Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
Use of Systems Biology for Design of inhibitor of Galectins as Cancer Therapeutic – Strategy and Software
Curator:Stephen J. Williams, Ph.D.
Below is a slide representation of the overall mission 4 to produce a PROTAC to inhibit Galectins 1, 3, and 9.
Using A Priori Knowledge of Galectin Receptor Interaction to Create a BioModel of Galectin 3 Binding
Now after collecting literature from PubMed on “galectin-3” AND “binding” to determine literature containing kinetic data we generate a WordCloud on the articles.
This following file contains the articles needed for BioModels generation.
From the WordCloud we can see that these corpus of articles describe galectin binding to the CRD (carbohydrate recognition domain). Interestingly there are many articles which describe van Der Waals interactions as well as electrostatic interactions. Certain carbohydrate modifictions like Lac NAc and Gal 1,4 may be important. Many articles describe the bonding as well as surface interactions. Many studies have been performed with galectin inhibitors like TDGs (thio-digalactosides) like TAZ TDG (3-deoxy-3-(4-[m-fluorophenyl]-1H-1,2,3-triazol-1-yl)-thio-digalactoside). This led to an interesting article
.
Dual thio-digalactoside-binding modes of human galectins as the structural basis for the design of potent and selective inhibitors
Human galectins are promising targets for cancer immunotherapeutic and fibrotic disease-related drugs. We report herein the binding interactions of three thio-digalactosides (TDGs) including TDG itself, TD139 (3,3′-deoxy-3,3′-bis-(4-[m-fluorophenyl]-1H-1,2,3-triazol-1-yl)-thio-digalactoside, recently approved for the treatment of idiopathic pulmonary fibrosis), and TAZTDG (3-deoxy-3-(4-[m-fluorophenyl]-1H-1,2,3-triazol-1-yl)-thio-digalactoside) with human galectins-1, -3 and -7 as assessed by X-ray crystallography, isothermal titration calorimetry and NMR spectroscopy. Five binding subsites (A-E) make up the carbohydrate-recognition domains of these galectins. We identified novel interactions between an arginine within subsite E of the galectins and an arene group in the ligands. In addition to the interactions contributed by the galactosyl sugar residues bound at subsites C and D, the fluorophenyl group of TAZTDG preferentially bound to subsite B in galectin-3, whereas the same group favored binding at subsite E in galectins-1 and -7. The characterised dual binding modes demonstrate how binding potency, reported as decreased Kd values of the TDG inhibitors from μM to nM, is improved and also offer insights to development of selective inhibitors for individual galectins.
Figures
Figure 1. Chemical structures of L3, TDG…
Figure 2. Structural comparison of the carbohydrate…
2022 World Medical Innovation Forum, GENE & CELL THERAPY • MAY 2–4, 2022 • BOSTON • IN-PERSON
Reporter: Aviva Lev-Ari, PhD, RN
World Medical Innovation Forum as we bring together global leaders to assess the latest opportunities and challenges, from the investment landscape to key technology developments to manufacturing and regulatory barriers. Gain first-hand insights on medicine’s ultimate game changer.
World Medical Innovation Forum will be held June 12 – 14 in Boston, MA. We hope you’ll join us for #WMIF2023!
From: “Rieck, Lucy (BOS-WSW)” <LRieck@webershandwick.com> Date: Tuesday, April 12, 2022 at 10:25 AM To: Aviva Lev-Ari <avivalev-ari@alum.berkeley.edu> Subject: You’re Invited: Mass General Brigham’s World Medical Innovation Forum
Hi Aviva,
I’m reaching out to extend free registration for you or a colleague to the 8th annual World Medical Innovation Forum (WMIF), taking place May 2-4 at the Westin Copley Place in Boston. This year’s event, co-sponsored with Bank of America, will explore gene and cell therapies (GCT), including the latest opportunities and challenges – from the investment landscape to key technology developments to manufacturing and regulatory barriers.
The event will feature 200 speakers – including CEOs of leading companies in the GCT and biotech fields, investors, entrepreneurs, Harvard clinicians and scientists, government officials and other key influencers – who discover, invest in, and cultivate GCT breakthroughs. Notable speakers include:
Peter Marks: Director, Center for Biologics Evaluation and Research at the FDA
Brian Moynihan: CEO, Bank of America
Anne Klibansky: President & CEO, Mass General Brigham
Senior executives from biopharma and academic institutions of all sizes (including Novartis, BMS, Takeda, Verve, UPenn)
You can view the full list of speakers here and the program agenda here.
WMIF is hosted by the Mass General Brigham health system, which comprises 14 hospitals, including two world-renowned medical centers: Mass General and Brigham & Women’s. Since 2015, the Forum has brought together global leaders to assess medical breakthroughs, the investment landscape and technology developments that have the potential to transform the industry.
In addition to a packed agenda, the 2022 “Disruptive Dozen” – 12 breakthrough technologies most likely to have significant impact on gene and cell therapy in the next 18 months – will also be announced.
Please let me know if you would be interested in attending.
Understanding long-term Gene and Cell Therapy investment complexities requires a keen awareness of where the science and the markets are headed. That’s why “The Doctor is In” in these updates on the latest GCT technologies. Presented by Mass General Brigham clinicians and innovators from the front lines of care, the sessions are co-hosted by expert analysts from Bank of America and include interactive discussion and Q&A.
In this session, Dr. Eichler will discuss the impact of gene defects across the lifespan and how timing and delivery of new genetic therapies is transforming the field of neurogenetics.
In this session, hear from experts in their field as they discuss the need and importance of regenerative medicine for the advancement in the treatment of diseases such as diabetes, kidney disease and blood disorders.
In this session, Dr. Artzi will share how an integrative approach of combining materials science, chemistry, imaging, and biology enables targeted delivery of gene therapy.
In this session, Drs. Robert Green and Adam Shaywitz will discuss how the early detection and prevention of rare diseases is imminent and represents an enormous public health opportunity.
In this session, Dr. Poznansky will share how research and development of vaccines and immunotherapy are safely accelerated from research lab to the patient leveraging novel mechanisms.
This panel features industry leaders who will discuss what the future may hold for gene and cell therapy. Which applications are likely to have the greatest impact? What are the key hurdles to be overcome? What specific platforms and technologies may enable optimal solutions? In what disease areas? Learn more about these and other questions as the panelists discuss the future potential of GCT.
Manufacturing quality and cost are critical for enabling rapid growth in GCT. Panelists will explore a variety of critical questions in this space. For example, are there historic parallels that can be drawn between GCT manufacturing and other groundbreaking technologies? How do key manufacturing concerns in GCT differ from those for more conventional pharmaceutical? What are the long-term opportunities for non-viral vectors? Will manufacturing capacity be a limiting factor in GCT growth over the next 5 to 10 years?
At the end of 2021, roughly 410 novel drugs had been approved in the past decade. On average, there were 40 approvals per year with over 150 of them being between 2018 and 2020. What has changed in the approval process and what is the vision of the future state? What will happen over the next 1–3 years? What does the new iteration of the Prescription Drug User Fees Act (PDUFA) need to do in this area and which fields show the greatest potential for innovation in CGT?
This panel will delve into clinical trials for GCT. How do these trials differ from those for conventional therapeutics? What are the key lessons learned from completed GCT trials? How is the regulatory landscape shifting and what will that mean for the future of GCT?
Dr. Bourla will share what Pfizer has learned from its leadership on mRNA and the development of the Covid vaccine that can be extrapolated to other R&D.
As we enter the third year of the coronavirus pandemic, the world is shifting to a new strategy: living with and managing COVID as a part of our everyday lives. What will the coming year look like? How will mitigation measures differ in this new phase? What about treatment strategies? Should we be bracing for another surge?
This panel will feature a discussion of global biotech clusters with a deep dive into the New England/Boston area. How does the capital availability, scale, and density of New England drive local growth in GCT? Also, the influx of large biopharmaceutical companies into the region has fueled global outcomes. What is the future impact of these investments and when will they peak? How will the biopharmaceutical landscape in New England appear in 2030?
The role of patients and their experiences are critical as the promise of GCT unfolds. This panel will discuss the patient experience and explore the challenges different patient populations face, both in rare diseases and more common conditions. Panelists will also discuss financial considerations, clinical trial access, and the role of advocacy groups in GCT.
As many countries begin to turn the corner on COVID-19, they face a resurgence of chronic illnesses, such as cancer and cardiovascular disease, that were not adequately addressed during the pandemic, and for which new treatments are urgently needed. Population aging – and the resulting increase in chronic diseases associated with aging – has compounded the challenge. There’s never been a greater need for biopharmaceutical innovation – or, fortunately, a greater ability to innovate. Amgen is investing in new discovery research capabilities that portend a revolution in drug design and development.
Understanding long-term Gene and Cell Therapy investment complexities requires a keen awareness of where the science and the markets are headed. That’s why “The Doctor is In” in these updates on the latest GCT technologies. Presented by Mass General Brigham clinicians and innovators from the front lines of care, the sessions are co-hosted by expert analysts from Bank of America and include interactive discussion and Q&A.
In this session, Dr. Vavvas will discuss examples of clinical trials in rare diseases and share insights into how clinical trials should be approached for rare and ultra-rare diseases and how study design is not a one-size fits all.
In this session, hear experts weigh in on the possibilities of cell therapy development and transplantation for the treatment of Parkinson’s Disease. What does the futures hold and how do we get there?
In this session, Dr. Nikiforow will provide insights into the world of gene therapy manufacturing and the complexities of scaling, costs and insurance reimbursement.
In this session, Dr. Marks will discuss the ins and outs of regulatory challenges for biological products and therapies in gene and cell therapy and the responsibility to assure safety and effectiveness.
Dark genome, accounting for ~98.5% of the human genome and containing the non-coding part, offers unprecedented opportunity to look for novel elements that could play a role in human health. This non-coding region consists of repeat elements, enhancers, regulatory sequences and non-coding RNAs. This session will explore this exciting new frontier in biology and how to translate this so called “junk” and previously ignored genome into potential novel therapeutics.
Panelists will discuss the life sciences capital markets environment with particular emphasis on private and public fundraising for GCT companies. What trends do panelists observe that will impact the availability and cost of capital for GCT? Are there novel fundraising structures that will serve GCT in the future?
As one of the foremost researchers of CAR-T cancer treatments, Dr. June will share what he believes is the next wave of cell-and-gene based oncology research and how his work set the stage for breakthrough developments in cancer.
Richard W. Vague Professor in Immunotherapy, Director, Center for Cellular Immunotherapies, Director, Parker Institute for Cancer Immunotherapy, University of Pennsylvania Perelman School of Medicine
This panel will examine the role of academia in driving the promise of GCT. How does academic innovation contribute to the success of GCT? What are the risks and opportunities? Which models have proven most successful and what is the impact on clinical translation? How can these partnerships be accelerated?
Richard W. Vague Professor in Immunotherapy, Director, Center for Cellular Immunotherapies, Director, Parker Institute for Cancer Immunotherapy, University of Pennsylvania Perelman School of Medicine
This panel will bring together gene and cell therapy leaders from across the world to discuss the latest opportunities and challenges in the field, from the investment landscape to key technology developments to manufacturing and regulatory barriers. These global experts will offer first-hand insights on the systemic complexity of this advancing field and its therapeutic promise.
Chronic inflammation in the brain is now recognized as a contributor to many neurodegenerative diseases, ranging from Parkinson’s disease to multiple sclerosis to Alzheimer’s disease. Are solutions to these historically intractable neurological diseases imminent or several years away? Are market-making platforms identifiable for neurological diseases? Are there novel genetic targets that can be explored? What are the prospects for cell therapies?
Cell therapies, ranging from CAR-T cells to stem-cell-based approaches, are emerging as a transformative therapeutic modality. Panelists will examine this emerging landscape and discuss a range of key topics. What drives differentiation in this space given the high number of competing technologies? How will the uptake of autologous cell therapies and allogeneic versions evolve? When will the regenerative medicine market mature?
This panel will explore how GCT technology could lead to disruptions in other areas of medicine, including surgery and medical devices, over the next several years. Could cell replacement therapy in diabetes advance enough to reduce the need for diabetes pumps or insulin? Will stem-cell-based methods for regenerating cartilage advance rapidly enough to disrupt the number of patients seeking hip and knee replacements? How is GCT driving innovations in surgical techniques?
What is the new generation of approaches to gene therapy manufacturing and delivery? What are the lessons learned from Covid and how can it be applied to custom disease response and the ability to custom design biologic organisms?
This panel will feature an in-depth discussion of the safety of gene and cell therapies. What are the unique safety concerns in this field, both acute and potential long-term risks? Which of these concerns are supported by clinical data versus the presumption of theoretical risk? What are the key issues for AAV-based gene therapies? Will redosing become feasible? What are the predominant safety concerns for in vivo versus ex vivo GCT modalities, including base editing?
The label “RNA” encompasses a wide array of biologically active agents spanning therapeutic modalities, vaccines, non-coding controls, and other forms. In this panel we will discuss a number of these forms, discuss examples of recent developments and illustrate why RNA developments represent a promising source of novel therapies and therapeutic approaches.
The Disruptive Dozen identifies and ranks the GCT technologies that Mass General Brigham faculty feel will break through over the next one to five years to significantly improve health care.
Recent genetic studies have identified variants associated with bipolar disorder (BD), but it remains unclear how brain gene expression is altered in BD and how genetic risk for BD may contribute to these alterations. Here, we obtained transcriptomes from subgenual anterior cingulate cortex and amygdala samples from post-mortem brains of individuals with BD and neurotypical controls, including 511 total samples from 295 unique donors. We examined differential gene expression between cases and controls and the transcriptional effects of BD-associated genetic variants. We found two coexpressed modules that were associated with transcriptional changes in BD: one enriched for immune and inflammatory genes and the other with genes related to the postsynaptic membrane. Over 50% of BD genome-wide significant loci contained significant expression quantitative trait loci (QTL) (eQTL), and these data converged on several individual genes, including SCN2A and GRIN2A. Thus, these data implicate specific genes and pathways that may contribute to the pathology of BP.
Gene Expression Markers for Bipolar Disorder Pinpointed
The work was led by researchers at Johns Hopkins’ Lieber Institute for Brain Development. The findings, published this week in Nature Neuroscience, represent the first time that researchers have been able to apply large-scale genetic research to brain samples from hundreds of patients with bipolar disorder (BD). They used 511 total samples from 295 unique donors.
“This is the first deep dive into the molecular biology of the brain in people who died with bipolar disorder—studying actual genes, not urine, blood or skin samples,” said Thomas Hyde of the Lieber Institute and a lead author of the paper. “If we can figure out the mechanisms behind BD, if we can figure out what’s wrong in the brain, then we can begin to develop new targeted treatments of what has long been a mysterious condition.”
Bipolar disorder is characterized by extreme mood swings, with episodes of mania alternating with episodes of depression. It usually emerges in people in their 20s and 30s and remains with them for life. This condition affects approximately 2.8% of the adult American population, or about 7 million people. Patients face higher rates of suicide, poorer quality of life, and lower productivity than the general population. Some estimates put the annual cost of the condition in the U.S. alone at $219.1 billion.
While drugs can be useful in treating BD, many patients find they have bothersome side effects, and for some patients, current medications don’t work at all.
In this study, researchers measured levels of messenger RNA in the brain samples. They observed almost eight times more differentially expressed gene features in the sACC versus the amygdala, suggesting that the sACC may play an especially prominent role—both in mood regulation in general and BD specifically.
In patients who died with BD, the researchers found abnormalities in two families of genes: one containing genes related to the synapse and the second related to immune and inflammatory function.
“There finally is a study using modern technology and our current understanding of genetics to uncover how the brain is doing,” Hyde said. “We know that BD tends to run in families, and there is strong evidence that there are inherited genetic abnormalities that put an individual at risk for bipolar disorder. Unlike diseases such as sickle-cell anemia, bipolar disorder does not result from a single genetic abnormality. Rather, most patients have inherited a group of variants spread across a number of genes.”
“Bipolar disorder, also known as manic-depressive disorder, is a highly damaging and paradoxical condition,” said Daniel R. Weinberger, chief executive and director of the Lieber Institute and a co-author of the study. “It can make people very productive so they can lead countries and companies, but it can also hurl them into the meat grinder of dysfunction and depression. Patients with BD may live on two hours of sleep a night, saving the world with their abundance of energy, and then become so self-destructive that they spend their family’s fortune in a week and lose all friends as they spiral downward. Bipolar disorder also has some shared genetic links to other psychiatric disorders, such as schizophrenia, and is implicated in overuse of drugs and alcohol.”
Tweets and Re-Tweets of Tweets by @pharma_BI@AVIVA1950 at 2021 Virtual World Medical Innovation Forum, Mass General Brigham, Gene and Cell Therapy, VIRTUAL May 19–21, 2021
REAL TIME EVENT COVERAGE as PRESS by invitation from 2021 Virtual World Medical Innovation Forumat #WMIF2021 @MGBInnovation:
for sharing this screen capture of the impressive lineup of #GCT “Disruptive Dozen” panelists at #WMIF2021
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Aviva Lev-Ari
@AVIVA1950
· May 21
@MGBInnovation #WMIF Best Global event on Gene Cell Therapy covered in real time @AVIVA1950 @pharma_BI Disruptive Dozen technologies four are based on Gene Editing, AAV and non viral vector for drug delivery are included
PART 1: ALL THE TWEETS PRODUCED by @AVIVA1950 on May 21, 2021
Bob Carter, MD, PhD Chairman, Department of Neurosurgery, MGH William and Elizabeth Sweet, Professor of Neurosurgery, HMS Neurogeneration REVERSAL or slowing down?
Penelope Hallett, PhD NRL, McLean Assistant Professor Psychiatry, HMS efficacy Autologous cell therapy transplantation approach program T cells into dopamine genetating cells greater than Allogeneic cell transplantation
Roger Kitterman VP, Venture, Mass General Brigham Saturation reached or more investment is coming in CGT Multi OMICS and academia originated innovations are the most attractive areas
Peter Kolchinsky, PhD Founder and Managing Partner, RA Capital Management Future proof for new comers disruptors Ex Vivo gene therapy to improve funding products what tool kit belongs to
Chairman, Department of Neurosurgery, MGH, Professor of Neurosurgery, HMS Cell therapy for Parkinson to replace dopamine producing cells lost ability to produce dopamine skin cell to become autologous cells reprogramed
Kapil Bharti, PhD Senior Investigator, Ocular and Stem Cell Translational Research Section, NIH Off-th-shelf one time treatment becoming cure Intact tissue in a dish is fragile to maintain metabolism to become like semiconductors
Ole Isacson, MD, PhD Director, Neuroregeneration Research Institute, McLean Professor, Neurology and Neuroscience, MGH, HMS Opportunities in the next generation of the tactical level Welcome the oprimism and energy level of all
Erin Kimbrel, PhD Executive Director, Regenerative Medicine, Astellas In the ocular space immunogenecity regulatory communication use gene editing for immunogenecity Cas1 and Cas2 autologous cells
Nabiha Saklayen, PhD CEO and Co-Founder, Cellino scale production of autologous cells foundry using semiconductor process in building cassettes by optic physicists
Joe Burns, PhD VP, Head of Biology, Decibel Therapeutics Ear inside the scall compartments and receptors responsible for hearing highly differentiated tall ask to identify cell for anticipated differentiation control by genomics
Kapil Bharti, PhD Senior Investigator, Ocular and Stem Cell Translational Research Section, NIH first drug required to establish the process for that innovations design of animal studies not done before
Robert Nelsen Managing Director, Co-founder, ARCH Venture Partners Manufacturing change is not a new clinical trial FDA need to be presented with new rethinking for big innovations Drug pricing cheaper requires systematization
David Berry, MD, PhD CEO, Valo Health GP, Flagship Pioneering Bring disruptive frontier platform reliable delivery CGT double knockout disease cure all change efficiency scope human centric vs mice centered right scale acceleration
Kush Parmar, MD, PhD Managing Partner, 5AM Ventures build it yourself, benefit for patients FIrst Look at MGB shows MEE innovation on inner ear worthy investment
Robert Nelsen Managing Director, Co-founder, ARCH Venture Partners Frustration with supply chain during the Pandemic, GMC anticipation in advance CGT rapidly prototype rethink and invest proactive investor .edu and Pharma
The # of US patients with Parkinson’s Disease is expected to double over next 30 years. Penelope Hallett PhD, Co-Director of the Neuroregeneration Research Inst
Marcela Maus, MD PhD, are working to expand the reach of this transformative technology. #WMIF2021
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Mass General Brigham Innovation
@MGBInnovation
· 3h
Disruptive Dozen: 12 Technologies that Will Reinvent GCT #9. Building the Next Wave of CAR-T-cell Therapies #WMIF2021 #GCT #GeneAndCellTherapy #CellTherapy #CarT #DisruptiveDozen
and global colleagues at #WMIF2021. On Thursday, May 20, my colleagues and I will discuss the advantages of RNA-targeted medicines and how they might shape the future of medicine for patients.
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Mass General Brigham Innovation
@MGBInnovation
· May 10
Are you part of the @MassGenBrigham network and interested in #GeneAndCellTherapy? Join us at the World Medical Innovation Forum on 5/19-5/21. Register today! https://worldmedicalinnovation.org/register/ #WMIF2021
Incredible opportunity to get up to speed with the most innovative technologies in medicine ! Gene and cell therapy are revolutionizing healthcare ! #WMIF2021#MedTwitter
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Mass General Brigham Innovation
@MGBInnovation
· May 11
#WMIF2021 is an opportunity for innovators from around the globe to meet, explore, challenge, and reflect on the issues influencing the adoption of novel technologies in #healthcare. Register now to join the conversation: https://worldmedicalinnovation.org/register/
Currently, the only cure for some common blood disorders is a bone marrow transplant, which can be risky. Now, gene therapies are also in the works, including a CRISPR-based #genetherapy being tested in clinical trials with encouraging early results. #WMIF2021
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Mass General Brigham Innovation
@MGBInnovation
· 3h
Disruptive Dozen: 12 Technologies that Will Reinvent GCT #2. A Genetic Fix for Two Common Blood Disorders #WMIF2021 #GCT #GeneAndCellTherapy #BloodDisorders #DisruptiveDozen
Researchers have pinpointed key genes involved in cholesterol and lipid metabolism that represent promising targets for new cholesterol-lowering treatments. #WMIF2021
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Mass General Brigham Innovation
@MGBInnovation
· 3h
Disruptive Dozen: 12 Technologies that Will Reinvent GCT #1. A New Generation of Cholesterol-Loweing Therapies #WMIF2021 #GCT #GeneAndCellTherapy #DisruptiveDozen
I really enjoyed this remarkable panel #WMIF2021. Thank you Meredith Fisher for moderating and thank you David, Bob and Kush for openly sharing your big picture view
Variability, delays, manufacturing as an afterthought make #GCT challenging from an investment POV — need to rethink the ecosystem and drive efficiency, invest in tech innovation says Bob Nelson ARCH Venture Partners
We need to change the scale and scope of how #GCT is advancing from discovery to development — systematization critical. Can’t have thousands of one-off therapies say early-stage investors. Major mis-match between where things are now and what could be.
Today I moderated a panel on Gene and Cell Therapy Delivery, Perfecting the Technology. We highlighted non-viral delivery technologies as key enablers of gene therapy and editing. Learn more: https://lnkd.in/d-Xqzqh#WMIF2021
Congratulations to the 2021 Innovation Discovery Grants winners: @lynchielydia, Peter Sage, @GrishchukL, Benjamin Kleinstiver, Petr Baranov, announced at the #WMIF2021. It’s exciting to see the range of breakthrough research in #geneticdisease at @MassGenBrigham…
for sharing this screen capture of the impressive lineup of #GCT “Disruptive Dozen” panelists at #WMIF2021
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Aviva Lev-Ari
@AVIVA1950
· May 21
@MGBInnovation #WMIF Best Global event on Gene Cell Therapy covered in real time @AVIVA1950 @pharma_BI Disruptive Dozen technologies four are based on Gene Editing, AAV and non viral vector for drug delivery are included
PART 1: ALL THE TWEETS PRODUCED by @AVIVA1950 on May 20, 2021
Bob Brown, PhD CSO, EVP of R&D, Dicerna small molecule vs capacity of nanoparticles to deliver therapeutics quantity for more molecule is much larger CNS delivery most difficult
Jeannie Lee, MD, PhD Molecular Biologist, MGH Prof Genetics, HMS 200 disease X chromosome unlock for neurological genetic diseases: Rett Syndrome, autism spectrum disorders female model vs male mice model restore own protein
Suneet Varma Global President of Rare Disease, Pfizer review of protocols and CGT for Hemophilia Pfizer: You can’t buy Time With MIT Pfizer is developing a model for Hemophilia CGT treatment
Gallia Levy, MD, PhD CMO, Spark Therapeutics Hemophilia CGT is the highest potential for Global access logistics in underdev countries working with NGOs practicality of the Tx Roche reached 120 Counties great to be part of the Roche
Theresa Heggie CEO, Freeline Therapeutics Safety concerns, high burden of treatment CGT has record of safety and risk/benefit adoption of Tx functional cure CGT is potent Tx relative small quantity of protein needs be delivered
Suneet Varma Global President of Rare Disease, Pfizer Gene therapy at Pfizer small, large molecule and CGT – spectrum of choice allowing Hemophilia patients to marry 1/3 internal 1/3 partnership 1/3 acquisitions review of protocols
Ron Renaud CEO, Translate Bio What strain of Flu vaccine will come back in the future when people do not use masks. AAV vectors small transcript size fit reach cytoplasm more development coming
Melissa Moore Chief Scientific Officer, Moderna Many years of mRNA pivoting for new diseases, DARPA, nucleic Acids global deployment of a manufacturing unit on site where the need arise Elan Musk funds new directions at Moderna
Lindsey Baden, MD Director, Clinical Research, Division of Infectious Diseases, BWH Associate Professor, HMS In vivo delivery process regulatory for new opportunities for same platform new indication using multi valence vaccines
Melissa Moore Chief Scientific Officer, Moderna Many years of mRNA pivoting for new diseases, DARPA, nucleic Acids global deployment of a manufacturing unit on site where the need arise Elan Musk funds new directions at Moderna
Ron Renaud CEO, Translate Bio 1.6 Billion doses produced rare disease monogenic correct mRNA like CF multiple mutation infection disease and oncology applications
Melissa Moore CSO, Moderna mRNA vaccine 98% efficacy for Pfizer and Moderna more then 10 years 2015 mRNA was ready (ZIKA, RSV), as the proteine is identify manufacturing temp less of downside in the future ability to store at Ref
Richard Wang, PhD CEO, Fosun Kite Biotechnology Co. Ltd Possibilities to be creative and capitalize the new technologies for new drug Support of the ecosystem by funding new companies Autologous in patients differences cost challenge
Tian Xu, PhD Vice President, Westlake University ICH Chinese FDA -r regulation similar to the US Difference is the population recruitment, in China patients are active participants Dev of transposome non-viral methods, price
Alvin Luk, PhD CEO, Neuropath Therapeutics Monogenic rare disease with clear genomic target Increase of 30% in patient enrollment Regulatory reform approval is 60 days no delay
We’re excited to attend this week’s #WMIF2021 to talk all things cell and genetic therapies. Join our Chief of VCGT Bastiano Sanna tomorrow at 9:50am EDT for a discussion on the promise of cell therapies for type 1 diabetes. Register now! https://bit.ly/3otngYd
John Fish, Board Chair, Brigham Health, Chairman & CEO, Suffolk on the Novartis Main Stage to introduce the “Collaboration is Key: GCT R&D of the Future” fireside chat with Jay Bradner, MD, President, NIBR
Thomas VanCott, PhD, Chief Technology & Strategy Officer, Catalent Cell & Gene Therapy, says that time, improvements and scaling up in manufacturing will lead to allogeneic cell therapies. He recognizes that upfront costs are high, but will decrease in the long term #WMIF2021
Today Lisa Michaels, Editas CMO, will participate in the panel “Gene Editing – Achieving Therapeutic Mainstream” at the World Medical Innovation Forum #WMIF2021 in Boston. For those attending, be sure to tune in!
, views GCT as the ultimate precision medicine. AI, machine learning, and data science comprise one of the big disruptive forces that will address misdiagnosis, smooth out workflow, reduce cost and enhance recovery. #WMIF2021
CSO Laura Sepp-Lorenzino, PhD, in our “GCT Delivery | Perfecting the Technology” panel this afternoon! #WMIF2021
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Intellia Therapeutics
@intelliatweets
· 6h
Today, Intellia CSO, @LauraSeppLore will be participating in the World Medical Innovation Forum’s panel on Gene and Cell Therapy Delivery, Perfecting the Technology. #WMIF2021 @MGBInnovation. Click here to learn more: https://worldmedicalinnovation.org
is back with us this afternoon sharing a First Look at “Versatile Polymer-Based Nanocarriers for Targeted Therapy and Immunomodulation.” #WMIF2021#GCT#geneandcelltherapy
VP of Clinical Development, Manasi Jaiman, during the “Diabetes | Grand Challenge” panel today. #WMIF2021
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ViaCyte
@ViaCyte
· 8h
Join us at #WMIF2021 today! Our own Manasi Jaiman, VP, Clinical Development, will participate in the Diabetes: Grand Challenge panel to discuss regenerative medicine approaches for T1D utilizing stem-cell derived islet cell replacement therapy.
, discusses how GCT is in the embryonic phase. Bayer is ready to treat its first Parkinson’s patient, and is exploring therapeutic technologies to treat diseases with single gene defects #WMIF2021
Today Lisa Michaels, Editas CMO, will participate in the panel “Gene Editing – Achieving Therapeutic Mainstream” at the World Medical Innovation Forum #WMIF2021 in Boston. For those attending, be sure to tune in! @MassGenBrigham https://bit.ly/3hx1XTV #geneediting #biotechnology
to discuss the current state of CAR-T and its future prospects. These conversations are important for the development of potential #CART therapies. #WMIF2021
‘s #WMIF2021 — Thanks to the MGB team for facilitating a great discussion!
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Mass General Brigham Innovation
@MGBInnovation
· 7h
Overview of our #mRNA Vaccines panel today, highlighting improved manufacturing capabilities & potential for #personalizedmedicine. Thank you to Lindsey Baden @bwh_id & panelists Kate Bingham, SV Health Investors, Melissa Moore @moderna_tx and Ron Renaud @TranslateBio #WMIF2021
investigators are ready to give you an early preview of their #GCT research in the First Look sessions at #WMIF2021. Exciting opportunities to dramatically change how disease is treated!
Our “Rare and Ultra Rare Diseases | GCT Breaks Through” panelists on the role of family organizations & patient advocacy groups in moving us forward on the regulatory side – “It’s absolutely essential” #WMIF2021
Congratulations! Lydia Lynch PhD, Brigham and Women’s Hospital receives an Innovation Discovery Grant for “Generating Superior ‘Killers’ for Adoptive Cell Therapy in Cancer” at #WMIF2021.
Looking forward to the Diabetes Grand Challenge and how #GCT could help millions of people. Read about what facing this disease and how cell therapies could lessen the burden from Manasi Jaiman, MD, VP, Clinical Development
Today is Day 2 of the World Medical Innovation Forum. Which panel you are most excited to see today? Reply and let us know! #WMIF2021 https://worldmedicalinnovation.org/agenda/
Cell and gene therapies hold promising potential for rare disease, blood cancers, and viral diseases. Register for #WMIF21 to hear about our work to pioneer cutting-edge science across our pipeline to advance breakthroughs that change patients’ lives: https://on.pfizer.com/3f3CGzj
Congratulations! Peter Sage PhD, Brigham and Women’s Hospital receives an Innovation Discovery Grant for “Novel Strategies to Enhance Tfr Treatment of Autoimmunity” at #WMIF2021
Congratulations! Yulia Grishchuk PhD, Massachusetts General Hospital, receives an Innovation Discovery Grant for “AAV-Based Gene Replacement Therapy Improves Targeting and Clinical Outcomes in a Childhood CNS Disorder” at #WMIF2021
Congratulations! Jinjun Shi, PhD, Brigham and Women’s Hospital, receives an Innovation Discovery Grant for “Long-Lasting mRNA Therapy for Genetic Disorders” at #WMIF2021
Final thoughts from “Benign Blood Disorders” panelists on academic/industry collaboration — the pace of #innovation is incredibly exciting, and I think it will be even faster together. #WMIF2021
Congratulations! Benjamin Kleinstiver PhD, Massachusetts General Hospital, receives an Innovation Discovery Grant for “Towards a Permanent Genetic Cure for Spinal Muscular Atrophy” at #WMIF2021
FDA’s Peter Marks, at #WMIF2021, notes # of INDs for gene therapies was flat in 2020 vs. 2019. But the fact IND submissions didn’t decline, he said, is a sign of how strong the gene therapy field is, given pandemic’s disruption.
Melissa Moore/Moderna- one advantage of mRNA is ability to do multivalent vaccines she said. She said they are already testing multivalent covid vaccines in clinical trials & testing flu vaccines. #wmif2021
Kate Bingham/SV Health & former head of UK Vaccine Taskforce: they haven’t seen escape variants in UK yet she said. mRNA is quickest platform to address escape variants probably. Needle delivery w/ supply cold chain has been the challenge. Deploying 3 vaccines in UK #WMIF2021
, notes that the science behind gene cell therapies is converging with technological development. How therapies are brought to market is still the question, as there is no roadmap when reimagining medicine #WMIF2021
Melissa Moore/Moderna: clear advantage of mRNA vaccine is how quickly we can manufacture the vaccines. Downsides- need 2store at low temperatures & limited shelflife 4storage in refrigerator. I know that both companies [Moderna, Pfizer/BioNTech] r working 2change this #wmif2021
We’re committed to addressing the unmet needs of people living with rare genetic diseases. Our SVP, External Innovation and Strategic Alliances, Leah Bloom, discusses the promise #genetherapy holds for communities impacted by rare diseases during #WMIF2021.
Speed of vaccination is critical to prevent escape variants says Kate Bingham, SV Health Investors, UK, at #WMIF2021, exploring what’s next for the technology w panel led by Lindsey Baden MD,
for sharing this screen capture of the impressive lineup of #GCT “Disruptive Dozen” panelists at #WMIF2021
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Aviva Lev-Ari
@AVIVA1950
· May 21
@MGBInnovation #WMIF Best Global event on Gene Cell Therapy covered in real time @AVIVA1950 @pharma_BI Disruptive Dozen technologies four are based on Gene Editing, AAV and non viral vector for drug delivery are included
PART 1: ALL THE TWEETS PRODUCED by @AVIVA1950 on May 19, 2021
Thomas VanCott, PhD Global Head of Product Dev, Gene & Cell Therapy, Catalent 2/3 autologous 1/3 allogeneic CAR-T high doses scale up is not done today logistics issues centralized vs decentralized allogeneic are health donors
Ropa Pike, Director, Enterprise Science & Partnerships, Thermo FIsher Scientific Centralized biopharma industry is moving to decentralized models site specific license
Rahul Singhvi, ScD CEO and Co-Founder, National Resilience, Inc. Investment company in platforms to be shared by start ups in CGT. Production cost of allogeneic: cost of quality 30% reagents 30% cell 30% Test is very expensive
Oladapo Yeku, MD, PhD Clinical Assistant in Medicine, MGH Outstanding moderator and most gifted panel on solid tumor success window of opportunities studies
Knut Niss, PhD CTO, Mustang Bio tumor hot start in 12 month clinical trial solid tumors Combination therapy will be an experimental treatment long journey checkpoint inhibitors to be used in combination maintenance
Barbra Sasu, PhD CSO, Allogene T cell response at prostate cancer tumor specific cytokine tumor specific signals move from solid to metastatic cell type for easier infiltration
Jennifer Brogdon Executive Director, Head of Cell Therapy Research, Exploratory Immuno-Oncology, NIBR 2017 CAR-T first approval M&A and research collaborations TCR tumor specific antigens avoid tissue toxicity
Jay Short, PhD Chairman, CEO, Cofounder, BioAlta, Inc. Tumor type is not enough for R&D therapeutics other organs are involved in periphery difficult to penetrate solid tumors biologics activated in the tumor only, positive changes
Stefan Hendriks Global Head, Cell & Gene, Novartis Confirmation the effectiveness of CAR-T therapies, 1 year response to 5 years 26 months Patient not responding a lot to learn Patient after 8 months of chemo can be helped by CAR-T
Jeffrey Infante, MD , Oncology, Janssen R&D Direct effect with intra-tumor single injection with right payload Platform approach Prime with 1 and Boost with 2 – not yet experimented with Do not have the data at trial
Nino Chiocca, MD, PhD Neurosurgeon-in-Chief BWH, HMS Oncolytic therapy DID NOT WORK Pancreatic Cancer and Glioblastoma Intra-tumoral heterogeniety hinders success Oncolytic VIRUSES – “coldness” GADD-34 20,000 GBM 40,000 pancreatic
Loic Vincent, PhD Head of Oncology Drug Discovery Unit, Takeda Classification of Patients by prospective response type id UNKNOWN yet, population of patients require stratification
Loic Vincent, PhD Head of Oncology Drug Discovery Unit, Takeda R&D in collaboration with Academic Vaccine platform to explore different payload IV administration may not bring sufficient concentration to the tumor is administer IV
Nino Chiocca, MD, PhD Neurosurgeon-in-Chief and Chairman, Neurosurgery, BWH Harvey W. Cushing Professor of Neurosurgery, HMS Challenges of manufacturing at Amgen what are they?
David Reese, MD Executive Vice President, R&D , Amgen Inter lesion injection of agent vs systemic therapeutics cold tumors immune resistant render them immune susptible Oncolytic virus is a Mono therapy addressing the unknown
David Reese, MD Executive Vice President, Research and Development, Amgen Inter lesion injection of agent vs systemic therapeutics cold tumors immune resistant render them immune suseptible Oncolytic virus is a Mono therapy
Robert Coffin, PhD Chief R&D Officer, Replimune 2002 in UK promise in oncolytic therapy GNCSF Phase III melanoma 2015 M&A with Amgen oncolytic therapy remains non effecting on immune response data is key for commercialization
Ann Silk, MD Physician, Dana Farber-Brigham and Women’s Cancer Center, HMS Which person gets oncolytics virus if patient has immune supression due to other indications Safety of oncolytic virus greater than Systemic treatment
Marianne De Backer/Bayer on post M&A & company culture: They acquired AskBio & thought about how to preserve their freedom so they could continue to operate. Bayer decided to keep them independent & so they can operate at arm’s length. #wmif2021
Merit Cudkowicz, MD Chief of Neurology, MGH ALS – Man 1in 300, Women 1 in 400, next decade increase 7% 10% ALS is heredity 160 pharma in ALS space diagnosis is late 1/3 of people are not diagnosed active community for clinical trials @pharma_BI@AVIVA1950
Adam Koppel, MD, PhD Managing Director, Bain Capital Life Sciences What acquirers are looking for?? What is the next generation vs what is real where is the industry going?
Debby Baron, Worldwide Business Development, Pfizer Scalability and manufacturing regulatory conversations, clinical programs safety in parallel to planning getting drug to patients
Marianne De Backer, PhD Head of Strategy, BD & Licensing, Bayer Absolute Leadership: Gene editing, gene therapy, via acquisition and alliances Operating model of the acquired company discussed acquired continue independence
Sean Nolan Board Chairman, Encoded Therapeutics & Affinia Executive Chairman Jaguar Gene Therapy Istari Oncology As acquiree multiple M&A acquirer looks at integration and cultures companies Traditional integration vs acquisition
Debby Baron, Worldwide Business Development, Pfizer CGT is an important area Pfizer is active looking for innovators, advancing forward programs of innovation with the experience Pfizer has internally
Marianne De Backer, PhD Head of Strategy, Business Development & Licensing, and Member of the Executive Committee, Bayer Absolute Leadership in Gene editing, gene therapy, via acquisition and strategic alliance
Manny Simons, PhD CEO, Akouos Biology across species nerve ending in the cochlea engineer out of the caspid, lowest dose possible, get desired effect by vector use, 2022 new milestones
Mathew Pletcher, PhD SVP, Head of Gene Therapy Research and Technical Operations, Astellas Continue to explore large animal guinea pig not the mice, not primates (ethical issues) for understanding immunogenicity and immune response
Mathew Pletcher, PhD SVP, Head of Gene Therapy Research and Technical Operations, Astellas Work with diseases poorly understood, collaborations needs example of existing: DMD is a great example explain dystrophin share placedo data
Rick Modi CEO, Affinia Therapeutics Speed R&D Speed better gene construct get to clinic with better design vs ASAP Data sharing clinical experience patients selection, vector selection, mitigation, patient type specific
Dave Lennon, PhD President, Novartis Gene Therapies big pharma therapeutics not one drug across Tx areas: cell, gene iodine therapy collective learning infrastructure development Acquisitions growth # applications for scaling
Rick Modi CEO, Affinia Therapeutics Copy, paste EDIT from product A to B novel vectors variant of vector coder optimization choice of indication is critical exploration on larger populations Speed to R&D to better gene construct get
Louise Rodino-Klapac, PhD EVP, Chief Scientific Officer, Sarepta Therapeutics AV based platform 15 years in development 1 disease indication vs more than one indication stereotype, analytics as hurdle 1st was 10 years 2nd was 3 years
Katherine High, MD President, Therapeutics, AskBio Three drugs approved in Europe in the CGT Regulatory Infrastructure CGT drug approval – as new class of therapeutics Participants investigators, regulators, patients i.e., MDM
Peter Marks, MD, PhD Director, Center for Biologics Evaluation and Research, FDA Immune modulators Immunotherapy Genome editing can make use of viral vectors future technologies nanoparticles and liposome encapsulation 50% more staff
Peter Marks, MD, PhD Director, Center for Biologics Evaluation and Research, FDA Recover Work load for the pandemic Gene Therapies IND application remained flat Rare diseases urgency remains Guidance T-Cell therapy vs Regulation
Peter Marks, MD, PhD Director, Center for Biologics Evaluation and Research, FDA June 2020 belief that vaccine challenge manufacture scaling up FDA did not predicted the efficacy of mRNA vaccine vs other approaches expected to work
Jim Holland CEO, http://Backcountry.com Parkinson patient Constraints by regulatory on participation in clinical trial wish to take Information dissemination is critical
Patricia Musolino, MD, PhD Co-Director Pediatric Stroke and Cerebrovascular Program What is the Power of One – the impact that a patient can have on their own destiny connecting with other participants in same trial can be beneficial
Barbara Lavery Chief Program Officer, ACGT Foundation Patient has the knowledge of the symptoms and recording all input needed for diagnosis by multiple clinicians Early application for CGT
Jack Hogan Patient, MEE Constraints by regulatory on participation in #clinicaltrials advance stage is approved participation Patients to determine the level of #risk they wish to take
Barbara Lavery Chief Program Officer, ACGT Foundation Advocacy agency beginning of work Global Genes educational content and out reach to access the information
Dave Lennon, PhD President, Novartis Gene Therapies Modality one time intervention, long duration of impart, reimbursement, ecosystem FDA works by indications and risks involved, Standards manufacturing payments over time payers
Dave Lennon, PhD President, Novartis Gene Therapies Promise of CGT realized, what part? #FDA role and interaction in CGT #Manufacturing aspects which is critical
Julian Harris, MD Partner, Deerfield Hope that CGT emerging, how therapies work, #neuro, #muscular, #ocular, #genetic diseases of #liver and of #heart revolution for the industry 900 #IND application 25 approvals #Economic driver
Luk Vandenberghe, PhD Grousbeck Family Chair, Gene Therapy, MEE Associate Professor, Ophthalmology, HMS #Pharmacology#Gene-Drug, Interface academic centers and industry many CGT drugs emerged in Academic center
Ravi Thadhani, MD CAO, Mass General Brigham Professor, Medicine and Faculty Dean, HMS Role of #academia special to spear head the #Polygenic#therapy – multiple #genes involved, #plug-play #delivery
The field of #genetherapy is growing. New therapies will come to market for rare and chronic diseases, and new therapies will drive scientific innovation and economic growth. #WMIF2021 (2/6)
In our First Look sessions clinicians/researchers from Harvard-affiliated hospitals highlight the potential of their research & new technologies. Next we’ll hear from Khalid Shah PhD, Vice Chair of Research
Tomorrow is Day 1 of #WMIF2021! Hear from the world-renowned CEOs, investors, clinicians and scientists bringing game-changing discoveries and insights to #GCT. Register to attend today: https://worldmedicalinnovation.org/register/
‘s World Medical Innovation Forum this week, discussing the future of #genetherapy. Here are our five predictions for where the industry is headed. #WMIF2021 (1/6)
explains at #WMIF2021 why the first FDA-approved gene therapy for inherited disease was for an inherited retinal degeneration, and what lessons have been learned from the success of that treatment.
Together with @BayerPharma, we are pleased to be part of #WMIF2021, organized by @MassGenBrigham. This year’s event focuses on the transformative potential of #cellandgene therapy (#GCT).
“We are more committed to our mission than ever before – laser-focused on realizing the transformative potential of #genetherapy for patients.” – Dave Lennon, President, during #WMIF2021
Patricia Musolino, MD PhD, Co-Director Pediatric Stroke and Cerebrovascular Program at MGH, discusses her work developing #genetherapy treatments for cerebral genetic vasculopathies #GCT #geneandcelltherapy #WMIF2021
chair Dr. Joan Miller moderates a panel on AAV gene therapy featuring director of Inherited Retinal Disorders Service and Ocular Genomics Institute, Dr. Eric Pierce.
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Mass General Brigham Innovation
@MGBInnovation
· 23h
Our “AAV Success Studies | Retinal Dystrophy | Spinal Muscular Atrophy” panelists have taken the stage. #WMIF2021 @MassEyeAndEar @REGENXBIO @spark_tx @NovartisGene
We are proud sponsors of the Virtual World Medical Innovation Forum (#WMIF2021). This year’s program will focus on the impact of gene and cell therapy as a way to potentially advance quality patient care, reduce cost and improve outcomes. Learn more:
Jonathan Kraft introducing #wmif2021 session with Pfizer CSO & president of R&D Mikael Dolsten and MGH oncologist & chair of MGH Cancer Center Daniel Haber.
president Dave Lennon & Deerfield partner Julian Harris having a “fireside chat.” Dave/Novartis: sees gene therapy as driver for economy generating need for highly skilled workers Incl manufacturing
Kite Pharma CEO (Gilead subsidiary) Christi Shaw said there are 120 biopharma companies working on CAR-T cell therapy & they are continuing to look for new partnerships. She also mentioned logistical challenges currently getting to Israel & helping patients there. #WMIF2021
FDA’s Dir of Center for Biologics Evaluation & Research Peter Marks interviewed by Vicki Sato- chairwoman of Vir Biotechnology, ex Vertex president & ex Biogen VP Research. Around June ’20, started 2c progress in covid vaccines w/ enough candidates moving forward #WMIF2021 1/n
“Once you work on cell and gene therapy, its really hard to go back and work on anything else” says moderator Marcela Maus, MD PhD in our “CAR-T | Lessons Learned | What’s Next” panel #WMIF2021#GCT#geneandcelltherapy
Ex Merck president R&D Roger Perlmutter is now Eikon Therapeutics CEO & is on #WMIF2021 oncolytic virus in cancer panel w/Amgen EVP R&D David Reese, ex BioVex CTO (T-VEC inventor
, join our leaders for panels and presentations discussing what’s next for #genetherapy and the key trends shaping the industry as it evolves. #WMIF2021https://bit.ly/3eYYls4
Dolsten/Pfizer discussed covid vaccines and real world evidence study in Israel. Was sole provider of vaccines in Israel. 95%-98% efficacy replicated in real world. Well above 90% efficacy in asymptomatic disease. #wmif2021
ICYMI: An illustration depicting the “AAV Delivery” panel discussion about advances in the area of #AAVGeneTherapy delivery. Thank you to the panelists from
Casey Maguire PhD, Associate Professor of Neurology, at the podium to present his work developing improved #genetherapy vectors. #WMIF2021 “First Look: Enhanced Gene Delivery and Immunoevasion of AAV Vectors without Capsid Modification”
Casey Maguire PhD, Associate Professor of Neurology, at the podium to present his work developing improved #genetherapy vectors. #WMIF2021 “First Look: Enhanced Gene Delivery and Immunoevasion of AAV Vectors without Capsid Modification”
Mikael Dolsten, MD PhD, CSO & President, Worldwide Research, Development and Medical @pfizer takes the stage for a Fireside Chat, moderated by @MGHCancerCenter Daniel Haber, MD, PhD. “Pfizer’s Future in Cell and Gene Therapy” #WMIF2021
Dave Lennon/Novartis: manufacturing has been a roadblock for many cell & gene therapy companies. Expects to see more investments earlier. Engineering advances will unlock scale & address bigger & bigger patient populations. Oppty to ID patients early #WMIF2021
Marianne De Backer/Bayer on post M&A & company culture: They acquired AskBio & thought about how to preserve their freedom so they could continue to operate. Bayer decided to keep them independent & so they can operate at arm’s length. #wmif2021
Ken Custer/Eli Lilly-said they’re relatively new in cell & gene therapy. They invested in 1 of Sean Nolan’s (ex AveXis CEO) new companies,Jaguar Gene Therapy. Lilly’s legacy in neuroscience is noted & bought Prevail last yr. Clinical trial w/ Parkinson’s w/GBA1 mutation #wmif2021
, was the first in the U.S. to be approved for FDA gene therapy surgery. In 2018 he underwent therapy to treat retinitis pigmentosa by having a synthetic gene inserted into his retina. With improved eyesight he can now play sports #WMIF2021
The acquisition market in #GCT: looking for breakthroughs for patients, technologies for intractable diseases, manufacturing expertise, pioneering companies with deep experience — all for “the modality of the future”. M&A panel at #WMIF2021
Christi Shaw/Kite Pharma: Only 4 out of 10 patients eligible for CAR-T are being referred for CAR-T cell therapy by oncologists. The other 6 out of 10, referred to palliative care only. Consistency of manufacturing is also very important. #wmif2021 1/n
Marianne De Backer/Bayer on post M&A & company culture: They acquired AskBio & thought about how to preserve their freedom so they could continue to operate. Bayer decided to keep them independent & so they can operate at arm’s length. #wmif2021
From High-Throughput Assay to Systems Biology: New Tools for Drug Discovery
Curator: Stephen J. Williams, PhD
Marc W. Kirschner*
Department of Systems Biology Harvard Medical School
Boston, Massachusetts 02115
With the new excitement about systems biology, there is understandable interest in a definition. This has proven somewhat difficult. Scientific fields, like species, arise by descent with modification, so in their earliest forms even the founders of great dynasties are only marginally different than their sister fields and species. It is only in retrospect that we can recognize the significant founding events. Before embarking on a definition of systems biology, it may be worth remembering that confusion and controversy surrounded the introduction of the term “molecular biology,” with claims that it hardly differed from biochemistry. Yet in retrospect molecular biology was new and different. It introduced both new subject matter and new technological approaches, in addition to a new style.
As a point of departure for systems biology, consider the quintessential experiment in the founding of molecular biology, the one gene one enzyme hypothesis of Beadle and Tatum. This experiment first connected the genotype directly to the phenotype on a molecular level, although efforts in that direction can certainly be found in the work of Archibald Garrod, Sewell Wright, and others. Here a protein (in this case an enzyme) is seen to be a product of a single gene, and a single function; the completion of a specific step in amino acid biosynthesis is the direct result. It took the next 30 years to fill in the gaps in this process. Yet the one gene one enzyme hypothesis looks very different to us today. What is the function of tubulin, of PI-3 kinase or of rac? Could we accurately predict the phenotype of a nonlethal mutation in these genes in a multicellular organism? Although we can connect structure to the gene, we can no longer infer its larger purpose in the cell or in the organism. There are too many purposes; what the protein does is defined by context. The context also includes a history, either developmental or physiological. Thus the behavior of the Wnt signaling pathway depends on the previous lineage, the “where and when” questions of embryonic development. Similarly the behavior of the immune system depends on previous experience in a variable environment. All of these features stress how inadequate an explanation for function we can achieve solely by trying to identify genes (by annotating them!) and characterizing their transcriptional control circuits.
That we are at a crossroads in how to explore biology is not at all clear to many. Biology is hardly in its dotage; the process of discovery seems to have been perfected, accelerated, and made universally applicable to all fields of biology. With the completion of the human genome and the genomes of other species, we have a glimpse of many more genes than we ever had before to study. We are like naturalists discovering a new continent, enthralled with the diversity itself. But we have also at the same time glimpsed the finiteness of this list of genes, a disturbingly small list. We have seen that the diversity of genes cannot approximate the diversity of functions within an organism. In response, we have argued that combinatorial use of small numbers of components can generate all the diversity that is needed. This has had its recent incarnation in the simplistic view that the rules of cis-regulatory control on DNA can directly lead to an understanding of organisms and their evolution. Yet this assumes that the gene products can be linked together in arbitrary combinations, something that is not assured in chemistry. It also downplays the significant regulatory features that involve interactions between gene products, their localization, binding, posttranslational modification, degradation, etc. The big question to understand in biology is not regulatory linkage but the nature of biological systems that allows them to be linked together in many nonlethal and even useful combinations. More and more we come to realize that understanding the conserved genes and their conserved circuits will require an understanding of their special properties that allow them to function together to generate different phenotypes in different tissues of metazoan organisms. These circuits may have certain robustness, but more important they have adaptability and versatility. The ease of putting conserved processes under regulatory control is an inherent design feature of the processes themselves. Among other things it loads the deck in evolutionary variation and makes it more feasible to generate useful phenotypes upon which selection can act.
Systems biology offers an opportunity to study how the phenotype is generated from the genotype and with it a glimpse of how evolution has crafted the phenotype. One aspect of systems biology is the development of techniques to examine broadly the level of protein, RNA, and DNA on a gene by gene basis and even the posttranslational modification and localization of proteins. In a very short time we have witnessed the development of high-throughput biology, forcing us to consider cellular processes in toto. Even though much of the data is noisy and today partially inconsistent and incomplete, this has been a radical shift in the way we tear apart problems one interaction at a time. When coupled with gene deletions by RNAi and classical methods, and with the use of chemical tools tailored to proteins and protein domains, these high-throughput techniques become still more powerful.
High-throughput biology has opened up another important area of systems biology: it has brought us out into the field again or at least made us aware that there is a world outside our laboratories. Our model systems have been chosen intentionally to be of limited genetic diversity and examined in a highly controlled and reproducible environment. The real world of ecology, evolution, and human disease is a very different place. When genetics separated from the rest of biology in the early part of the 20th century, most geneticists sought to understand heredity and chose to study traits in the organism that could be easily scored and could be used to reveal genetic mechanisms. This was later extended to powerful effect to use genetics to study cell biological and developmental mechanisms. Some geneticists, including a large school in Russia in the early 20th century, continued to study the genetics of natural populations, focusing on traits important for survival. That branch of genetics is coming back strongly with the power of phenotypic assays on the RNA and protein level. As human beings we are most concerned not with using our genetic misfortunes to unravel biology’s complexity (important as that is) but with the role of our genetics in our individual survival. The context for understanding this is still not available, even though the data are now coming in torrents, for many of the genes that will contribute to our survival will have small quantitative effects, partially masked or accentuated by other genetic and environmental conditions. To understand the genetic basis of disease will require not just mapping these genes but an understanding of how the phenotype is created in the first place and the messy interactions between genetic variation and environmental variation.
Extracts and explants are relatively accessible to synthetic manipulation. Next there is the explicit reconstruction of circuits within cells or the deliberate modification of those circuits. This has occurred for a while in biology, but the difference is that now we wish to construct or intervene with the explicit purpose of describing the dynamical features of these synthetic or partially synthetic systems. There are more and more tools to intervene and more and more tools to measure. Although these fall short of total descriptions of cells and organisms, the detailed information will give us a sense of the special life-like processes of circuits, proteins, cells in tissues, and whole organisms in their environment. This meso-scale systems biology will help establish the correspondence between molecules and large-scale physiology.
You are probably running out of patience for some definition of systems biology. In any case, I do not think the explicit definition of systems biology should come from me but should await the words of the first great modern systems biologist. She or he is probably among us now. However, if forced to provide some kind of label for systems biology, I would simply say that systems biology is the study of the behavior of complex biological organization and processes in terms of the molecular constituents. It is built on molecular biology in its special concern for information transfer, on physiology for its special concern with adaptive states of the cell and organism, on developmental biology for the importance of defining a succession of physiological states in that process, and on evolutionary biology and ecology for the appreciation that all aspects of the organism are products of selection, a selection we rarely understand on a molecular level. Systems biology attempts all of this through quantitative measurement, modeling, reconstruction, and theory. Systems biology is not a branch of physics but differs from physics in that the primary task is to understand how biology generates variation. No such imperative to create variation exists in the physical world. It is a new principle that Darwin understood and upon which all of life hinges. That sounds different enough for me to justify a new field and a new name. Furthermore, the success of systems biology is essential if we are to understand life; its success is far from assured—a good field for those seeking risk and adventure.
Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no “one size fits all” approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.
5.1.5. Large-Scale Proteomics
While FITExP is based on protein expression regulation during apoptosis, a study of Ruprecht et al. showed that proteomic changes are induced both by cytotoxic and non-cytotoxic compounds, which can be detected by mass spectrometry to give information on a compound’s mechanism of action. They developed a large-scale proteome-wide mass spectrometry analysis platform for MOA studies, profiling five lung cancer cell lines with over 50 drugs. Aggregation analysis over the different cell lines and the different compounds showed that one-quarter of the drugs changed the abundance of their protein target. This approach allowed target confirmation of molecular degraders such as PROTACs or molecular glues. Finally, this method yielded unexpected off-target mechanisms for the MAP2K1/2 inhibitor PD184352 and the ALK inhibitor ceritinib [97]. While such a mapping approach clearly provides a wealth of information, it might not be easily attainable for groups that are not equipped for high-throughput endeavors.
All-in-all, mass spectrometry methods have gained a lot of traction in recent years and have been successfully applied for target deconvolution and MOA studies of small molecules. As with all high-throughput methods, challenges lie in the accessibility of the instruments (both from a time and cost perspective) and data analysis of complex and extensive data sets.
5.2. Genetic Approaches
Both label-based and mass spectrometry proteomic approaches are based on the physical interaction between a small molecule and a protein target, and focus on the proteome for target deconvolution. It has been long realized that genetics provides an alternative avenue to understand a compound’s action, either through precise modification of protein levels, or by inducing protein mutations. First realized in yeast as a genetically tractable organism over 20 years ago, recent advances in genetic manipulation of mammalian cells have opened up important opportunities for target identification and MOA studies through genetic screening in relevant cell types [98]. Genetic approaches can be roughly divided into two main areas, with the first centering on the identification of mutations that confer compound resistance (Figure 3a), and the second on genome-wide perturbation of gene function and the concomitant changes in sensitivity to the compound (Figure 3b). While both methods can be used to identify or confirm drug targets, the latter category often provides many additional insights in the compound’s mode of action.
Figure 3. Genetic methods for target identification and mode of action studies. Schematic representations of (a) resistance cloning, and (b) chemogenetic interaction screens.
5.2.1. Resistance Cloning
The “gold standard” in drug target confirmation is to identify mutations in the presumed target protein that render it insensitive to drug treatment. Conversely, different groups have sought to use this principle as a target identification method based on the concept that cells grown in the presence of a cytotoxic drug will either die or develop mutations that will make them resistant to the compound. With recent advances in deep sequencing it is now possible to then scan the transcriptome [99] or genome [100] of the cells for resistance-inducing mutations. Genes that are mutated are then hypothesized to encode the protein target. For this approach to be successful, there are two initial requirements: (1) the compound needs to be cytotoxic for resistant clones to arise, and (2) the cell line needs to be genetically unstable for mutations to occur in a reasonable timeframe.
In 2012, the Kapoor group demonstrated in a proof-of-concept study that resistance cloning in mammalian cells, coupled to transcriptome sequencing (RNA-seq), yields the known polo-like kinase 1 (PLK1) target of the small molecule BI 2536. For this, they used the cancer cell line HCT-116, which is deficient in mismatch repair and consequently prone to mutations. They generated and sequenced multiple resistant clones, and clustered the clones based on similarity. PLK1 was the only gene that was mutated in multiple groups. Of note, one of the groups did not contain PLK1 mutations, but rather developed resistance through upregulation of ABCBA1, a drug efflux transporter, which is a general and non-specific resistance mechanism [101]. In a following study, they optimized their pipeline “DrugTargetSeqR”, by counter-screening for these types of multidrug resistance mechanisms so that these clones were excluded from further analysis (Figure 3a). Furthermore, they used CRISPR/Cas9-mediated gene editing to determine which mutations were sufficient to confer drug resistance, and as independent validation of the biochemical relevance of the obtained hits [102].
While HCT-116 cells are a useful model cell line for resistance cloning because of their genomic instability, they may not always be the cell line of choice, depending on the compound and process that is studied. Povedana et al. used CRISPR/Cas9 to engineer mismatch repair deficiencies in Ewing sarcoma cells and small cell lung cancer cells. They found that deletion of MSH2 results in hypermutations in these normally mutationally silent cells, resulting in the formation of resistant clones in the presence of bortezomib, MLN4924, and CD437, which are all cytotoxic compounds [103]. Recently, Neggers et al. reasoned that CRISPR/Cas9-induced non-homologous end-joining repair could be a viable strategy to create a wide variety of functional mutants of essential genes through in-frame mutations. Using a tiled sgRNA library targeting 75 target genes of investigational neoplastic drugs in HAP1 and K562 cells, they generated several KPT-9274 (an anticancer agent with unknown target)-resistant clones, and subsequent deep sequencing showed that the resistant clones were enriched in NAMPT sgRNAs. Direct target engagement was confirmed by co-crystallizing the compound with NAMPT [104]. In addition to these genetic mutation strategies, an alternative method is to grow the cells in the presence of a mutagenic chemical to induce higher mutagenesis rates [105,106].
When there is already a hypothesis on the pathway involved in compound action, the resistance cloning methodology can be extended to non-cytotoxic compounds. Sekine et al. developed a fluorescent reporter model for the integrated stress response, and used this cell line for target deconvolution of a small molecule inhibitor towards this pathway (ISRIB). Reporter cells were chemically mutagenized, and ISRIB-resistant clones were isolated by flow cytometry, yielding clones with various mutations in the delta subunit of guanine nucleotide exchange factor eIF2B [107].
While there are certainly successful examples of resistance cloning yielding a compound’s direct target as discussed above, resistance could also be caused by mutations or copy number alterations in downstream components of a signaling pathway. This is illustrated by clinical examples of acquired resistance to small molecules, nature’s way of “resistance cloning”. For example, resistance mechanisms in Hedgehog pathway-driven cancers towards the Smoothened inhibitor vismodegib include compound-resistant mutations in Smoothened, but also copy number changes in downstream activators SUFU and GLI2 [108]. It is, therefore, essential to conduct follow-up studies to confirm a direct interaction between a compound and the hit protein, as well as a lack of interaction with the mutated protein.
5.2.3. “Chemogenomics”: Examples of Gene-Drug Interaction Screens
When genetic perturbations are combined with small molecule drugs in a chemogenetic interaction screen, the effect of a gene’s perturbation on compound action is studied. Gene perturbation can render the cells resistant to the compound (suppressor interaction), or conversely, result in hypersensitivity and enhanced compound potency (synergistic interaction) [5,117,121]. Typically, cells are treated with the compound at a sublethal dose, to ascertain that both types of interactions can be found in the final dataset, and often it is necessary to use a variety of compound doses (i.e., LD20, LD30, LD50) and timepoints to obtain reliable insights (Figure 3b).
An early example of successful coupling of a phenotypic screen and downstream genetic screening for target identification is the study of Matheny et al. They identified STF-118804 as a compound with antileukemic properties. Treatment of MV411 cells, stably transduced with a high complexity, genome-wide shRNA library, with STF-118804 (4 rounds of increasing concentration) or DMSO control resulted in a marked depletion of cells containing shRNAs against nicotinamide phosphoribosyl transferase (NAMPT) [122].
The Bassik lab subsequently directly compared the performance of shRNA-mediated knockdown versus CRISPR/Cas9-knockout screens for the target elucidation of the antiviral drug GSK983. The data coming out of both screens were complementary, with the shRNA screen resulting in hits leading to the direct compound target and the CRISPR screen giving information on cellular mechanisms of action of the compound. A reason for this is likely the level of protein depletion that is reached by these methods: shRNAs lead to decreased protein levels, which is advantageous when studying essential genes. However, knockdown may not result in a phenotype for non-essential genes, in which case a full CRISPR-mediated knockout is necessary to observe effects [123].
Another NAMPT inhibitor was identified in a CRISPR/Cas9 “haplo-insufficiency (HIP)”-like approach [124]. Haploinsuffiency profiling is a well-established system in yeast which is performed in a ~50% protein background by heterozygous deletions [125]. As there is no control over CRISPR-mediated loss of alleles, compound treatment was performed at several timepoints after addition of the sgRNA library to HCT116 cells stably expressing Cas9, in the hope that editing would be incomplete at early timepoints, resulting in residual protein levels. Indeed, NAMPT was found to be the target of phenotypic hit LB-60-OF61, especially at earlier timepoints, confirming the hypothesis that some level of protein needs to be present to identify a compound’s direct target [124]. This approach was confirmed in another study, thereby showing that direct target identification through CRISPR-knockout screens is indeed possible [126].
An alternative strategy was employed by the Weissman lab, where they combined genome-wide CRISPR-interference and -activation screens to identify the target of the phase 3 drug rigosertib. They focused on hits that had opposite action in both screens, as in sensitizing in one but protective in the other, which were related to microtubule stability. In a next step, they created chemical-genetic profiles of a variety of microtubule destabilizing agents, rationalizing that compounds with the same target will have similar drug-gene interactions. For this, they made a focused library of sgRNAs, based on the most high-ranking hits in the rigosertib genome-wide CRISPRi screen, and compared the focused screen results of the different compounds. The profile for rigosertib clustered well with that of ABT-571, and rigorous target validation studies confirmed rigosertib binding to the colchicine binding site of tubulin—the same site as occupied by ABT-571 [127].
From the above examples, it is clear that genetic screens hold a lot of promise for target identification and MOA studies for small molecules. The CRISPR screening field is rapidly evolving, sgRNA libraries are continuously improving and increasingly commercially available, and new tools for data analysis are being developed [128]. The challenge lies in applying these screens to study compounds that are not cytotoxic, where finding the right dosage regimen will not be trivial.
SYSTEMS BIOLOGY AND CANCER RESEARCH & DRUG DISCOVERY
Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence
The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question
1. Introduction
The development and widespread use of high-throughput technologies founded the era of big data in biology and medicine. In particular, it led to an accumulation of large-scale data sets that opened a vast amount of possible applications for data-driven methodologies. In cancer, these applications range from fundamental research to clinical applications: molecular characteristics of tumors, tumor heterogeneity, drug discovery and potential treatments strategy. Therefore, data-driven bioinformatics research areas have tailored data mining technologies such as systems biology, machine learning, and deep learning, elaborated in this review paper (see Figure 1 and Figure 2). For example, in systems biology, data-driven approaches are applied to identify vital signaling pathways [1]. This pathway-centric analysis is particularly crucial in cancer research to understand the characteristics and heterogeneity of the tumor and tumor subtypes. Consequently, this high-throughput data-based analysis enables us to explore characteristics of cancers with a systems biology and a systems medicine point of view [2].Combining high-throughput techniques, especially next-generation sequencing (NGS), with appropriate analytical tools has allowed researchers to gain a deeper systematic understanding of cancer at various biological levels, most importantly genomics, transcriptomics, and epigenetics [3,4]. Furthermore, more sophisticated analysis tools based on computational modeling are introduced to decipher underlying molecular mechanisms in various cancer types. The increasing size and complexity of the data required the adaptation of bioinformatics processing pipelines for higher efficiency and sophisticated data mining methodologies, particularly for large-scale, NGS datasets [5]. Nowadays, more and more NGS studies integrate a systems biology approach and combine sequencing data with other types of information, for instance, protein family information, pathway, or protein–protein interaction (PPI) networks, in an integrative analysis. Experimentally validated knowledge in systems biology may enhance analysis models and guides them to uncover novel findings. Such integrated analyses have been useful to extract essential information from high-dimensional NGS data [6,7]. In order to deal with the increasing size and complexity, the application of machine learning, and specifically deep learning methodologies, have become state-of-the-art in NGS data analysis.
Figure 1. Next-generation sequencing data can originate from various experimental and technological conditions. Depending on the purpose of the experiment, one or more of the depicted omics types (Genomics, Transcriptomics, Epigenomics, or Single-Cell Omics) are analyzed. These approaches led to an accumulation of large-scale NGS datasets to solve various challenges of cancer research, molecular characterization, tumor heterogeneity, and drug target discovery. For instance, The Cancer Genome Atlas (TCGA) dataset contains multi-omics data from ten-thousands of patients. This dataset facilitates a variety of cancer researches for decades. Additionally, there are also independent tumor datasets, and, frequently, they are analyzed and compared with the TCGA dataset. As the large scale of omics data accumulated, various machine learning techniques are applied, e.g., graph algorithms and deep neural networks, for dimensionality reduction, clustering, or classification. (Created with BioRender.com.)
Figure 2. (a) A multitude of different types of data is produced by next-generation sequencing, for instance, in the fields of genomics, transcriptomics, and epigenomics. (b) Biological networks for biomarker validation: The in vivo or in vitro experiment results are considered ground truth. Statistical analysis on next-generation sequencing data produces candidate genes. Biological networks can validate these candidate genes and highlight the underlying biological mechanisms (Section 2.1). (c) De novo construction of Biological Networks: Machine learning models that aim to reconstruct biological networks can incorporate prior knowledge from different omics data. Subsequently, the model will predict new unknown interactions based on new omics information (Section 2.2). (d) Network-based machine learning: Machine learning models integrating biological networks as prior knowledge to improve predictive performance when applied to different NGS data (Section 2.3). (Created with BioRender.com).
Therefore, a large number of studies integrate NGS data with machine learning and propose a novel data-driven methodology in systems biology [8]. In particular, many network-based machine learning models have been developed to analyze cancer data and help to understand novel mechanisms in cancer development [9,10]. Moreover, deep neural networks (DNN) applied for large-scale data analysis improved the accuracy of computational models for mutation prediction [11,12], molecular subtyping [13,14], and drug repurposing [15,16].
2. Systems Biology in Cancer Research
Genes and their functions have been classified into gene sets based on experimental data. Our understandings of cancer concentrated into cancer hallmarks that define the characteristics of a tumor. This collective knowledge is used for the functional analysis of unseen data.. Furthermore, the regulatory relationships among genes were investigated, and, based on that, a pathway can be composed. In this manner, the accumulation of public high-throughput sequencing data raised many big-data challenges and opened new opportunities and areas of application for computer science. Two of the most vibrantly evolving areas are systems biology and machine learning which tackle different tasks such as understanding the cancer pathways [9], finding crucial genes in pathways [22,53], or predicting functions of unidentified or understudied genes [54]. Essentially, those models include prior knowledge to develop an analysis and enhance interpretability for high-dimensional data [2]. In addition to understanding cancer pathways with in silico analysis, pathway activity analysis incorporating two different types of data, pathways and omics data, is developed to understand heterogeneous characteristics of the tumor and cancer molecular subtyping. Due to its advantage in interpretability, various pathway-oriented methods are introduced and become a useful tool to understand a complex diseases such as cancer [55,56,57].
In this section, we will discuss how two related research fields, namely, systems biology and machine learning, can be integrated with three different approaches (see Figure 2), namely, biological network analysis for biomarker validation, the use of machine learning with systems biology, and network-based models.
2.1. Biological Network Analysis for Biomarker Validation
The detection of potential biomarkers indicative of specific cancer types or subtypes is a frequent goal of NGS data analysis in cancer research. For instance, a variety of bioinformatics tools and machine learning models aim at identify lists of genes that are significantly altered on a genomic, transcriptomic, or epigenomic level in cancer cells. Typically, statistical and machine learning methods are employed to find an optimal set of biomarkers, such as single nucleotide polymorphisms (SNPs), mutations, or differentially expressed genes crucial in cancer progression. Traditionally, resource-intensive in vitro analysis was required to discover or validate those markers. Therefore, systems biology offers in silico solutions to validate such findings using biological pathways or gene ontology information (Figure 2b) [58]. Subsequently, gene set enrichment analysis (GSEA) [50] or gene set analysis (GSA) [59] can be used to evaluate whether these lists of genes are significantly associated with cancer types and their specific characteristics. GSA, for instance, is available via web services like DAVID [60] and g:Profiler [61]. Moreover, other applications use gene ontology directly [62,63]. In addition to gene-set-based analysis, there are other methods that focuse on the topology of biological networks. These approaches evaluate various network structure parameters and analyze the connectivity of two genes or the size and interconnection of their neighbors [64,65]. According to the underlying idea, the mutated gene will show dysfunction and can affect its neighboring genes. Thus, the goal is to find abnormalities in a specific set of genes linked with an edge in a biological network. For instance, KeyPathwayMiner can extract informative network modules in various omics data [66]. In summary, these approaches aim at predicting the effect of dysfunctional genes among neighbors according to their connectivity or distances from specific genes such as hubs [67,68]. During the past few decades, the focus of cancer systems biology extended towards the analysis of cancer-related pathways since those pathways tend to carry more information than a gene set. Such analysis is called Pathway Enrichment Analysis (PEA) [69,70]. The use of PEA incorporates the topology of biological networks. However, simultaneously, the lack of coverage issue in pathway data needs to be considered. Because pathway data does not cover all known genes yet, an integration analysis on omics data can significantly drop in genes when incorporated with pathways. Genes that can not be mapped to any pathway are called ‘pathway orphan.’ In this manner, Rahmati et al. introduced a possible solution to overcome the ‘pathway orphan’ issue [71]. At the bottom line, regardless of whether researchers consider gene-set or pathway-based enrichment analysis, the performance and accuracy of both methods are highly dependent on the quality of the external gene-set and pathway data [72].
2.2. De Novo Construction of Biological Networks
While the known fraction of existing biological networks barely scratches the surface of the whole system of mechanisms occurring in each organism, machine learning models can improve on known network structures and can guide potential new findings [73,74]. This area of research is called de novo network construction (Figure 2c), and its predictive models can accelerate experimental validation by lowering time costs [75,76]. This interplay between in silico biological networks building and mining contributes to expanding our knowledge in a biological system. For instance, a gene co-expression network helps discover gene modules having similar functions [77]. Because gene co-expression networks are based on expressional changes under specific conditions, commonly, inferring a co-expression network requires many samples. The WGCNA package implements a representative model using weighted correlation for network construction that leads the development of the network biology field [78]. Due to NGS developments, the analysis of gene co-expression networks subsequently moved from microarray-based to RNA-seq based experimental data [79]. However, integration of these two types of data remains tricky. Ballouz et al. compared microarray and NGS-based co-expression networks and found the existence of a bias originating from batch effects between the two technologies [80]. Nevertheless, such approaches are suited to find disease-specific co-expressional gene modules. Thus, various studies based on the TCGA cancer co-expression network discovered characteristics of prognostic genes in the network [81]. Accordingly, a gene co-expression network is a condition-specific network rather than a general network for an organism. Gene regulatory networks can be inferred from the gene co-expression network when various data from different conditions in the same organism are available. Additionally, with various NGS applications, we can obtain multi-modal datasets about regulatory elements and their effects, such as epigenomic mechanisms on transcription and chromatin structure. Consequently, a gene regulatory network can consist of solely protein-coding genes or different regulatory node types such as transcription factors, inhibitors, promoter interactions, DNA methylations, and histone modifications affecting the gene expression system [82,83]. More recently, researchers were able to build networks based on a particular experimental setup. For instance, functional genomics or CRISPR technology enables the high-resolution regulatory networks in an organism [84]. Other than gene co-expression or regulatory networks, drug target, and drug repurposing studies are active research areas focusing on the de novo construction of drug-to-target networks to allow the potential repurposing of drugs [76,85].
2.3. Network Based Machine Learning
A network-based machine learning model directly integrates the insights of biological networks within the algorithm (Figure 2d) to ultimately improve predictive performance concerning cancer subtyping or susceptibility to therapy. Following the establishment of high-quality biological networks based on NGS technologies, these biological networks were suited to be integrated into advanced predictive models. In this manner, Zhang et al., categorized network-based machine learning approaches upon their usage into three groups: (i) model-based integration, (ii) pre-processing integration, and (iii) post-analysis integration [7]. Network-based models map the omics data onto a biological network, and proper algorithms travel the network while considering both values of nodes and edges and network topology. In the pre-processing integration, pathway or other network information is commonly processed based on its topological importance. Meanwhile, in the post-analysis integration, omics data is processed solely before integration with a network. Subsequently, omics data and networks are merged and interpreted. The network-based model has advantages in multi-omics integrative analysis. Due to the different sensitivity and coverage of various omics data types, a multi-omics integrative analysis is challenging. However, focusing on gene-level or protein-level information enables a straightforward integration [86,87]. Consequently, when different machine learning approaches tried to integrate two or more different data types to find novel biological insights, one of the solutions is reducing the search space to gene or protein level and integrated heterogeneous datatypes [25,88].
In summary, using network information opens new possibilities for interpretation. However, as mentioned earlier, several challenges remain, such as the coverage issue. Current databases for biological networks do not cover the entire set of genes, transcripts, and interactions. Therefore, the use of networks can lead to loss of information for gene or transcript orphans. The following section will focus on network-based machine learning models and their application in cancer genomics. We will put network-based machine learning into the perspective of the three main areas of application, namely, molecular characterization, tumor heterogeneity analysis, and cancer drug discovery.
3. Network-Based Learning in Cancer Research
As introduced previously, the integration of machine learning with the insights of biological networks (Figure 2d) ultimately aims at improving predictive performance and interpretability concerning cancer subtyping or treatment susceptibility.
3.1. Molecular Characterization with Network Information
Various network-based algorithms are used in genomics and focus on quantifying the impact of genomic alteration. By employing prior knowledge in biological network algorithms, performance compared to non-network models can be improved. A prominent example is HotNet. The algorithm uses a thermodynamics model on a biological network and identifies driver genes, or prognostic genes, in pan-cancer data [89]. Another study introduced a network-based stratification method to integrate somatic alterations and expression signatures with network information [90]. These approaches use network topology and network-propagation-like algorithms. Network propagation presumes that genomic alterations can affect the function of neighboring genes. Two genes will show an exclusive pattern if two genes complement each other, and the function carried by those two genes is essential to an organism [91]. This unique exclusive pattern among genomic alteration is further investigated in cancer-related pathways. Recently, Ku et al. developed network-centric approaches and tackled robustness issues while studying synthetic lethality [92]. Although synthetic lethality was initially discovered in model organisms of genetics, it helps us to understand cancer-specific mutations and their functions in tumor characteristics [91].
Furthermore, in transcriptome research, network information is used to measure pathway activity and its application in cancer subtyping. For instance, when comparing the data of two or more conditions such as cancer types, GSEA as introduced in Section 2 is a useful approach to get an overview of systematic changes [50]. It is typically used at the beginning of a data evaluation [93]. An experimentally validated gene set can provide information about how different conditions affect molecular systems in an organism. In addition to the gene sets, different approaches integrate complex interaction information into GSEA and build network-based models [70]. In contrast to GSEA, pathway activity analysis considers transcriptome data and other omics data and structural information of a biological network. For example, PARADIGM uses pathway topology and integrates various omics in the analysis to infer a patient-specific status of pathways [94]. A benchmark study with pan-cancer data recently reveals that using network structure can show better performance [57]. In conclusion, while the loss of data is due to the incompleteness of biological networks, their integration improved performance and increased interpretability in many cases.
3.2. Tumor Heterogeneity Study with Network Information
The tumor heterogeneity can originate from two directions, clonal heterogeneity and tumor impurity. Clonal heterogeneity covers genomic alterations within the tumor [95]. While de novo mutations accumulate, the tumor obtains genomic alterations with an exclusive pattern. When these genomic alterations are projected on the pathway, it is possible to observe exclusive relationships among disease-related genes. For instance, the CoMEt and MEMo algorithms examine mutual exclusivity on protein–protein interaction networks [96,97]. Moreover, the relationship between genes can be essential for an organism. Therefore, models analyzing such alterations integrate network-based analysis [98].
In contrast, tumor purity is dependent on the tumor microenvironment, including immune-cell infiltration and stromal cells [99]. In tumor microenvironment studies, network-based models are applied, for instance, to find immune-related gene modules. Although the importance of the interaction between tumors and immune cells is well known, detailed mechanisms are still unclear. Thus, many recent NGS studies employ network-based models to investigate the underlying mechanism in tumor and immune reactions. For example, McGrail et al. identified a relationship between the DNA damage response protein and immune cell infiltration in cancer. The analysis is based on curated interaction pairs in a protein–protein interaction network [100]. Most recently, Darzi et al. discovered a prognostic gene module related to immune cell infiltration by using network-centric approaches [101]. Tu et al. presented a network-centric model for mining subnetworks of genes other than immune cell infiltration by considering tumor purity [102].
3.3. Drug Target Identification with Network Information
In drug target studies, network biology is integrated into pharmacology [103]. For instance, Yamanishi et al. developed novel computational methods to investigate the pharmacological space by integrating a drug-target protein network with genomics and chemical information. The proposed approaches investigated such drug-target network information to identify potential novel drug targets [104]. Since then, the field has continued to develop methods to study drug target and drug response integrating networks with chemical and multi-omic datasets. In a recent survey study by Chen et al., the authors compared 13 computational methods for drug response prediction. It turned out that gene expression profiles are crucial information for drug response prediction [105].
Moreover, drug-target studies are often extended to drug-repurposing studies. In cancer research, drug-repurposing studies aim to find novel interactions between non-cancer drugs and molecular features in cancer. Drug-repurposing (or repositioning) studies apply computational approaches and pathway-based models and aim at discovering potential new cancer drugs with a higher probability than de novo drug design [16,106]. Specifically, drug-repurposing studies can consider various areas of cancer research, such as tumor heterogeneity and synthetic lethality. As an example, Lee et al. found clinically relevant synthetic lethality interactions by integrating multiple screening NGS datasets [107]. This synthetic lethality and related-drug datasets can be integrated for an effective combination of anticancer therapeutic strategy with non-cancer drug repurposing.
4. Deep Learning in Cancer Research
DNN models develop rapidly and become more sophisticated. They have been frequently used in all areas of biomedical research. Initially, its development was facilitated by large-scale imaging and video data. While most data sets in the biomedical field would not typically be considered big data, the rapid data accumulation enabled by NGS made it suitable for the application of DNN models requiring a large amount of training data [108]. For instance, in 2019, Samiei et al. used TCGA-based large-scale cancer data as benchmark datasets for bioinformatics machine learning research such as Image-Net in the computer vision field [109]. Subsequently, large-scale public cancer data sets such as TCGA encouraged the wide usage of DNNs in the cancer domain [110]. Over the last decade, these state-of-the-art machine learning methods have been incorporated in many different biological questions [111].
In addition to public cancer databases such as TCGA, the genetic information of normal tissues is stored in well-curated databases such as GTEx [112] and 1000Genomes [113]. These databases are frequently used as control or baseline training data for deep learning [114]. Moreover, other non-curated large-scale data sources such as GEO (https://www.ncbi.nlm.nih.gov/geo/, accessed on 20 May 2021) can be leveraged to tackle critical aspects in cancer research. They store a large-scale of biological data produced under various experimental setups (Figure 1). Therefore, an integration of GEO data and other data requires careful preprocessing. Overall, an increasing amount of datasets facilitate the development of current deep learning in bioinformatics research [115].
4.1. Challenges for Deep Learning in Cancer Research
Many studies in biology and medicine used NGS and produced large amounts of data during the past few decades, moving the field to the big data era. Nevertheless, researchers still face a lack of data in particular when investigating rare diseases or disease states. Researchers have developed a manifold of potential solutions to overcome this lack of data challenges, such as imputation, augmentation, and transfer learning (Figure 3b). Data imputation aims at handling data sets with missing values [116]. It has been studied on various NGS omics data types to recover missing information [117]. It is known that gene expression levels can be altered by different regulatory elements, such as DNA-binding proteins, epigenomic modifications, and post-transcriptional modifications. Therefore, various models integrating such regulatory schemes have been introduced to impute missing omics data [118,119]. Some DNN-based models aim to predict gene expression changes based on genomics or epigenomics alteration. For instance, TDimpute aims at generating missing RNA-seq data by training a DNN on methylation data. They used TCGA and TARGET (https://ocg.cancer.gov/programs/target/data-matrix, accessed on 20 May 2021) data as proof of concept of the applicability of DNN for data imputation in a multi-omics integration study [120]. Because this integrative model can exploit information in different levels of regulatory mechanisms, it can build a more detailed model and achieve better performance than a model build on a single-omics dataset [117,121]. The generative adversarial network (GAN) is a DNN structure for generating simulated data that is different from the original data but shows the same characteristics [122]. GANs can impute missing omics data from other multi-omics sources. Recently, the GAN algorithm is getting more attention in single-cell transcriptomics because it has been recognized as a complementary technique to overcome the limitation of scRNA-seq [123]. In contrast to data imputation and generation, other machine learning approaches aim to cope with a limited dataset in different ways. Transfer learning or few-shot learning, for instance, aims to reduce the search space with similar but unrelated datasets and guide the model to solve a specific set of problems [124]. These approaches train models with data of similar characteristics and types but different data to the problem set. After pre-training the model, it can be fine-tuned with the dataset of interest [125,126]. Thus, researchers are trying to introduce few-shot learning models and meta-learning approaches to omics and translational medicine. For example, Select-ProtoNet applied the ProtoTypical Network [127] model to TCGA transcriptome data and classified patients into two groups according to their clinical status [128]. AffinityNet predicts kidney and uterus cancer subtypes with gene expression profiles [129].
Figure 3. (a) In various studies, NGS data transformed into different forms. The 2-D transformed form is for the convolution layer. Omics data is transformed into pathway level, GO enrichment score, or Functional spectra. (b) DNN application on different ways to handle lack of data. Imputation for missing data in multi-omics datasets. GAN for data imputation and in silico data simulation. Transfer learning pre-trained the model with other datasets and fine-tune. (c) Various types of information in biology. (d) Graph neural network examples. GCN is applied to aggregate neighbor information. (Created with BioRender.com).
4.2. Molecular Charactization with Network and DNN Model
DNNs have been applied in multiple areas of cancer research. For instance, a DNN model trained on TCGA cancer data can aid molecular characterization by identifying cancer driver genes. At the very early stage, Yuan et al. build DeepGene, a cancer-type classifier. They implemented data sparsity reduction methods and trained the DNN model with somatic point mutations [130]. Lyu et al. [131] and DeepGx [132] embedded a 1-D gene expression profile to a 2-D array by chromosome order to implement the convolution layer (Figure 3a). Other algorithms, such as the deepDriver, use k-nearest neighbors for the convolution layer. A predefined number of neighboring gene mutation profiles was the input for the convolution layer. It employed this convolution layer in a DNN by aggregating mutation information of the k-nearest neighboring genes [11]. Instead of embedding to a 2-D image, DeepCC transformed gene expression data into functional spectra. The resulting model was able to capture molecular characteristics by training cancer subtypes [14].
Another DNN model was trained to infer the origin of tissue from single-nucleotide variant (SNV) information of metastatic tumor. The authors built a model by using the TCGA/ICGC data and analyzed SNV patterns and corresponding pathways to predict the origin of cancer. They discovered that metastatic tumors retained their original cancer’s signature mutation pattern. In this context, their DNN model obtained even better accuracy than a random forest model [133] and, even more important, better accuracy than human pathologists [12].
4.3. Tumor Heterogeneity with Network and DNN Model
As described in Section 4.1, there are several issues because of cancer heterogeneity, e.g., tumor microenvironment. Thus, there are only a few applications of DNN in intratumoral heterogeneity research. For instance, Menden et al. developed ’Scaden’ to deconvolve cell types in bulk-cell sequencing data. ’Scaden’ is a DNN model for the investigation of intratumor heterogeneity. To overcome the lack of training datasets, researchers need to generate in silico simulated bulk-cell sequencing data based on single-cell sequencing data [134]. It is presumed that deconvolving cell types can be achieved by knowing all possible expressional profiles of the cell [36]. However, this information is typically not available. Recently, to tackle this problem, single-cell sequencing-based studies were conducted. Because of technical limitations, we need to handle lots of missing data, noises, and batch effects in single-cell sequencing data [135]. Thus, various machine learning methods were developed to process single-cell sequencing data. They aim at mapping single-cell data onto the latent space. For example, scDeepCluster implemented an autoencoder and trained it on gene-expression levels from single-cell sequencing. During the training phase, the encoder and decoder work as denoiser. At the same time, they can embed high-dimensional gene-expression profiles to lower-dimensional vectors [136]. This autoencoder-based method can produce biologically meaningful feature vectors in various contexts, from tissue cell types [137] to different cancer types [138,139].
4.4. Drug Target Identification with Networks and DNN Models
In addition to NGS datasets, large-scale anticancer drug assays enabled the training train of DNNs. Moreover, non-cancer drug response assay datasets can also be incorporated with cancer genomic data. In cancer research, a multidisciplinary approach was widely applied for repurposing non-oncology drugs to cancer treatment. This drug repurposing is faster than de novo drug discovery. Furthermore, combination therapy with a non-oncology drug can be beneficial to overcome the heterogeneous properties of tumors [85]. The deepDR algorithm integrated ten drug-related networks and trained deep autoencoders. It used a random-walk-based algorithm to represent graph information into feature vectors. This approach integrated network analysis with a DNN model validated with an independent drug-disease dataset [15].
The authors of CDRscan did an integrative analysis of cell-line-based assay datasets and other drug and genomics datasets. It shows that DNN models can enhance the computational model for improved drug sensitivity predictions [140]. Additionally, similar to previous network-based models, the multi-omics application of drug-targeted DNN studies can show higher prediction accuracy than the single-omics method. MOLI integrated genomic data and transcriptomic data to predict the drug responses of TCGA patients [141].
4.5. Graph Neural Network Model
In general, the advantage of using a biological network is that it can produce more comprehensive and interpretable results from high-dimensional omics data. Furthermore, in an integrative multi-omics data analysis, network-based integration can improve interpretability over traditional approaches. Instead of pre-/post-integration of a network, recently developed graph neural networks use biological networks as the base structure for the learning network itself. For instance, various pathways or interactome information can be integrated as a learning structure of a DNN and can be aggregated as heterogeneous information. In a GNN study, a convolution process can be done on the provided network structure of data. Therefore, the convolution on a biological network made it possible for the GNN to focus on the relationship among neighbor genes. In the graph convolution layer, the convolution process integrates information of neighbor genes and learns topological information (Figure 3d). Consequently, this model can aggregate information from far-distant neighbors, and thus can outperform other machine learning models [142].
In the context of the inference problem of gene expression, the main question is whether the gene expression level can be explained by aggregating the neighboring genes. A single gene inference study by Dutil et al. showed that the GNN model outperformed other DNN models [143]. Moreover, in cancer research, such GNN models can identify cancer-related genes with better performance than other network-based models, such as HotNet2 and MutSigCV [144]. A recent GNN study with a multi-omics integrative analysis identified 165 new cancer genes as an interactive partner for known cancer genes [145]. Additionally, in the synthetic lethality area, dual-dropout GNN outperformed previous bioinformatics tools for predicting synthetic lethality in tumors [146]. GNNs were also able to classify cancer subtypes based on pathway activity measures with RNA-seq data. Lee et al. implemented a GNN for cancer subtyping and tested five cancer types. Thus, the informative pathway was selected and used for subtype classification [147]. Furthermore, GNNs are also getting more attention in drug repositioning studies. As described in Section 3.3, drug discovery requires integrating various networks in both chemical and genomic spaces (Figure 3d). Chemical structures, protein structures, pathways, and other multi-omics data were used in drug-target identification and repurposing studies (Figure 3c). Each of the proposed applications has a specialty in the different purposes of drug-related tasks. Sun et al. summarized GNN-based drug discovery studies and categorized them into four classes: molecular property and activity prediction, interaction prediction, synthesis prediction, and de novo drug design. The authors also point out four challenges in the GNN-mediated drug discovery. At first, as we described before, there is a lack of drug-related datasets. Secondly, the current GNN models can not fully represent 3-D structures of chemical molecules and protein structures. The third challenge is integrating heterogeneous network information. Drug discovery usually requires a multi-modal integrative analysis with various networks, and GNNs can improve this integrative analysis. Lastly, although GNNs use graphs, stacked layers still make it hard to interpret the model [148].
4.6. Shortcomings in AI and Revisiting Validity of Biological Networks as Prior Knowledge
The previous sections reviewed a variety of DNN-based approaches that present a good performance on numerous applications. However, it is hardly a panacea for all research questions. In the following, we will discuss potential limitations of the DNN models. In general, DNN models with NGS data have two significant issues: (i) data requirements and (ii) interpretability. Usually, deep learning needs a large proportion of training data for reasonable performance which is more difficult to achieve in biomedical omics data compared to, for instance, image data. Today, there are not many NGS datasets that are well-curated and -annotated for deep learning. This can be an answer to the question of why most DNN studies are in cancer research [110,149]. Moreover, the deep learning models are hard to interpret and are typically considered as black-boxes. Highly stacked layers in the deep learning model make it hard to interpret its decision-making rationale. Although the methodology to understand and interpret deep learning models has been improved, the ambiguity in the DNN models’ decision-making hindered the transition between the deep learning model and translational medicine [149,150].
As described before, biological networks are employed in various computational analyses for cancer research. The studies applying DNNs demonstrated many different approaches to use prior knowledge for systematic analyses. Before discussing GNN application, the validity of biological networks in a DNN model needs to be shown. The LINCS program analyzed data of ’The Connectivity Map (CMap) project’ to understand the regulatory mechanism in gene expression by inferring the whole gene expression profiles from a small set of genes (https://lincsproject.org/, accessed on 20 May 2021) [151,152]. This LINCS program found that the gene expression level is inferrable with only nearly 1000 genes. They called this gene list ’landmark genes’. Subsequently, Chen et al. started with these 978 landmark genes and tried to predict other gene expression levels with DNN models. Integrating public large-scale NGS data showed better performance than the linear regression model. The authors conclude that the performance advantage originates from the DNN’s ability to model non-linear relationships between genes [153].
Following this study, Beltin et al. extensively investigated various biological networks in the same context of the inference of gene expression level. They set up a simplified representation of gene expression status and tried to solve a binary classification task. To show the relevance of a biological network, they compared various gene expression levels inferred from a different set of genes, neighboring genes in PPI, random genes, and all genes. However, in the study incorporating TCGA and GTEx datasets, the random network model outperformed the model build on a known biological network, such as StringDB [154]. While network-based approaches can add valuable insights to analysis, this study shows that it cannot be seen as the panacea, and a careful evaluation is required for each data set and task. In particular, this result may not represent biological complexity because of the oversimplified problem setup, which did not consider the relative gene-expressional changes. Additionally, the incorporated biological networks may not be suitable for inferring gene expression profiles because they consist of expression-regulating interactions, non-expression-regulating interactions, and various in vivo and in vitro interactions.
“ However, although recently sophisticated applications of deep learning showed improved accuracy, it does not reflect a general advancement. Depending on the type of NGS data, the experimental design, and the question to be answered, a proper approach and specific deep learning algorithms need to be considered. Deep learning is not a panacea. In general, to employ machine learning and systems biology methodology for a specific type of NGS data, a certain experimental design, a particular research question, the technology, and network data have to be chosen carefully.”
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Use of Systems Biology in Anti-Microbial Drug Development
Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance. Asma Munir, Sundeep Chaitanya Vedithi, Amanda K. Chaplin and Tom L. Blundell. Front. Genet., 04 September 2020 | https://doi.org/10.3389/fgene.2020.00965
In an earlier review article (Waman et al., 2019), we discussed various computational approaches and experimental strategies for drug target identification and structure-guided drug discovery. In this review we discuss the impact of the era of precision medicine, where the genome sequences of pathogens can give clues about the choice of existing drugs, and repurposing of others. Our focus is directed toward combatting antimicrobial drug resistance with emphasis on tuberculosis and leprosy. We describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.
Genome Sequences and Proteomic Structural Databases
In recent years, there have been many focused efforts to define the amino-acid sequences of the M. tuberculosis pan-genome and then to define the three-dimensional structures and functional interactions of these gene products. This work has led to essential genes of the bacteria being revealed and to a better understanding of the genetic diversity in different strains that might lead to a selective advantage (Coll et al., 2018). This will help with our understanding of the mode of antibiotic resistance within these strains and aid structure-guided drug discovery. However, only ∼10% of the ∼4128 proteins have structures determined experimentally.
Several databases have been developed to integrate the genomic and/or structural information linked to drug resistance in Mycobacteria (Table 1). These invaluable resources can contribute to better understanding of molecular mechanisms involved in drug resistance and improvement in the selection of potential drug targets.
There is a dearth of information related to structural aspects of proteins from M. leprae and their oligomeric and hetero-oligomeric organization, which has limited the understanding of physiological processes of the bacillus. The structures of only 12 proteins have been solved and deposited in the protein data bank (PDB). However, the high sequence similarity in protein coding genes between M. leprae and M. tuberculosis allows computational methods to be used for comparative modeling of the proteins of M. leprae. Mainly monomeric models using single template modeling have been defined and deposited in the Swiss Model repository (Bienert et al., 2017), in Modbase (Pieper et al., 2014), and in a collection with other infectious disease agents (Sosa et al., 2018). There is a need for multi-template modeling and building homo- and hetero-oligomeric complexes to better understand the interfaces, druggability and impacts of mutations.
We are now exploiting Vivace, a multi-template modeling pipeline developed in our lab for modeling the proteomes of M. tuberculosis (CHOPIN, see above) and M. abscessus [Mabellini Database (Skwark et al., 2019)], to model the proteome of M. leprae. We emphasize the need for understanding the protein interfaces that are critical to function. An example of this is that of the RNA-polymerase holoenzyme complex from M. leprae. We first modeled the structure of this hetero-hexamer complex and later deciphered the binding patterns of rifampin (Vedithi et al., 2018; Figures 1A,B). Rifampin is a known drug to treat tuberculosis and leprosy. Owing to high rifampin resistance in tuberculosis and emerging resistance in leprosy, we used an approach known as “Computational Saturation Mutagenesis”, to identify sites on the protein that are less impacted by mutations. In this study, we were able to understand the association between predicted impacts of mutations on the structure and phenotypic rifampin-resistance outcomes in leprosy.
FIGURE 2
Figure 2.(A) Stability changes predicted by mCSM for systematic mutations in the ß-subunit of RNA polymerase in M. leprae. The maximum destabilizing effect from among all 19 possible mutations at each residue position is considered as a weighting factor for the color map that gradients from red (high destabilizing effects) to white (neutral to stabilizing effects) (Vedithi et al., 2020). (B) One of the known mutations in the ß-subunit of RNA polymerase, the S437H substitution which resulted in a maximum destabilizing effect [-1.701 kcal/mol (mCSM)] among all 19 possibilities this position. In the mutant, histidine (residue in green) forms hydrogen bonds with S434 and Q438, aromatic interactions with F431, and other ring-ring and π interactions with the surrounding residues which can impact the shape of the rifampin binding pocket and rifampin affinity to the ß-subunit [-0.826 log(affinity fold change) (mCSM-lig)]. Orange dotted lines represent weak hydrogen bond interactions. Ring-ring and intergroup interactions are depicted in cyan. Aromatic interactions are represented in sky-blue and carbonyl interactions in pink dotted lines. Green dotted lines represent hydrophobic interactions (Vedithi et al., 2020).
Examples of Understanding and Combatting Resistance
The availability of whole genome sequences in the present era has greatly enhanced the understanding of emergence of drug resistance in infectious diseases like tuberculosis. The data generated by the whole genome sequencing of clinical isolates can be screened for the presence of drug-resistant mutations. A preliminary in silico analysis of mutations can then be used to prioritize experimental work to identify the nature of these mutations.
FIGURE 3
Figure 3.(A) Mechanism of isoniazid activation and INH-NAD adduct formation. (B) Mutations mapped (Munir et al., 2019) on the structure of KatG (PDB ID:1SJ2; Bertrand et al., 2004).
Other articles related to Computational Biology, Systems Biology, and Bioinformatics on this online journal include:
2021 Virtual World Medical Innovation Forum, Mass General Brigham, Gene and Cell Therapy, VIRTUAL May 19–21, 2021
The 2021 Virtual World Medical Innovation Forum will focus on the growing impact of gene and cell therapy. Senior healthcare leaders from all over look to shape and debate the area of gene and cell therapy. Our shared belief: no matter the magnitude of change, responsible healthcare is centered on a shared commitment to collaborative innovation–industry, academia, and practitioners working together to improve patients’ lives.
About the World Medical Innovation Forum
Mass General Brigham is pleased to present the World Medical Innovation Forum (WMIF) virtual event Wednesday, May 19 – Friday, May 21. This interactive web event features expert discussions of gene and cell therapy (GCT) and its potential to change the future of medicine through its disease-treating and potentially curative properties. The agenda features 150+ executive speakers from the healthcare industry, venture, startups, life sciences manufacturing, consumer health and the front lines of care, including many Harvard Medical School-affiliated researchers and clinicians. The annual in-person Forum will resume live in Boston in 2022. The World Medical Innovation Forum is presented by Mass General Brigham Innovation, the global business development unit supporting the research requirements of 7,200 Harvard Medical School faculty and research hospitals including Massachusetts General, Brigham and Women’s, Massachusetts Eye and Ear, Spaulding Rehab and McLean Hospital. Follow us on Twitter: twitter.com/@MGBInnovation
Accelerating the Future of Medicine with Gene and Cell Therapy What Comes Next
Co-Chairs identify the key themes of the Forum – set the stage for top GCT opportunities, challenges, and where the field might take medicine in the future. Moderator: Susan Hockfield, PhD
President Emerita and Professor of Neuroscience, MIT
Hope that CGT emerging, how the therapies work, neuro, muscular, ocular, genetic diseases of liver and of heart revolution for the industry 900 IND application 25 approvals Economic driver Skilled works, VC disease. Modality one time intervention, long duration of impart, reimbursement, ecosystem to be built around CGT
FDA works by indications and risks involved, Standards and expectations for streamlining manufacturing, understanding of process and products
payments over time payers and Innovators relations Moderator: Julian Harris, MD
Partner, Deerfield
Promise of CGT realized, what part?
FDA role and interaction in CGT
Manufacturing aspects which is critical Speaker: Dave Lennon, PhD
President, Novartis Gene Therapies
Hope that CGT emerging, how the therapies work, neuro, muscular, ocular, genetic diseases of liver and of heart revolution for the industry 900 IND application 25 approvals Economic driver Skilled works, VC disease. Modality one time intervention, long duration of impart, reimbursement, ecosystem to be built around CGT
FDA works by indications and risks involved, Standards and expectations for streamlining manufacturing, understanding of process and products
payments over time payers and Innovators relations
GCT development for rare diseases is driven by patient and patient-advocate communities. Understanding their needs and perspectives enables biomarker research, the development of value-driving clinical trial endpoints and successful clinical trials. Industry works with patient communities that help identify unmet needs and collaborate with researchers to conduct disease natural history studies that inform the development of biomarkers and trial endpoints. This panel includes patients who have received cutting-edge GCT therapy as well as caregivers and patient advocates. Moderator: Patricia Musolino, MD, PhD
Co-Director Pediatric Stroke and Cerebrovascular Program, MGH
Assistant Professor of Neurology, HMS
What is the Power of One – the impact that a patient can have on their own destiny by participating in Clinical Trials Contacting other participants in same trial can be beneficial Speakers: Jack Hogan
Parkinson patient Constraints by regulatory on participation in clinical trial advance stage is approved participation Patients to determine the level of risk they wish to take Information dissemination is critical Barbara Lavery
Chief Program Officer, ACGT Foundation
Advocacy agency beginning of work Global Genes educational content and out reach to access the information
Patient has the knowledge of the symptoms and recording all input needed for diagnosis by multiple clinicians Early application for CGTDan Tesler
Clinical Trial Patient, BWH/DFCC
Experimental Drug clinical trial patient participation in clinical trial is very important to advance the state of scienceSarah Beth Thomas, RN
Professional Development Manager, BWH
Outcome is unknown, hope for good, support with resources all advocacy groups,
Process at FDA generalize from 1st entry to rules more generalizable Speaker: Peter Marks, MD, PhD
Director, Center for Biologics Evaluation and Research, FDA
Last Spring it became clear that something will work a vaccine by June 2020 belief that enough candidates the challenge manufacture enough and scaling up FDA did not predicted the efficacy of mRNA vaccine vs other approaches expected to work
Recover Work load for the pandemic will wean & clear, Gene Therapies IND application remained flat in the face of the pandemic Rare diseases urgency remains Consensus with industry advisory to get input gene therapy Guidance T-Cell therapy vs Regulation best thinking CGT evolve speedily flexible gained by Guidance
Immune modulators, Immunotherapy Genome editing can make use of viral vectors future technologies nanoparticles and liposome encapsulation
big pharma has portfolios of therapeutics not one drug across Tx areas: cell, gene iodine therapy
collective learning infrastructure features manufacturing at scale early in development Acquisitions strategy for growth # applications for scaling Rick Modi
CEO, Affinia Therapeutics
Copy, paste EDIT from product A to B novel vectors leverage knowledge varient of vector, coder optimization choice of indication is critical exploration on larger populations Speed to R&D and Speed to better gene construct get to clinic with better design vs ASAP
Data sharing clinical experience with vectors strategies patients selection, vector selection, mitigation, patient type specific Louise Rodino-Klapac, PhD
AAV based platform 15 years in development same disease indication vs more than one indication stereotype, analytics as hurdle 1st was 10 years 2nd was 3 years
Safety to clinic vs speed to clinic, difference of vectors to trust
Recent AAV gene therapy product approvals have catalyzed the field. This new class of therapies has shown the potential to bring transformative benefit to patients. With dozens of AAV treatments in clinical studies, all eyes are on the field to gauge its disruptive impact.
The panel assesses the largest challenges of the first two products, the lessons learned for the broader CGT field, and the extent to which they serve as a precedent to broaden the AAV modality.
Is AAV gene therapy restricted to genetically defined disorders, or will it be able to address common diseases in the near term?
Lessons learned from these first-in-class approvals.
Challenges to broaden this modality to similar indications.
Reflections on safety signals in the clinical studies?
Tissue types additional administrations, tech and science, address additional diseases, more science for photoreceptors a different tissue type underlying pathology novelties in last 10 years
Laxterna success to be replicated platform, paradigms measurement visual improved
More science is needed to continue develop vectors reduce toxicity,
AAV can deliver different cargos reduce adverse events improve vectorsRon Philip
Chief Operating Officer, Spark Therapeutics
The first retinal gene therapy, voretigene neparvovec-rzyl (Luxturna, Spark Therapeutics), was approved by the FDA in 2017.Meredith Schultz, MD
Executive Medical Director, Lead TME, Novartis Gene Therapies
Impact of cell therapy beyond muscular dystrophy, translational medicine, each indication, each disease, each group of patients build platform unlock the promise
Monitoring for Safety signals real world evidence remote markers, home visits, clinical trial made safer, better communication of information
AAV a complex driver in Pharmacology durable, vector of choice, administer in vitro, gene editing tissue specificity, pharmacokinetics side effects and adverse events manufacturability site variation diversify portfolios,
This panel will address the advances in the area of AAV gene therapy delivery looking out the next five years. Questions that loom large are: How can biodistribution of AAV be improved? What solutions are in the wings to address immunogenicity of AAV? Will patients be able to receive systemic redosing of AAV-based gene therapies in the future? What technical advances are there for payload size? Will the cost of manufacturing ever become affordable for ultra-rare conditions? Will non-viral delivery completely supplant viral delivery within the next five years?What are the safety concerns and how will they be addressed? Moderators: Xandra Breakefield, PhD
Ataxia requires therapy targeting multiple organ with one therapy, brain, spinal cord, heart several IND, clinical trials in 2022Mathew Pletcher, PhD
SVP, Head of Gene Therapy Research and Technical Operations, Astellas
Work with diseases poorly understood, collaborations needs example of existing: DMD is a great example explain dystrophin share placedo data
Continue to explore large animal guinea pig not the mice, not primates (ethical issues) for understanding immunogenicity and immune response Manny Simons, PhD
CEO, Akouos
AAV Therapy for the fluid of the inner ear, CGT for the ear vector accessible to surgeons translational work on the inner ear for gene therapy right animal model
Biology across species nerve ending in the cochlea
engineer out of the caspid, lowest dose possible, get desired effect by vector use, 2022 new milestones
The GCT M&A market is booming – many large pharmas have made at least one significant acquisition. How should we view the current GCT M&A market? What is its impact of the current M&A market on technology development? Are these M&A trends new are just another cycle? Has pharma strategy shifted and, if so, what does it mean for GCT companies? What does it mean for patients? What are the long-term prospects – can valuations hold up? Moderator: Adam Koppel, MD, PhD
Managing Director, Bain Capital Life Sciences
What acquirers are looking for??
What is the next generation vs what is real where is the industry going? Speakers:
Debby Baron,
Worldwide Business Development, Pfizer
CGT is an important area Pfizer is active looking for innovators, advancing forward programs of innovation with the experience Pfizer has internally
Scalability and manufacturing regulatory conversations, clinical programs safety in parallel to planning getting drug to patients
ALS – Man 1in 300, Women 1 in 400, next decade increase 7%
10% ALS is heredity 160 pharma in ALS space, diagnosis is late 1/3 of people are not diagnosed, active community for clinical trials Challenges: disease heterogeneity cases of 10 years late in diagnosis. Clinical Trials for ALS in Gene Therapy targeting ASO1 protein therapies FUS gene struck youngsters
Cell therapy for ACTA2 Vasculopathy in the brain and control the BP and stroke – smooth muscle intima proliferation. Viral vector deliver aiming to change platform to non-viral delivery rare disease , gene editing, other mutations of ACTA2 gene target other pathway for atherosclerosis
Oncolytic viruses represent a powerful new technology, but so far an FDA-approved oncolytic (Imlygic) has only occurred in one area – melanoma and that what is in 2015. This panel involves some of the protagonists of this early success story. They will explore why and how Imlygic became approved and its path to commercialization. Yet, no other cancer indications exist for Imlygic, unlike the expansion of FDA-approved indication for immune checkpoint inhibitors to multiple cancers. Why? Is there a limitation to what and which cancers can target? Is the mode of administration a problem?
No other oncolytic virus therapy has been approved since 2015. Where will the next success story come from and why? Will these therapies only be beneficial for skin cancers or other easily accessible cancers based on intratumoral delivery?
The panel will examine whether the preclinical models that have been developed for other cancer treatment modalities will be useful for oncolytic viruses. It will also assess the extent pre-clinical development challenges have slowed the development of OVs. Moderator: Nino Chiocca, MD, PhD
Neurosurgeon-in-Chief and Chairman, Neurosurgery, BWH
Harvey W. Cushing Professor of Neurosurgery, HMS
Challenges of manufacturing at Amgen what are they? Speakers: Robert Coffin, PhD
Chief Research & Development Officer, Replimune
2002 in UK promise in oncolytic therapy GNCSF
Phase III melanoma 2015 M&A with Amgen
oncolytic therapy remains non effecting on immune response
data is key for commercialization
do not belief in systemic therapy achieve maximum immune response possible from a tumor by localized injection Roger Perlmutter, MD, PhD
Chairman, Merck & Co.
response rates systemic therapy like PD1, Keytruda, OPTIVA well tolerated combination of Oncolytic with systemic
Physician, Dana Farber-Brigham and Women’s Cancer Center
Assistant Professor of Medicine, HMS
Which person gets oncolytics virus if patient has immune suppression due to other indications
Safety of oncolytic virus greater than Systemic treatment
series biopsies for injected and non injected tissue and compare Suspect of hot tumor and cold tumors likely to have sme response to agent unknown all potential
There are currently two oncolytic virus products on the market, one in the USA and one in China. As of late 2020, there were 86 clinical trials 60 of which were in phase I with just 2 in Phase III the rest in Phase I/II or Phase II. Although global sales of OVs are still in the ramp-up phase, some projections forecast OVs will be a $700 million market by 2026. This panel will address some of the major questions in this area:
What regulatory challenges will keep OVs from realizing their potential? Despite the promise of OVs for treating cancer only one has been approved in the US. Why has this been the case? Reasons such have viral tropism, viral species selection and delivery challenges have all been cited. However, these are also true of other modalities. Why then have oncolytic virus approaches not advanced faster and what are the primary challenges to be overcome?
Will these need to be combined with other agents to realize their full efficacy and how will that impact the market?
Why are these companies pursuing OVs while several others are taking a pass?
In 2020 there were a total of 60 phase I trials for Oncolytic Viruses. There are now dozens of companies pursuing some aspect of OV technology. This panel will address:
How are small companies equipped to address the challenges of developing OV therapies better than large pharma or biotech?
Will the success of COVID vaccines based on Adenovirus help the regulatory environment for small companies developing OV products in Europe and the USA?
Is there a place for non-viral delivery and other immunotherapy companies to engage in the OV space? Would they bring any real advantages?
Systemic delivery Oncolytic Virus IV delivery woman in remission
Collaboration with Regeneron
Data collection: Imageable reporter secretable reporter, gene expression
Field is intense systemic oncolytic delivery is exciting in mice and in human, response rates are encouraging combination immune stimulant, check inhibitors
Few areas of potential cancer therapy have had the attention and excitement of CAR-T. This panel of leading executives, developers, and clinician-scientists will explore the current state of CAR-T and its future prospects. Among the questions to be addressed are:
Is CAR-T still an industry priority – i.e. are new investments being made by large companies? Are new companies being financed? What are the trends?
What have we learned from first-generation products, what can we expect from CAR-T going forward in novel targets, combinations, armored CAR’s and allogeneic treatment adoption?
Early trials showed remarkable overall survival and progression-free survival. What has been observed regarding how enduring these responses are?
Most of the approvals to date have targeted CD19, and most recently BCMA. What are the most common forms of relapses that have been observed?
Is there a consensus about what comes after these CD19 and BCMA trials as to additional targets in liquid tumors? How have dual-targeted approaches fared?
The potential application of CAR-T in solid tumors will be a game-changer if it occurs. The panel explores the prospects of solid tumor success and what the barriers have been. Questions include:
How would industry and investor strategy for CAR-T and solid tumors be characterized? Has it changed in the last couple of years?
Does the lack of tumor antigen specificity in solid tumors mean that lessons from liquid tumor CAR-T constructs will not translate well and we have to start over?
Whether due to antigen heterogeneity, a hostile tumor micro-environment, or other factors are some specific solid tumors more attractive opportunities than others for CAR-T therapy development?
Given the many challenges that CAR-T faces in solid tumors, does the use of combination therapies from the start, for example, to mitigate TME effects, offer a more compelling opportunity.
Executive Director, Head of Cell Therapy Research, Exploratory Immuno-Oncology, NIBR
2017 CAR-T first approval
M&A and research collaborations
TCR tumor specific antigens avoid tissue toxicity Knut Niss, PhD
CTO, Mustang Bio
tumor hot start in 12 month clinical trial solid tumors , theraties not ready yet. Combination therapy will be an experimental treatment long journey checkpoint inhibitors to be used in combination maintenance Lipid tumor Barbra Sasu, PhD
CSO, Allogene
T cell response at prostate cancer
tumor specific
cytokine tumor specific signals move from solid to metastatic cell type for easier infiltration
Where we might go: safety autologous and allogeneic Jay Short, PhD
Chairman, CEO, Cofounder, BioAlta, Inc.
Tumor type is not enough for development of therapeutics other organs are involved in the periphery
difficult to penetrate solid tumors biologics activated in the tumor only, positive changes surrounding all charges, water molecules inside the tissue acidic environment target the cells inside the tumor and not outside
The modes of GCT manufacturing have the potential of fundamentally reordering long-established roles and pathways. While complexity goes up the distance from discovery to deployment shrinks. With the likelihood of a total market for cell therapies to be over $48 billion by 2027, groups of products are emerging. Stem cell therapies are projected to be $28 billion by 2027 and non-stem cell therapies such as CAR-T are projected be $20 billion by 2027. The manufacturing challenges for these two large buckets are very different. Within the CAR-T realm there are diverging trends of autologous and allogeneic therapies and the demands on manufacturing infrastructure are very different. Questions for the panelists are:
Help us all understand the different manufacturing challenges for cell therapies. What are the trade-offs among storage cost, batch size, line changes in terms of production cost and what is the current state of scaling naïve and stem cell therapy treatment vs engineered cell therapies?
For cell and gene therapy what is the cost of Quality Assurance/Quality Control vs. production and how do you think this will trend over time based on your perspective on learning curves today?
Will point of care production become a reality? How will that change product development strategy for pharma and venture investors? What would be the regulatory implications for such products?
How close are allogeneic CAR-T cell therapies? If successful what are the market implications of allogenic CAR-T? What are the cost implications and rewards for developing allogeneic cell therapy treatments?
Global Head of Product Development, Gene & Cell Therapy, Catalent
2/3 autologous 1/3 allogeneic CAR-T high doses and high populations scale up is not done today quality maintain required the timing logistics issues centralized vs decentralized allogeneic are health donors innovations in cell types in use improvements in manufacturing
China embraced gene and cell therapies early. The first China gene therapy clinical trial was in 1991. China approved the world’s first gene therapy product in 2003—Gendicine—an oncolytic adenovirus for the treatment of advanced head and neck cancer. Driven by broad national strategy, China has become a hotbed of GCT development, ranking second in the world with more than 1,000 clinical trials either conducted or underway and thousands of related patents. It has a booming GCT biotech sector, led by more than 45 local companies with growing IND pipelines.
In late 1990, a T cell-based immunotherapy, cytokine-induced killer (CIK) therapy became a popular modality in the clinic in China for tumor treatment. In early 2010, Chinese researchers started to carry out domestic CAR T trials inspired by several important reports suggested the great antitumor function of CAR T cells. Now, China became the country with the most registered CAR T trials, CAR T therapy is flourishing in China.
The Chinese GCT ecosystem has increasingly rich local innovation and growing complement of development and investment partnerships – and also many subtleties.
This panel, consisting of leaders from the China GCT corporate, investor, research and entrepreneurial communities, will consider strategic questions on the growth of the gene and cell therapy industry in China, areas of greatest strength, evolving regulatory framework, early successes and products expected to reach the US and world market. Moderator: Min Wu, PhD
Managing Director, Fosun Health Fund
What are the area of CGT in China, regulatory similar to the US Speakers: Alvin Luk, PhD
CEO, Neuropath Therapeutics
Monogenic rare disease with clear genomic target
Increase of 30% in patient enrollment
Regulatory reform approval is 60 days no delayPin Wang, PhD
CSO, Jiangsu Simcere Pharmaceutical Co., Ltd.
Similar starting point in CGT as the rest of the World unlike a later starting point in other biologicalRichard Wang, PhD
CEO, Fosun Kite Biotechnology Co., Ltd
Possibilities to be creative and capitalize the new technologies for innovating drug
Support of the ecosystem by funding new companie allowing the industry to be developed in China
Autologous in patients differences cost challengeTian Xu, PhD
Vice President, Westlake University
ICH committee and Chinese FDA -r regulation similar to the US
Difference is the population recruitment, in China patients are active participants in skin disease
Active in development of transposome
Development of non-viral methods, CRISPR still in D and transposome
In China price of drugs regulatory are sensitive Shunfei Yan, PhD
The COVID vaccine race has propelled mRNA to the forefront of biomedicine. Long considered as a compelling modality for therapeutic gene transfer, the technology may have found its most impactful application as a vaccine platform. Given the transformative industrialization, the massive human experience, and the fast development that has taken place in this industry, where is the horizon? Does the success of the vaccine application, benefit or limit its use as a therapeutic for CGT?
How will the COVID success impact the rest of the industry both in therapeutic and prophylactic vaccines and broader mRNA lessons?
How will the COVID success impact the rest of the industry both on therapeutic and prophylactic vaccines and broader mRNA lessons?
Beyond from speed of development, what aspects make mRNA so well suited as a vaccine platform?
Will cost-of-goods be reduced as the industry matures?
How does mRNA technology seek to compete with AAV and other gene therapy approaches?
Many years of mRNA pivoting for new diseases, DARPA, nucleic Acids global deployment of a manufacturing unit on site where the need arise Elan Musk funds new directions at Moderna
How many mRNA can be put in one vaccine: Dose and tolerance to achieve efficacy
45 days for Personalized cancer vaccine one per patient
Hemophilia has been and remains a hallmark indication for the CGT. Given its well-defined biology, larger market, and limited need for gene transfer to provide therapeutic benefit, it has been at the forefront of clinical development for years, however, product approval remains elusive. What are the main hurdles to this success? Contrary to many indications that CGT pursues no therapeutic options are available to patients, hemophiliacs have an increasing number of highly efficacious treatment options. How does the competitive landscape impact this field differently than other CGT fields? With many different players pursuing a gene therapy option for hemophilia, what are the main differentiators? Gene therapy for hemophilia seems compelling for low and middle-income countries, given the cost of currently available treatments; does your company see opportunities in this market? Moderator: Nancy Berliner, MD
Safety concerns, high burden of treatment CGT has record of safety and risk/benefit adoption of Tx functional cure CGT is potent Tx relative small quantity of protein needs be delivered
Potency and quality less quantity drug and greater potency
risk of delivery unwanted DNA, capsules are critical
analytics is critical regulator involvement in potency definition
Director, Center for Rare Neurological Diseases, MGH
Associate Professor, Neurology, HMS
Single gene disorder NGS enable diagnosis, DIagnosis to Treatment How to know whar cell to target, make it available and scale up Address gap: missing components Biomarkers to cell types lipid chemistry cell animal biology
crosswalk from bone marrow matter
New gene discovered that causes neurodevelopment of stagnant genes Examining new Biology cell type specific biomarkers
The American Diabetes Association estimates 30 million Americans have diabetes and 1.5 million are diagnosed annually. GCT offers the prospect of long-sought treatment for this enormous cohort and their chronic requirements. The complexity of the disease and its management constitute a grand challenge and highlight both the potential of GCT and its current limitations.
Islet transplantation for type 1 diabetes has been attempted for decades. Problems like loss of transplanted islet cells due to autoimmunity and graft site factors have been difficult to address. Is there anything different on the horizon for gene and cell therapies to help this be successful?
How is the durability of response for gene or cell therapies for diabetes being addressed? For example, what would the profile of an acceptable (vs. optimal) cell therapy look like?
Advanced made, Patient of Type 1 Outer and Inner compartments of spheres (not capsule) no immune suppression continuous secretion of enzyme Insulin independence without immune suppression
Volume to have of-the-shelf inventory oxegenation in location lymphatic and vascularization conrol the whole process modular platform learning from others
Keep eyes open, waiting the Pandemic to end and enable working back on all the indications
Portfolio of MET, Mimi Emerging Therapies
Learning from the Pandemic – operationalize the practice science, R&D leaders, new collaboratives at NIH, FDA, Novartis
Pursue programs that will yield growth, tropic diseases with Gates Foundation, Rising Tide pods for access CGT within Novartis Partnership with UPenn in Cell Therapy
Cost to access to IP from Academia to a Biotech CRISPR accessing few translations to Clinic
Protein degradation organization constraint valuation by parties in a partnership
Novartis: nuclear protein lipid nuclear particles, tamplate for Biotech to collaborate
Game changing: 10% of the Portfolio, New frontiers human genetics in Ophthalmology, CAR-T, CRISPR, Gene Therapy Neurological and payloads of different matter
The Voice of Dr. Seidman – Her abstract is cited below
The ultimate opportunity presented by discovering the genetic basis of human disease is accurate prediction and disease prevention. To enable this achievement, genetic insights must enable the identification of at-risk
individuals prior to end-stage disease manifestations and strategies that delay or prevent clinical expression. Genetic cardiomyopathies provide a paradigm for fulfilling these opportunities. Hypertrophic cardiomyopathy (HCM) is characterized by left ventricular hypertrophy, diastolic dysfunction with normal or enhanced systolic performance and a unique histopathology: myocyte hypertrophy, disarray and fibrosis. Dilated cardiomyopathy (DCM) exhibits enlarged ventricular volumes with depressed systolic performance and nonspecific histopathology. Both HCM and DCM are prevalent clinical conditions that increase risk for arrhythmias, sudden death, and heart failure. Today treatments for HCM and DCM focus on symptoms, but none prevent disease progression. Human molecular genetic studies demonstrated that these pathologies often result from dominant mutations in genes that encode protein components of the sarcomere, the contractile unit in striated muscles. These data combined with the emergence of molecular strategies to specifically modulate gene expression provide unparalleled opportunities to silence or correct mutant genes and to boost healthy gene expression in patients with genetic HCM and DCM. Many challenges remain, but the active and vital efforts of physicians, researchers, and patients are poised to ensure success.
Cyprus Island, kidney disease by mutation causing MUC1 accumulation and death BRD4780 molecule that will clear the misfolding proteins from the kidney organoids: pleuripotent stem cells small molecule developed for applications in the other cell types in brain, eye, gene mutation build mechnism for therapy clinical models transition from Academia to biotech
One of the most innovative segments in all of healthcare is the development of GCT driven therapies for rare and ultra-rare diseases. Driven by a series of insights and tools and funded in part by disease focused foundations, philanthropists and abundant venture funding disease after disease is yielding to new GCT technology. These often become platforms to address more prevalent diseases. The goal of making these breakthroughs routine and affordable is challenged by a range of issues including clinical trial design and pricing.
What is driving the interest in rare diseases?
What are the biggest barriers to making breakthroughs ‘routine and affordable?’
What is the role of retrospective and prospective natural history studies in rare disease? When does the expected value of retrospective disease history studies justify the cost?
Related to the first question, what is the FDA expecting as far as controls in clinical trials for rare diseases? How does this impact the collection of natural history data?
The power of GCT to cure disease has the prospect of profoundly improving the lives of patients who respond. Planning for a disruption of this magnitude is complex and challenging as it will change care across the spectrum. Leading chief executives shares perspectives on how the industry will change and how this change should be anticipated. Moderator: Meg Tirrell
Senior Health and Science Reporter, CNBC
CGT becoming staple therapy what are the disruptors emerging Speakers: Lisa Dechamps
SVP & Chief Business Officer, Novartis Gene Therapies
Reimagine medicine with collaboration at MGH, MDM condition in children
The Science is there, sustainable processes and systems impact is transformational
Value based pricing, risk sharing Payers and Pharma for one time therapy with life span effect
Head, Pharmaceuticals Research & Development, Bayer AG
CGT – 2016 and in 2020 new leadership and capability
Disease Biology and therapeutics
Regenerative Medicine: CGT vs repair building pipeline in ophthalmology and cardiovascular
During Pandemic: Deliver Medicines like Moderna, Pfizer – collaborations between competitors with Government Bayer entered into Vaccines in 5 days, all processes had to change access innovations developed over decades for medical solutions
GCT represents a large and growing market for novel therapeutics that has several segments. These include Cardiovascular Disease, Cancer, Neurological Diseases, Infectious Disease, Ophthalmology, Benign Blood Disorders, and many others; Manufacturing and Supply Chain including CDMO’s and CMO’s; Stem Cells and Regenerative Medicine; Tools and Platforms (viral vectors, nano delivery, gene editing, etc.). Bayer’s pharma business participates in virtually all of these segments. How does a Company like Bayer approach the development of a portfolio in a space as large and as diverse as this one? How does Bayer approach the support of the production infrastructure with unique demands and significant differences from its historical requirements? Moderator:
EVP, Pharmaceuticals, Head of Cell & Gene Therapy, Bayer AG
CGT will bring treatment to cure, delivery of therapies
Be a Leader repair, regenerate, cure
Technology and Science for CGT – building a portfolio vs single asset decision criteria development of IP market access patients access acceleration of new products
Bayer strategy: build platform for use by four domains
Gener augmentation
Autologeneic therapy, analytics
Gene editing
Oncology Cell therapy tumor treatment: What kind of cells – the jury is out
Of 23 product launch at Bayer no prediction is possible some high some lows
Gene delivery uses physical, chemical, or viral means to introduce genetic material into cells. As more genetically modified therapies move closer to the market, challenges involving safety, efficacy, and manufacturing have emerged. Optimizing lipidic and polymer nanoparticles and exosomal delivery is a short-term priority. This panel will examine how the short-term and long-term challenges are being tackled particularly for non-viral delivery modalities. Moderator: Natalie Artzi, PhD
Gene editing was recognized by the Nobel Committee as “one of gene technology’s sharpest tools, having a revolutionary impact on life sciences.” Introduced in 2011, gene editing is used to modify DNA. It has applications across almost all categories of disease and is also being used in agriculture and public health.
Today’s panel is made up of pioneers who represent foundational aspects of gene editing. They will discuss the movement of the technology into the therapeutic mainstream.
Successes in gene editing – lessons learned from late-stage assets (sickle cell, ophthalmology)
When to use what editing tool – pros and cons of traditional gene-editing v. base editing. Is prime editing the future? Specific use cases for epigenetic editing.
When we reach widespread clinical use – role of off-target editing – is the risk real? How will we mitigate? How practical is patient-specific off-target evaluation?
There are several dozen companies working to develop gene or cell therapies for Sickle Cell Disease, Beta Thalassemia, and Fanconi Anemia. In some cases, there are enzyme replacement therapies that are deemed effective and safe. In other cases, the disease is only managed at best. This panel will address a number of questions that are particular to this class of genetic diseases:
What are the pros and cons of various strategies for treatment? There are AAV-based editing, non-viral delivery even oligonucleotide recruitment of endogenous editing/repair mechanisms. Which approaches are most appropriate for which disease?
How can companies increase the speed of recruitment for clinical trials when other treatments are available? What is the best approach to educate patients on a novel therapeutic?
How do we best address ethnic and socio-economic diversity to be more representative of the target patient population?
How long do we have to follow up with the patients from the scientific, patient’s community, and payer points of view? What are the current FDA and EMA guidelines for long-term follow-up?
Where are we with regards to surrogate endpoints and their application to clinically meaningful endpoints?
What are the emerging ethical dilemmas in pediatric gene therapy research? Are there challenges with informed consent and pediatric assent for trial participation?
Are there differences in reimbursement policies for these different blood disorders? Clearly durability of response is a big factor. Are there other considerations?
Oligonucleotide drugs have recently come into their own with approvals from companies such as Biogen, Alnylam, Novartis and others. This panel will address several questions:
How important is the delivery challenge for oligonucleotides? Are technological advancements emerging that will improve the delivery of oligonucleotides to the CNS or skeletal muscle after systemic administration?
Will oligonucleotides improve as a class that will make them even more effective? Are further advancements in backbone chemistry anticipated, for example.
Will oligonucleotide based therapies blaze trails for follow-on gene therapy products?
Are small molecules a threat to oligonucleotide-based therapies?
Beyond exon skipping and knock-down mechanisms, what other roles will oligonucleotide-based therapies take mechanistically — can genes be activating oligonucleotides? Is there a place for multiple mechanism oligonucleotide medicines?
Are there any advantages of RNAi-based oligonucleotides over ASOs, and if so for what use?
What is occurring in the GCT venture capital segment? Which elements are seeing the most activity? Which areas have cooled? How is the investment market segmented between gene therapy, cell therapy and gene editing? What makes a hot GCT company? How long will the market stay frothy? Some review of demographics — # of investments, sizes, etc. Why is the market hot and how long do we expect it to stay that way? Rank the top 5 geographic markets for GCT company creation and investing? Are there academic centers that have been especially adept at accelerating GCT outcomes? Do the business models for the rapid development of coronavirus vaccine have any lessons for how GCT technology can be brought to market more quickly? Moderator: Meredith Fisher, PhD
The promise of stem cells has been a highlight in the realm of regenerative medicine. Unfortunately, that promise remains largely in the future. Recent breakthroughs have accelerated these potential interventions in particular for treating neurological disease. Among the topics the panel will consider are:
Stem cell sourcing
Therapeutic indication growth
Genetic and other modification in cell production
Cell production to final product optimization and challenges
The dynamics of venture/PE investing and IPOs are fast evolving. What are the drivers – will the number of investors grow will the size of early rounds continue to grow? How is this reflected in GCT target areas, company design, and biotech overall? Do patients benefit from these trends? Is crossover investing a distinct class or a little of both? Why did it emerge and what are the characteristics of the players? Will SPACs play a role in the growth of the gene and cell therapy industry. What is the role of corporate investment arms eg NVS, Bayer, GV, etc. – has a category killer emerged? Are we nearing the limit of what the GCT market can absorb or will investment capital continue to grow unabated? Moderator: Roger Kitterman
Nearly one hundred senior Mass General Brigham Harvard faculty contributed to the creation of this group of twelve GCT technologies that they believe will breakthrough in the next two years. The Disruptive Dozen identifies and ranks the GCT technologies that will be available on at least an experimental basis to have the chance of significantly improving health care. 11:35 AM – 11:45 AM
Computer connection to the iCloud of WordPress.com FROZE completely at 10:30AM EST and no file update was possible. COVERAGE OF MAY 21, 2021 IS RECORDED BELOW FOLLOWING THE AGENDA BY COPY AN DPASTE OF ALL THE TWEETS I PRODUCED ON MAY 21, 2021 8:30 AM – 8:55 AM
What is occurring in the GCT venture capital segment? Which elements are seeing the most activity? Which areas have cooled? How is the investment market segmented between gene therapy, cell therapy and gene editing? What makes a hot GCT company? How long will the market stay frothy? Some review of demographics — # of investments, sizes, etc. Why is the market hot and how long do we expect it to stay that way? Rank the top 5 geographic markets for GCT company creation and investing? Are there academic centers that have been especially adept at accelerating GCT outcomes? Do the business models for the rapid development of coronavirus vaccine have any lessons for how GCT technology can be brought to market more quickly? Moderator: Meredith Fisher, PhD
The promise of stem cells has been a highlight in the realm of regenerative medicine. Unfortunately, that promise remains largely in the future. Recent breakthroughs have accelerated these potential interventions in particular for treating neurological disease. Among the topics the panel will consider are:
Stem cell sourcing
Therapeutic indication growth
Genetic and other modification in cell production
Cell production to final product optimization and challenges
The dynamics of venture/PE investing and IPOs are fast evolving. What are the drivers – will the number of investors grow will the size of early rounds continue to grow? How is this reflected in GCT target areas, company design, and biotech overall? Do patients benefit from these trends? Is crossover investing a distinct class or a little of both? Why did it emerge and what are the characteristics of the players? Will SPACs play a role in the growth of the gene and cell therapy industry. What is the role of corporate investment arms eg NVS, Bayer, GV, etc. – has a category killer emerged? Are we nearing the limit of what the GCT market can absorb or will investment capital continue to grow unabated? Moderator: Roger Kitterman
Nearly one hundred senior Mass General Brigham Harvard faculty contributed to the creation of this group of twelve GCT technologies that they believe will breakthrough in the next two years. The Disruptive Dozen identifies and ranks the GCT technologies that will be available on at least an experimental basis to have the chance of significantly improving health care. 11:35 AM – 11:45 AM
The co-chairs convene to reflect on the insights shared over the three days. They will discuss what to expect at the in-person GCT focused May 2-4, 2022 World Medical Innovation Forum.
The co-chairs convene to reflect on the insights shared over the three days. They will discuss what to expect at the in-person GCT focused May 2-4, 2022 World Medical Innovation Forum.Christine Seidman, MD
Cyprus Island, kidney disease by mutation causing MUC1 accumulation and death BRD4780 molecule that will clear the misfolding proteins from the kidney organoids: pleuripotent stem cells small molecule developed for applications in the other cell types in brain, eye, gene mutation build mechnism for therapy clinical models transition from Academia to biotech
One of the most innovative segments in all of healthcare is the development of GCT driven therapies for rare and ultra-rare diseases. Driven by a series of insights and tools and funded in part by disease focused foundations, philanthropists and abundant venture funding disease after disease is yielding to new GCT technology. These often become platforms to address more prevalent diseases. The goal of making these breakthroughs routine and affordable is challenged by a range of issues including clinical trial design and pricing.
What is driving the interest in rare diseases?
What are the biggest barriers to making breakthroughs ‘routine and affordable?’
What is the role of retrospective and prospective natural history studies in rare disease? When does the expected value of retrospective disease history studies justify the cost?
Related to the first question, what is the FDA expecting as far as controls in clinical trials for rare diseases? How does this impact the collection of natural history data?
The power of GCT to cure disease has the prospect of profoundly improving the lives of patients who respond. Planning for a disruption of this magnitude is complex and challenging as it will change care across the spectrum. Leading chief executives shares perspectives on how the industry will change and how this change should be anticipated. Moderator: Meg Tirrell
Senior Health and Science Reporter, CNBC
CGT becoming staple therapy what are the disruptors emerging Speakers: Lisa Dechamps
SVP & Chief Business Officer, Novartis Gene Therapies
Reimagine medicine with collaboration at MGH, MDM condition in children
The Science is there, sustainable processes and systems impact is transformational
Value based pricing, risk sharing Payers and Pharma for one time therapy with life span effect
Head, Pharmaceuticals Research & Development, Bayer AG
CGT – 2016 and in 2020 new leadership and capability
Disease Biology and therapeutics
Regenerative Medicine: CGT vs repair building pipeline in ophthalmology and cardiovascular
During Pandemic: Deliver Medicines like Moderna, Pfizer – collaborations between competitors with Government Bayer entered into Vaccines in 5 days, all processes had to change access innovations developed over decades for medical solutions
GCT represents a large and growing market for novel therapeutics that has several segments. These include Cardiovascular Disease, Cancer, Neurological Diseases, Infectious Disease, Ophthalmology, Benign Blood Disorders, and many others; Manufacturing and Supply Chain including CDMO’s and CMO’s; Stem Cells and Regenerative Medicine; Tools and Platforms (viral vectors, nano delivery, gene editing, etc.). Bayer’s pharma business participates in virtually all of these segments. How does a Company like Bayer approach the development of a portfolio in a space as large and as diverse as this one? How does Bayer approach the support of the production infrastructure with unique demands and significant differences from its historical requirements? Moderator:
EVP, Pharmaceuticals, Head of Cell & Gene Therapy, Bayer AG
CGT will bring treatment to cure, delivery of therapies
Be a Leader repair, regenerate, cure
Technology and Science for CGT – building a portfolio vs single asset decision criteria development of IP market access patients access acceleration of new products
Bayer strategy: build platform for use by four domains
Gener augmentation
Autologeneic therapy, analytics
Gene editing
Oncology Cell therapy tumor treatment: What kind of cells – the jury is out
Of 23 product launch at Bayer no prediction is possible some high some lows
Gene delivery uses physical, chemical, or viral means to introduce genetic material into cells. As more genetically modified therapies move closer to the market, challenges involving safety, efficacy, and manufacturing have emerged. Optimizing lipidic and polymer nanoparticles and exosomal delivery is a short-term priority. This panel will examine how the short-term and long-term challenges are being tackled particularly for non-viral delivery modalities. Moderator: Natalie Artzi, PhD
Gene editing was recognized by the Nobel Committee as “one of gene technology’s sharpest tools, having a revolutionary impact on life sciences.” Introduced in 2011, gene editing is used to modify DNA. It has applications across almost all categories of disease and is also being used in agriculture and public health.
Today’s panel is made up of pioneers who represent foundational aspects of gene editing. They will discuss the movement of the technology into the therapeutic mainstream.
Successes in gene editing – lessons learned from late-stage assets (sickle cell, ophthalmology)
When to use what editing tool – pros and cons of traditional gene-editing v. base editing. Is prime editing the future? Specific use cases for epigenetic editing.
When we reach widespread clinical use – role of off-target editing – is the risk real? How will we mitigate? How practical is patient-specific off-target evaluation?
There are several dozen companies working to develop gene or cell therapies for Sickle Cell Disease, Beta Thalassemia, and Fanconi Anemia. In some cases, there are enzyme replacement therapies that are deemed effective and safe. In other cases, the disease is only managed at best. This panel will address a number of questions that are particular to this class of genetic diseases:
What are the pros and cons of various strategies for treatment? There are AAV-based editing, non-viral delivery even oligonucleotide recruitment of endogenous editing/repair mechanisms. Which approaches are most appropriate for which disease?
How can companies increase the speed of recruitment for clinical trials when other treatments are available? What is the best approach to educate patients on a novel therapeutic?
How do we best address ethnic and socio-economic diversity to be more representative of the target patient population?
How long do we have to follow up with the patients from the scientific, patient’s community, and payer points of view? What are the current FDA and EMA guidelines for long-term follow-up?
Where are we with regards to surrogate endpoints and their application to clinically meaningful endpoints?
What are the emerging ethical dilemmas in pediatric gene therapy research? Are there challenges with informed consent and pediatric assent for trial participation?
Are there differences in reimbursement policies for these different blood disorders? Clearly durability of response is a big factor. Are there other considerations?
Oligonucleotide drugs have recently come into their own with approvals from companies such as Biogen, Alnylam, Novartis and others. This panel will address several questions:
How important is the delivery challenge for oligonucleotides? Are technological advancements emerging that will improve the delivery of oligonucleotides to the CNS or skeletal muscle after systemic administration?
Will oligonucleotides improve as a class that will make them even more effective? Are further advancements in backbone chemistry anticipated, for example.
Will oligonucleotide based therapies blaze trails for follow-on gene therapy products?
Are small molecules a threat to oligonucleotide-based therapies?
Beyond exon skipping and knock-down mechanisms, what other roles will oligonucleotide-based therapies take mechanistically — can genes be activating oligonucleotides? Is there a place for multiple mechanism oligonucleotide medicines?
Are there any advantages of RNAi-based oligonucleotides over ASOs, and if so for what use?
Computer connection to the iCloud of WordPress.com FROZE completely at 10:30AM EST and no file update was possible. COVERAGE OF MAY 21, 2021 IS RECORDED BELOW FOLLOWING THE AGENDA BY COPY AN DPASTE OF ALL THE TWEETS I PRODUCED ON MAY 21, 2021
What is occurring in the GCT venture capital segment? Which elements are seeing the most activity? Which areas have cooled? How is the investment market segmented between gene therapy, cell therapy and gene editing? What makes a hot GCT company? How long will the market stay frothy? Some review of demographics — # of investments, sizes, etc. Why is the market hot and how long do we expect it to stay that way? Rank the top 5 geographic markets for GCT company creation and investing? Are there academic centers that have been especially adept at accelerating GCT outcomes? Do the business models for the rapid development of coronavirus vaccine have any lessons for how GCT technology can be brought to market more quickly? Moderator: Meredith Fisher, PhD
Partner, Mass General Brigham Innovation Fund
Strategies, success what changes are needed in the drug discovery process Speakers:
Bring disruptive frontier as a platform with reliable delivery CGT double knock out disease cure all change efficiency and scope human centric vs mice centered right scale of data converted into therapeutics acceleratetion
Innovation in drugs 60% fails in trial because of Toxicology system of the future deal with big diseases
Moderna is an example in unlocking what is inside us Microbiome and beyond discover new drugs epigenetics
Manufacturing change is not a new clinical trial FDA need to be presented with new rethinking for big innovations Drug pricing cheaper requires systematization How to systematically scaling up systematize the discovery and the production regulatory innovations
The promise of stem cells has been a highlight in the realm of regenerative medicine. Unfortunately, that promise remains largely in the future. Recent breakthroughs have accelerated these potential interventions in particular for treating neurological disease. Among the topics the panel will consider are:
Stem cell sourcing
Therapeutic indication growth
Genetic and other modification in cell production
Cell production to final product optimization and challenges
Director, Neuroregeneration Research Institute, McLean
Professor, Neurology and Neuroscience, MGH, HMS
Opportunities in the next generation of the tactical level Welcome the oprimism and energy level of all Translational medicine funding stem cells enormous opportunities
Ear inside the scall compartments and receptors responsible for hearing highly differentiated tall ask to identify cell for anticipated differentiation
The dynamics of venture/PE investing and IPOs are fast evolving. What are the drivers – will the number of investors grow will the size of early rounds continue to grow? How is this reflected in GCT target areas, company design, and biotech overall? Do patients benefit from these trends? Is crossover investing a distinct class or a little of both? Why did it emerge and what are the characteristics of the players? Will SPACs play a role in the growth of the gene and cell therapy industry. What is the role of corporate investment arms eg NVS, Bayer, GV, etc. – has a category killer emerged? Are we nearing the limit of what the GCT market can absorb or will investment capital continue to grow unabated? Moderator: Roger Kitterman
VP, Venture, Mass General Brigham
Saturation reached or more investment is coming in CGT
Pharmacologic agent in existing cause another disorders locomo-movement related
efficacy Autologous cell therapy transplantation approach program T cells into dopamine generating neurons greater than Allogeneic cell transplantation
Current market does not have delivery mechanism that a drug-delivery is the solution Trials would fail on DELIVERY
Immune suppressed patients during one year to avoid graft rejection Autologous approach of Parkinson patient genetically mutated reprogramed as dopamine generating neuron – unknowns are present
Circuitry restoration
Microenvironment disease ameliorate symptoms – education of patients on the treatment
Nearly one hundred senior Mass General Brigham Harvard faculty contributed to the creation of this group of twelve GCT technologies that they believe will breakthrough in the next two years. The Disruptive Dozen identifies and ranks the GCT technologies that will be available on at least an experimental basis to have the chance of significantly improving health care. 11:35 AM – 11:45 AM
The co-chairs convene to reflect on the insights shared over the three days. They will discuss what to expect at the in-person GCT focused May 2-4, 2022 World Medical Innovation Forum.
ALL THE TWEETS PRODUCED ON MAY 21, 2021 INCLUDE THE FOLLOWING:
Bob Carter, MD, PhD Chairman, Department of Neurosurgery, MGH William and Elizabeth Sweet, Professor of Neurosurgery, HMS Neurogeneration REVERSAL or slowing down?
Penelope Hallett, PhD NRL, McLean Assistant Professor Psychiatry, HMS efficacy Autologous cell therapy transplantation approach program T cells into dopamine genetating cells greater than Allogeneic cell transplantation
Roger Kitterman VP, Venture, Mass General Brigham Saturation reached or more investment is coming in CGT Multi OMICS and academia originated innovations are the most attractive areas
Peter Kolchinsky, PhD Founder and Managing Partner, RA Capital Management Future proof for new comers disruptors Ex Vivo gene therapy to improve funding products what tool kit belongs to
Chairman, Department of Neurosurgery, MGH, Professor of Neurosurgery, HMS Cell therapy for Parkinson to replace dopamine producing cells lost ability to produce dopamine skin cell to become autologous cells reprogramed
Kapil Bharti, PhD Senior Investigator, Ocular and Stem Cell Translational Research Section, NIH Off-th-shelf one time treatment becoming cure Intact tissue in a dish is fragile to maintain metabolism to become like semiconductors
Ole Isacson, MD, PhD Director, Neuroregeneration Research Institute, McLean Professor, Neurology and Neuroscience, MGH, HMS Opportunities in the next generation of the tactical level Welcome the oprimism and energy level of all
Erin Kimbrel, PhD Executive Director, Regenerative Medicine, Astellas In the ocular space immunogenecity regulatory communication use gene editing for immunogenecity Cas1 and Cas2 autologous cells
Nabiha Saklayen, PhD CEO and Co-Founder, Cellino scale production of autologous cells foundry using semiconductor process in building cassettes by optic physicists
Joe Burns, PhD VP, Head of Biology, Decibel Therapeutics Ear inside the scall compartments and receptors responsible for hearing highly differentiated tall ask to identify cell for anticipated differentiation control by genomics
Kapil Bharti, PhD Senior Investigator, Ocular and Stem Cell Translational Research Section, NIH first drug required to establish the process for that innovations design of animal studies not done before
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Cryo-EM disclosed how the D614G mutation changes SARS-CoV-2 spike protein structure.
Reporter: Dr. Premalata Pati, Ph.D., Postdoc
SARS-CoV-2, the virus that causes COVID-19, has had a major impact on human health globally; infecting a massive quantity of people around 136,046,262 (John Hopkins University); causing severe disease and associated long-term health sequelae; resulting in death and excess mortality, especially among older and prone populations; altering routine healthcare services; disruptions to travel, trade, education, and many other societal functions; and more broadly having a negative impact on peoples physical and mental health.
It’s need of the hour to answer the questions like what allows the variants of SARS-CoV-2 first detected in the UK, South Africa, and Brazil to spread so quickly? How can current COVID-19 vaccines better protect against them?
Bing Chen, HMS professor of pediatrics at Boston Children’s, and colleagues analyzed the changes in the structure of the spike proteins with the genetic change by D614G mutation by all three variants. Hence they assessed the structure of the coronavirus spike protein down to the atomic level and revealed the reason for the quick spreading of these variants.
This model shows the structure of the spike protein in its closed configuration, in its original D614 form (left) and its mutant form (G614). In the mutant spike protein, the 630 loop (in red) stabilizes the spike, preventing it from flipping open prematurely and rendering SARS-CoV-2 more infectious.
Fig. 1. Cryo-EM structures of the full-length SARS-CoV-2 S protein carrying G614.
(A) Three structures of the G614 S trimer, representing a closed, three RBD-down conformation, an RBD-intermediate conformation and a one RBD-up conformation, were modeled based on corresponding cryo-EM density maps at 3.1-3.5Å resolution. Three protomers (a, b, c) are colored in red, blue and green, respectively. RBD locations are indicated. (B) Top views of superposition of three structures of the G614 S in (A) in ribbon representation with the structure of the prefusion trimer of the D614 S (PDB ID: 6XR8), shown in yellow. NTD and RBD of each protomer are indicated. Side views of the superposition are shown in fig. S8.
The mutant spikes were imaged by Cryo-Electron microscopy (cryo-EM), which has resolution down to the atomic level. They found that the D614G mutation (substitution of in a single amino acid “letter” in the genetic code for the spike protein) makes the spike more stable as compared with the original SARS-CoV-2 virus. As a result, more functional spikes are available to bind to our cells’ ACE2 receptors, making the virus more contagious.
Fig. 2. Cryo-EM revealed how the D614G mutation changes SARS-CoV-2 spike protein structure.
Say the original virus has 100 spikes,” Chen explained. “Because of the shape instability, you may have just 50 percent of them functional. In the G614 variants, you may have 90 percent that is functional. So even though they don’t bind as well, the chances are greater and you will have an infection
Forthcoming directions by Bing Chen and Team
The findings suggest the current approved COVID-19 vaccines and any vaccines in the works should include the genetic code for this mutation. Chen has quoted:
Since most of the vaccines so far—including the Moderna, Pfizer–BioNTech, Johnson & Johnson, and AstraZeneca vaccines are based on the original spike protein, adding the D614G mutation could make the vaccines better able to elicit protective neutralizing antibodies against the viral variants
Chen proposes that redesigned vaccines incorporate the code for this mutant spike protein. He believes the more stable spike shape should make any vaccine based on the spike more likely to elicit protective antibodies. Chen also has his sights set on therapeutics. He and his colleagues are further applying structural biology to better understand how SARS-CoV-2 binds to the ACE2 receptor. That could point the way to drugs that would block the virus from gaining entry to our cells.
In January, the team showed that a structurally engineered “decoy” ACE2 protein binds to SARS-CoV-2 200 times more strongly than the body’s own ACE2. The decoy potently inhibited the virus in cell culture, suggesting it could be an anti-COVID-19 treatment. Chen is now working to advance this research into animal models.
Main Source:
Abstract
Substitution for aspartic acid by glycine at position 614 in the spike (S) protein of severe acute respiratory syndrome coronavirus 2 appears to facilitate rapid viral spread. The G614 strain and its recent variants are now the dominant circulating forms. We report here cryo-EM structures of a full-length G614 S trimer, which adopts three distinct prefusion conformations differing primarily by the position of one receptor-binding domain. A loop disordered in the D614 S trimer wedges between domains within a protomer in the G614 spike. This added interaction appears to prevent premature dissociation of the G614 trimer, effectively increasing the number of functional spikes and enhancing infectivity, and to modulate structural rearrangements for membrane fusion. These findings extend our understanding of viral entry and suggest an improved immunogen for vaccine development.
Comparing COVID-19 Vaccine Schedule Combinations, or “Com-COV” – First-of-its-Kind Study will explore the Impact of using eight different Combinations of Doses and Dosing Intervals for Different COVID-19 Vaccines
Intellia announced in its fourth-quarter earnings report that Novartis had ended development of sickle cell treatment OTQ923/HIX763. (Getty Images)
Novartis will no longer develop an ex vivo sickle cell disease program that was part of an older deal with Intellia, and the gene editing biotech’s CEO John Leonard, M.D., thinks he knows why.
“We’ve always believed that the future lies with the in vivo approaches, and that’s been a focus of the work that we do,” Leonard said. “I’m sure they looked at the ex vivo space and may have had some of the same realizations that we had some years ago.”
Leonard, of course, said he wasn’t completely sure why Novartis opted to cut the program, but noted that the Big Pharma is undergoing a broad pipeline reorganization.
Novartis confirmed just that in an emailed statement to Fierce Biotech, saying that the program was discontinued for strategic reasons. The overall partnership with Intellia remains intact, however, the spokesperson said.
Intellia announced in its fourth-quarter earnings report Thursday that the Swiss pharma ended development of OTQ923/HIX763 this month.
The therapy uses autologous, ex vivo, CRISPR-edited hematopoietic stem cells to target fetal hemoglobin for treating sickle cell disease. Novartis initiated dosing on a phase 1/2 trial for the Intellia-partnered program in 2021.
Intellia has both types of candidate in its pipeline, but the in vivo list is longer and more advanced, with NTLA-2001 in transthyretin (ATTR) amyloidosis leading the pack.
Novartis and Intellia have had a cell therapy partnership since January 2015, which was three months after Intellia launched from Atlas Venture and Caribou Biosciences. The agreement was revised in 2018 to expand to ex vivo development of cell therapies using certain ocular stem cells. At that time, Intellia received a $10 million payment, but other financial details of the agreement have not been disclosed. Novartis gained the rights to opt in on one or more programs, while Intellia earned the right to use the pharma’s lipid nanoparticle technology for all genome editing applications in both in vivo and ex vivo settings.
Intellia, working with its partner Regeneron, has shown over the past year that CRISPR/Cas9 in vivo gene editing can cause high, seemingly durable levels of gene knockdown in humans. While questions about the Intellia data, and the concept more broadly, remain unanswered, there is now early evidence that the approach may be effective and, as importantly, safe. Precision is one of a clutch of companies barreling toward the clinic in the wake of Intellia, and the potential of its Arcus platform to provide greater precision and versatility than CRISPR/Cas9 and zinc finger nuclease has now attracted a suitor.
To add to its in vivo capabilities, Novartis is set to pay $50 million in cash to partner with Precision. The deal also features a $25 million equity investment priced at $2.01 per share, a 20% premium over the recent average for the stock, as well as up to $1.4 billion in milestones, research funding and royalties ranging from the mid-single-digit to low-double-digit percentages.
Alnylam Announces First-Ever FDA Approval of an RNAi Therapeutic, ONPATTRO™ (patisiran) for the Treatment of the Polyneuropathy of Hereditary Transthyretin-Mediated Amyloidosis in Adults
Plant Cells of Different Species Can Swap Organelles
Reporter : Irina Robu, PhD
Farmers have used plant grafts to grow fruit trees and grapevines, but plant grafts also occur in nature when closely related plants that touch each other eventually fuse, or when parasitic plants form connections to their hosts. At the graft site, the plants form a kind of scar or callus, that reestablishes the flow of water and nutrients through vascular tissues across the wound and sometimes gives rise to new shoots. Plant geneticists noticed that two plants that grew together, the cells of each plant showed signs of having picked up substantial amounts of DNA from the other one. They knew that horizontal transfer of genes is not uncommon in bacteria, even animals, fungi and plants but in this case, the transferred DNA seems to be the entire intact genome of chloroplasts.
And in order to understand this, researchers at Max Planck Institute of Molecular Plant Physiology, in Dr. Ralph Bock’s laboratory discovered that not only are cell walls sometimes more porous than was thought, but plants seem to have developed a mechanism that enables whole organelles to crawl through the cell wall into adjacent cells. The genetic transfer between plants was not only interesting, but a challenging puzzle. The fact that the only openings in cell walls were tiny narrow bridges (0.05 microns) that allow adjacent plant cells to exchange proteins and RNA molecules. The chloroplast, typically about 5 microns in diameter looked like it miraculously showed up in the other cell.
Researchers in Dr. Brock’s lab were determined to see what exactly was going on with the callus at graft site. He was able to observe that the cells had openings larger than previously noticed, up to 1.5 microns across. While seeing live cells in the callus, he noticed that the chloroplasts can migrate. Some of the chloroplasts changed into more primitive, more motile proto-plastids that could get as small as 0.2 microns and the proto-plastids crawled along the inside of the cell membrane positions underneath the fresh discovered holes in the cell wall. Budlike protrusions of the cell membranes then protruded into neighboring cells and transported the organelles. As the tissue organization in the graft reestablished itself, the plastids returned to the normal size for chloroplasts.
Even though the metamorphosis of the chloroplasts is not understood, it seems that carbon starvation can lead to photosynthesis. And how well transferred plastids function in their new host cells depend on the related the two species are. If the genetic If the genetic mismatch with the nuclear DNA is too extreme, the organelles may fail to work and will eventually be lost. But they could thrive in the cells of close relatives. Whole-organelle migration can help clarify the observation that the chloroplasts from clumps of different species. They hypothesized that plants move chloroplasts between cells routinely in response to injuries or other events. The researchers point out that once a graft callus starts to produce roots, shoots and flowers, it could give rise to a new species or subspecies.
Biomolecular Condensates: A new approach to biology originated @MIT – Drug Discovery at DewPoint Therapeutics, Cambridge, MA gets new leaders, Ameet Nathwani, MD (ex-Sanofi, ex-Novartis) as Chief Executive Officer and Arie Belldegrun, PhD (ex-Kite Therapeutics) on R&D
“The real voyage of discovery consists, not in seeking new landscapes, but in having new eyes.” Marcel Proust
Starting with the study of P granules in C.elegans embryos in 2009, Tony Hyman, working with his collaborators like Frank Julicher, Cliff Brangwynne, Simon Alberti, Mike Rosen, and Rohit Pappu, began to unravel the mysteries of biomolecular condensates. These scientists realized that P granules behave like liquid droplets that form by phase separation (think of oil droplets in salad dressing) and called them condensates.
In subsequent studies, they found to their surprise that many compartments inside cells had the behavior of condensates: they are liquid-like and form by phase separation.
Inspired by the work of Tony and his colleagues, Richard Young, Phillip Sharp, and Arup Chakraborty at MIT applied these approaches to the study of gene expression, similarly shedding light on many important questions in gene control.
Other related article published in this Online Open Access Scientific Journal include:
Economic Potential of a Drug Invention (Prof. Zelig Eshhar, Weitzman Institute, registered the patent) versus a Cancer Drug in Clinical Trials: CAR-T as a Case in Point, developed by Kite Pharma, under Arie Belldegrun, CEO, acquired by Gilead for $11.9 billion, 8/2017.
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