Leaders in Pharmaceutical Business Intelligence (LPBI) Group

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

Reporter: Aviva Lev-Ari, PhD, RN

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