Genome-Based Biomarkers for Adverse Drug Effects, Patient Enrichment and Prediction of Drug Response, and their Incorporation into Clinical Trial Design

Abstract

Classic examples of pharmacogenomic biomarkers for drug efficacy include genetic variation in the drug target (including its expression level) and drug metabolizing enzymes (DMEs). Recent US FDA approvals of tests for cytochrome P450 2D6/2C9 and uridine diphosphate glucuronsyltransferase (UGT)1A1 have given regulatory endorsement to biomarkers that can improve drug safety by identifying individuals at risk for drug toxicity. Markers that predict risk for disease can identify patients who will have a greater than average benefit from therapy. This creates a new opportunity to enrich clinical trials with patients who are likely to have more events and to achieve earlier drug approval. Markers that predict for risk of cardiovascular, thrombotic and liver diseases may also identify a subset of individuals at substantially elevated risk for adverse drug effects. The adaptive clinical trial design provides a mechanism for incorporating genomic information during clinical trials, while providing sufficient time for diagnostic product development and co-registration with a new drug application.

Keywords: :

• adaptive clinical trial design
• adverse drug events
• biomarkers
• drug metabolizing enzymes

Acknowledgments

The authors wish to thank John Sninsky for helpful comments on the manuscript and Eric Lai for the concept of Figure 1.