Dr. Monte's research gathers clinical, genomic, cytochrome (CYP) metabolism phenotype, and metabolomic data in a prospective trial of a patients beginning metoprolol therapy for uncontrolled hypertension. These factors will be compared to identify which variables predict the drug response to metoprolol. These data will be integrated into a model to predict systolic blood pressure reduction due to metoprolol. This innovative approach integrates clinical, genomic, CYP metabolism phenotype, and metabolomic data to predict the drug response of metoprolol. The integrative approach can be applied to other diseases and therapeutics to improve drug efficacy and safety. This may eliminate time intensive up-titration, eliminate therapies that are destined to be ineffective, and minimize adverse drug effects.
A clinical example of the proposed integrated method for predicting drug response. (1) Patients are given 10 mg of hydrocodone. (2) Clinical phenotypes are captured fully and completely. These may include (among others) development of ADRs, chronicity of treatment, ethnic differences, and demographic factors. (3) Association studies may contribute to characterization of the clinical phenotypes (e.g. RNA-sequencing may help distinguish chronicity of treatment). (4) The drug response is categorized into phenotypically pertinent groups. (5) Relevant biological pathways are identified and linked by individual metabolomic markers. (6) Stratification of drug response is refined by accounting for biological pathway polymorphisms and controlled for phenotypic variables captured in #2 above. (7) The final stepwise model is built, allowing for a high, although not perfect, receiver operating characteristic (ROC).