Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network

Utility of physiologically based pharmacokinetic modeling in predicting and characterizing clinical drug interactions

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions (DDIs). With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late-stage drug development and is often used to support regulatory packages for new drug applications. This Minireview will briefly describe the approaches to predicting DDI using PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models, and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations, and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules. SIGNIFICANCE STATEMENT: Physiologically based pharmacokinetic (PBPK) models have been a key tool in the characterization of various pharmacokinetic phenomena, including drug-drug interactions. This Minireview will highlight recent advancements and publications around physiologically based pharmacokinetic drug-drug interaction modeling, an important area of drug discovery and development research in light of the increasing prevalence of polypharmacology in clinical settings.

Physiologically based pharmacokinetic modeling to predict the effect of risperidone on aripiprazole pharmacokinetics in subjects with different CYP2D6 genotypes and individuals with hepatic impairment

Background

Aripiprazole and risperidone, widely used atypical antipsychotics, are commonly adjunctively prescribed in clinical practice. When aripiprazole was combined with risperidone, the genotype of drug-metabolizing enzymes and liver impairment may lead to complex pharmacokinetic changes. The Physiologically Based Pharmacokinetic (PBPK) model can predict the influence of these factors on plasma concentration and optimize dosage regimens.

Objectives

This study aims to investigate the pharmacokinetic changes of aripiprazole caused by various influencing factors when it was co-administered with risperidone through PBPK models.

Design

The PBPK models of aripiprazole and risperidone were developed by gathering physicochemical parameters and drug-specific parameters. Then, by combining the inhibitory parameters, the enzymatic kinetic parameters of CYP2D6 genotypes, and the changes in anatomical and physiological parameters when liver function is damaged, the corresponding PBPK models were further established. Finally, this study put forward dosage optimization recommendations for situations where risks may exist.

Methods

The comparison between predicted and observed plasma concentration data, along with the assessment of pharmacokinetic parameters, was utilized to evaluate the fit performance of the models.

Results

The simulations of the PBPK model revealed that co-administration of risperidone did not result in significant changes in aripiprazole pharmacokinetics. However, in individuals with mild hepatic impairment and CYP2D6 normal metabolizer, a dose reduction of approximately 11% was advised when aripiprazole was combined with risperidone. When individuals with mild liver damage have CYP2D6 genotypes of intermediate metabolizer (IM) and poor metabolizer (PM), aripiprazole doses should be further reduced to 61% and 51%, respectively.

Conclusion

The co-administration of aripiprazole and risperidone is generally considered safe from a pharmacokinetic perspective. However, if individuals have a CYP2D6 genotype of IM or PM and/or if they have mild hepatic impairment, adjusting the dose of aripiprazole is advisable to mitigate potential risks when combining it with risperidone.

Drug transporters in drug disposition - highlights from the year 2023

Drug transporter field is rapidly evolving with significant progress in in vitro and in vivo tools and, computational models to assess transporter-mediated drug disposition and drug-drug interactions (DDIs) in humans. On behalf of all coauthors, I am pleased to share the fourth annual review highlighting articles published and deemed influential in the field of drug transporters in the year 2023. Each coauthor independently selected peer-reviewed articles published or available online in the year 2023 and summarized them as shown previously (Chothe et al. 2021; Chothe et al. 2022, 2023) with unbiased perspectives. Based on selected articles, this review was categorized into four sections: (1) transporter structure and in vitro evaluation, (2) novel in vitro/ex vivo models, (3) endogenous biomarkers, and (4) PBPK modeling for evaluating transporter DDIs (Table 1). As the scope of this review is not to comprehensively review each article, readers are encouraged to consult original paper for specific details. Finally, I appreciate all the authors for their time and continued support in writing this review.

Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine's Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes

Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ's nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent-metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus®. The model was utilized for Drug-Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ's nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUClast ratio was 0.49 (slightly outside of the two-fold range). CBZ's nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

Pharmacogenetic Influence on Stereoselective Steady-State Disposition of Bupropion

Bupropion is used for treating depression, obesity, and seasonal affective disorder, and for smoking cessation. Bupropion is commonly prescribed, but has complex pharmacokinetics and interindividual variability in metabolism and bioactivation may influence therapeutic response, tolerability, and safety. Bupropion is extensively and stereoselectively metabolized, the metabolites are pharmacologically active, and allelic variation in cytochrome P450 (CYP) 2B6 affects clinical hydroxylation of single-dose bupropion. Genetic effects on stereoselective disposition of steady-state bupropion are not known. In this preplanned secondary analysis of a prospective, randomized, double-blinded, crossover study which compared brand and generic bupropion XL 300 mg drug products, we measured steady-state enantiomeric plasma and urine parent bupropion and primary and secondary metabolite concentrations. This investigation evaluated the influence of genetic polymorphisms in CYP2B6, CYP2C19, and P450 oxidoreductase on the disposition of Valeant Pharmaceuticals Wellbutrin brand bupropion in 67 participants with major depressive disorder. We found that hydroxylation of both bupropion enantiomers was lower in carriers of the CYP2B6*6 allele and in carriers of the CYP2B6 516G>T variant, with correspondingly greater bupropion and lesser hydroxybupropion plasma concentrations. Hydroxylation was 25-50% lower in CYP2B6*6 carriers and one-third to one-half less in 516T carriers. Hydroxylation of the bupropion enantiomers was comparably affected by CYP2B6 variants. CYP2C19 polymorphisms did not influence bupropion plasma concentrations or hydroxybupropion formation but did influence the minor pathway of 4'-hydroxylation of bupropion and primary metabolites. P450 oxidoreductase variants did not influence bupropion disposition. Results show that CYP2B6 genetic variants affect steady-state metabolism and bioactivation of Valeant brand bupropion, which may influence therapeutic outcomes. SIGNIFICANCE STATEMENT: Bupropion, used for depression, obesity, and smoking cessation, undergoes metabolic bioactivation, with incompletely elucidated interindividual variability. We evaluated cytochrome P450 (CYP) 2B6, CYP2C19 and P450 oxidoreductase genetic variants and steady-state bupropion and metabolite enantiomers disposition. Both enantiomers hydroxylation was lower in CYP2B6*6 and CYP2B6 516G>T carriers, with greater bupropion and lesser hydroxybupropion plasma concentrations. CYP2C19 polymorphisms did not affect bupropion or hydroxybupropion but did influence minor 4'-hydroxylation of bupropion and primary metabolites. CYP2B6 variants affect steady-state bupropion bioactivation, which may influence therapeutic outcomes.

[Exploring the therapeutic mechanism of Liuwei Suanzao decoction for perimenopausal insomnia based on network pharmacology and animal experiments]

OBJECTIVE

To explore the therapeutic mechanism of Liuwei Suanzao decoction (LWSZD) for perimenopausal insomnia (PI) based on network pharmacology.

METHODS

TCMSP and Batman-TCM databases were searched for the active ingredients and targets of LWSZD and a herb-active ingredient-target network was constructed, and the disease targets were obtained from the OMIM, Genecards and Gene databases.The common targets were imported into STRING database and Cytoscape software to screen the core therapeutic targets, and GO enrichment and KEGG pathway analyses were performed using DAVID database.Molecular docking of the main active ingredients of LWSZD and the core targets was conducted using AutoDock, and the results were verified by observing the therapeutic effects of LWSZD and zolpidem in a rat model of PI induced by bilateral ovariectomy and intraperitoneal p-chlorophenylalanine injection.

RESULTS

A total of 99 active ingredients, 389 drug targets, 187 PI-related targets, and 15 drug-PI common targets were screened.The core active ingredients were armepavine, sanjoinenine and mairin, and the core targets included ESR1, SIRT1, SERPINE1, COMT and CCL2, which were involved in the positive regulation of transcription from RNA polymerase II promoter, signal transduction, response to drug and positive regulation of transcription and in the pathways of dopaminergic synapses, tyrosine metabolism and tryptophan metabolism.Molecular docking results showed that LWSZD had a strong binding with ESR1, SIRT1 and SERPINE1 and was comparable to zolpidem.In the rat models of PI, treatment with LWSZD effectively alleviated the symptoms of insomnia (P<0.01), improved the levels of estrogen and other HPO axis-related hormones (P<0.05), and promoted the mRNA and protein expressions of ESR1 and SIRT1 in the hypothalamus tissues (P<0.01).

CONCLUSION

The active ingredients armepavine, sanjoinenine and mairin in LWSZD may synergistically regulate the expressions of ESR1, SIRT1 and SERPINE1 to improve PI in rats.

Physiologically based pharmacokinetic (PBPK) modeling of flurbiprofen in different CYP2C9 genotypes

The aim of this study was to establish the physiologically based pharmacokinetic (PBPK) model of flurbiprofen related to CYP2C9 genetic polymorphism and describe the pharmacokinetics of flurbiprofen in different CYP2C9 genotypes. PK-Sim® software was used for the model development and validation. A total of 16 clinical pharmacokinetic data for flurbiprofen in different CYP2C9 genotypes, dose regimens, and age groups were used for the PBPK modeling. Turnover number (k_cat) of CYP2C9 values were optimized to capture the observed profiles in different CYP2C9 genotypes. In the simulation, predicted fraction metabolized by CYP2C9, fraction excreted to urine, bioavailability, and volume of distribution were similar to previously reported values. Predicted plasma concentration-time profiles in different CYP2C9 genotypes were visually similar to the observed profiles. Predicted AUC_inf in CYP2C9*1/*2, CYP2C9*1/*3, and CYP2C9*3/*3 genotypes were 1.44-, 2.05-, and 3.67-fold higher than the CYP2C9*1/*1 genotype. The ranges of fold errors for AUC_inf, C_max, and t_1/2 were 0.84-1.00, 0.61-1.22, and 0.74-0.94 in development and 0.59-0.98, 0.52-0.97, and 0.61-1.52 in validation, respectively, which were within the acceptance criterion. Thus, the PBPK model was successfully established and described the pharmacokinetics of flurbiprofen in different CYP2C9 genotypes, dose regimens, and age groups. The present model could guide the decision-making of tailored drug administration strategy by predicting the pharmacokinetics of flurbiprofen in various clinical scenarios.

Quantitative Prediction of Drug Interactions Caused by Cytochrome P450 2B6 Inhibition or Induction

BACKGROUND

Numerous drugs have the potential to be affected by cytochrome P450 (CYP) 2B6-mediated drug-drug interactions (DDIs).

OBJECTIVES

In this work, we extend a static approach to the prediction of the extent of pharmacokinetics DDIs between substrates and inhibitors or inducers of CYP2B6.

METHODS

This approach is based on the calculation of two parameters (the contribution ratio [CR], representing the fraction of dose of the substrate metabolized via this pathway and the inhibitory or inducing potency of the perpetrator [IR or IC, respectively]) calculated from the area under the concentration-time curve (AUC) ratios obtained in in-vivo DDI studies.

RESULTS

Forty-eight studies involving 5 substrates, 11 inhibitors and 18 inducers of CYP2B6 (overall 15 inhibition and 33 induction studies) were divided into test and validation sets and considered for estimation of the parameters. The proposed approach demonstrated a fair accuracy for predicting the extent of DDI related to CYP2B6 inhibition and induction, all predictions related to the validation test (N = 18) being 50-200% of the observed ratios.

CONCLUSIONS

This methodology can be used for proposing initial dose adaptations to be adopted, for example in clinical use or for designing DDI studies involving this enzyme.

Physiologically Based Pharmacokinetic (PBPK) Modeling of Clopidogrel and Its Four Relevant Metabolites for CYP2B6, CYP2C8, CYP2C19, and CYP3A4 Drug–Drug–Gene Interaction Predictions

The antiplatelet agent clopidogrel is listed by the FDA as a strong clinical index inhibitor of cytochrome P450 (CYP) 2C8 and weak clinical inhibitor of CYP2B6. Moreover, clopidogrel is a substrate of—among others—CYP2C19 and CYP3A4. This work presents the development of a whole-body physiologically based pharmacokinetic (PBPK) model of clopidogrel including the relevant metabolites, clopidogrel carboxylic acid, clopidogrel acyl glucuronide, 2-oxo-clopidogrel, and the active thiol metabolite, with subsequent application for drug–gene interaction (DGI) and drug–drug interaction (DDI) predictions. Model building was performed in PK-Sim^® using 66 plasma concentration-time profiles of clopidogrel and its metabolites. The comprehensive parent-metabolite model covers biotransformation via carboxylesterase (CES) 1, CES2, CYP2C19, CYP3A4, and uridine 5′-diphospho-glucuronosyltransferase 2B7. Moreover, CYP2C19 was incorporated for normal, intermediate, and poor metabolizer phenotypes. Good predictive performance of the model was demonstrated for the DGI involving CYP2C19, with 17/19 predicted DGI AUC_last and 19/19 predicted DGI C_max ratios within 2-fold of their observed values. Furthermore, DDIs involving bupropion, omeprazole, montelukast, pioglitazone, repaglinide, and rifampicin showed 13/13 predicted DDI AUC_last and 13/13 predicted DDI C_max ratios within 2-fold of their observed ratios. After publication, the model will be made publicly accessible in the Open Systems Pharmacology repository.

Novel models for the prediction of drug-gene interactions

INTRODUCTION

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

AREAS COVERED

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

EXPERT OPINION

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.