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Archive for the ‘Scientific & Biotech Conferences: Press Coverage’ Category


16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Reporter: Aviva Lev-Ari, PhD, RN

 

Summer Symposium 2017

https://ki.mit.edu/news/symposium/2017

A leader in Convergence, MIT’s Koch Institute for Integrative Cancer Research will, on June 16, present its 16th annual Summer Symposium: the Convergence of Science and Engineering in Cancer Research. Convergence—the merging of historically distinct disciplines such as engineering, physics, computer science, chemistry, mathematics, and the life sciences—has created extraordinary opportunities in cancer research and care. Leaders in this emerging field will discuss innovative new approaches and technologies to better detect, monitor, treat, and prevent cancer. The symposium will also feature a panel of experts to discuss the impact of Convergence on the future of medical care.


INTRODUCTORY REMARKS

Tyler Jacks Tyler Jacks, PhD
Director, Koch Institute, MIT
David H. Koch Professor of Biology, MIT

 

Phillip A. Sharp  

Phillip A. Sharp, PhD
Institute Professor, MIT
Koch Institute, MIT

 

KEYNOTE SPEAKER

Eric Lander

30 Years of Convergence

Eric Lander, PhD
President and Founding Director, Broad Institute of Harvard and MIT
Professor of Biology, Department of Biology, MIT
Koch Institute, MIT
Professor of Systems Biology, Harvard Medical School

 

SPEAKERS

James Collins

Synthetic biology and next-generation diagnostics

James Collins, PhD
Termeer Professor of Medical Engineering and Science and Professor of Biological Engineering, MIT
Broad Institute of Harvard and MIT
Wyss Institute

 

Gad Getz

Cancer Genome and the Cloud

Gad Getz, PhD
Director, Cancer Genome Computational Analysis Group, Broad Institute of Harvard and MIT

 

Paula Hammond

Targeting Aggressive Cancers Nanolayers at a Time: A Platform Approach to Engineered Nanomedicine

Paula T. Hammond, PhD
David H. Koch Professor in Engineering, MIT
Head of the Department of Chemical Engineering, MIT
Koch Institute, MIT

 

Robert Langer

New chemical engineering approaches to convergence

Robert S. Langer, ScD
David H. Koch Institute Professor, MIT
Koch Institute, MIT

 

Daniel Larson

Understanding transcription and splicing heterogeneity in cancer progression

Daniel Larson, PhD
NIH Stadtman Investigator, Center for Cancer Research
Head, Systems Biology of Gene Expression, National Cancer Institute

 

Franziska Michor

Computational Models of Cancer

Franziska Michor, PhD
Professor of Computational Biology, Dana-Farber Cancer Institute
Harvard T.H. Chan School of Public Health

 

Chad A. Mirkin

Spherical Nucleic Acids as a Powerful New Platform for Cancer Therapy

Chad A. Mirkin, PhD
Director, International Institute for Nanotechnology
George B. Rathmann Professor of Chemistry, Department of Chemistry, Northwestern University

 

Aviv Regev

Dissecting the tumor ecosystem with single cell genomics

Aviv Regev, PhD
Core Institute Member, Chair of the Faculty, Broad Institute of Harvard and MIT
Co-director of the Cell Circuits Program, Broad Institute of Harvard and MIT
Koch Institute, MIT

 

Xiaowei Zhuang

Illuminating biology at the nanoscale and systems scale using single-molecule and super-resolution imaging

Xiaowei Zhuang, PhD
David B. Arnold Professor of Science, Harvard University
Howard Hughes Medical Institute Investigator

 

EXPERT PANEL: CONVERGENCE IN HEALTH

Cori Bargmann  

Cori Bargmann, PhD
President of Science, Chan Zuckerberg Initiative

 

Marc N. Casper  

Marc N. Casper, MBA
President and CEO, Thermo Fisher Scientific

 

Victor Dzau  

Victor Dzau, MD
President, National Academy of Medicine

 

Tyler Jacks  

Tyler Jacks, PhD
Director, Koch Institute, MIT
David H. Koch Professor of Biology, MIT

 

Nancy Simonian  

Nancy Simonian, MD
CEO, Syros Pharmaceuticals, Inc.

 

Elias Zerhouni  

Elias Zerhouni, MD
President for Global Research and Development, Sanofi
Former Director, NIH

 

Susan Hockfield

Moderated by:

Susan Hockfield, PhD
President Emerita, MIT
Professor of Neuroscience, MIT
Koch Institute, MIT

 

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LIVE 11/17 1:45PM – 5PM – The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

 

Leaders in Pharmaceutical Business intelligence (LPBI) Group

Covering in Real Time using Social Media this Event on

Personalized Medicine

Aviva Lev-Ari, PhD, RN, Founder LPBI Group & Editor-in-Chief

http://pharmaceuticalintelligence.com

Streaming LIVE @ HARVARD MEDICAL SCHOOL,

Joseph B. Martin Conference Center

@pharma_BI

@AVIVA1950

November 17

#PMConf

1:45 p.m. — Leadership in Personalized Medicine Award

  • Presenter: William S. Dalton, Ph.D., M.D., CEO, M2Gen, Chairman, Personalized Medicine Coalition

Science, Business and Patents: Millenium, Celgenics, and Medicine/Desease – Member of AAAS

co-Chair Cancer Consorcium

PM – 1990’s on. How Human Genome at Harvard will start a new center – reach out to the Global community, conference was born. PM as subject of a Global Conference, effirt started with Genzyme, Eric Launder, Broad, Collins at NIH – effort led to Obama Initiative in PM, Duke Medical System.

Challenge: Reimbursement for Genomics diagnosis

  • PM – P care – by sequencing of Genome – become available commercially inexpensivelly
  • Genetic component to become part and parcial of Medicine and Patient care

2:15 p.m. — Networking Break

2:45 p.m. — The Data Dilemma: Fulfilling Expectations of Big Data in the Future of Personalized Medicine

There is consensus that the massive amounts of genomic, clinical, claims and other types of data could yield important insights for research and clinical care. But for years, obstacles around technical standards, interoperability, privacy and confidentiality, data security, and consent have been held up as daunting challenges that inevitably slowed progress.  During this discussion, a panel of academic and industry experts will discuss their respective organizations’ strategies to obtain and analyze the data, including what has worked and what has not; the programs and processes that have led to the most productive data usage; examples of important knowledge that has been derived from data analysis; and the infrastructure they believe is needed to achieve fulfillment of the potential of big data in personalized medicine nationwide.

  • Moderator: Marcia A. Kean, M.B.A., Chairman, Strategic Initiatives, Feinstein Kean Healthcare
  1. How one works with 20 Partners at once?

 

  • Paul Bleicher, M.D., Ph.D., CEO, OptumLabs
  1. Data collaboration of 35 Partners – bring value to Medicine, Like Bell Labs
  2. Academics, Hospitals, Physician offices – Constellations – groups of projects
  3. DATA is KEY — Public and Private Partnerships
  • Christophe G. Lambert, Ph.D., Associate Professor, Center for Global Health, Division of Translational Informatics, Department of Internal Medicine, University of New Mexico
  1. VA Data, Co-Chair of Informatics, clinical , pharmaceutical stackholders
  2. Focus Groups Patient research Partners – How to automate data
  3. Centralization above nad decentralization, below COntrol mechanism govern all variables: Increase fitness of system vs Personal Control
  4. 1984-1998 Bi-Partisan support for Data in HealthCare
  5. Big Data for early detection, prevention, `
  6. AGING, Infectious and Pediatric disease – Investment in these areas
  • Adam Margolin, Ph.D., Director, Computational Biology, Oregon Health & Science University School of Medicine
  1. Project with Intel – across institutions
  2. consorsium – success ration
  3. data sharing #1 Priority at the National Level
  4. Add value by data sharing, strategic investment in the healh system
  • Edward J. Stepanski, Ph.D., Chief Operating Officer, Vector Oncology
  1. Propriatory real time reporting to Physicians – systematic – core asset, originally,
  2. Research Group use Warehouse doing Analytics, Tools development linked with clinical data with PRO and studies based on data integretion
  3. Success is more data – PRO data informing clinical data
  4. Defragmenting the care vs drive across town for care several units disaggregated geography vs all deaprtments in one location

 

3:45 p.m. — Keynote Speaker
“Medicine and the Targeted Marketing Problem”

We live in the golden age of cloud computing and machine learning.  The organizing conundrum for the “big data era,” however, is a surprising one — the “targeted marketing problem” (i.e., the ability to better match the right customers to targeted messages). This talk will explore overlaps and similarities between the targeted marketing problem and precision medicine, and how advances in data sciences can be leveraged to create a learning medical system that in turn points to the health care system of the future.

  • Introduction: Amy Abernethy, M.D., Ph.D., Chief Scientific Officer, Senior Vice President, Oncology, Flatiron Health

 

  • Anthony Philippakis, M.D., Ph.D., Cardiologist, BWH, Chief Data Officer, Broad Institute and Partner, GV (Venture Capital)

Learning from Users

Five causes for cardiac death:

  • MI,
  • a-Fib
  • Structural
  • PE
  • Aorta dissection

PreventionGenomic Sequencingvalue in Cardiology:

  • Estonia BioBank – mutation carrier
  • Familial Hypercholesterolemia – 4 genes involved,
  • Prediction sudden cardiac death – larger data sets
  • New Model for Human Subjects Research; DIrect-to-Participant: Potentia Advantages:
  • cost, scalability, facilitate re-contact, frequent collection,
  • My Research Legacy: Broad & AHA – Launched November 13, 2016 
  • Quantified Self –>> Quantigied Physical Exam: Face dysformia, Dysarthia, Ataxia,
  • Identify every patient in the World  with this disease

 

Data sharing: Inverting the Model ; ALL OF US  – 1 Million – Precision Medicine with IBM – Mandate to innovate – Diversity: People, Geography, Health Status

Innovation in Genomic data sharing – bring data to researchers

SIX types od data wil be collected: Participant-provided Info, mHealth Data, Consent EMR

 

DATA Research CoreVanderbuilt, Verily Broad

  1. pharmacogenomics

Launch start ups cost

  1. Open source
  2. Cloud
  3. developers start ups

PLATFORM

 

GATK – workhorse of genomic data – Launched 4/2016

Partnerships: Amazon, google genomics, microsoft, IBM, Watson, 

 

Announcing: BROAD + Intel Center for Advanced Genomic Data Engineering, Anthony Philippakis, M.D., Ph.D., Chief Data Officer, Broad Institute

Reference Architecture: Design: Single node, small cluster,

 

4:30 p.m. — Closing Remarks

  • Edward Abrahams, Ph.D., President, Personalized Medicine Coalition

 

– See more at: http://www.personalizedmedicinecoalition.org/Conference/November_17_Program#sthash.zpTNQYKd.dpuf

 

#PMConf

SOURCE

http://www.personalizedmedicinecoalition.org/Conference/November_17_Program

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LIVE 11/17 8AM – 1:45PM – The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Leaders in Pharmaceutical Business intelligence (LPBI) Group

Covering in Real Time using Social Media this Event on

Personalized Medicine

Aviva Lev-Ari, PhD, RN, Founder LPBI Group & Editor-in-Chief

http://pharmaceuticalintelligence.com

Streaming LIVE @ HARVARD MEDICAL SCHOOL,

Joseph B. Martin Conference Center

@pharma_BI

@AVIVA1950

November 17

#PMConf

 

Joseph B. Martin Conference Center
77 Avenue Louis Pasteur
Boston, MA 02115

7:00 a.m. — Registration and Continental Breakfast

8:00 a.m. — Opening Remarks

  • Edward Abrahams, Ph.D., President, Personalized Medicine Coalition

8:15 a.m. — Fireside Chat

  • Moderator: Meg Tirrell, Reporter, CNBC
  1. How did the the Economics changed
  • Daniel O’Day, CEO, Roche Pharmaceuticals – Joined Roche at 1989
  1. Roche  – 60% of investments goes to Cancer with embedded diagnostics, 20-30% of the market
  2. Hypothesis in the Lab starts an innovation – Phase I, Phase II is extension of Phase I – continue understanding of the Biology of the Disease
  3. Treat only patient that will benefit – PM – transformational benefit
  4. Early stage of discovery – protection of IP – work inside ONE company — less of an issur the protection of IP
  5. Diagnostics area – Roche collaborates with other Pharma
  6. Setting infrastructure for testing
  7. Diagnostics and Pharma are coming together – availability of big data – discover and develop with Foundation Medicine – Deep analysis of Molecular Medicine, decide o better hypothesis, do it in shorter time – 2015 – Commercialize the platform around the Globe, One standard for Clinical Trials – in China hard to move Clinical Trials Data out of China –
  8. Harnessing Global Data in Oncology – Clinical Trials
  9. Data accuracy
  10. Genentech, Foundation Medicine and Roche capabilities – FDA
  11. Comprehensive Genomics side – has needs yet to be developed for Payers to participate
  12. Foundation One – 30% more positive Lung Cancers found vs standard of testing
  13. Over simplification is dangerous, technology/diagnostocs/histology/genomics – sequencing ENHANCES not replaces
  14. Data of Phase III – robust genomics profiling: no diagnostics and wrong diagnostics
  15. In the next five years, Cancer immunotherapy, when and how resistance occur.
  16. Blood based assay – Patient journey  – fine tuning the Science tissue based sample
  17. Biomarkers: Tumor microenvironment
  18. Diagnostics is not rewarded appropriately, genetics, CMS,
  19. Outcome value for TX and Dx
  20. Mission of Roche will not Change with change of Gov’t, public Sector in the US, FDA – requires being faster a respondent,
  21. ” I am optimistic”

Questions from the audience

  • Democratizing access to sequencing data
  • accuracy of test results, Oncologist and PCP ordering genomics tests, value added
  • PM after medicine SOC (biopsy, tissue histology)
  • Reimbursement: Diagnostics vs drugs

8:45 a.m. — Coverage is King: Identifying the Evidence That Leads to Reimbursement

Many innovators in personalized medicine are unclear on the kinds of evidence that inform the coverage and payment decisions of payers. That lack of clarity can have negative financial consequences for personalized medicine companies with products and services that are on the market but not paid for. During this panel, payer representatives will help define the reimbursement landscape for the field by providing examples of the evidence they consider appropriate for coverage and payment. Confirmed panelists include:

  • Moderator:Amy M. Miller, Ph.D., Executive Vice President, Personalized Medicine Coalition
  1. What each of the companies does in PM
  2. Targeted therapeutics: 25% are targeted,
  3. Value of Diagnostics
  • Kristine Bordenave, M.D., Lead Medical Director, Humana
  1. Clinical perspective, how much it cost to patient, clinician, Pharma – both need to be paid,
  2. Population Health, 100% of GDP to go to HealthCare — can’t be
  3. Humana Perspective: How to cover – organized criming: Charges for Testin – several month doen in one day, sharing drugs, expired drugs, repackaged and sold, drugs resold
  4. Independent Research Department: MS, Pharmacists, Statisticians — Looking at the Value of Test in Population context
  5. Large Medicare, Small managed care company, Value-based contracts: working with Pharma – early on at Phase II stage
  6. Testing: genetics, radiographic — 60 gene panel – done by two labs,
  7. Duplication in ordering genetic testing: optometrists, Physical therapist, ordering genetic profiling
  • Matthew Fontana, M.D., Vice President and Chief Medical Officer, Pharmacy, Health Care Service Corporation
  1. WHY REFERE TO US AS PAYERS – BUSINESS MODEL: MULTIPLE CUSTOMERS MEDICAID, BIG PROVIDERS, MANAGED CARE INDUSTRY
  2. Cost and Revenue
  3. FDA is been pushed to ignore the science (DMD), lack of coordination in HealthCare
  4. Pay for diagnostics if not linked to Therapeutics – morbidity
  5. elaboration of Diagnostics
  6. Accuracy of testing – expensive  – misinterpretation
  • Elaine Jeter, M.D., MolDx Medical Director, Palmetto GBA
  1. Access of Patients – 25 of 50 States are participants in MOlDx — NOT New England States
  2. All molecular assays to register for code specificity – to be able to control appropriate coding – Panel matched to unit of service issue between Genome Profiling and assay
  3. Reimbursement – Lung Cancer – Clinical utility  – genomic profiling ONLY IF THE DATA IS IN A REGISTRY — IF PROVED UTILITY AND THE REGISTRY SHOULD BE IN PUBLIC DOMAIN
  4. Analytical minimal standard accepted
  5. Developed Assessment meetings – Labs come to receive guidance  – clinical utility information, as a contractor – Central Office allowed PM vs Lab developing tests – assist Lab – pay for service obtain end points
  6. Assays for Prostate Cancer – innovative – high disease demand, define endpoints
  7. Paying premium for FDA approved genetic testing – onlu 2% of molecular assays — 98% are not FDA approved
  8. 65,000 molecular tests in the market in the Registry only 10,000, every day 2-3 new molecular assay tests are introduced
  9. By statue, no screening covered by Medicare for Genetic testing – congress need to act upon that – change coverage of Medicare. Memogram and colonoscopy, lung X-ray – are by statue – covered by Medicare

Questions from the Audience

  • Why premium paid if FDA approved a test?
  • Screening and early detection

9:45 a.m. — Networking Break

10:15 a.m. — Harvard Business School Case Study Presentation

DNA-editing technologies have been hailed as revolutionary with the possibility to edit out mutations that cause disease.  Yet the CRISPR-Cas system is currently locked in a legal dispute between two great research institutions involving, as one journalist put it, “who owns molecular biology.”  The CRISPR technology in short raises the broader issue of whether these new techniques should be privately owned or placed in the public domain. The technology also raises serious ethical issues. The case study will serve as the point of departure for our discussion of these issues.

  • Leader:Richard Hamermesh, D.B.A., Senior Fellow and Former MBA Class of 1961, Professor of Management Practice, Harvard Business School
  1. Ethical issues in the case
  2. Stacks are very high

11:15 a.m. — Keynote Speaker

  • Introduction:William Chin, M.D., Chief Medical Officer, Executive Vice President, PhRMA
  • Keynote:Victor Dzau, M.D., President, National Academy of Medicine (ex-IOM) – part of NIH
  1. Global Landscape of PM: Integration into HealthCare — Cost effectiveness
  2. Evidence for PM
  3. better health and well being
  4. high value health care
  5. strong science & technology – 150 papers in JAMA ans NAS
  6. Precision Medicine: patient/public engagement, NGS: omics, biomarkers, collection of clinical & research data, integration of omics, EHR
  7. Challenges: Tests & Therapies
  8. efficacy of PM AFTER actually being used in clinical practice
  9. Evidence of PM efficacy for implementation in Practice
  10. Regulatory and Reimbursement for utility
  11. Reward Value vs cost
  12. Aligning Results: 10% incidence reduction vs 50% incidence reduction
  13. Evidence generation:
  14. Modeling could be used to assess the potential economic impact of PM approaches
  15. Final regulatory & Payment Pathwaysand payer approval
  16. Analytic Validity — clinical validity — economic impact analysis– Assess Clinical Utility
  17. Strength of evidence Low to High
  18. investigational experiment
  19. Assess cost effectiveness: initial experimentation — Economic analysis — provisional approval — validation — final approval
  20. Integration with clinical Practice: Clinician Educatuin, integration pathways
  21. Genomic Medicine:guideline and care pathways — Clinical DSS —
  22. Data infrastructure and sharing: EHR (DIGITize) – genomics – GA4GH
  23. PATIENT/PUBLIC ENGAGEMENT – concern of Privacy and Data Ownership – Fear of discrimination  — consent — education of Patients
  24. Future needs: data, National scale learning system, Global collaboration G2GH
  25. Support Data infrastructure
  26. PM – quality, access, cost, improve clinical outcomes, inequality mitigate
  27. PRECISION PUBLIC HEALTH – ZIKA, EBOLA,
  28. GENOMICS and Population Health Action Collaborative
  29. working Groups: Evidence generation

11:45 a.m. — Bag Lunch

12:45 p.m. — Personalizing Care: Strategies for Integrating Personalized Medicine into Health Care

Personalized medicine lacks sufficient literature on how health care providers can integrate personalized medicine into clinical care, which makes it difficult for providers to take advantage of the growing number of personalized medicine products and services now available to them. During this session, panelists who have spearheaded integration efforts will share the strategies they found most useful for speeding the pace of personalized medicine’s adoption in clinical settings. Confirmed panelists include:

  • Moderator:Howard McLeod, Pharm.D., Medical Director, DeBartolo Family Personalized Medicine Institute, Moffitt Cancer Center
  1. Awareness & education
  2. Patient empowerment
  3. value recognition
  4. IT and Information Management
  5. ensuring Access to Care: Case if Neuropathy than 15 more visits causing 8 other Patients to be pushed in the queue

 

  • Amy Abernethy, M.D., Ph.D., Chief Medical Officer, Chief Scientific Officer, Senior Vice President, Oncology, Flatiron Health
  1. Family and Care givers need to understand as well
  2. More Advocacy in Washington

 

  • Dax Kurbegov, M.D., Physician Vice President, National Oncology Service Line, Catholic Health Initiatives – CHI (103 Hospitals) and DIgnity ospitals – Community system
  1. Provide speed of service
  2. Permeate piece by piece by each institution, economics – problematic IT infrastructure for Genomics is expensive, centralized system needed
  3. broader beyond Oncology
  4. complex Patients with polypharmacy
  5. If physician needs to write a special note for service , patients are lost in the way for testing
  6. as NGS become accessible in labs — CHI provide infrastructure to LINK Patients with Experts and Labs outside the system
  • Lincoln Nadauld, M.D., Ph.D., Executive Director of Precision Genomics, Intermountain Healthcare, UT (22 Hospital 107 physicians – Molecular Tumor Board)
  1. Pilot Project approach implemented in 3 hospitals, built lab, implement by Molecular Tumor Board placed on the report
  2. Barriers: getting drug is difficult – mutation exists, drug exists — How to get the drug ordered, approved and shipped
  3. Patients want to know that their oncology is up to date the care is best, Patient advocate for themselves, Patient empowerment
  4. Barriers to PM – Physician compensated better for next line IV chemo vs Targeted Genomic-based therapy
  5. Tumor Board
  6. Get Genomics EARLY not late – it will max the course of treatment
  7. MOST PATIENT CAN’T TRAVEL FOR CARE because it is expensive
  8. Utility and Value asked by Payors, cost saving need be demonstrated not only efficacy vs SOC – reduce cost must be demonstrated

 

  • Peter H. O’Donnell, M.D., Assistant Professor of Medicine and Associate Director for Clinical Implementation, Center for Personalized Therapeutics, The University of Chicago
  1. Lab Testing, way to long, system for Physician to look at Genomic data,
  2. If Physician is buying in shared decisions with Patient easier
  3. all tests are bundled and results are presented to PCPs – they love that
  4. Drugs fail because Patients do not take them vs Pharmacogenomics – more likely to help Patients

#PMConf

SOURCE

http://www.personalizedmedicinecoalition.org/Conference/November_17_Program

 

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LIVE 11/16 3:15PM – 5:30PM – The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

 

Leaders in Pharmaceutical Business intelligence (LPBI) Group

Covering in Real Time using Social Media this Event on

Personalized Medicine

Aviva Lev-Ari, PhD, RN, Founder LPBI Group & Editor-in-Chief

http://pharmaceuticalintelligence.com

Streaming LIVE @ HARVARD MEDICAL SCHOOL,

Joseph B. Martin Conference Center

@pharma_BI

@AVIVA1950

November 16

#PMConf

 

 

3:15 p.m. — Diagnostics Debate: Regulatory and Reimbursement Hurdles for Personalized Medicine Diagnostics

Nowhere are the regulatory and reimbursement challenges facing personalized medicine more evident than in the diagnostics industry, where the routes to market are often hampered by a lack of clarity regarding the possible changes to the regulatory pathway for laboratory-developed tests, ambiguity regarding the kinds of evidence that justify payment, and the need for large marketing budgets to sell low-cost procedures, all of which impede the development of sophisticated diagnostics with the power to transform medicine.

During this panel discussion, representatives from a diverse range of diagnostics companies, a payer and FDA will identify the most promising strategies to alter the landscape to encourage investment in personalized medicine diagnostic products, including the roles of other stakeholders such as the pharmaceutical industry and integrated health systems.

  • Moderator: Ronnie Andrews, Founder and Principal, The Bethesda Group

 

  • Suzanne Belinson, Ph.D., M.P.H., Executive Director, Center for Clinical Effectiveness, Blue Cross Blue Shield Association
  1. Centralized Evidence review – Payers making coverage decisions
  2. Blue Payers: Payers: can modify or use it as such
  3. Value proposition of Evidence Review: Medical Specialties, Payer, Providers, Manufecturer of Diagnostics, Clinical Research
  4. Where is the GAP, in evidence review
  5. engagement with Blue and non Blue, Community based Hospitals
  6. Test utilization – decision is made – withhold a treatment or modification of treatment — at the aggregate level – impact on outcome can be achieved
  7. reduce number of test is a health outcome,
  8. redude numbers of day in Hospital is a Health Outcome
  9. Curate information for Payers to use the information for policy
  10. Prime Therapeutics – PPM of 14 organization — are clients for Evidence Review by Center for Clinical Effectiveness, Blue Cross Blue Shield Association – 36 plans, IT issues, Patient is covered in IL, treatment is givrn in FL, different rules in FL.

 

  • Brad Gray, CEO, NanoString
  1. Seattle-based gene expression (emerged in academia), development of diagnosis
  2. IN VITRO DIAGNOSTIC KIT – PREDICTION OF RESPONSE TO BREAST CANCER
  3. Cengene – response to their druv
  4. Medidiation – prospate cancer
  5. Merck – response to Keytruda – multiple indications
  6. Universal asset development: Diagnostics several parameters in ONE test
  7. Policy change is not the focus bur the association of diagnostics with drugs

 

  • Alberto Perez, FDA

 

  • Michael Pellini, M.D., CEO, Foundation Medicine – had experience with Thermo Fisher Diagnostics
  • Molecular diagnostics space, information comapny not Lab or Diagnostics
  • Comprehensive tumor testing – ASSETS:
  1. EXTRACT MOLECULAR information from tissue vs multiple biopsies
  2. COMPEHENSIVE DB OF 1,000 PATIENTS – Genomic Profiling – Rows are cancer type Columns are Published studies providing evidence
  3. Decision Support: MD, Pathologists – Testing Platform submitted to FDA to get regulatory standards
  4. Biliary small tumor

 

4:30 p.m. — Visions of Value: Evaluating Evidence for Personalized Medicine

The fact that payers, providers, patients, industry representatives and regulators all define value differently makes it difficult for personalized medicine’s champions to contribute to and communicate about the body of evidence supporting the field. Participants in this panel discussion will bring the personalized medicine community closer to an accepted definition of value by identifying common elements in multiple stakeholders’ understanding of the concept.

  • Moderator: Susan Dentzer, President and CEO, Network for Excellence in Health Innovation
  1. Without Pricing adjustment Access can’t be accomplished
  2. drugs for HPC – patient is not sick enough to qualify for the drug
  • Donna Cryer, J.D., President and CEO, Global Liver Institute
  1. societal impact of a diagnosis
  2. Development of Value framework, development of he evidence that goes into Value frameworks, Outcomes desireable, Patient generated evidence
  3. Health high functioning citizens and a fibrant Health Care industry
  4. PM PARADIGM accepts Patients factors to be used in algorithm development
  5. Restrictive Formulary for non avarage patients – access at time of need

 

  • Michael Sherman, M.D., M.B.A., M.S., Senior Vice President, Chief Medical Officer, Harvard Pilgrim HealthCare
  1. Pay for Service is not connected to Value
  2. Charge the variable cost for testing
  3. DECISION re driven by costs: delivering drug lowering cholesterol to avoid facing expenses of trating CVD
  4. Higher price for success
  5. Care about the total cost of Care
  6. payment for innovations
  • Peter B. Bach, M.D., M.A.P.P., Director, Center for Health Policy and Outcomes, Memorial Sloan Kettering Cancer Center
  1. Oncology drugs and Priding to improve the decision making
  2. US and specialty drugs
  3. severe access problems for drugs to patient – colapse because of Pricing
  4. out performing in cost of treatment per patient
  5. Societal equation – drug prices
  6. AMA embrassed Value based Pricing on 11/16/2016
  7. Linking Price of Drug with what the drug does

 

  • Randy Barkholder, PhRMA
  1. consolidation Prices for Medicine and for Societal are different
  2. add value to the Healthcare system
  3. Performace and quality to measure value
  4. Drug companies representation and SOcietal values vs HealthCare system value

 

5:30 p.m. — Elements Café Cocktail Reception

#PMConf

SOURCE

http://www.personalizedmedicinecoalition.org/Conference/November_16_Program

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LIVE 11/16 1:15PM – 2:45PM – The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

 

Leaders in Pharmaceutical Business intelligence (LPBI) Group

Covering in Real Time using Social Media this Event on

Personalized Medicine

Aviva Lev-Ari, PhD, RN, Founder LPBI Group & Editor-in-Chief

http://pharmaceuticalintelligence.com

Streaming LIVE @ HARVARD MEDICAL SCHOOL,

Joseph B. Martin Conference Center

@pharma_BI

@AVIVA1950

November 16

#PMConf

1:15 p.m. — Update: Kraft Precision Medicine Accelerator & Trials Challenge Award

An update on the activities of the Kraft Precision Medicine Accelerator and interviews with the winners of Harvard Business School’s “Precision Trials Challenge,” sponsored by the Kraft Precision Medicine Accelerator.

  • Presenter: Richard Hamermesh, D.B.A., Faculty Co-Chair, Kraft Precision Medicine Accelerator, Harvard Business School
  • Winner: MatchMiner
    • Team Lead: Ethan Cerami, Ph.D., Director, Knowledge Systems Group, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute
  1. 17,000 patients now are genome sequenced
  2. Rational clinical trial design
  3. enroll patient n trial
  4. clinical decision support
  5. Trial-Centric Matching vs Patient Centric Matching
  6. Open Source PLATFORM
  7. Clinical Trial Markup Language (CTML)
  8. MatchMiner, Knowledge System, The Hyve

 

 

 

  • Runner Up: No Patients Left Behind
    • Team Lead: Gavin MacBeath, Ph.D., Co-Founder and Senior Vice President, Merrimack Pharmaceuticals
  1. Company brings Drug and biomarker assay match – for patient assignment to Trial
  2. Protein based not genomic based test

 

  • Runner Up: iCare for Cancer Patients
    • Team Lead: Leylah Drusbosky, Ph.D., Associate Professor of Medicine, University of Florida, Scientific Director, iCare for Cancer Patients
  1. iCare for Cancer Patients
  2. Protein Network Map: Drug interaction and Genomic aberrations
  3. Functional interactions
  4. Predictive SImulation Technology

Computational Biology Model: Proliferation, SUrvival apoptosis

VIrtual Cancer Clinical Trial Simulator – Bring new drugs to the RIGHT patient population: DIsease onhibition score

1:45 p.m. — Keynote Speaker
“Reforming Clinical Trials: How Alternative Trial Designs May Reshape Regulatory Review”

Traditional clinical trial designs are often too cumbersome and expensive to study the efficacy of personalized medicine products and services in sub-populations of patients. Yet there is no consensus on which methods have the most promise to speed trials and lower costs. During her keynote address, Dr. Woodcock will explore which of the latest progressive designs she believes are best suited to demonstrate the efficacy of personalized medicine based on past successes and proposed reforms.

  • Introduction: Steve BMS
  • Keynote: Janet Woodcock, M.D., Director, Center for Drug Evaluation and Research, U.S. Food and Drug Administration
  1. Not just the trial, but the Development Program
  2. knowledge that underpins the program vs Novel Trial Design
  3. +50%  of Trials FAIL at Phase 3 – simulate  best and worse scenarios
  4. Cut off points – affect the result of the Human Trial
  5. Outcome measures for disease have never or rarely, been tested
  6. Murphy’s law operate
  7. robust vs fragile design
  8. DEsign – conduct a seamless, adaptive development program
  9. Trade off – for benefits vs burden of Disease – Functionality vs Longevity – give up life for better functionality when aive
  10. More patient enroll or tril goes longer, treatment gets better and Endpoints needs to be revised
  11. heterogenious progression, fast progression
  12. DIsease heterogeniety — REDUCTION by beter Patient selection – more homogenious to reach similar progression
  13. Natural History: rare diseas vs heterogeneous
  14. Progression not known because longitudinal studies are limited or study is not representative
  15. projection of results of trial may be difict: Pharmacodynamic Markers (efficacy) dificult to reproduce
  16. Trial Design: Phase 1,2,3
  17. Alternatives: “Extended Phase 1 COhort” –>> Approval
  18. Endpoint (cancer): Response rate, Progression Free Survival (PFS) Time
  19. Mechanistics hypothesis, natural history data on non responding patients
  20. N-of -1 looking at disease trajectory
  21. Oncology: “basket” trials with biomarker defined targets across histologic diagnoses : NCI “MATCH” trial
  22. Emergencies: EBOLA trial
  23. DOse-finding can be randomized, adaptive,, include placedbo arm
  24. Serious, Rare or Uncommon DIsease wiht Existing Standard of Care (SOC) vs Placebo
  25. Common Diseaase with SOC
  26. Biomarkers: Predictive of Response: Magnitude of the response not Yes or No — Highest Biomarket Cutoff
  27. RWE – Rare WOrld Evidence
  28. Knowledge tht UNDERLIE – biological knowledge
  29. CUrative therappy with PM – promise is there

2:15 p.m. — Fireside Chat

  • Moderator: Alexander Vadas, Ph.D., Managing Director and Partner, L.E.K. Consulting
  1. Companion Diagnostics
  • Peer M. Schatz, M.B.A., CEO, QIAGEN (15 years around)
  1. PM and experience
  2. Value chain of PM does not work – Diagnostics is 2% of the HealthCare expenses. Reimbursement by COst of Production
  3. 30x smaller then Pharmaceuticals
  4. Standards to evaluate the value of diagnostics
  5. Biomarkers – 60,000 distinctive tests
  6. Benefits of Diagnostics not recognized
  7. HealthCare 2% spent on DIagnostics and Monitoring
  8. Value of information of Molecualr DIagnostics
  9. Lower quality evidence
  10. Reward value of diagnostics – Patients
  11. For LABs  diagnostics is a profit looser
  12. Molecular Diagnostics – new, PM is known in 1999 – AMP launched its Journal
  13. Value based Medicine, systems of rewards is to blame
  14. Reimbursement – Diagnostics and regulatory
  15. 30 Pharma Partnerships
  16. Industry organization
  17. Biopsy to Microbiome
  18. 70% of Cancer care is done at COmmunity Hospitals
  19. Human genomics data doubles annoually
  20. Data needs, 34% in accordance, 70% accordance with diagnosis – CONCORDANCE POOR CROSS GENOMICS OR AXON LABS
  21. PM recognize the value of information DIagnosis, improvemnet in Patient Diagnosis

Reply to interview by Alexander Vadas, Ph.D., Managing Director and Partner, L.E.K. Consulting

  • Education of Pathologists in Genomic Pathology
  • Approval of Companion Diagnostics in China requires infrastructure in regulatory interface with each Country
  • Seamless interaction with Pharma
  • If tests in Pathology are too expensive, LABS will not be able to be profitable
  • NGS – Variance – vs a frontline test Designed for Reimbursability and profitaility

2:45 p.m. — Networking Break

 

 

#PMConf

SOURCE

http://www.personalizedmedicinecoalition.org/Conference/November_16_Program

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LIVE 11/16 8AM – noon The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Leaders in Pharmaceutical Business intelligence (LPBI) Group

Covering in Real Time using Social Media this Event on

Personalized Medicine

Aviva Lev-Ari, PhD, RN, Founder LPBI Group & Editor-in-Chief

http://pharmaceuticalintelligence.com

Streaming LIVE @ HARVARD MEDICAL SCHOOL,

Joseph B. Martin Conference Center

@pharma_BI

@AVIVA1950

 

November 16

#PMConf

Joseph B. Martin Conference Center
77 Avenue Louis Pasteur
Boston, MA 02115

7:00 a.m. — Registration and Breakfast

8:00 a.m. — Opening Remarks – [Personalized Medicine = PM]

  • Edward Abrahams, Ph.D., President, Personalized Medicine Coalition
  • PM offers a more efficient HealthCare: Gov’t, Academia, research, pharma and Patients collaborations = working together
  • PMC goal – solutions in the field, stacks are high
  • Corporate sponsors: Genetech-Roache, astellas, Intermountain Precision Medicine

8:05 a.m. — The Personalized Medicine Report

  • William S. Dalton, Ph.D., M.D., CEO, M2Gen, Chairman, Personalized Medicine Coalition
  • Five medicines in 2016 are defined as PM
  • Efficiences in Care by Precision Medicine
  • Sequencing, molecular medicine, Genomics in all Medical Record

8:15 a.m. — Keynote Speaker

  • Greg Simon, Executive Director, Cancer Moonshot Task Force
  • 400 people in Washington at the Launch of Moonshot, 7,000 in audio/video attendance around the US
  • Video by all stackholders
  • Joe Bidan, today we are more ready than one year ago – ergency of now – a challenge we are not willing to postpone
  • Example CLL therapeutics today vs 20 years ago
  • Precision Medicine is the technology & Big Dat aand Science and Survivorship; PM is about the Patient
  • TASK FORCE on Cancer: added EPA, Council for the Arts, DoD
  • Double impact – How, Partnerships
  • VA – many Veterans with Cancers – Sequence effort, Watson-IBM, to review sequences from VA – new partnerships: IBM + VA – Walter Reed Initiative
  • VA tishue bank with 20 Companies – new Partnership
  • DoD and VA
  • Private initiatives on Moonshot topics: American Cancer SOciety – does their own, Foundations, Academia,
  • Health Systems, several not one — join together Dana Farbers, Sloan Katering
  • Accessible Medications is very important for Patients
  • The significance of 9 month to start changing the World

8:45 a.m. — Pioneering Precision: Charting a Course for Cutting-Edge Innovations

Many scientists believe innovations in personalized medicine are poised to yield major breakthroughs in coming years, but not all members of the health care system are clear on which research topics have the most potential. The participants in this panel will identify the most encouraging scientific directions for personalized medicine and point to the most promising topics for future research.

  • Moderator: Stephen Eck, M.D., Ph.D., Vice President, Oncology Medical Sciences, Astellas Pharma Global Development
  1. Oncology drugs were prescribed by histology no molecular diagnostics
  2. 2015 – 25% of FDA approved medicines are PM
  3. In 2016 – PM and molecular diagnostics – needs to be a SYSTEM not disjoint solutions
  4. Value for Price, small indications, cost containment
  5. Pricing treatment if several drugs are at use wihtout knowing the realtive contribution of each

 

  • David Altshuler, M.D., Ph.D., Executive Vice President, Global Research and Chief Scientific Officer, Vertex
  1. Prevent and modify the course, Human genetics and somatic genetics in cancer, discovery started with Mandelian, moved to somatic, now use of catelogue for common mutations,
  2. Test existing hypothesis on pathphysiology: LDL and heart disease vs HDL – genetics variance for Heart attack – predictor not modifier
  3. Predicting with Genome in PM, who respond to which medication
  4. New Hypothesis for new Paradigms
  5. Cyctic FIbrosis: Gene found, took a histology based disease CF – to foundational genomics – mutation in gene on cell surface – POTENTIATOR of opening the channel by a small molecule — these small molecule were developed in the last 15 years, follow Patients in Registries – reduction in rate of decline of lung function — Drug – is changing the course of the disease and intervention early
  6. small molecule in combination into cells that have only one copy, in a dish – two medications in Phase II
  7. Personalization is a statement of not ahving yet an effective therapy but it is identification of the Pathway
  8. Common genotype – small groupsidentification
  9. target diseases that will show doubling efficacy, curative therapies, new models: PENETRATION OF LYTHAL DISEASES
  10. Annuity model only in the future the outcome allows measurement – REGISTRIES are very important and need be used for outcomes in the future
  • Michael Panzara, M.D., Head of Neurology Franchise, WAVE Life Sciences
  1. Synthesis of nucleaic acid focusing on neurology – STEREOCHEMISTRY
  2. modify sulfor – phorphate new modification – mixure of isomeres stereo molecules – systhesize nuclaic acids
  3. design nucleic acids
  4. Pipeline: CNS, MUSCLES, EYE, Liver, Skin, GI
  5. HD – Huntington’s Disease [Mutant HTT] – cognitive decline
  6. WAVE approach: target only the mutant HTT while leaving wild-type intact – disease modifier
  7. Targeted therpy  mHTT RNA – transcript of mutant, SNP
  8. Complement activation assay vs HTT transcript – wtHTT: Patients with long CAG repeat associated with T isoform will be selected for trial.
  9. DMD – Exom Skipping,vs Dysfunctional Splicing: Dystrophin (increase production) vs Vinculin
  • Barbara Weber, M.D., Interim Chief Medical Officer, Neon Therapeutics
  1. Neoantigen biology: Native antigens (targets are expressed in both tumor and normal cells vs Neoantigens (Targets are specific to each Tumor)

Checkpoint inhibitor drug class: Neoantigen-based Therapies may expand ACTIVITY of Checkpoint Inhibitor – Nature 2013

  1. Ipilimumab in melanoma (NEJM 2014
  2. Pembrolizumab in NSCLC, Science 2015
  3. Pembrolizumab in colonorectal cancer, NEJM 2015

NEON: Personalized Neoantigens

  • personalized CAR-T
  • personalized Vaccines
  • Epitope Librart – Epitope selection
  • NT-001 PM Clinical Trial: combination of Neoantigen Vaccine wiht Checkpoint Inhibitor – PD-1
  • Monitor Immune response after insertion of neoantigents

9:45 a.m. — Networking Break

10:15 a.m. — Money Talks: The Future of Investment in Personalized Medicinemolecular diagnostics

Innovation requires investment. During this discussion, a panel of diverse investors will illuminate the most promising business opportunities for advancing personalized medicine, focusing on both macro and micro environments while also discussing the barriers to investment and potential solutions for removing them.

  • Moderator: Edward Winnick, Editor in Chief, GenomeWeb
  1. Beyond Ocology
  • Alexis Borisy, M.S., Partner, Third Rock Ventures
  1. What excite now – Breakthrough therapies in the RIGHT Patient populations
  2. FOundation Medicine – 1,000 Patients treated with FOundationOne across diagnostics and drug – genomics and disease and therapy in One
  3. New Diagnostics company creation – is very hard, not many, only if it is an extraordinary compelling idea, If FDA approves and the Payers do not endorse
  4. Therapeutics – have a solution, nucleaic based drug, gene editing, Payers are iterested, FDA is interested vs historical standdards, translational medicine – drug will drive diagnostics
  5. Reimbursement, Capital mobilization, new molecular diagnostics, challenges: Technical risk Clinical Risk
  6. Cut off for AstraZeneca vs BMS are different
  7. number tumor cells vs penetration of tumor cells vs infiltration of immune cells
  8. Merck  has a different strategy – precise indicator of I/O agents: Single vs. multiple combinations
  9. WHen population of patients is identified well: understand response rate go for 80% response rate inside this population identified
  10. beyond oncology MI and CVD, cardiomyopathy – genetic mutations, familial vs sporadic,
  11. Immunology: Inflammation, innume conditions, understand what is the microtrend in subpopulations
  12. Infectious diseases: organisms given narrow cast antibiotics and trade-offs PM antibiotics is a fundamental challengeresistence to antibiotics
  13. Diagnostics in Cancer – a small fraction vs the cost of Oncology drugs
  14. Longevity drive costs,
  15. Physician diagnostics is critical
  • Vamil Divan, M.D., M.B.A., Senior Research Analyst, Credit Suisse
  1. BMS is a leader in immunotherapy and Roache as well
  • Ryan Lindquist, M.B.A., Director, Investment Banking, Leerink Partners
  1. Diagnostics and Precision Medicine
  2. Diagnostics – reimbursement
  3. winners and loosers in Diagnostics
  4. Performance, cost reduction, Payers have rules of engagement
  5. Differentiation of generic drugs: COST OF TREATMENT FO RDRUG $30,000 COST RUNNING TEST $200, Pharma was willing to cut the cost creatively
  6. Drug pricing a point in investing in Biotech
  7. Neuroscience: many drugs are not effication – swab of mouth saliva allows to determine which pshychtropic drug will work for which patient on psych therapies
  8. Data companies with Regulatories
  9. Payers: Kaiser – has Outcome data and cost data
  10. Healthcare system: Payers and Delivery is separate
  11. Patient advocacy on reimbursement is critical and DOctor education on new Diagnostics at Academic Centers

Questions from the audience:

  1. Therapeutics: IP and reimbursement is straight forward
  2. Diagnostics: No IP therefore reimburcement more complex
  3. Data companies
  4. Foundation Medicine: Reimbursement is a challenge – what should the Payer do vs the DIagnostics companies, regulatory, American Pathology Guidelines
  5. Engagement Providers – what is their role?

 

11:15 a.m. — You + 999,999: How a One Million Person Cohort Can Pave the Way for Personalized Care

  • Introduction: Paolo Narvaez, Ph.D., Senior Principal Engineer, Director of Engineering, Intel Corporation
    1. How to use technology for PM – Intel Life Sciences

     

  • Keynote: Eric Dishman, Director, All of Us Research Program, National Institutes of Health
  1. PM Precision vs Personalized Health
  2. All of Us Research Program, National Institutes of Health funded – is PM 
  3. Framework Like the Framingham Study
  4. One million Patients,  – longitudinal: lifestyle, genes, environmental
  5. HUGE data not a study for one disease,  – accelerate Science
  6. Infectious,Neuro, mental, Heart& Lung, Musculo-Skeletal
  • DIrect Volunteer
  • Health Provider Org
  • Enroll & Consent
  • Surveys Journals
  • Baseline Measrement
  • Mission to accelerate Knowledge Turns & Breakthroughs
  • Participation – Transformational Approach
  • 1,000 vs One million
  • Diversity: People , geography health Status,
  • Data Types: Genomics, Claims – Data Access
  • High schools, community colleges
  • User-Centered approach
  • gathering Input
  • Personas: ready to go, determined, too much Govenment, No time, Suspitious
  • Platform Innovation Mindset & Process –>> Innovation Fannel
  • Baseline Physical Evaluation not a Physical Exam
  • PPI – Participant Provided Information
  • Mayo Clinic: Biospecimens: Blood and Urine

Research ROADMAP Workshops

  • Near Term
  • Mid Term
  • Long Term

Kind of Attendees: experts network (50 organization, co-funders, advoccy

Biobank – 35Million vials

IT infrafaces–>> security,  Enrollement 1-800-

New Eng PM Consortium, Bosotn, MA

 

 

 

12:00 p.m. — Luncheon

 

 

 

#PMConf

SOURCE

http://www.personalizedmedicinecoalition.org/Conference/November_16_Program

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LIVE 9/21 8AM to 10:55 AM Expoloring the Versatility of CRISPR/Cas9 at CHI’s 14th Discovery On Target, 9/19 – 9/22/2016, Westin Boston Waterfront, Boston

http://www.discoveryontarget.com/

http://www.discoveryontarget.com/crispr-therapies/

Leaders in Pharmaceutical Business Intelligence (LPBI) Group is a

Media Partner of CHI for CHI’s 14th Annual Discovery on Targettaking place September 19 – 22, 2016 in Boston.

In Attendance, streaming LIVE using Social Media

Aviva Lev-Ari, PhD, RN

Editor-in-Chief

http://pharmaceuticalintelligence.com

#BostonDOT16

@BostonDOT

 

COMMENTS BY Stephen J Williams, PhD

EXPLORING THE VERSATILITY OF CRISPR/Cas9

 

8:00 Chairperson’s Opening Remarks

TJ Cradick , Ph.D., Head of Genome Editing, CRISPR Therapeutics

 

@CRISPRTX

 

8:10 Functional Genomics Using CRISPR-Cas9: Technology and Applications

Neville Sanjana, Ph.D., Core Faculty Member, New York Genome Center and Assistant Professor, Department of Biology & Center for Genomics and Systems Biology, New York University

 

CRISPR Cas9 is easier to target to multiple genomic loci; RNA specifies DNA targeting; with zinc finger nucleases or TALEEN in the protein specifies DNA targeting

 

  • This feature of crisper allows you to make a quick big and cheap array of a GENOME SCALE Crisper Knock out (GeCKO) screening library
  • How do you scale up the sgRNA for whole genome?; for all genes in RefSeq, identify consitutive exons using RNA-sequencing data from 16 primary human tissue (alot of genes end with ‘gg’) changing the bases on 3’ side negates crisper system but changing on 5’ then crisper works fine
  • Rank sequences to be specific for target
  • Cloned array into lentiviral and put in selectable markers
  • GeCKO displays high consistency betweens reagents for the same gene versus siRNA; GeCKO has high screening sensitivity
  • 98% of genome is noncoding so what about making a library for intronic regions (miRNA, promoter regions?)
  • So you design the sgRNA library by taking 100kb of gene-adjacent regions
  • They looked at CUL3; (data will soon be published in Science)
  • Do a transcription CHIP to verify the lack of binding of transcription factor of interest
  • Can also target histone marks on promoter and enhancer elements
  • NYU wants to explore this noncoding screens
  • sanjanalab.org

 

@nyuniversity

 

8:40 Therapeutic Gene Editing With CRISPR/Cas9

TJ Cradick , Ph.D., Head of Genome Editing, CRISPR Therapeutics

 

NEHJ is down and dirty repair of single nonhomologous end but when have two breaks the NEHJ repair can introduce the inversions or deletions

 

    • High-throughput screens are fine but can limit your view of genomic context; genome searches pick unique sites so use bioinformatic programs  to design specific guide Rna
    • Bioinformatic directed, genome wide, functional screens
    • Compared COSMID and CCTOP; 320 COSMID off-target sites, 333 CCtop off target
    • Young lab GUIDESeq program genome wide assay useful to design guides
    • If shorten guide may improve specificity; also sometime better sensitivity if lengthen guide

 

  • Manufacturing of autologous gene corrected product ex vivo gene correction (Vertex, Bayer, are partners in this)

 

 

They need to use a clones from multiple microarrays before using the GUidESeq but GUIDEseq is better for REMOVING the off targets than actually producing the sgRNA library you want (seems the methods for library development are not fully advanced to do this)

 

The score sometimes for the sgRNA design programs do not always give the best result because some sgRNAs are genome context dependent

9:10 Towards Combinatorial Drug Discovery: Mining Heterogeneous Phenotypes from Large Scale RNAi/Drug Perturbations

Arvind Rao, Ph.D., Assistant Professor, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center

 

Bioinformatics in CRISPR screens:  they looked at image analysis of light microscopy of breast cancer cells and looked for phenotypic changes

 

  • Then they modeled in a small pilot and then used the algorithm for 20,000 images (made morphometric measurements)
  • Can formulate training statistical algorithms to make a decision tree how you classify data points
  • Although their algorithms worked well there was also human input from scientists

Aggregate ranking of hits programs available on web like LINKS

 

@MDAndersonNews

 

10:25 CRISPR in Stem Cell Models of Eye Disease

Alexander Bassuk, M.D., Ph.D., Associate Professor of Pediatrics, Department of Molecular and Cellular Biology, University of Iowa

 

Blind athlete Michael Stone, biathlete, had eye disease since teenager helped fund and start the clinical trial for Starbardt disease; had one bad copy of ABCA4, heterozygous (inheritable in Ahkenazi Jewish) – a recessive inheritable mutation with juvenile macular degeneration

  • Also had another male in family with disease but he had another mutation in the RPGR gene
  • December 2015 paper Precision Medicine: Genetic Repair of retinitis pigmentosa in patient derived stem cells
  • They were able to correct the iPSCs in the RPGR gene derived from patient however low efficiency of repair, scarless repair, leaves changes in DNA, need clinical grade iPSCs, and need a humanized model of RPGR

@uiowa

10:55 CRISPR in Mouse Models of Eye Disease

Vinit Mahajan, M.D., Ph.D., Assistant Professor of Ophthalmology and Visual Sciences, University of Iowa College of Medicine

  • degeneration of the retina will see brown spots, the macula will often be preserved but retinal cells damaged but with RPGR have problems with peripheral vision, retinitis pigmentosa get tunnel vision with no peripheral vision (a mouse model of PDE6 Knockout recapitulates this phenotype)
  • the PDE6 is linked to the rhodopsin GTP pathway
  • rd1 -/- mouse has something that looks like retinal pigmentosa; has mutant PDE6; is actually a nonsense mutation in rd1 so they tried a crisper to fix in mice
  • with crisper fix of rd1 nonsense mutation the optic nerve looked comparible to normal and the retina structure restored
  • photoreceptors layers- some recovery but not complete
  • sequence results show the DNA is a mosaic so not correcting 100% but only 35% but stil leads to a phenotypic recovery; NHEJ was about 12% to 25% with large deletions
  • histology is restored in crspr repaired mice
  • CRSPR off target effects: WGS and analyze for variants SNV/indels, also looked at on target and off target regions; there were no off target SNVs indels while variants that did not pass quality control screening not a single SNV
  • Rhodopsin mutation accounts for a large % of patients (RhoD190N)
  • injection of gene therapy vectors: AAV vector carrying CRSPR and cas9 repair templates

CAPN mouse models

  • family in Iowa have dominant mutation in CAPN5; retinal degenerates
  • used CRSPR to generate mouse model with mutation in CAPN5 similar to family mutation
  • compared to other transgenic methods CRSPR is faster to produce a mouse model

To Follow LIVE CONFERENCE COVERAGE PLEASE FOLLOW ON TWITTER USING

Meeting #: #BostonDOT16

Meeting @: @BostonDOT

 

Overall good meeting #s:

#personalizedmedicine

#innovation

#cancer

#immunology

#immunooncology

#pharmanews

#CRSPR

#geneediting

#crisper

#biotech

 

AND FOLLOW these @

@pharma_BI

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@BiotechNews

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