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Drug metabolism is a major determinant of drug concentrations in the body. Drug-drug interactions (DDIs) caused by the co-administration of multiple drugs can lead to alteration in the exposure of the victim drug, raising safety or effectiveness concerns. Assessment of the DDI potential starts with in vitro experiments to determine kinetic parameters and identify risks associated with the use of comedication that can inform future clinical studies. The diverse range of experimental models and techniques has significantly contributed to the examination of potential DDIs. Cytochrome P450 (CYP) enzymes are responsible for the biotransformation of many drugs on the market, making them frequently implicated in drug metabolism and DDIs. Consequently, there has been a growing focus on the assessment of DDI risk for CYPs. This review article provides mechanistic insights underlying CYP inhibition/induction and an overview of the in vitro assessment of CYP-mediated DDIs.

(S)-N-(1-amino-3,3-dimethyl-1-oxobutan-2-yl)-1-butyl-1H-indazole-3carboxamide (ADB-BUTINACA) is an emerging synthetic cannabinoid that was first identified in Europe in 2019 and entered Singapore's drug scene in January 2020. Due to the unavailable toxicological and metabolic data, there is a need to establish urinary metabolite biomarkers for detection of ADB-BUTINACA consumption and elucidate its biotransformation pathways for rationalizing its toxicological implications. We characterized the metabolites of ADB-BUTINACA in human liver microsomes using liquid chromatography Orbitrap mass spectrometry analysis. Enzyme-specific inhibitors and recombinant enzymes were adopted for the reaction phenotyping of ADB-BUTINACA. We further used recombinant enzymes to generate a pool of key metabolites in situ and determined their metabolic stability. By coupling in vitro metabolism and authentic urine analyses, a panel of urinary metabolite biomarkers of ADB-BUTINACA was curated. Fifteen metabolites of ADB-BUTINACA were identified with key biotransformations being hydroxylation, N-debutylation, dihydrodiol formation, and oxidative deamination. Reaction phenotyping established that ADB-BUTINACA was rapidly eliminated via CYP2C19-, CYP3A4-, and CYP3A5-mediated metabolism. Three major monohydroxylated metabolites (M6, M12, and M14) were generated in situ, which demonstrated greater metabolic stability compared to ADB-BUTINACA. Coupling metabolite profiling with urinary analysis, we identified four urinary biomarker metabolites of ADB-BUTINACA: 3 hydroxylated metabolites (M6, M11, and M14) and 1 oxidative deaminated metabolite (M15). Our data support a panel of four urinary metabolite biomarkers for diagnosing the consumption of ADB-BUTINACA.

5F-MDMB-PINACA and 4F-MDMB-BINACA are synthetic cannabinoids (SCs) that elicit cannabinoid psychoactive effects. Defining pharmacokinetic–pharmacodynamic (PK–PD) relationships governing SCs and their metabolites are paramount to investigating their in vivo toxicological outcomes. However, the disposition kinetics and cannabinoid receptor (CB) activities of the primary metabolites of SCs are largely unknown. Additionally, reasons underlying the selection of ester hydrolysis metabolites (EHMs) as urinary biomarkers are often unclear. Here, metabolic reaction phenotyping was performed to identify key metabolizing enzymes of the parent SCs. Hepatic clearances of parent SCs and their EHMs were estimated from microsomal metabolic stability studies. Renal clearances were simulated using a mechanistic kidney model incorporating in vitro permeability and organic anionic transporter 3 (OAT3)-mediated uptake data. Overall clearances were considered in tandem with estimated volumes of distribution for in vivo biological half-lives (t1/2) predictions. Interactions of the compounds with CB1 and CB2 were investigated using a G-protein coupled receptor activation assay. We demonstrated that similar enzymatic isoforms were implicated in the metabolism of 5F-MDMB-PINACA and 4F-MDMB-BINACA. Our in vivo t1/2 determinations verified the rapid elimination of parent SCs and suggest prolonged circulation of their EHMs. The pronounced attenuation of the potencies and efficacies of the metabolites against CB1 and CB2 further suggests how toxic manifestations of SC abuse are likely precipitated by augmented exposure to parent SCs. Notably, basolateral OAT3-mediated uptake of the EHMs substantiates their higher urinary abundance. These novel insights underscore the importance of mechanistic, quantitative and systematic characterization of PK–PD relationships in rationalizing the toxicities of SCs.

Lamotrigine is a phenyltriazine anticonvulsant that is primarily metabolized by phase II UDP-glucuronosyltransferases (UGT) to a quaternary N2-glucuronide, which accounts for ~ 90% of the excreted dose in humans. While there is consensus that UGT1A4 plays a predominant role in the formation of the N2-glucuronide, there is compelling evidence in the literature to suggest that the metabolism of lamotrigine is catalyzed by another UGT isoform. However, the exact identity of the UGT isoform that contribute to the formation of this glucuronide remains uncertain. In this study, we harnessed a robust reaction phenotyping strategy to delineate the identities and its associated fraction metabolized (f m ) of the UGTs involved in lamotrigine N2-glucuronidation. Foremost, human recombinant UGT mapping experiments revealed that the N2-glucuronide is catalyzed by multiple UGT isoforms. (i.e., UGT1A1, 1A3, 1A4, 1A9, 2B4, 2B7, and 2B10). Thereafter, scaling the apparent intrinsic clearances obtained from the enzyme kinetic experiments with our in-house liver-derived relative expression factors (REF) and relative activity factors (RAF) revealed that, in addition to UGT1A4, UGT2B10 was involved in the N2-glucuronidation of lamotrigine. This was further confirmed via chemical inhibition in human liver microsomes with the UGT1A4-selective inhibitor hecogenin and the UGT2B10-selective inhibitor desloratadine. By integrating various orthogonal approaches (i.e., REF- and RAF-scaling, and chemical inhibition), we quantitatively determined that the f m for UGT1A4 and UGT2B10 ranged from 0.42 – 0.64 and 0.32 – 0.57, respectively. Finally, we also provided nascent evidence that the pharmacokinetic interaction between lamotrigine and valproic acid likely arose from the in vivo inhibition of its UGT2B10-mediated pathway.

β‐eudesmol is a major bioactive component of Atractylodes lancea (AL). AL has been developed as the capsule formulation of standardized AL extract for treating cholangiocarcinoma (CCA). However, the complex constituents of herbal products increase the risk of adverse drug interactions. β‐eudesmol has demonstrated inhibitory effects on rCYP2C19 and rCYP3A4 in the previous research. This study aimed to identify the cytochrome P450 (CYP) isoforms responsible for the metabolism of β‐eudesmol and determine the enzyme kinetic parameters and the metabolic stability of β‐eudesmol metabolism in the microsomal system. Reaction phenotyping using human recombinant CYPs (rCYPs) and selective chemical inhibitors of CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 was performed, and enzyme kinetics and metabolic stability were investigated using human liver microsome (HLM). The results suggest that CYP2C19 and CYP3A4 play significant roles in β‐eudesmol metabolism. The disappearance half‐life (t1/2) and intrinsic clearance (CLint) of β‐eudesmol were 17.09 min and 0.20 mL/min·mg protein, respectively. Enzyme kinetic analysis revealed the Michaelis–Menten constant (Km) and maximum velocity (Vmax) of 16.76 μM and 3.35 nmol/min·mg protein, respectively. As a component of AL, β‐eudesmol, as a substrate and inhibitor of CYP2C19 and CYP3A4, has a high potential for drug–drug interactions when AL is co‐administered with other herbs or conventional medicines. CYP2C19 and CYP3A4 are major CYP enzymes in β‐eudesmol metabolism. As a component of AL, β‐eudesmol as a substrate and inhibitor of CYP2C19 and CYP3A4, has a high potential for drug‐drug interactions when AL is co‐administered with other herbs or conventional medicines.

Ataluren promotes ribosomal readthrough of premature termination codons in mRNA which result from nonsense mutations. In vitro studies were performed to characterize the metabolism and enzyme kinetics of ataluren and its interaction potential with CYP enzymes. Incubation of [14C]‐ataluren with human liver microsomes indicated that the major metabolic pathway for ataluren is via direct glucuronidation and that the drug is not metabolized via cytochrome P450 (CYP). Glucuronidation was also observed in the incubation in human intestinal and kidney microsomes, but not in human pulmonary microsomes. UGT1A9 was found to be the major uridine diphosphate glucuronosyltransferase (UGT) responsible for ataluren glucuronidation in the liver and kidney microsomes. Enzyme kinetic analysis of the formation of ataluren acyl glucuronide, performed in human liver, kidney, and intestinal microsomes and recombinant human UGT1A9, found that increasing bovine serum albumin (BSA) levels enhanced the glucuronidation Michaelis‐Menten constant (Km) and ataluren protein binding but had a minimal effect on maximum velocity (Vmax) of glucuronidation. Due to the decreased unbound Michaelis‐Menten constant (Km,u), the ataluren unbound intrinsic clearance (CLint,u) increased for all experimental systems and BSA concentrations. Human kidney microsomes were about 3.7‐fold more active than human liver microsomes, in terms of CLint,u/mg protein, indicating that the kidney is also a key organ for the metabolism and disposition of ataluren in humans. Ataluren showed no or little potential to inhibit or induce most of the CYP enzymes.

Genetic variants of drug metabolism enzymes and transporters can result in high pharmacokinetic and pharmacodynamic variability, unwanted characteristics of efficacious and safe drugs. Ideally, the contributions of these enzymes and transporters to drug disposition can be predicted from experiments and modeling in discovery or early development, and then be utilized during clinical development. Recently, regulatory agencies have provided guidance on the preclinical investigation of pharmacogenetics, for application to clinical drug development. This white paper summarizes the results of an industry survey conducted by the Industry Pharmacogenomics Working Group on current practice and challenges with using systems and models to understand pharmacogenetic causes of variability in drug disposition.

This chapter contains sections titled: Introduction Initial Considerations CYP Reaction Phenotyping Non‐P450 Reaction Phenotyping UGT Conjugation Reaction Phenotyping Reaction Phenotyping for Other Conjugation Reactions Integration of Reaction Phenotyping and Prediction of DDI Conclusion References

This chapter contains sections titled: Introduction Metabolic Clearance Metabolite Profiling/Mass Balance Studies Safety Testing of Drug Metabolites Reaction Phenotyping Assessment of Potential Toxicology of Metabolites Assessment of Potential for Active Metabolites Summary References

With increasing cohort size and phenotyping, GWAS have identified more than 70,000 associated variants1. Because as many as 90% of GWAS variants fall within non-coding regions, most of them have unknown functional importance2. [...]LocusCompare integrates eQTLs from 48 tissues in the GTEx study (version 7)3; eQTLs and splicing QTLs from coronary artery smooth muscle cells9 and retinal pigment epithelial cells10; and methylation QTLs from brain tissues11,12 and whole blood13. 'Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA. 6Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA. 7Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA.8Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. 9