Pharmacogenomics and Pharmacoepidemiology
Slide 1 of 7: Cancer Pharmacogenomics Workshop Panel Discussion
David A. Flockhart, M.D., Ph.D.
July 21, 2009
Slide 2 of 7: Introduction
Identify infrastructures, resources, and pressing clinical issues, as well as current limitations that need to be addressed to advance cancer pharmacoepidemiology and pharmacogenomics research.
Slide 3 of 7: Infrastructures
- Support for training a new generation of scientists trained in epidemiology, pharmacogenomics and clinical therapeutics
- Not T32 but integrated into multidisciplinary oncology pharmacogenomics research centers.
- Support for collection, transport and extraction of DNA from all trials independent of an individual specific hypothesis.
- Support for highly collaborative multidisciplinary groups to carry out the pharmacoepidemiologic pharmacogenomic research needed outside RPCTs. Access to quality medication and phenotypic outcome data key.
- Database access with cancer outcomes
- Toxicity outcomes
- Highly Granular Medication Data, e.g. Marshfield, Regenstrief, Mayo, Vanderbilt, Medco
Slide 4 of 7: Resources
- Access to cores able to carry out Next Gen Sequencing
- Support for the final stages of moving tests to clinic
Slide 5 of 7: Pressing Clinical Issues
- Economic Consequences of Pharmacogenomic Testing in Different Resource Environments:
- Need to demonstrate substantial cost savings may be most evident with biologic pharmacogenomics.
- Performance of pharmacogenomic and genetic tests outside the RPCT environment in the real world.
Slide 6 of 7: Where We Need to Go Next
An integrated approach that addresses:
- Clinical Practice
Requires Integration of clinical and biomarker data into clinically meaningful, easily communicable “indices” validated in large retrospective datasets or randomized trials
- E.g. Oncotype Dx™
- CYP2D6, UGT2B7, ER
- TPMT +, UGT1A1
Requires high sensitivity and specificity:
- Next Gen Sequencing for relatively rare variants
- Improved specificity using pattern recognition informatics
Slide 7 of 7: Where We Need to Go Next
- Clinical Pharmacology in Large Datasets
- Careful characterization of drug effect and toxicity
- Careful medication data collection
- Adherence research and the effects of genomics on adherence
- Integration with Basic pharmacology:
- Pathway analysis to identify variants relevant to drug development and most likely worth testing in RPCTs and large datasets.