Interindividual variability in drug metabolism can significantly affect drug concentrations in the body and subsequent drug response. Understanding an individual’s drug metabolism capacity is important for predicting drug exposure and developing precision medicine strategies. The goal of precision medicine is to individualize drug treatment for patients to maximize efficacy and minimize drug toxicity. While advances in pharmacogenomics have improved our understanding of how genetic variations in drug-metabolizing enzymes (DMEs) affect drug response, nongenetic factors are also known to influence drug metabolism phenotypes. This minireview discusses approaches beyond pharmacogenetic testing to phenotype DMEs—particularly the cytochrome P450 enzymes—in clinical settings. Several phenotyping approaches have been proposed: traditional approaches include phenotyping with exogenous probe substrates and the use of endogenous biomarkers; newer approaches include evaluating circulating noncoding RNAs and liquid biopsy-derived markers relevant to DME expression and function. The goals of this minireview are to 1) provide a high-level overview of traditional and novel approaches to phenotype individual drug metabolism capacity, 2) describe how these approaches are being applied or can be applied to pharmacokinetic studies, and 3) discuss perspectives on future opportunities to advance precision medicine in diverse populations. SIGNIFICANCE STATEMENT This minireview provides an overview of recent advances in approaches to characterize individual drug metabolism phenotypes in clinical settings. It highlights the integration of existing pharmacokinetic biomarkers with novel approaches; also discussed are current challenges and existing knowledge gaps. The article concludes with perspectives on the future deployment of a liquid biopsy-informed physiologically based pharmacokinetic strategy for patient characterization and precision dosing.

Masitinib is a small molecule tyrosine kinase inhibitor under investigation for the treatment of amyotrophic lateral sclerosis, mastocytosis, and COVID-19. Hepatotoxicity has been reported in some patients while taking masitinib. The liver injury is thought to involve hepatic metabolism of masitinib by cytochrome P450 (P450) enzymes to form chemically reactive, potentially toxic metabolites. The goal of the current investigation was to determine the P450 enzymes involved in the metabolic activation of masitinib in vitro. In initial studies, masitinib (30 μM) was incubated with pooled human liver microsomes in the presence of NADPH and potassium cyanide to trap reactive iminium ion metabolites as cyano adducts. Masitinib metabolites and cyano adducts were analyzed using reversed-phase liquid chromatography–tandem mass spectrometry. The primary active metabolite, N-desmethyl masitinib (M485), and several oxygenated metabolites were detected along with four reactive metabolite cyano adducts (MCN510, MCN524, MCN526, and MCN538). To determine which P450 enzymes were involved in metabolite formation, reaction phenotyping experiments were conducted by incubation of masitinib (2 μM) with a panel of recombinant human P450 enzymes and by incubation of masitinib with human liver microsomes in the presence of P450-selective chemical inhibitors. In addition, enzyme kinetic assays were conducted to determine the relative kinetic parameters (apparent K m and V max) of masitinib metabolism and cyano adduct formation. Integrated analysis of the results from these experiments indicates that masitinib metabolic activation is catalyzed primarily by P450 3A4 and 2C8, with minor contributions from P450 3A5 and 2D6. These findings provide further insight into the pathways involved in the generation of reactive, potentially toxic metabolites of masitinib. Future studies are needed to evaluate the impact of masitinib metabolism on the toxicity of the drug in vivo.

Masitinib is an orally bioavailable inhibitor of protein kinases such as c‐kit, Lyn, Fyn and MCSFR‐1. These pathways play a key role in cell survival and proliferation of mast cells and microglia. Due to the selectivity of masitinib for these pathways, this drug is under investigation for treating neurodegenerative disorders, inflammatory disorders, as well as pancreatic and prostate cancer. Hepatotoxicity has been observed in some patients taking masitinib during clinical trials and is thought to arise from reactive metabolites formed by cytochrome P450 (CYP) enzymes in the liver. The purpose of this study was to elucidate the metabolism and bioactivation pathways of masitinib using in vitro phenotyping methods to identify the enzymes involved in masitinib metabolic activation. Masitinib (2 μM) was incubated with pooled human liver microsomes in the presence and absence of NADPH. Potassium cyanide was added to trap reactive metabolites as cyano adducts. Metabolites were analyzed using reversed‐phase liquid chromatography ‐ tandem mass spectrometry (LC‐MS/MS). To determine which enzymes are involved in forming masitinib metabolites and reactive metabolites, masitinib was incubated with human liver microsomes in the presence of P450‐selective reversible inhibitors as well as time‐dependent inhibitors. To validate the inhibitor panel findings, masitinib was also incubated with recombinant P450 enzymes. From these assays we found that the primary metabolite, N‐desmethyl masitinib, was primarily formed by CYP2C8 and CYP3A4, with contributions from CYP2D6 and CYP3A5. Other primary metabolites produced by monooxygenation of masitinib, M515a, M515b, and M515d, were formed by CYP3A4 and CYP3A5, while M515c was formed by CYP3A4 with minor contribution from CYP2D6. We also found that reactive metabolite cyano adducts MCN510 and MCN538 were formed by CYP3A4 and CYP3A5. MCN524 and MCN526 were generated by CYP3A4 with contributions from CYP2D6, CYP2C8, and CYP3A5. By elucidating these pathways, we gain further insight into the routes that generate reactive and potentially toxic metabolites of masitinib. Given the polymorphic nature of the enzymes involved, future studies are needed to determine the impact of CYP genetic polymorphisms on masitinib metabolism. This information could ultimately provide evidence to inform precision prescribing for masitinib to ensure safety and efficacy for future patients.