Translating Pharmacogenomics Discoveries into Clinical Practice: The Role of Curated Databases

Abstract

Pharmacogenomics-related genotype information is growing at a supra-linear rate, and phenotyperelated information, as determined by computer simulations, in vitro experiments and clinical studies, is also growing. Even when phenotypic information is confirmed via clinical research, numerous barriers exist in translating these discoveries into clinical practice. We consider two of them here: the uncertainty regarding the practical relevance of research observations, and translation of significant research findings into clinical practice and research through electronic information access. This form of access is critical because even leading clinical pharmacologists cannot fully retain mentally today's large volume of drug-related information. Clinical Importance of Individual Pharmacogenomics StudiesIt has been suggested that HIV viral genotyping, which is part of routine patient management in the USA, can serve as a paradigm for introduction of pharmacogenomics into patient care [1]. The important difference, however, is that viral genotyping predicts drug resistance very accurately, in addition to helping to trace the transmission of specific viral strains through susceptible communities. By contrast, detection of most human polymorphic gene variants (which, by definition, are reasonably common in individual populations) is often of less clear consequence: their effects are typically much less dramatic than those of the rare variants, such as atypical butyrylcholinesterase, which spurred classical pharmacogenetics Even for well-characterized polymorphisms that are widely accepted to be clinically important, the variation in phenotypic end-points such as metabolic ratios (MRs) of non-metabolized to metabolized drug is considerable. For example, in a study of 200 Iranian subjects phenotyped for CYP2D6 polymorphisms with dextromethorphan, a 520-fold inter-individual MR variation was observed across all subjects, with variation for poor metabolizers itself being 19.5-fold [2]. Even when genotype and phenotype correlate with high statistical significance, clinical importance is harder to assess. Thus, in a recent study of warfarin kinetics [3], multiple regression analysis of warfarin maintenance dose as a function of CYP2C9 genotype, age, lean body weight and concomitant treatment with warfarin yielded a statistically significant R 2 (coefficient of determination) of 0.37. This number, however, implies that 63% of the experimental variance is unaccounted for.Consequently, there are differences between the viewpoints of those generating the results and those who are expected to act on them. Despite recommendations that CYP2D6 genotyping "should be performed routinely because genotyping costs offset medical costs" [4], a recent Australasian survey of 629 individuals/centers [5] found that only one center was routinely