Deriving rules and assertions from pharmacogenomics knowledge resources in support of patient drug metabolism efficacy predictions

Abstract

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.

Figure 1

Prototype reasoning system architecture: a prototype implementation of the PEMRIC model. SuperCYP is our primary source for genotype–phenotype association knowledge. PharmGKB is our primary source for pharmacokinetic pathway knowledge. The clinical data source contains patient data. The dashed line linking PharmGKB to the evidence base represents the inclusion of PharmGKB curated publications into the evidence base only for the drug-oriented approach. The user interface element is included for illustrative purposes.

Figure 2

Distribution of genotypes: CYP3A5 Wt=*1, Vt=*3,*6; CYP2D6 Wt=*1, Vt=*4,*6; CYP2C9 Wt=*1, Vt=*2,*3; and CYP2C19 Wt=*1, Vt=*2.

Figure 3

Scatterplot and quantile regression fit. The plots show scatter plots of the endoxifen/N-desmethyltamoxifen (NDM) ratio versus phenotype scores. Superimposed on the plots are the median fit (solid line) and the least squares estimate of the conditional mean function (dashed line). (A) Phenotype scoring systems that assign numeric values according to ‘allelic variant’–‘enzyme activity’ association assertions. (B) Phenotype scoring systems that assign numeric values according to ‘genotype’–‘metabolizer activity’ association assertions. ^aA p value of ≤0.05 is considered statistically significant, indicating significant difference in the regression coefficient medians.

Figure 4

(A) Simple metrics for individual genes with genotype values as Vt/Vt=2, Wt/Vt=1, and Wt/Wt=0: CYP3A5 Wt=*1, Vt=*3,*6; CYP2D6 Wt=*1, Vt=*4,*6; CYP2C9 Wt=*1, Vt=*2,*3; and CYP2C19 Wt=*1, Vt=*2. Wt/Wt=0, Wt/Vt=1, Vt/Vt=2. The plots show box-plots of the endoxifen/N-desmethyltamoxifen (NDM) ratio versus genotype values. Superimposed on the plots are the median fit (solid line) and the least squares estimate of the conditional mean function (dashed line). (B) Simple metric for the sum of genotype values for genes CYP3A5, CYP2D6, CYP2C9, and CYP2C19. The lines indicate median bands. The plots show scatter plots of the endoxifen/NDM ratio versus simple metrics. Superimposed on the plots are the median fit (solid line) and the least squares estimate of the conditional mean function (dashed line).