Pharmacogenomics and the drug discovery pipeline: when should it be implemented?

Abstract

One of the key factors in developing improved medicines lies in understanding the molecular basis of the complex diseases we treat. Investigation of genetic associations with disease utilizing advances in linkage disequilibrium-based whole genome association strategies will provide novel targets for therapy and define relevant pathways contributing to disease pathogenesis. Genetic studies in conjunction with gene expression, proteomic, and metabonomic analyses provide a powerful tool to identify molecular subtypes of disease. Using these molecular data, pharmacogenomics has the potential to impact on the drug discovery and development process at many stages of the pipeline, contributing to both target identification and increased confidence in the therapeutic rationale. This is exemplified by the identified association of 5-lipoxygenase-activating protein (ALOX5AP/FLAP) with increased risk of myocardial infarction, and of the chemokine receptor 5 (CCR5) with HIV infection and therapy. Pharmacogenomics has already been used in oncology to demonstrate that molecular data facilitates assessment of disease heterogeneity, and thus identification of molecular markers of response to drugs such as imatinib mesylate (Gleevec) and trastuzumab (Herceptin). Knowledge of genetic variation in a target allows early assessment of the clinical significance of polymorphism through the appropriate design of preclinical studies and use of relevant animal models. A focussed pharmacogenomic strategy at the preclinical phase of drug development will produce data to inform the pharmacogenomic plan for exploratory and full development of compounds. Opportunities post-approval show the value of large well-characterized data sets for a systematic assessment of the contribution of genetic determinants to adverse drug reactions and efficacy. The availability of genomic samples in large phase IV trials also provides a valuable resource for further understanding the molecular basis of disease heterogeneity, providing data that feeds back into the drug discovery process in target identification and validation for the next generation of improved medicines.