Integration analysis of metabolites and single nucleotide polymorphisms improves the prediction of drug response of celecoxib

Predicting Range of Initial Warfarin Dose Based on Pharmacometabolomic and Genetic Inputs

Anticoagulation response to warfarin during the initial stage of therapy varies among individuals. In this study, we aimed to combine pharmacometabolomic and pharmacogenetic data to predict interindividual variation in warfarin response, and, on this basis, suggest an initial daily dose range. The baseline metabolic profiles, genotypes, and clinical information of 160 patients with heart valve disease served as the variables of the function of the last international normalized ratio measured before a patient's discharge (INR_day7 ) to screen for potential biomarkers. The partial least-squares model showed that two baseline metabolites (uridine and guanosine), one single-nucleotide variation (VKORC1), and four clinical parameters (weight, creatinine level, amiodarone usage, and initial daily dose) had good predictive power for INR_day7 (R²  = 0.753 for the training set, 0.643 for the test set). With these biomarkers, a machine learning algorithm (two-dimensional linear discriminant analysis-multinomial logit model) was used to predict the subgroups with extremely warfarin-sensitive or less warfarin-sensitive patients with a prediction accuracy of 91% for the training set and 90% for the test set, indicating that individual responses to warfarin could be effectively predicted. Based on this model, we have successfully designed an algorithm,"IniWarD," for predicting an effective dose range in the initial 7-day warfarin therapy. The results indicate that the daily dose range suggested by the IniWarD system is more appropriate than that of the conventional genotype-based method, and the risk of bleeding or thrombus due to warfarin could thus be avoided.

Individualizing pharmacogenomic test results in the context of the microbiome

The field of pharmacogenetic testing was hailed as one of the early successful clinical applications arising from the personalized (or precision) medicine revolution. Substantial progress has been made to identify genes and genetic variants involved in drug response and establish clinical implementation programs. Yet, drug response is a complex trait and recent work has highlighted the key role played by the gut microbiome. As the study of the gut microbiome and pharmacogenetics converge, it may be possible to generate more precise predictions of drug response and improve health outcomes to treatments. Substantial effort will be needed to understand the dynamic impact of the microbiome and the interplay with host genetics and how to implement expanded pharmacogenetic testing.