Drug Safety Pre- and Post-Approval: Bridging the Gap

May 5, 2011

Overview

Increasing scientific, regulatory and public scrutiny focuses on the obligation of the medical community, pharmaceutical industry and health authorities to ensure that marketed drugs have acceptable benefit-risk profiles. This is an intricate and ongoing process that begins with carefully designed randomized clinical trials prior to approval but continues after regulatory market authorization when the drug is in widespread clinical use. In the post-approval environment, surveillance schemes based on spontaneous reporting systems (SRSs) represent a cornerstone for the early detection of drug hazards that are novel by virtue of their clinical nature, severity and/or frequency. However, newer data sources such as claims databases, electronic health record databases and social media data have emerged in recent years as important resources. Methods development for these sources currently attracts considerable research attention.

This workshop will discuss the research challenges surrounding the following question: How can we combine information from pre- and post-approval clinical trials, spontaneous reports, and longitudinal observational databases to provide more complete safety profiles at arbitrary moments in time?

Agenda

Morning Session

8:00 am : Welcome and Opening Remarks

8:30am - 9:30am : Trends, Challenges and Opportunities

1. Government Perspective
   • Gerald Dal Pan, FDA (30 min)
2. Industry Perspective
   • Paul Stang, Johnson & Johnson (30 min)
3. 'Contrarian' Perspective
   • Stan Young, Statistics Institute of North Carolina (30 min)

9:30am - 9:45am : Q&A with first session speakers

9:45 - 10:00am : BREAK

10:00 am - 11:30 am : "Nuts & Bolts" of Drug Safety Monitoring

1. Larry Gould, Merck (drug safety in clinical trials) (30 min)
2. Andrew Bate, Pfizer (state-of-the-art in spontaneous reporting) (30 min)
3. Patrick Ryan (OMOP) (mining observational databases) (30 min)

11:30am - 11:45am : Q&A with second session speakers

12:00 pm - 1:00 pm: Lunch

Afternoon Session

1:00 pm - 3:00 pm : Methodology

1. Statistics perspective (David Madigan, Columbia) (30 min)
2. Sheila Weiss from U. Maryland (30 min)
3. Bayesian Belief Networks (Marcello Trovati, IBM Ireland) (30 min)
4. Social Media Mining (Rick Lawrence, IBM Watson) (30 min)

3:15 pm - 3:30pm : BREAK

3:30pm - 5:00pm: Panel Discussion (Facilitated by David Madigan)

1. OMOP (public-private partnership)
2. Guna Rajagopal
3. Jeremy Rassen
4. Gerald Dal Pan
5. Paul Stang
6. Marianthi Markatou

5:00pm : Closing Remarks