Insights into pharmacogenetics, drug-gene interactions, and drug-drug-gene interactions.

This review explores genetic contributors to drug interactions, known as drug-gene and drug-drug-gene interactions (DGI and DDGI, respectively). This article is part of a mini-review issue led by the International Society for the Study of Xenobiotics (ISSX) New Investigators Group. Pharmacogenetics (PGx) is the study of the impact of genetic variation on pharmacokinetics (PK), pharmacodynamics (PD), and adverse drug reactions. Genetic variation in pharmacogenes, including drug metabolizing enzymes and drug transporters, is common and can increase the risk of adverse drug events or contribute to reduced efficacy. In this review, we summarize clinically actionable genetic variants, and touch on methodologies such as genotyping patient DNA to identify genetic variation in targeted genes, and deep mutational scanning as a high-throughput in vitro approach to study the impact of genetic variation on protein function and/or expression in vitro. We highlight the utility of physiologically based pharmacokinetic (PBPK) models to integrate genetic and chemical inhibitor and inducer data for more accurate human PK simulations. Additionally, we analyze the limitations of historical ethnic descriptors in pharmacogenomics research. Altogether, the work herein underscores the importance of identifying and understanding complex DGI and DDGIs with the intention to provide better treatment outcomes for patients. We also highlight current barriers to wide-scale implementation of PGx-guided dosing as standard or care in clinical settings.

Transporter-mediated drug-drug interactions: regulatory guidelines, in vitro and in vivo methodologies and translation, special populations, and the blood-brain barrier.

This review, part of a special issue on drug-drug interactions (DDIs) spearheaded by the International Society for the Study of Xenobiotics (ISSX) New Investigators, explores the critical role of drug transporters in absorption, disposition, and clearance in the context of DDIs. Over the past two decades, significant advances have been made in understanding the clinical relevance of these transporters. Current knowledge on key uptake and efflux transporters that affect drug disposition and development is summarized. Regulatory guidelines from the FDA, EMA, and PMDA that inform the evaluation of potential transporter-mediated DDIs are discussed in detail. Methodologies for preclinical and clinical testing to assess potential DDIs are reviewed, with an emphasis on the utility of physiologically based pharmacokinetic (PBPK) modeling. This includes the application of relative abundance and expression factors to predict human pharmacokinetics (PK) using preclinical data, integrating the latest regulatory guidelines. Considerations for assessing transporter-mediated DDIs in special populations, including pediatric, hepatic, and renal impairment groups, are provided. Additionally, the impact of transporters at the blood-brain barrier (BBB) on the disposition of CNS-related drugs is explored. Enhancing the understanding of drug transporters and their role in drug disposition and toxicity can improve efficacy and reduce adverse effects. Continued research is essential to bridge remaining gaps in knowledge, particularly in comparison with cytochrome P450 (CYP) enzymes.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic.

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Assessing and mitigating pH-mediated DDI risks in drug development - formulation approaches and clinical considerations.

pH-mediated drug-drug interactions (DDI) is a prevalent DDI in drug development, especially for weak base compounds with highly pH-dependent solubility. FDA has released a guidance on the evaluation of pH-mediated DDI assessments using in vitro testing and clinical studies. Currently, there is no common practice of ways of testing across the academia and industry. The development of biopredictive method and physiologically-based biopharmaceutics modeling (PBBM) approaches to assess acid-reducing agent (ARA)-DDI have been proven with accurate prediction and could decrease drug development burden, inform clinical design and potentially waive clinical studies. Formulation strategies and careful clinical design could help mitigate the pH-mediated DDI to avoid more clinical studies and label restrictions, ultimately benefiting the patient. In this review paper, a detailed introduction on biorelevant dissolution testing, preclinical and clinical study requirement and PBPK modeling approaches to assess ARA-DDI are described. An improved decision tree for pH-mediated DDI is proposed. Potential mitigations including clinical or formulation strategies are discussed.

Epigenetics in drug disposition & drug therapy: symposium report of the 24th North American meeting of the International Society for the Study of Xenobiotics (ISSX).

The 24th North American International Society for the Study of Xenobiotics (ISSX) meeting, held virtually from September 13 to 17, 2021, embraced the theme of "Broadening Our Horizons." This reinforces a key mission of ISSX: striving to share innovative science related to drug discovery and development. Session speakers and the ISSX New Investigators Group, which supports the scientific and professional development of student and early career ISSX members, elected to highlight the scientific content presented during the captivating session titled, "Epigenetics in Drug Disposition & Drug Therapy." The impact genetic variation has on drug response is well established; however, this session underscored the importance of investigating the role of epigenetics in drug disposition and drug discovery. Session speakers, Drs. Ning, McClay, and Lazarus, detailed mechanisms by which epigenetic players including long non-coding RNA (lncRNAs), microRNA (miRNAs), DNA methylation, and histone acetylation can alter the expression of genes involved in pharmacokinetics, pharmacodynamics, and toxicity. Dr. Ning detailed current knowledge about miRNAs and lncRNAs and the mechanisms by which they can affect the expression of drug metabolizing enzymes (DMEs) and nuclear receptors. Dr. Lazarus discussed the potential role of miRNAs on UDP-glucuronosyltransferase (UGT) expression and activity. Dr. McClay provided evidence that aging alters methylation and acetylation of DMEs in the liver, affecting gene expression and activity. These topics, compiled by the symposium organizers, presenters, and the ISSX New Investigators Group, are herein discussed, along with exciting future perspectives for epigenetics in drug disposition and drug discovery research.

Using partition analysis as a facile method to derive net clearances.

Partition analysis has been described previously by W.W. Cleland to derive net rate constants and simplify the derivation of enzyme kinetic equations. Here, we show that partition analysis can be used to derive elimination and transfer (distribution) net clearances for use in pharmacokinetic models. For elimination clearances, the net clearance approach is exemplified with a mammillary two-compartment model with peripheral elimination, and the established well-stirred and full hepatic clearance models. The intrinsic hepatic clearance associated with an observed average hepatic clearance can be easily calculated with net clearances. Expressions for net transfer clearances are easily derived, including models with explicit membranes (e.g., monolayer permeability and blood-brain barrier models). Together, these approaches can be used to derive equations for physiologically based and hybrid compartmental/ physiologically based models. This tutorial describes how net clearances can be used to derive relationships for simple models as well as increasingly complex models, such as inclusion of active transport and target mediated processes.

Numerical Methods for Modeling Enzyme Kinetics.

Differential equations are used to describe time-dependent changes in enzyme kinetics and pharmacokinetics. Analytical and numerical methods can be used to solve differential equations. This chapter describes the use of numerical methods in solving differential equations and its applications in characterizing the complexities observed in enzyme kinetics. A discussion is included on the use of numerical methods to overcome limitations of explicit equations in the analysis of metabolism kinetics, reversible inhibition kinetics, and inactivation kinetics. The chapter describes the advantages of using numerical methods when Michaelis-Menten assumptions do not hold.

Recent developments in in vitro and in vivo models for improved translation of preclinical pharmacokinetics and pharmacodynamics data.

Improved pharmacokinetics/pharmacodynamics (PK/PD) prediction in the early stages of drug development is essential to inform lead optimization strategies and reduce attrition rates. Recently, there have been significant advancements in the development of new in vitro and in vivo strategies to better characterize pharmacokinetic properties and efficacy of drug leads. Herein, we review advances in experimental and mathematical models for clearance predictions, advancements in developing novel tools to capture slowly metabolized drugs, in vivo model developments to capture human etiology for supporting drug development, limitations and gaps in these efforts, and a perspective on the future in the field.

Mechanisms of Herb-Drug Interactions Involving Cinnamon and CYP2A6: Focus on Time-Dependent Inhibition by Cinnamaldehyde and 2-Methoxycinnamaldehyde.

Information is scarce regarding pharmacokinetic-based herb-drug interactions (HDI) with trans-cinnamaldehyde (CA) and 2-methoxycinnamaldehyde (MCA), components of cinnamon. Given the presence of cinnamon in food and herbal treatments for various diseases, HDIs involving the CYP2A6 substrates nicotine and letrozole with MCA (KS = 1.58 µM; Hill slope = 1.16) and CA were investigated. The time-dependent inhibition (TDI) by MCA and CA of CYP2A6-mediated nicotine metabolism is a complex process involving multiple mechanisms. Molecular dynamic simulations showed that CYP2A6's active site accommodates two dynamic ligands. The preferred binding orientations for MCA and CA were consistent with the observed metabolism: epoxidation, O-demethylation, and aromatic hydroxylation of MCA and cinnamic acid formation from CA. The percent remaining activity plots for TDI by MCA and CA were curved, and they were analyzed with a numerical method using models of varying complexity. The best-fit models support multiple inactivator binding, inhibitor depletion, and partial inactivation. Deconvoluted mass spectra indicated that MCA and CA modified CYP2A6 apoprotein with mass additions of 156.79 (142.54-171.04) and 132.67 (123.37-141.98), respectively, and it was unaffected by glutathione. Heme degradation was observed in the presence of MCA (48.5% ± 13.4% loss; detected by liquid chromatography-tandem mass spectrometry). In the absence of clinical data, HDI predictions were made for nicotine and letrozole using inhibition parameters from the best-fit TDI models and parameters scaled from rats. Predicted area under the concentration-time curve fold changes were 4.29 (CA-nicotine), 4.92 (CA-letrozole), 4.35 (MCA-nicotine), and 5.00 (MCA-letrozole). These findings suggest that extensive exposure to cinnamon (corresponding to ≈ 275 mg CA) would lead to noteworthy interactions. SIGNIFICANCE STATEMENT: Human exposure to cinnamon is common because of its presence in food and cinnamon-based herbal treatments. Little is known about the risk for cinnamaldehyde and methoxycinnamaldehyde, two components of cinnamon, to interact with drugs that are eliminated by CYP2A6-mediated metabolism. The interactions with CYP2A6 are complex, involving multiple-ligand binding, time-dependent inhibition of nicotine metabolism, heme degradation, and apoprotein modification. An herb-drug interaction prediction suggests that extensive exposure to cinnamon would lead to noteworthy interactions with nicotine.

Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions.

Cytochrome P450 (CYP) enzyme kinetics often do not conform to Michaelis-Menten assumptions, and time-dependent inactivation (TDI) of CYPs displays complexities such as multiple substrate binding, partial inactivation, quasi-irreversible inactivation, and sequential metabolism. Additionally, in vitro experimental issues such as lipid partitioning, enzyme concentrations, and inactivator depletion can further complicate the parameterization of in vitro TDI. The traditional replot method used to analyze in vitro TDI datasets is unable to handle complexities in CYP kinetics, and numerical approaches using ordinary differential equations of the kinetic schemes offer several advantages. Improvement in the parameterization of CYP in vitro kinetics has the potential to improve prediction of clinical drug-drug interactions (DDIs). This manuscript discusses various complexities in TDI kinetics of CYPs, and numerical approaches to model these complexities. The extrapolation of CYP in vitro TDI parameters to predict in vivo DDIs with static and dynamic modeling is discussed, along with a discussion on current gaps in knowledge and future directions to improve the prediction of DDI with in vitro data for CYP catalyzed drug metabolism.

Impact of Lipid Partitioning on the Design, Analysis, and Interpretation of Microsomal Time-Dependent Inactivation.

Nonspecific drug partitioning into microsomal membranes must be considered for in vitro-in vivo correlations. This work evaluated the effect of including lipid partitioning in the analysis of complex TDI kinetics with numerical methods. The covariance between lipid partitioning and multiple inhibitor binding was evaluated. Simulations were performed to test the impact of lipid partitioning on the interpretation of TDI kinetics, and experimental TDI datasets for paroxetine (PAR) and itraconazole (ITZ) were modeled. For most kinetic schemes, modeling lipid partitioning results in statistically better fits. For MM-IL simulations (KI,u = 0.1 µM, kinact = 0.1 minute-1), concurrent modeling of lipid partitioning for an fumic range (0.01, 0.1, and 0.5) resulted in better fits compared with post hoc correction (AICc: -526 vs. -496, -579 vs. -499, and -636 vs. -579, respectively). Similar results were obtained with EII-IL. Lipid partitioning may be misinterpreted as double binding, leading to incorrect parameter estimates. For the MM-IL datasets, when fumic = 0.02, MM-IL, and EII model fits were indistinguishable (δAICc = 3). For less partitioned datasets (fumic = 0.1 or 0.5), the inclusion of partitioning resulted in better models. The inclusion of lipid partitioning can lead to markedly different estimates of KI,u and kinact A reasonable alternate experimental design is nondilution TDI assays, with post hoc fumic incorporation. The best fit models for PAR (MIC-M-IL) and ITZ (MIC-EII-M-IL and MIC-EII-M-Seq-IL) were consistent with their reported mechanism and kinetics. Overall, experimental fumic values should be concurrently incorporated into TDI models with complex kinetics, when dilution protocols are used.

Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A.

Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min-1, the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min-1. These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

Mechanism-Based Inhibition of CYP3A4 by Podophyllotoxin: Aging of an Intermediate Is Important for in Vitro/in Vivo Correlations.

An in vitro observation of time-dependent inhibition (TDI) of metabolic enzymes often results in removing a potential drug from the drug pipeline. However, the accepted method for predicting TDIs of the important drug metabolizing cytochrome P450 enzymes often overestimates the drug interaction potential. Better models that take into account the complexities of the cytochrome P450 enzyme system will lead to better predictions. Herein we report the use of our previously described models for complex kinetics of podophyllotoxin. Spectral characterization of the kinetics indicates that an intermediate MI complex is formed, which slowly progresses to an essentially irreversible MI complex. The intermediate MI complex can release free enzyme during the time course of a typical 30 min TDI experiment. This slow rate of MI complex conversion results in an overprediction of the kinact value if this process is not included in the analysis of the activity versus time profile. In vitro kinetic experiments in rat liver microsomes predicted a lack of drug interaction between podophyllotoxin and midazolam. In vivo rat pharmacokinetic studies confirmed this lack of drug interaction.