Full title: Deriving rules and assertions from pharmacogenomics knowledge resources in support of patient drug metabolism efficacy predictions...
Full title: Deriving rules and assertions from pharmacogenomics knowledge resources in support of patient drug metabolism efficacy predictions
Objective Pharmacogenomics evaluations of variability in drug metabolic processes may be useful for making individual drug response predictions. We present an approach to deriving ‘phenotype scores’ based on existing pharmacogenomics knowledge and a patient's genomics data. Pharmacogenomics plays an important role in the bioactivation of tamoxifen, a prodrug administered to patients for breast cancer treatment. Tamoxifen is therefore considered a model for many drugs requiring bioactivation. We investigate whether this knowledge-based approach can be applied to produce a phenotype score that is predictive of the endoxifen/N-desmethyltamoxifen (NDM) plasma concentration ratio in patients taking tamoxifen. Materials and methods We implement a knowledge-based model for calculating phenotype scores from patient-specific genotype data. These data include allelic variants of genes encoding enzymes involved in the bioactivation of tamoxifen. We performed quantile linear regression to evaluate whether six phenotype scoring algorithms are predictive of patient endoxifen/NDM plasma concentration ratio, and validate our scoring methods.
Results Our model illustrates a knowledge-based approach to predict drug metabolism efficacy given patient genomics data. Results showed that for one phenotype scoring algorithm, scores were weakly correlated with patient endoxifen/NDM plasma concentration ratios. This algorithm performed better than simple metrics for variation in individual and multiple genes. Discussion We discuss advantages of the model, challenges to its implementation in a personalized medicine context, and provide example future directions. Conclusions We demonstrate the utility of our model in a tamoxifen case study context. We also provide evidence that more complicated polygenic models are needed to represent heterogeneity in clinical outcomes.