Archive for the ‘Genetics & Pharmaceutical’ Category
Protected: KOL on Competitive Dynamics: LPBI Group’s Founder’s Radar Screens on AI in Health, Life Sciences & BioPharma
Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, BioTechnology - Venture Creation, BioTechnology - Venture Creation, Venture Capital, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Drug Delivery Platform Technology, Drug Development Process, Drug Discovery Chemistry, drug repurposing, Genetics & Pharmaceutical, Global Market of Medical Devices Technology, Global Partnering & Biotech Investment, HealthCare IT, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment, Pharmacogenomics, Precision Cancer Medicine, Rapid automation of plasma protein pools on June 15, 2026|
Unlocking the Secrets of Longevity: A 117-Year-Old Woman’s Genes Defied Aging
Posted in Age and Life Expectancy, Cognition, Epigenetics and Environmental Factors, Gene Editing Impact on Longevity, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Longevity, Medical and Population Genetics, Microbiologial genetics, Molecular Genetics & Pharmaceutical, Personal Health Applications: Tech Innovations serves HealhCare, Personalized and Precision Medicine & Genomic Research, Population genetics, Population Health Management, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, Transformative Technologies in Healthcare, tagged ageing, bond, diet, exercise, family, gene, lifestyle, longevity, physical activity, young on March 18, 2025| Leave a Comment »
Unlocking the Secrets of Longevity: A 117-Year-Old Woman’s Genes Defied Aging
Curator: Dr. Sudipta Saha, Ph.D.
A recent study led by the University of Barcelona has shed light on the genetic factors contributing to exceptional human longevity. The research focused on Maria Branyas Morera, who was recognized as the world’s oldest living person until her passing at age 117 in August 2024. The findings revealed that her unique genetic makeup allowed her cells to function as if they were 17 years younger, and her gut microbiota resembled that of an infant.
Branyas Morera attributed her remarkable lifespan to “luck and good genetics.” Beyond her genetic advantages, she maintained a healthy lifestyle characterized by a Mediterranean diet, regular physical activity, and strong family bonds. These factors likely contributed to her prolonged cognitive clarity and minimal health issues, primarily limited to joint pain and hearing loss.
This study adds to a growing body of research exploring the genetic foundations of longevity. For instance, the Okinawa Centenarian Study has examined over 600 centenarians from Okinawa, Japan, uncovering genetic markers associated with extended lifespan and reduced incidence of age-related diseases.
Similarly, the New England Centenarian Study has identified specific genetic variations linked to longevity, providing insights into the biological mechanisms that allow some individuals to live significantly longer than average.
Researchers hope that understanding these genetic factors can inform the development of treatments for age-related diseases, challenging the notion that aging and illness are inextricably linked. By studying individuals like Branyas Morera, scientists aim to uncover strategies to promote healthier aging across the broader population.
However, it’s important to note that while genetics play a crucial role in exceptional longevity, lifestyle factors such as diet, exercise, and social connections also significantly impact overall health and lifespan. The interplay between genetic predisposition and environmental influences continues to be a critical area of research in understanding human aging.
References
Bridging the Gender Gap in Healthcare: Unlocking Biopharma’s Potential in Women’s Health
Posted in AI Models in Healthcare, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Digital HealthCare – biotech & internet joint ventures, Electronic Health Record, Genetics & Pharmaceutical, Genomics Pharmacy, Glycobiology: Biopharmaceutical Production, Health Care System by Country, Health Economics and Outcomes Research, Health Law & Patient Safety, Health Law Policy, Healthcare costs and reimbursement, HealthCare IT, Healthcare Reform, Human Immune System in Health and in Disease, Mobile Healthcare, Personal Health Applications: Tech Innovations serves HealhCare, Pharmaceutical Analytics, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Informatics, Pharmaceutical R&D Investment, Pharmacogenomics, Population Health Management, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, Technology Transfer: Biotech and Pharmaceutical, Transformative Technologies in Healthcare, Voices of Patients and Healthcare Providers, Wearable Tech + Digital Health, Women Health, tagged advance therapy, Biopharmaceutical, gap, gender, Targeted therapy, translational therapy, womens' health on February 4, 2025| Leave a Comment »
Curator: Dr. Sudipta Saha, Ph.D.
Nearly half of the global population—and 80 percent of patients in therapeutic areas such as immunology—are women. Yet, treatments are frequently developed without tailored insights for female patients, often ignoring critical biological differences such as hormonal impacts, genetic factors, and cellular sex. Historically, women’s health has been narrowly defined through the lens of reproductive organs, while for non-reproductive conditions, women were treated as “small men.” This lack of focus on sex-specific biology has contributed to significant gaps in healthcare.
A recent analysis found that women spend 25 percent more of their lives in poor health compared with men due to the absence of sex-based treatments. Addressing this disparity could not only improve women’s quality of life but also unlock over $1 trillion in annual global GDP by 2040.
Four key factors contribute to the women’s health gap: limited understanding of sex-based biological differences, healthcare systems designed around male physiology, incomplete data that underestimates women’s disease burden, and chronic underfunding of female-focused research. For instance, despite women representing 78 percent of U.S. rheumatoid arthritis patients, only 7 percent of related NIH funding in 2019 targeted female-specific studies.
However, change is happening. Companies have demonstrated how targeted R&D can drive better outcomes for women. These therapies achieved expanded FDA approvals after clinical trials revealed their unique benefits for female patients. Similarly, addressing sex-based treatment gaps in asthma, atrial fibrillation, and tuberculosis could prevent millions of disability-adjusted life years.
By closing the women’s health gap, biopharma companies can drive innovation, improve therapeutic outcomes, and build high-growth markets while addressing long-standing inequities. This untapped opportunity holds the potential to transform global health outcomes for women and create a more equitable future.
References
Nobel Prize in Chemistry 2024 to David Baker, Demis Hassabis and John M. Jumper
Posted in Amino acids, Gene Therapy & Gene Editing Development, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Genome Biology, Genomic Testing: Methodology for Diagnosis, Genomics Pharmacy, Interviews with Scientific Leaders, mRNA Therapeutics, Mutagenesis, Nobel Prize Winners, Patient-centered Medicine, Personalized and Precision Medicine & Genomic Research, Precision Cancer Medicine, Proteins, Proteomics, Single Cell Genomics, Single-cell sequencing, Variation in human protein-coding regions on October 9, 2024| Leave a Comment »
Nobel Prize in Chemistry 2024 to David Baker, Demis Hassabis and John M. Jumper
Reporter: Aviva Lev-Ari, PhD, RN
UPDATED on 10/22/2024
ProteinMPNN, which is now available free on the open-source software repository GitHub, will give researchers the tools to make unlimited new designs. “The challenge, of course … is what are you going to design?” Baker says.
Hallucinating symmetric protein assemblies
Science
15 Sep 2022
Vol 378, Issue 6615
- 56-61
https://www.science.org/doi/10.1126/science.add1964
Robust deep learning–based protein sequence design using ProteinMPNN
Science
15 Sep 2022
Vol 378, Issue 6615
- 49-56
https://www.science.org/doi/10.1126/science.add2187
UPDATED on 10/13/2024
In a second Nobel win for AI, the Royal Swedish Academy of Sciences has awarded half the 2024 prize in chemistry to Demis Hassabis, the cofounder and CEO of Google DeepMind, and John M. Jumper, a director at the same company, for their work on using artificial intelligence to predict the structures of proteins. The other half goes to David Baker, a professor of biochemistry at the University of Washington, for his work on computational protein design. The winners will share a prize pot of 11 million Swedish kronor ($1 million).
The potential impact of this research is enormous. Proteins are fundamental to life, but understanding what they do involves figuring out their structure—a very hard puzzle that once took months or years to crack for each type of protein. By cutting down the time it takes to predict a protein’s structure, computational tools such as those developed by this year’s award winners are helping scientists gain a greater understanding of how proteins work and opening up new avenues of research and drug development. The technology could unlock more efficient vaccines, speed up research on cures for cancer, or lead to completely new materials.
Hassabis and Jumper created AlphaFold, which in 2020 solved a problem scientists have been wrestling with for decades: predicting the three-dimensional structure of a protein from a sequence of amino acids. The AI tool has since been used to predict the shapes of all proteins known to science.
Their latest model, AlphaFold 3, can predict the structures of DNA, RNA, and molecules like ligands, which are essential to drug discovery. DeepMind has also released the source code and database of its results to scientists for free.
“I’ve dedicated my career to advancing AI because of its unparalleled potential to improve the lives of billions of people,” said Demis Hassabis. “AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery. I hope we’ll look back on AlphaFold as the first proof point of AI’s incredible potential to accelerate scientific discovery,” he added.
Baker has created several AI tools for designing and predicting the structure of proteins, such as a family of programs called Rosetta. In 2022, his lab created an open-source AI tool called ProteinMPNN that could help researchers discover previously unknown proteins and design entirely new ones. It helps researchers who have an exact protein structure in mind find amino acid sequences that fold into that shape.
Most recently, in late September, Baker’s lab announced it had developed custom molecules that allow scientists to precisely target and eliminate proteins associated with diseases in living cells.
“[Proteins] evolved over the course of evolution to solve the problems that organisms faced during evolution. But we face new problems today, like covid. If we could design proteins that were as good at solving new problems as the ones that evolved during evolution are at solving old problems, it would be really, really powerful,” Baker told MIT Technology Review in 2022.
10/9/2024
David Baker “for computational protein design”
born 1962 in Seattle, WA, USA. PhD 1989 from University of California, Berkeley, CA, USA. Professor at University of Washington, Seattle, WA, USA and Investigator, Howard Hughes Medical Institute, USA.
University of Washington, Seattle, WA, USA
Howard Hughes Medical Institute, USA
Demis Hassabis “for protein structure prediction”
born 1976 in London, UK. PhD 2009 from University College London, UK. CEO of Google DeepMind, London, UK.
Google DeepMind, London, UK
John M. Jumper “for protein structure prediction”
born 1985 in Little Rock, AR, USA. PhD 2017 from University of Chicago, IL, USA. Senior Research Scientist at Google DeepMind, London, UK.
Google DeepMind, London, UK
The Nobel Prize in Chemistry 2024 is about proteins, life’s ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures. These discoveries hold enormous potential.
“One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities,” says Heiner Linke, Chair of the Nobel Committee for Chemistry.
Proteins generally consist of 20 different amino acids, which can be described as life’s building blocks. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors.
The second discovery concerns the prediction of protein structures. In proteins, amino acids are linked together in long strings that fold up to make a three-dimensional structure, which is decisive for the protein’s function. Since the 1970s, researchers had tried to predict protein structures from amino acid sequences, but this was notoriously difficult. However, four years ago, there was a stunning breakthrough.
In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
Life could not exist without proteins. That we can now predict protein structures and design our own proteins confers the greatest benefit to humankind.
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SOURCE
https://www.linkedin.com/company/nobelprize/posts/?feedView=all
Reference
Scientific background: Computational protein design and protein structure prediction (pdf)
SOURCE
https://www.nobelprize.org/prizes/chemistry/2024/press-release/
2024 Nobel Prize in Physiology or Medicine jointly to Victor Ambros and Gary Ruvkun for the discovery of microRNA and its role in post-transcriptional gene regulation
Posted in Biological Networks, Gene Regulation and Evolution, Cell Biology, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Clinical & Translational, Clinical Genomics, Cytoskeleton, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Gene Regulation, Gene Regulation and Evolution, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Genome Biology, Genomic Expression, Interviews with Scientific Leaders, Meta-analysis of transcriptome data, mRNA Therapeutics, Nobel Prize Winners, Single Cell Genomics, Single-cell sequencing, Variation in human protein-coding regions on October 8, 2024| Leave a Comment »
2024 Nobel Prize in Physiology or Medicine jointly to Victor Ambros and Gary Ruvkun for the discovery of microRNA and its role in post-transcriptional gene regulation
Reporter: Aviva Lev-Ari, PhD, RN
Updated 10/22/2024
The revolution in our understanding of transcriptional regulation and dark regions of the genome
The genome of higher eukaryotes are comprised of multiple exonic and intronic regions, with coding and noncoding DNA respectively. Much of the DNA sequence between exonic regions of genes, the sequences encoding the amino acids of a polypeptide, was considered either promoter regions regulating an exonic sequence or ‘junk DNA’, which had merely separated exons and their regulatory elements. It was not considered that this dark DNA or junk DNA was important in regulating transcription of genes. It was felt that most gene regulation occurred in promoter regions by response element factors which bound to specific sequences within these regions.
MicroRNA (miRNA), originally discovered in Caenorhabditis elegans, is found in most eukaryotes, including humans [1–3]. It is predicted that miRNA account for 1-5% of the human genome and regulate at least 30% of protein-coding genes [4–8]. To date, 940 distinct miRNAs molecules have been identified within the human genome [9–12] (http://microrna.sanger.ac.uk accessed July 20, 2010). Although little is currently known about the specific targets and biological functions of miRNA molecules thus far, it is evident that miRNA plays a crucial role in the regulation of gene expression controlling diverse cellular and metabolic pathways.
MiRNA are small, evolutionary conserved, single-stranded, non-coding RNA molecules that bind target mRNA to prevent protein production by one of two distinct mechanisms. Mature miRNA is generated through two-step cleavage of primary miRNA (pri-miRNA), which incorporates into the effector complex RNA-induced silencing complex (RISC). The miRNA functions as a guide by base-pairing with target mRNA to negatively regulate its expression. The level of complementarity between the guide and mRNA target determines which silencing mechanism will be employed; cleavage of target messenger RNA (mRNA) with subsequent degradation or translation inhibition
Fig. (1). MicroRNA maturation and function.
Figure. miRNA maturation and function. Source: Macfarlane LA, Murphy PR. MicroRNA: Biogenesis, Function and Role in Cancer. Curr Genomics. 2010 Nov;11(7):537-61. doi: 10.2174/138920210793175895.
The following is an interview in the journal Journal of Cellular Biology with Dr, Victor Ambros on his discovery of miRNA.
Source: Ambros V. Victor Ambros: the broad scope of microRNAs. Interview by Caitlin Sedwick. J Cell Biol. 2013 May 13;201(4):492-3. doi: 10.1083/jcb.2014pi. PMID: 23671307; PMCID: PMC3653358.
Once, we thought we understood all there was to know about how gene expression is regulated: A cell can tinker with the expression level of a given protein’s messenger RNA by modifying the activity, abundance, and type of transcription factors in the nucleus or with the RNA’s stability once it is made. But then came a surprising story about a short RNA in C. elegans called lin-4, which didn’t encode a protein but prevented expression of the protein encoded by another gene, lin-14, through antisense binding to lin-14 mRNA (1, 2). Today, we know that lin-4 was just the first example of a large number of small RNAs, called microRNAs, which regulate the expression of various other proteins in a similar way.
Victor Ambros, whose lab published that first story about lin-4, has been studying microRNAs (3, 4) and their regulation (5, 6) ever since, pushing forward our understanding of this powerful mechanism. We called him at his office at the University of Massachusetts Medical School to get some perspective on microRNAs and his career and to learn about some of the latest developments in his lab.
“That shared discovery is one of the most precious moments in my career.”
FROM FARM TO LAB TABLE
How did you end up doing a PhD with David Baltimore?
I was the first scientist in my family. My dad was an immigrant from Poland. He came to the States just after World War II and met my mom. They got married, moved to a farm in Vermont, and started farming. My siblings and I grew up amongst the cows and pigs and helped with the haying and cutting corn, stuff like that.
When I was about nine, I got interested in science, and after that I always wanted to be a scientist. I was an amateur astronomer; I built a telescope and started to imagine that I could actually do astronomy or physics as an occupation. But I quickly changed my mind when I reached college, in part because I realized that my math skills weren’t really up to the task of being a physicist and also because I discovered molecular biology and genetics and just fell in love with both subjects. David taught one of the advanced biology classes I took as an undergraduate at MIT, and that probably had some influence on my decision to work with him. After college, I worked as a technician in David’s lab for a year. I liked it a lot and stayed on in his lab when I entered graduate school at MIT. I was lucky because I had gotten a little bit of traction on a project and continued on that as a grad student, so I ended up finishing grad school fairly efficiently.
Had you any idea at the time what the nature of the lin-4 mutant was?
The assumption was that it was a protein product. I mean, nobody ever thought that there would be any other kind of regulator. There really wasn’t any reason to imagine that there were any other kinds of molecules necessary, other than proteins, to carry out everything that’s done in a cell—especially with regard to the regulation of gene expression. The complexity of gene regulation by proteins alone was so enormous that I never imagined—and nobody I knew imagined—that we needed to look for new kinds of regulatory molecules. The realization that lin-4 was antisense to the 3′-untranslated region of lin-14 was totally the result of communication between Gary and me. That shared discovery is one of the most precious moments in my career. But at the time I didn’t realize that this might be the first example of a general mechanism for regulating gene expression because I was prone to thinking that whatever I was studying in the worm was not generally applicable. It wasn’t until genome sequences were made available that the prevalence of this mechanism became clear.
THE RIGHT CONTEXT
You’ve moved to studying processes that modulate microRNA function…
One protein we’ve studied is called Nhl-2. It’s an example of an emerging class of proteins that can modulate, positively or negatively, the RNA-induced silencing complex (RISC) that inhibits mRNAs targeted by microRNAs. This class of genes may have either general effects on RISC activity or, in some cases, more specific effects. One area of interest in the lab right now is trying to understand the specific outcomes for the regulation of particular microRNAs. Do they always interact with all their targets, or is their activity on some targets promoted or inhibited at the expense of other targets? Can their interaction with certain targets be modified depending on context? We’re using genetic and genomic approaches to identify new modulatory cofactors.
Watch Video
Victor Ambros was born in 1953 in Hanover, New Hampshire, USA. He received his PhD from Massachusetts Institute of Technology (MIT), Cambridge, MA, in 1979 where he also did postdoctoral research 1979-1985. He became a Principal Investigator at Harvard University, Cambridge, MA in 1985. He was Professor at Dartmouth Medical School from 1992-2007 and he is now Silverman Professor of Natural Science at the University of Massachusetts Medical School, Worcester, MA.
Gary Ruvkun was born in Berkeley, California, USA in 1952. He received his PhD from Harvard University in 1982. He was a postdoctoral fellow at Massachusetts Institute of Technology (MIT), Cambridge, MA, 1982-1985. He became a Principal Investigator at Massachusetts General Hospital and Harvard Medical School in 1985, where he is now Professor of Genetics.
This year’s Nobel Prize honors two scientists for their discovery of a fundamental principle governing how gene activity is regulated.
The information stored within our chromosomes can be likened to an instruction manual for all cells in our body. Every cell contains the same chromosomes, so every cell contains exactly the same set of genes and exactly the same set of instructions. Yet, different cell types, such as muscle and nerve cells, have very distinct characteristics. How do these differences arise? The answer lies in gene regulation, which allows each cell to select only the relevant instructions. This ensures that only the correct set of genes is active in each cell type.
Victor Ambros and Gary Ruvkun were interested in how different cell types develop. They discovered microRNA, a new class of tiny RNA molecules that play a crucial role in gene regulation. Their groundbreaking discovery revealed a completely new principle of gene regulation that turned out to be essential for multicellular organisms, including humans. It is now known that the human genome codes for over one thousand microRNAs. Their surprising discovery revealed an entirely new dimension to gene regulation. MicroRNAs are proving to be fundamentally important for how organisms develop and function.
Ambros and Ruvkun were interested in genes that control the timing of activation of different genetic programs, ensuring that various cell types develop at the right time. They studied two mutant strains of worms, lin-4 and lin-14, that displayed defects in the timing of activation of genetic programs during development. The laureates wanted to identify the mutated genes and understand their function. Ambros had previously shown that the lin-4 gene appeared to be a negative regulator of the lin-14 gene. However, how the lin-14 activity was blocked was unknown. Ambros and Ruvkun were intrigued by these mutants and their potential relationship and set out to resolve these mysteries.
Ambros and Ruvkun performed further experiments showing that the lin-4 microRNA turns off lin-14 by binding to the complementary sequences in its mRNA, blocking the production of lin-14 protein. A new principle of gene regulation, mediated by a previously unknown type of RNA, microRNA, had been discovered! The results were published in 1993 in two articles in the journal Cell.
Ruvkun cloned let-7, a second gene encoding a microRNA. The gene is conserved in evolution, and it is now known that microRNA regulation is universal among multicellular organisms.
Andrew Z. Fire and Craig C. Mello, awarded the Nobel Prize in 2006, described RNA interference, where specific mRNA-molecules are inactivated by adding double-stranded RNA to cells.
Mutations in one of the proteins required for microRNA production result in the DICER1 syndrome, a rare but severe syndrome linked to cancer in various organs and tissues.
Reference
SOURCE
https://www.nobelprize.org/prizes/medicine/2024/press-release/
Mission 4: Use of Systems Biology for Design of inhibitor of Galectins as Cancer Therapeutic – Strategy and Software
Posted in Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Applications in Therapeutics, BioIT: BioInformatics, BioSimilars, BioTechnology - Venture Creation, Cancer - General, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Cancer Informatics, cancer metabolism, Cell Biology, Signaling & Cell Circuits, Computational Biology/Systems and Bioinformatics, Genetics & Pharmaceutical, Genome Biology, Genomic Expression, Glycobiology: Biopharmaceutical Production, Glycobiology: Biopharmaceutical Production, Pharmacodynamics and Pharmacokinetics, Innovations, LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management, MicroEngineering Cell-Tissue & Systems, Natural Language Processing (NLP), Phosphorylation, S-nitrosylation, Signaling, Signaling & Cell Circuits, Small Molecules in Development of Therapeutic Drugs, Ubiquitin, Ubiquitinylation, tagged angiogenesis, Cancer, carbohydrate, galectin-3, LPBI, metastasis, PROTAC, Synthetic biology, systems biology on December 13, 2022| Leave a Comment »
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
Affiliations 2016 Jul 15;6:29457.
doi: 10.1038/srep29457.
- PMID: 27416897
- PMCID: PMC4945863
- DOI: 10.1038/srep29457
Abstract
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
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
Posted in Cell Biology, Cell Biology, Signaling & Cell Circuits, Chemical Genetics, Conference Coverage with Social Media, Gene Therapy & Gene Editing Development, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Genome Biology, mRNA Therapeutics, Personalized and Precision Medicine & Genomic Research, Regenerative Biology and Medicine, tagged Cell therapy, gene therapy on October 7, 2021| Leave a Comment »
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 Forum at #WMIF2021 @MGBInnovation:
Aviva Lev-Ari, PhD, RN
Tweet Collection Curator:
Aviva Lev-Ari, PhD, RN
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May 2021 • 31 days
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Top Tweet earned 611 impressions
@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 pic.twitter.com/9Q2dWikhNd 1 2
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Christian, father, husband. Owner @RaconteurMC. Strategist for comms, digital. Former award-winning journalist. Proverbs 3:5-6 View profile
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#COVID#vaccines by @Pfizer, @AstraZeneca are probed in @Europe after reports of #heart#inflammation, rare #nerve#disorderpharmaceuticalintelligence.com/2021/05/14/cov… via @pharma_BI@AVIVA1950 1 3View all Tweet activityView Tweet activity
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These are the Tweets and the Re-Tweets
by Day, 5/21, 5/20, 5/19 for
2021 Virtual World Medical Innovation Forum, Mass General Brigham, Gene and Cell Therapy, VIRTUAL May 19–21, 2021
Real Time coverage: Aviva Lev-Ari, PhD, RN
May 21, 2021
TWEETS AND RE-TWEETS for 2021 World Medical Innovation Forum, Mass General Brigham, Gene and Cell Therapy, VIRTUAL May 19–21, 2021
PART 1: ALL THE TWEETS PRODUCED by @AVIVA1950 on May 21, 2021
Part 2: ALL THE RE-TWEETS by @AVIVA1950 on
May 21, 2021
Tweets Originator for Part 1: Aviva Lev-Ari, PhD, RN
From: Mass General Brigham <innovations@partners.org>
Reply-To: <innovations@partners.org>
Date: Monday, May 24, 2021 at 9:31 AM
To: “Aviva Lev-Ari, PhD, RN” <AvivaLev-Ari@alum.berkeley.edu>
Subject: RECAP | World Forum | Day 3 | GCT | CEOs | Harvard | Investors
| Notable Tweets |
Disruptive Dozen
2021 World Medical Innovation Forum on
YouTube
https://www.youtube.com/results?search_query=Disruptive+Dozen+2021+World+Medical+Innovation+Forum
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#WMIF Best Global event on Gene Cell Therapy covered in real time
Disruptive Dozen technologies four are based on Gene Editing, AAV and non viral vector for drug delivery are included
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Mass General Brigham Innovation
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Thanks
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
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Erwan Bezard, PhD INSERM Research Director, Institute of Neurodegenerative Diseases Cautious on reversal
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Nikola Kojic, PhD CEO and Co-Founder, Oryon Cell Therapies Autologus cell therapy placed focal replacing missing synapses reestablishment of neural circutary
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Bob Carter, MD, PhD Chairman, Department of Neurosurgery, MGH William and Elizabeth Sweet, Professor of Neurosurgery, HMS Neurogeneration REVERSAL or slowing down?
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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
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Penelope Hallett, PhD NRL, McLean Assistant Professor Psychiatry, HMS Pharmacologic agent in existing cause another disorders locomo-movement related
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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
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Roger Kitterman VP, Venture, Mass General Brigham Saturation reached or more investment is coming in CGT
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Oleg Nodelman Founder & Managing Partner, EcoR1 Capital Invest in company next round of investment will be IPO 20% discount
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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
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Deep Nishar Senior Managing Partner, SoftBank Investment Advisors Young field vs CGT started in the 80s high payloads is a challenge
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Bob Carter, MD, PhD MGH, HMS cells producing dopamine transplantation fibroblast cells metabolic driven process lower mutation burden Quercetin inhibition elimination undifferentiated cells graft survival oxygenation increased
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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
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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
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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
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Nabiha Saklayen, PhD CEO and Co-Founder, Cellino scale production of autologous cells foundry using semiconductor process in building cassettes by optic physicists
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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
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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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Meredith Fisher, PhD Partner, Mass General Brigham Innovation Fund Strategies, success what changes are needed in the drug discovery process@pharma_BI
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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
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Kush Parmar, MD, PhD Managing Partner, 5AM Ventures Responsibility mismatch should be and what is “are”
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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
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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
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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
Part 2: ALL THE RE-TWEETS by @AVIVA1950 on
May 21, 2021
Mass General Brigham Innovation
·
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
, presents a #regenerativemedicine approach that could alter that trajectory. #WMIF2021
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Mass General Brigham Innovation
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Our “Capital Formation ’21-30 | Investing Modes Driving GCT Technology and Timing” panelists have taken the stage. #WMIF2021
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Mass General Brigham Innovation
·
CAR-T therapies have proven remarkably effective. Now,
researchers including
Marcela Maus, MD PhD, are working to expand the reach of this transformative technology. #WMIF2021
Quote Tweet
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
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Mass General Brigham Innovation
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Disruptive Dozen: 12 Technologies that Will Reinvent GCT #6. Eyes and Ears: Expanding Gene Therapy’s Reach #WMIF2021 #GCT #GeneAndCellTherapy #GeneTherapy #DisruptiveDozen
Mass General Brigham Innovation
·
If you’ve missed some First Look sessions, don’t worry! We’ve got you covered. Our First Look On-Demand videos, featuring 18
investigators giving previews of their #GCT research, are available to view on the #WMIF2021 conference platform. https://worldmedicalinnovation.org/register/
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This morning at 10:20 a.m. ET, our CEO, Ken Mills, will be participating live on the AAV Success Studies virtual panel at the #WMIF2021, hosted by
. Click here to register: https://bit.ly/33tHTti #Genetherapy
Register | World Medical Innovation Forum – Gene and Cell Therapy
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Looking forward to joining
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.
Quote Tweet
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
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Maria Luiza Gutierrez de Andrade Seixas
·
Incredible opportunity to get up to speed with the most innovative technologies in medicine ! Gene and cell therapy are revolutionizing healthcare ! #WMIF2021 #MedTwitter
Quote Tweet
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/
Mass General Brigham Innovation
·
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
Quote Tweet
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
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Mass General Brigham Innovation
·
Researchers have pinpointed key genes involved in cholesterol and lipid metabolism that represent promising targets for new cholesterol-lowering treatments. #WMIF2021
Quote Tweet
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
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The
#WMIF2021 event kicks of this morning! Congratulations to faculty member and event Co-Chair
on putting together such a terrific program. Register: https://bit.ly/3uWYB0E
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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
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Mass General Brigham Innovation
·
Thank you to our World Medical Innovation Forum Collaborators
·
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
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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.
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Mass General Brigham Innovation
·
Disruptive Dozen: 12 Technologies that Will Reinvent GCT #8. Replacing What’s Lost: Stem Cell Therapies for Diabetes #WMIF2021 #GCT #GeneAndCellTherapy #StemCell #StemCellResearch #Diabetes #DisruptiveDozen
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Mass General Brigham Innovation
·
An overview of our CEO Panel featuring Lisa Deschamps of
, Kieran Murphy of
and Christian Rommel PhD, of
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Gene and cell therapies could change the future of medicine for patients w chronic disease or rare/ultra-rare disease – hear how
is working w the GCT ecosystem to drive new discoveries from bench to bedside #GCT #WMIF2021
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Mass General Brigham Innovation
·
That’s a wrap! Thank you to everyone who helped make #WMIF2021 such a success, especially our incredible sponsors:
and more. Full list: https://worldmedicalinnovation.org/sponsors/
Mass General Brigham Innovation
·
Disruptive Dozen: 12 Technologies that Will Reinvent GCT #1. A New Generation of Cholesterol-Loweing Therapies #WMIF2021 #GCT #GeneAndCellTherapy #DisruptiveDozen
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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
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Thank you
and
for this award! Congratulations to
and all other winners!
Quote Tweet
Leaps by Bayer
@LeapsByBayer
· 6h
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…
Show this thread
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An artistic description of an exciting panel I led today, at the World Biomedical Innovation Forum, discussing the future of non-viral delivery systems for gene therapy. #MatthewStanton #LauraSeppLorenzino #DouglasWilliams #SonyaMontgomery #WMIF2021
May 20, 2021
TWEETS AND RE-TWEETS for 2021 World Medical Innovation Forum, Mass General Brigham, Gene and Cell Therapy, VIRTUAL May 19–21, 2021
PART 1: ALL THE TWEETS PRODUCED by @AVIVA1950 on May 20, 2021
Part 2: ALL THE RE-TWEETS by @AVIVA1950 on
May 20, 2021
Tweets Originator for Part 1: Aviva Lev-Ari, PhD, RN
·
#WMIF Best Global event on Gene Cell Therapy covered in real time
Disruptive Dozen technologies four are based on Gene Editing, AAV and non viral vector for drug delivery are included
2
2
Mass General Brigham Innovation
·
Thanks
for sharing this screen capture of the impressive lineup of #GCT “Disruptive Dozen” panelists at #WMIF2021
Quote Tweet
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
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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
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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
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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
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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
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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
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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
1
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Melissa Moore Chief Scientific Officer, Moderna Flu vaccine knowing the virus variant 45 days for Personalized cancer vaccine one per patient
1
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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
1
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Melissa Moore Chief Scientific Officer, Moderna How many mRNA can be put in one vaccine: Dose and tolerance to achieve efficacy and the
1
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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
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Ron Renaud CEO, Translate Bio Platform allowing to swap cargo reusing same nanoparticles address disease beyond Big Pharma options for biotech
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
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Ron Renaud CEO, Translate Bio 1.6 Billion doses produced rare disease monogenic correct mRNA like CF multiple mutation infection disease and oncology applications
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Kate Bingham, UK Vaccine Taskforce July 2020, AAV vs mRNA delivery across UK local centers administered both types supply and delivery uplift
1
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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
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Shunfei Yan, PhD Investment Manager, InnoStar Capital Indication driven: Hymophilia, Allogogenic efficiency therapies Licensing opportunities
1
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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
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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
1
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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
Part 2: ALL THE RE-TWEETS by @AVIVA1950 on
May 20, 2021
·
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
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Mass General Brigham Innovation
·
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
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Mass General Brigham Innovation
·
In our next First Look presentation we’ll hear from Xandra Breakefield PhD & Koen Breyne PhD
about their work focused on developing non-viral vectors to enhance #genedelivery. #WMIF2021 #GCT #genetherapy
Mass General Brigham Innovation
·
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
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Mass General Brigham Innovation
·
Ravi Thadhani, CAO
and Juergen Eckhardt, Head of
, are announcing the 2021 Innovation Discovery Grants this afternoon at #WMIF2021.
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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!
https://bit.ly/3hx1XTV #geneediting #biotechnology
Gene Editing | Achieving Therapeutic Mainstream – 2021 World Medical Innovation Forum
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Mass General Brigham Innovation
·
A behind the scenes peek at our “Gene Editing | Achieving Therapeutic Mainstream” moderator & panelists preparing to go live. #WMIF2021
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Mass General Brigham Innovation
·
Thank you to the “Common Blood Disorders | Gene Therapy” moderator David Scadden, MD
and panelists Leslie Kean, MD PhD
, Samarth Kulkarni, PhD
, Nick Leschly
, Mike McCune, MD PhD
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1
Mass General Brigham Innovation
·
Kieran Murphy, CEO,
, 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
Mass General Brigham Innovation
·
Wrapping up Day 2 of #WMIF2021 with the “Gene Expression | Modulating with Oligonucleotide-Based Therapies” panel.
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Mass General Brigham Innovation
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Juergen Eckhardt, Head of
, announces new Bayer mentoring program for Innovation Discovery Grant winners at #WMIF2021.
3
Mass General Brigham Innovation
·
In our final First Look session of the day, Pierpaolo Peruzzi, MD PhD,
presents “RNA Therapy for Brain Cancer” #WMIF2021
1
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Mass General Brigham Innovation
·
Hear from
CSO Laura Sepp-Lorenzino, PhD, in our “GCT Delivery | Perfecting the Technology” panel this afternoon! #WMIF2021
Quote Tweet
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
1
1
Mass General Brigham Innovation
·
Natalie Artzi, PhD, Assistant Professor
is back with us this afternoon sharing a First Look at “Versatile Polymer-Based Nanocarriers for Targeted Therapy and Immunomodulation.” #WMIF2021 #GCT #geneandcelltherapy
Mass General Brigham Innovation
·
We look forward to hearing from
VP of Clinical Development, Manasi Jaiman, during the “Diabetes | Grand Challenge” panel today. #WMIF2021
Quote Tweet
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.
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Mass General Brigham Innovation
·
We’ll see you back here after the break for the “GCT Delivery | Perfecting the Technology” panel, featuring moderator Natalie Artzi, PhD,
and panelists from
,
,
and
1
Mass General Brigham Innovation
·
Christian Rommel, PhD,EVP, Head, Pharmaceuticals Research & Development,
, 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
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Mass General Brigham Innovation
·
Next up is the #Diabetes | Grand Challenge panel at #WMIF2021 featuring speakers from
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Mass General Brigham Innovation
·
The “Gene Editing | Achieving Therapeutic Mainstream” panel today at 2:55 pm Eastern will discuss the movement of #geneediting technology into the therapeutic mainstream. Join us! #WMIF2021 https://worldmedicalinnovation.org/register/
Quote Tweet
Editas Medicine
@editasmed
· 10h
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
·
Global Head of R&D
joined this week’s World Medical Innovation Forum hosted by
to discuss the current state of CAR-T and its future prospects. These conversations are important for the development of potential #CART therapies. #WMIF2021
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8
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“We can get to an “n of 1” with mRNA technology says Melissa Moore, PhD, CSO Platform Research,
1
1
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Today, Intellia CSO,
will be participating in the World Medical Innovation Forum’s panel on Gene and Cell Therapy Delivery, Perfecting the Technology. #WMIF2021
. Click here to learn more: https://worldmedicalinnovation.org
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Graphical representation of this morning’s #mRNA #vaccines panel at
‘s #WMIF2021 — Thanks to the MGB team for facilitating a great discussion!
Quote Tweet
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
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18
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!
Mass General Brigham Innovation
·
Please welcome Marc Casper, CEO
to the stage for a Fireside Chat moderated by Erin Harris
, Editor in Chief
“Partnering Across the GCT Spectrum” #WMIF2021 #GCT #geneandcelltherapy
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Mass General Brigham Innovation
·
The “CEO Panel | Anticipating Disruption | Planning for Widespread GCT” panelists have joined the stage. #WMIF2021
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Mass General Brigham Innovation
·
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
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Mass General Brigham Innovation
·
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.
2
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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
here http://bit.ly/T1Dcelltherapies… #WMIF2021
Quote Tweet
Mass General Brigham Innovation
@MGBInnovation
· 11h
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/
2
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Mass General Brigham Innovation
·
Coming up at 12:05 pm Eastern: “CEO Panel | Anticipating Disruption | Planning for Widespread GCT” featuring panelists from
and moderated by
Senior Health and Science Reporter
Mass General Brigham Innovation
·
Join us at #WMIF2021 to hear from Suneet Varma, Global President of Rare Disease
, during the “Benign Blood Disorders” today at 9:00 am Eastern. https://worldmedicalinnovation.org/register/
Quote Tweet
Pfizer Inc.
@pfizer
· May 19
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
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Melissa Moore/Moderna said they are working with Merck on developing personalized cancer vaccines, n of 1 #wmif2021
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Mass General Brigham Innovation
·
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
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Mass General Brigham Innovation
·
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
2
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Mass General Brigham Innovation
·
Congratulations! Jinjun Shi, PhD, Brigham and Women’s Hospital, receives an Innovation Discovery Grant for “Long-Lasting mRNA Therapy for Genetic Disorders” at #WMIF2021
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Mass General Brigham Innovation
·
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
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Mass General Brigham Innovation
·
Congratulations! Benjamin Kleinstiver PhD, Massachusetts General Hospital, receives an Innovation Discovery Grant for “Towards a Permanent Genetic Cure for Spinal Muscular Atrophy” at #WMIF2021
2
·
Listening to mRNA vaccines #wmif2021 panel w/ speakers incl SV Health managing partner & ex UK Vaccine Taskforce
, Moderna CSO Platform Rsrch Melissa Moore,
CEO Ron Renaud
, Brigham/BWH Dir Clinical Research Infectious Disease Lindsey Baden
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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.
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Mass General Brigham Innovation
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Coming up this afternoon: the 2021 Innovation Discovery Grants in #geneandcelltherapy. Who will secure additional funding for research to advance #GCT? Join us to watch live. #WMIF2021 https://worldmedicalinnovation.org/register/
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1
Mass General Brigham Innovation
·
Thank you Jeannie Lee, MD PhD
, Bob Brown, PhD
, Brett Monia, PhD
, and Alfred Sandrock, MD PhD
for sharing your perspectives on oligonucleotide-based therapies. #WMIF2021
1
2
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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
1
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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
1
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1
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Marc Casper
says gene and cell therapy represents a “phenomenal opportunity to improve patients’ lives” #WMIF2021 #GCT
1
2
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Today, our CEO Ron Renaud
participated in
‘s 2021 World Medical Innovation Forum to discuss the impact of #messengerRNA #vaccines on the industry #WMIF2021 #mRNA
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Mass General Brigham Innovation
·
Lisa Deschamps, SVP & Chief Business Officer,
, 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
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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.
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Diagnostics and data tools key part of precision medicine complementing gene and cell therapy says
at
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Debating the value of natural history studies in rare/ultra rare disease — panel led by Susan Slaugenhaupt, PhD, scientific director,
at #WMIF2021. Challenges include costs, feasibility, timing, comparative data.
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Rett’s Syndrome, which primarily affects young girls, has historically been studied in male mice! Jeannie Lee, MD, PhD,
, and team are exploring how to treat the disease w X chromosome reactivation… and using a female mouse model. Hear more on #GCT at #WMIF2021
2
5
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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,
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May 19, 2021
TWEETS AND RE-TWEETS for 2021 World Medical Innovation Forum, Mass General Brigham, Gene and Cell Therapy, VIRTUAL May 19–21, 2021
PART 1: ALL THE TWEETS PRODUCED by @AVIVA1950 on May 19, 2021
Part 2: ALL THE RE-TWEETS by @AVIVA1950 on
May 19, 2021
Tweets Originator for Part 1: Aviva Lev-Ari, PhD, RN
·
#WMIF Best Global event on Gene Cell Therapy covered in real time
Disruptive Dozen technologies four are based on Gene Editing, AAV and non viral vector for drug delivery are included
2
2
Mass General Brigham Innovation
·
Thanks
for sharing this screen capture of the impressive lineup of #GCT “Disruptive Dozen” panelists at #WMIF2021
Quote Tweet
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
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Marcela Maus, MD, PhD Director, Cancer Center, MGH, HMS Fit-to-purpose CAR-T cells: 3 lead programs Tr-fill CAR-T induce response myeloma and multiple myeloma GBM 27 patents on CAR-T +400 patients treaded 40 Clinical Trials
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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
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Ropa Pike, Director, Enterprise Science & Partnerships, Thermo FIsher Scientific Centralized biopharma industry is moving to decentralized models site specific license
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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
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Oladapo Yeku, MD, PhD Clinical Assistant in Medicine, MGH Outstanding moderator and most gifted panel on solid tumor success window of opportunities studies
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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
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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
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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
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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
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Christi Shaw CEO, Kite CAR-T is priority 120 companies in the space Manufacturing consistency Patients respond with better quality of life
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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
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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
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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
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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
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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
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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?
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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
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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
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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
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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
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amazing Conference on the frontier od Science Cell & Gene Therapy
top programs for ALS, Brain genetic vasculopathologies and Occular, MEE
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Pearl Freier
@PearlF
· 21h
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
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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
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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?
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Debby Baron, Worldwide Business Development, Pfizer Scalability and manufacturing regulatory conversations, clinical programs safety in parallel to planning getting drug to patients
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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
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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
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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
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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
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2 people unfollowed me // automatically checked by
fllwrs – keep track of who follows and unfollows you on twitter
fllwrs is the easiest way to keep track of your twitter followers
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Jim Holland CEO, http://Backcountry.com Parkinson patient Constraints by regulatory on participation in clinical trial wish to take Information dissemination is critical
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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
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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
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Sarah Beth Thomas, RN Professional Development Manager, BWH Outcome is unknown, hope for good, support with resources all advocacy groups,
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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
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Barbara Lavery Chief Program Officer, ACGT Foundation Advocacy agency beginning of work Global Genes educational content and out reach to access the information
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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
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Dave Lennon, PhD President, Novartis Gene Therapies Promise of CGT realized, what part? #FDA role and interaction in CGT #Manufacturing aspects which is critical
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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
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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
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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
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Nino Chiocca, MD, PhD Neurosurgeon-in-Chief and Chairman, Neurosurgery, BWH #Oncolytic #Viruses triple threats #Toxic, #braintumors #immunological requires #combination #therapies with #anticancer
Part 2: ALL THE RE-TWEETS by @AVIVA1950 on
May 19, 2021
Mass General Brigham Innovation
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Will point of care production become a reality? “Short answer is yes” says Rupa Pike PhD, Director, Enterprise Science & Innovation Partnerships,
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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)
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Very creative two targets
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Mass General Brigham Innovation
@MGBInnovation
· 16h
A behind the scenes look at David Scadden, MD @ScaddenLab presenting his FIRST LOOK: Regenerating T Cell Immunity #WMIF2021 #GCT #Tcells
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Mass General Brigham Innovation
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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
#WMIF2021 https://bwhclinicalandresearchnews.org/2021/05/11/look-whos-talking-world-medical-innovation-forum-first-look-speakers/…
Mass General Brigham Innovation
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“Entrepreneurial Growth | Oncolytic Virus” panel, moderated by Reid Huber PhD, Partner
, discusses how small companies can address the challenges of developing #oncolyticvirus therapies. #WMIF2021
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The World Medical Innovation Forum is here! During his fireside chat, our President Dave Lennon shares the immense promise ahead for #genetherapy.
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Mass General Brigham Innovation
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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/
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We’re at
‘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)
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Mass General Brigham Innovation
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Some incredible #visualnotes from this morning’s co-chair’s panel “The Grand Challenge of Widespread GCT Patient Benefits” #WMIF2021 #GCT #geneandcelltherapy
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Mass General Brigham Innovation
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The World Medical Innovation Forum #WMIF2021 is just two days away! Join us to hear the latest in #geneandcelltherapy #healthcare innovation. https://worldmedicalinnovation.org/register/
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“We anticipate that our engineered tumor cell platform will have major contributions in finding a cure for #glioblastoma patients,” says
of
. Catch a preview of his #WMIF2021 First Look talk here: https://fal.cn/3fpUL
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Dr. Eric Pierce
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.
Mass General Brigham Innovation
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Mass General Brigham Innovation
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Ravi Thadhani, CAO @MassGeneralBrigham and Juergen Eckhardt, Head of
, will be announcing the 2021 Innovation Discovery Grants at #WMIF2021 tomorrow, 5/20 @ 2:00 pm Eastern. https://worldmedicalinnovation.org
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Leaps by Bayer
@LeapsByBayer
· 22h
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).
Show this thread
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Mass General Brigham Innovation
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Welcome back! Our next #WMIF2021 panel, Oncolytic Viruses in #Cancer | Curing #Melanoma and Beyond, features panelists from
and
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“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
Outstanding researcher and speaker
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Mass General Brigham Innovation
@MGBInnovation
· 21h
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
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Happening now at #WMIF2021.
chief and
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
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Attending
World Medical Innovation Forum? Tune in to hear our CEO
speak tomorrow at 3:25pm ET on innovations in cell and gene therapy, followed by a Q&A. Learn more: https://bit.ly/3eWb66R #WMIF2021
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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:
World Medical Innovation Forum
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Jonathan Kraft introducing #wmif2021 session with Pfizer CSO & president of R&D Mikael Dolsten and MGH oncologist & chair of MGH Cancer Center Daniel Haber.
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MEE is the leader in cell therapy for retina genetic disease
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Tracy Doyle
@doylet
· May 19
Great discussion to open #WMIF2021 on the patient impact of #GCT @MGBInnovation World Medical Innovation Forum twitter.com/AVIVA1950/stat…
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Tuning into
#WMIF2021 cell & gene therapy meeting.
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
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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
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Dolsten/Pfizer discussing their partnership with Ionis.https://ir.ionispharma.com/news-releases/news-release-details/ionis-and-akcea-announce-pfizer-has-initiated-phase-2b-clinical… #wmif2021
Ionis and Akcea announce that Pfizer has initiated a Phase 2b clinical study of vupanorsen (AKCEA…
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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
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FDA staffing up on gene therapies personnel by 50% says Peter Marks, MD, PhD, Center for Biologics Evaluation and Research
at #WMIF2021
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Mass General Brigham Innovation
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“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
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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
now
founder/president, Dana-Farber physician Ann Silk, BWH’s Nino Chiocca
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During this week’s World Medical Innovation Forum with
, join our leaders for panels and presentations discussing what’s next for #genetherapy and the key trends shaping the industry as it evolves. #WMIF2021 https://bit.ly/3eYYls4
59 views
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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
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Is CART-T therapy still an industry priority? Panelists say yes! Join us to hear more at the
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CAR-T #WMIF2021 panel w/ MGH’s
,
EVP R&D
, BMS SVP Hematology/Oncology & Cell Therapy
,
CEO Christi Shaw, Novartis Global Head Cell & Gene
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Mass General Brigham Innovation
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ICYMI: An illustration depicting the “AAV Delivery” panel discussion about advances in the area of #AAVGeneTherapy delivery. Thank you to the panelists from
,
,
and
. #geneandcelltherapy #GCT #WMIF2021
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Like that presentation a lot
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Mass General Brigham Innovation
@MGBInnovation
· 22h
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”
Mass General Brigham Innovation
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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”
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Best interview of a CSO in the history of Big Pharma
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Mass General Brigham Innovation
@MGBInnovation
· 16h
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
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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
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Mass General Brigham Innovation
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Nino Chiocca, MD PhD,
presents FIRST LOOK: Oncolytic Viruses: Turning Pathogens into Anticancer Agents #WMIF2021
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M&A cell & gene therapy #WMIF2021 panel incl Bain Capital’s Adam Koppel, Bayer’s Head Strategy Business Development & Licensing
, Pfizer’s SVP Worldiwde BD Debbie Baron, Eli Lilly VP BD Ken Custer, ex AveXis CEO Sean Nolan now Affinia & Encoded Therapeutics Board Chair
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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
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Happening now: our CEO, Rahul Singhvi, speaking at the virtual 2021 World Medical Innovation Forum: http://worldmedicalinnovation.org #WMIF2021 https://pic.twitter.com/Nyc2lXbvUR
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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
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Mass General Brigham Innovation
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Jack Hogan, a patient
, 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
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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
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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
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AAV gene therapy expert
presents on the future potential of this revolutionary technology at #WMIF2021
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amazing Conference on the frontier od Science Cell & Gene Therapy
top programs for ALS, Brain genetic vasculopathologies and Occular, MEE
Quote Tweet
Pearl Freier
@PearlF
· 21h
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
Posted in Anticancer Resistance, Antimalarial Preparation, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Applications in Therapeutics, Bacterial Resistance, Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Biological Networks, Gene Regulation and Evolution, Cancer - General, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Cancer Informatics, cancer metabolism, CRISPR/Cas9 & Gene Editing, Deep Learning in Pathology, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Drug Development/Formulation using 3D Printing, Gene Therapy & Gene Editing Development, Genetics & Pharmaceutical, Genome Biology, Genomic Expression, Machine Learning, Natural Language Processing (NLP), Pharmaceutical Drug Discovery, tagged antimicrobial resistance, Bioinformatics, Cancer Drug Discovery, cancer drug resistance, computational biology, Computational Biology/Systems and Bioinformatics, CRISPR –Cas9 system for genetic engineering, crspr screens, drug screens, highthroughput, HTS, Mycoplasma genitalium, NGS, Proteomics, systems biology, Transcriptomics on July 19, 2021| Leave a Comment »
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.
Source: “Meaning of Systems Biology” Cell, Vol. 121, 503–504, May 20, 2005, DOI 10.1016/j.cell.2005.05.005
Old High-throughput Screening, Once the Gold Standard in Drug Development, Gets a Systems Biology Facelift
From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems
Quentin T. L. Pasquer, Ioannis A. Tsakoumagkos and Sascha Hoogendoorn
Molecules 2020, 25(23), 5702; https://doi.org/10.3390/molecules25235702
Abstract
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
Youngjun Park, Dominik Heider and Anne-Christin Hauschild. Cancers 2021, 13(13), 3148; https://doi.org/10.3390/cancers13133148
Abstract
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.”
References
- Janes, K.A.; Yaffe, M.B. Data-driven modelling of signal-transduction networks. Nat. Rev. Mol. Cell Biol. 2006, 7, 820–828. [Google Scholar] [CrossRef] [PubMed]
- Kreeger, P.K.; Lauffenburger, D.A. Cancer systems biology: A network modeling perspective. Carcinogenesis 2010, 31, 2–8. [Google Scholar] [CrossRef] [PubMed]
- Vucic, E.A.; Thu, K.L.; Robison, K.; Rybaczyk, L.A.; Chari, R.; Alvarez, C.E.; Lam, W.L. Translating cancer ‘omics’ to improved outcomes. Genome Res. 2012, 22, 188–195. [Google Scholar] [CrossRef]
- Hoadley, K.A.; Yau, C.; Wolf, D.M.; Cherniack, A.D.; Tamborero, D.; Ng, S.; Leiserson, M.D.; Niu, B.; McLellan, M.D.; Uzunangelov, V.; et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 2014, 158, 929–944. [Google Scholar] [CrossRef] [PubMed]
- Hutter, C.; Zenklusen, J.C. The cancer genome atlas: Creating lasting value beyond its data. Cell 2018, 173, 283–285. [Google Scholar] [CrossRef]
- Chuang, H.Y.; Lee, E.; Liu, Y.T.; Lee, D.; Ideker, T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 2007, 3, 140. [Google Scholar] [CrossRef]
- Zhang, W.; Chien, J.; Yong, J.; Kuang, R. Network-based machine learning and graph theory algorithms for precision oncology. NPJ Precis. Oncol. 2017, 1, 25. [Google Scholar] [CrossRef] [PubMed]
- Ngiam, K.Y.; Khor, W. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019, 20, e262–e273. [Google Scholar] [CrossRef]
- Creixell, P.; Reimand, J.; Haider, S.; Wu, G.; Shibata, T.; Vazquez, M.; Mustonen, V.; Gonzalez-Perez, A.; Pearson, J.; Sander, C.; et al. Pathway and network analysis of cancer genomes. Nat. Methods 2015, 12, 615. [Google Scholar]
- Reyna, M.A.; Haan, D.; Paczkowska, M.; Verbeke, L.P.; Vazquez, M.; Kahraman, A.; Pulido-Tamayo, S.; Barenboim, J.; Wadi, L.; Dhingra, P.; et al. Pathway and network analysis of more than 2500 whole cancer genomes. Nat. Commun. 2020, 11, 729. [Google Scholar] [CrossRef]
- Luo, P.; Ding, Y.; Lei, X.; Wu, F.X. deepDriver: Predicting cancer driver genes based on somatic mutations using deep convolutional neural networks. Front. Genet. 2019, 10, 13. [Google Scholar] [CrossRef]
- Jiao, W.; Atwal, G.; Polak, P.; Karlic, R.; Cuppen, E.; Danyi, A.; De Ridder, J.; van Herpen, C.; Lolkema, M.P.; Steeghs, N.; et al. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nat. Commun. 2020, 11, 728. [Google Scholar] [CrossRef]
- Chaudhary, K.; Poirion, O.B.; Lu, L.; Garmire, L.X. Deep learning–based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 2018, 24, 1248–1259. [Google Scholar] [CrossRef]
- Gao, F.; Wang, W.; Tan, M.; Zhu, L.; Zhang, Y.; Fessler, E.; Vermeulen, L.; Wang, X. DeepCC: A novel deep learning-based framework for cancer molecular subtype classification. Oncogenesis 2019, 8, 44. [Google Scholar] [CrossRef]
- Zeng, X.; Zhu, S.; Liu, X.; Zhou, Y.; Nussinov, R.; Cheng, F. deepDR: A network-based deep learning approach to in silico drug repositioning. Bioinformatics 2019, 35, 5191–5198. [Google Scholar] [CrossRef]
- Issa, N.T.; Stathias, V.; Schürer, S.; Dakshanamurthy, S. Machine and deep learning approaches for cancer drug repurposing. In Seminars in Cancer Biology; Elsevier: Amsterdam, The Netherlands, 2020. [Google Scholar]
- Weinstein, J.N.; Collisson, E.A.; Mills, G.B.; Shaw, K.R.M.; Ozenberger, B.A.; Ellrott, K.; Shmulevich, I.; Sander, C.; Stuart, J.M.; Network, C.G.A.R.; et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 2013, 45, 1113. [Google Scholar] [CrossRef]
- The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 2020, 578, 82. [Google Scholar] [CrossRef] [PubMed]
- King, M.C.; Marks, J.H.; Mandell, J.B. Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2. Science 2003, 302, 643–646. [Google Scholar] [CrossRef] [PubMed]
- Courtney, K.D.; Corcoran, R.B.; Engelman, J.A. The PI3K pathway as drug target in human cancer. J. Clin. Oncol. 2010, 28, 1075. [Google Scholar] [CrossRef] [PubMed]
- Parker, J.S.; Mullins, M.; Cheang, M.C.; Leung, S.; Voduc, D.; Vickery, T.; Davies, S.; Fauron, C.; He, X.; Hu, Z.; et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 2009, 27, 1160. [Google Scholar] [CrossRef]
- Yersal, O.; Barutca, S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J. Clin. Oncol. 2014, 5, 412. [Google Scholar] [CrossRef] [PubMed]
- Zhao, L.; Lee, V.H.; Ng, M.K.; Yan, H.; Bijlsma, M.F. Molecular subtyping of cancer: Current status and moving toward clinical applications. Brief. Bioinform. 2019, 20, 572–584. [Google Scholar] [CrossRef] [PubMed]
- Jones, P.A.; Issa, J.P.J.; Baylin, S. Targeting the cancer epigenome for therapy. Nat. Rev. Genet. 2016, 17, 630. [Google Scholar] [CrossRef] [PubMed]
- Huang, S.; Chaudhary, K.; Garmire, L.X. More is better: Recent progress in multi-omics data integration methods. Front. Genet. 2017, 8, 84. [Google Scholar] [CrossRef]
- Chin, L.; Andersen, J.N.; Futreal, P.A. Cancer genomics: From discovery science to personalized medicine. Nat. Med. 2011, 17, 297. [Google Scholar] [CrossRef] [PubMed]
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).
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This year’s Nobel Prize laureates in chemistry Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures.
In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
Read more about their story: https://bit.ly/4diKiJ2