Prediction of in vivo drug-drug interactions from in vitro data: impact of incorporating parallel pathways of drug elimination and inhibitor absorption rate constant

Quantitative prediction of CYP3A induction-mediated drug-drug interactions in clinical practice

There have been no reports on the quantitative prediction of CYP3A induction-mediated decreases in AUC and Cmax for drug candidates identified as a "victims" of CYP3A induction. Our previous study separately evaluated the fold-induction of hepatic and intestinal CYP3A by known inducers using clinical induction data and revealed that we were able to quantitatively predict the AUC ratio (AUCR) of a few CYP3A substrates in the presence and absence of CYP3A inducers. In the present study, we investigate the predictability of AUCR and also Cmax ratio (CmaxR) in additional 54 clinical studies. The fraction metabolized by CYP3A (fm), the intestinal bioavailability (Fg), and the hepatic intrinsic clearance (CLint) of substrates were determined by the in vitro experiments as well as clinical data used for calculating AUCR and CmaxR. The result showed that 65-69% and 65-67% of predictions were within 2-fold of observed AUCR and CmaxR, respectively. A simulation using multiple parameter combinations suggested that the variability of fm and Fg within a certain range might have a minimal impact on the calculation output. These findings suggest that clinical AUCR and CmaxR of CYP3A substrates can be quantitatively predicted from the preclinical stage.

Clinical Trial Data-Driven Risk Assessment of Drug-Drug Interactions: A Rapid and Accurate Decision-Making Tool

BACKGROUND

In clinical practice, the vast array of potential drug combinations necessitates swift and accurate assessments of pharmacokinetic drug-drug interactions (DDIs), along with recommendations for adjustments. Current methodologies for clinical DDI evaluations primarily rely on basic extrapolations from clinical trial data. However, these methods are limited in accuracy owing to their lack of a comprehensive consideration of various critical factors, including the inhibitory potency, dosage, and type of the inhibitor, as well as the metabolic fraction and intestinal availability of the substrate.

OBJECTIVE

This study aims to propose an efficient and accurate clinical pharmacokinetic-mediated DDI assessment tool, which comprehensively considers the effects of inhibitory potency and dosage of inhibitors, intestinal availability and fraction metabolized of substrates on DDI outcomes.

METHODS

This study focuses on DDIs caused by cytochrome P450 3A4 enzyme inhibition, utilizing extensive clinical trial data to establish a methodology to calculate the metabolic fraction and intestinal availability for substrates, as well as the concentration and inhibitory potency for inhibitors ( K i ork inact / K I ). These parameters were then used to predict the outcomes of DDIs involving 33 substrates and 20 inhibitors. We also defined the risk index for substrates and the potency index for inhibitors to establish a clinical DDI risk scale. The training set for parameter calculation consisted of 73 clinical trials. The validation set comprised 89 clinical DDI trials involving 53 drugs. which was used to evaluate the reliability of in vivo values of K i andk inact / K I , the accuracy of DDI predictions, and the false-negative rate of risk scale.

RESULTS

First, the reliability of the in vivo K i andk inact / K I values calculated in this study was assessed using a basic static model. Compared with values obtained from other methods, this study values showed a lower geometric mean fold error and root mean square error. Additionally, incorporating these values into the physiologically based pharmacokinetic-DDI model facilitated a good fitting of the C-t curves when the substrate's metabolic enzymes are inhibited. Second, area under the curve ratio predictions of studied drugs were within a 1.5 × margin of error in 81% of cases compared with clinical observations in the validation set. Last, the clinical DDI risk scale developed in this study predicted the actual risks in the validation set with only a 5.6% incidence of serious false negatives.

CONCLUSIONS

This study offers a rapid and accurate approach for assessing the risk of pharmacokinetic-mediated DDIs in clinical practice, providing a foundation for rational combination drug use and dosage adjustments.

An Artificial Gut/Absorption Simulator: Understanding the Impact of Absorption on In Vitro Dissolution, Speciation, and Precipitation of Amorphous Solid Dispersions

Upon dissolution, amorphous solid dispersions (ASDs) of poorly water-soluble compounds can generate supersaturated solutions consisting of bound and free drug species that are in dynamic equilibrium with each other. Only free drug is available for absorption. Drug species bound to bile micelles, polymer excipients, and amorphous and crystalline precipitate can reduce the drug solute's activity to permeate, but they can also serve as reservoirs to replenish free drug in solution lost to absorption. However, with multiple processes of dissolution, absorption, and speciation occurring simultaneously, it may become challenging to understand which processes lead to an increase or decrease in drug solution concentration. Closed, nonsink dissolution testing methods used routinely, in the absence of drug removal, allow only for static equilibrium to exist and obscure the impact of each drug species on absorption. An artificial gut simulator (AGS) introduced recently consists of a hollow fiber-based absorption module and allows mass transfer of the drug from the dissolution media at a physiological rate after tuning the operating parameters. In the present work, ASDs of varying drug loadings were prepared with a BCS-II model compound, ketoconazole (KTZ), and hypromellose acetate succinate (HPMCAS) polymer. Simultaneous dissolution and absorption testing of the ASDs was conducted with the AGS, and simple analytical techniques were utilized to elucidate the impact of bound drug species on absorption. In all cases, a lower amount of crystalline precipitate was formed in the presence of absorption relative to the nonsink dissolution "control". However, formation of HPMCAS-bound drug species and crystalline precipitate significantly reduced KTZ absorption. Moreover, at high drug loading, inclusion of an absorption module was shown to enhance ASD dissolution. The rank ordering of the ASDs with respect to dissolution was significantly different when nonsink dissolution versus AGS was used, and this discrepancy could be mechanistically elucidated by understanding drug dissolution and speciation in the presence of absorption.

Studying the Interaction between Bendamustine and DNA Molecule with SERS Based on AuNPs/ZnCl2/NpAA Solid-State Substrate

Bendamustine (BENDA) is a bifunctional alkylating agent with alkylating and purinergic antitumor activity, which exerts its anticancer effects by direct binding to DNA, but the detailed mechanism of BENDA-DNA interaction is poorly understood. In this paper, the interaction properties of the anticancer drug BENDA with calf thymus DNA (ctDNA) were systematically investigated based on surface-enhanced Raman spectroscopy (SERS) technique mainly using a novel homemade AuNPs/ZnCl2/NpAA (NpAA: nano porous anodic alumina) solid-state substrate and combined with ultraviolet-visible spectroscopy and molecular docking simulation to reveal the mechanism of their interactions. We experimentally compared and studied the SERS spectra of ctDNA, BENDA, and BENDA-ctDNA complexes with different molar concentrations (1:1, 2:1, 3:1), and summarized their important characteristic peak positions, their peak position differences, and hyperchromic/hypochromic effects. The results showed that the binding modes include covalent binding and hydrogen bonding, and the binding site of BENDA to DNA molecules is mainly the N7 atom of G base. The results of this study help to understand and elucidate the mechanism of BENDA at the single-molecule level, and provide guidance for the further development of effective new drugs with low toxicity and side effects.

Species Specificity and Selection of Models for Drug Oxidations Mediated by Polymorphic Human Enzymes

Many drug oxygenations are mainly mediated by polymorphic cytochromes P450 (P450s) and also by flavin-containing monooxygenases (FMOs). More than 50 years of research on P450/FMO-mediated drug oxygenations have clarified their catalytic roles. The natural product coumarin causes hepatotoxicity in rats via the reactive coumarin 3,4-epoxide, a reaction catalyzed by P450 1A2; however, coumarin undergoes rapid 7-hydroxylation by polymorphic P450 2A6 in humans. The primary oxidation product of the teratogen thalidomide in rats is deactivated 5'-hydroxythalidomide plus sulfate and glucuronide conjugates; however, similar 5'-hydroxythalidomide and 5-hydroxythalidomide are formed in rabbits in vivo. Thalidomide causes human P450 3A enzyme induction in liver (and placenta) and is also activated in vitro and in vivo by P450 3A through the primary human metabolite 5-hydroxythalidomide (leading to conjugation with glutathione/nonspecific proteins). Species differences exist in terms of drug metabolism in rodents and humans, and such differences can be very important when determining the contributions of individual enzymes. The approaches used for investigating the roles of human P450 and FMO enzymes in understanding drug oxidations and clinical therapy have not yet reached maturity and still require further development. SIGNIFICANCE STATEMENT: Drug oxidations in animals and humans mediated by P450s and FMOs are important for understanding the pharmacological properties of drugs, such as the species-dependent teratogenicity of the reactive metabolites of thalidomide and the metabolism of food-derived odorous trimethylamine to non-odorous (but proatherogenic) trimethylamine N-oxide. Recognized differences exist in terms of drug metabolism between rodents, non-human primates, and humans, and such differences are important when determining individual liver enzyme contributions with substrates in in vitro and in vivo systems.

An Artificial Gut/Absorption Simulator: Simultaneous Evaluation of Desupersaturation and Absorption from Ketoconazole Supersaturated Solutions

For supersaturating formulations of BCS-II compounds, which by definition have high intestinal permeability, a closed USP apparatus does not provide the necessary absorptive conditions during dissolution. To address this, an artificial gut simulator (AGS) has been constructed consisting of a 2.5 mL donor compartment in which a hollow fiber-based absorption module is suspended. Drug from donor diffuses across the hollow fiber membrane to be absorbed by the continuously flowing intraluminal receiver fluid. The membrane surface area and intraluminal fluid flow rate are tuned to obtain the physiologically observed absorption rate constant for a weakly basic, poorly water-soluble model compound, ketoconazole (KTZ). Supersaturated solutions of KTZ were generated in the donor in pH 6.5 phosphate buffer by the pH-shift method in the absence (closed system, control) and presence (open system, biorelevant) of an optimally or suboptimally tuned absorption module. Drug concentrations in the donor and intraluminal fluids were determined by in-line UV spectroscopy. The presence of an absorptive sink reduced the supersaturated solution's crystallization propensity, more so in the case of the optimally tuned AGS. This study demonstrates the significance of simulating absorption of drug at a physiological rate during dissolution studies, especially to predict the performance of formulations of BCS-II drugs.

Roles of human cytochrome P450 1A2 in coumarin 3,4-epoxidation mediated by untreated hepatocytes and by those metabolically inactivated with furafylline in previously transplanted chimeric mice

Coumarin is a naturally occurring component of food products but is of clinical interest for its potential hepatotoxicity in humans. In the current study, the pharmacokinetics of coumarin in humanized-liver mice after oral and intravenous administrations (30 mg/kg) were investigated for its transformations to metabolically active coumarin 3,4-epoxide (as estimated by the levels of o-hydroxyphenylacetic acid) and to excretable 7-hydroxycoumarin. After oral administration, control mice metabolized coumarin to o-hydroxyphenylacetic acid at roughly the same rate as that to 7-hydroxycoumarin (total of unconjugated and conjugated forms). In contrast, the in vivo biotransformation of coumarin to o-hydroxyphenylacetic acid by humanized-liver mice was around two orders of magnitude less than that to conjugated and unconjugated 7-hydroxycoumarin. After intravenous administrations of coumarin, differences were observed in the plasma concentrations of o-hydroxyphenylacetic acid between humanized-liver mice treated with furafylline (daily oral doses of 13 mg/kg for 3 days) and untreated humanized-liver mice. The mean values of the areas under the plasma concentration versus time curves and the maximum concentrations for o-hydroxyphenylacetic acid were significantly lower in the group treated with furafylline (45% and 57% of the untreated values, respectively). These results suggested that the metabolic activation of coumarin in humans was mediated mainly by P450 1A2, which was suppressed by furafylline, and that humanized-liver mice orally treated with furafylline might constitute an in vivo model for metabolically inactivated P450 1A2 in human hepatocytes transplanted into chimeric mice.

Cytochrome P450 enzymes: a review on drug metabolizing enzyme inhibition studies in drug discovery and development

Assessment of drug candidate's potential to inhibit cytochrome P450 (CYP) enzymes remains crucial in pharmaceutical drug discovery and development. Both direct and time-dependent inhibition of drug metabolizing CYP enzymes by the concomitant administered drug is the leading cause of drug-drug interactions (DDIs), resulting in the increased toxicity of the victim drug. In this context, pharmaceutical companies have grown increasingly diligent in limiting CYP inhibition liabilities of drug candidates in the early stages and examining risk assessments throughout the drug development process. This review discusses different strategies and decision-making processes for assessing the drug-drug interaction risks by enzyme inhibition and lays particular emphasis on in vitro study designs and interpretation of CYP inhibition data in a stage-appropriate context.

Model-Based Drug-Drug Interaction Extrapolation Strategy From Adults to Children: Risdiplam in Pediatric Patients With Spinal Muscular Atrophy

Risdiplam (Evrysdi) improves motor neuron function in patients with spinal muscular atrophy (SMA) and has been approved for the treatment of patients ≥2 months old. Risdiplam exhibits time‐dependent inhibition of cytochrome P450 (CYP) 3A in vitro. While many pediatric patients receive risdiplam, a drug–drug interaction (DDI) study in pediatric patients with SMA was not feasible. Therefore, a novel physiologically‐based pharmacokinetic (PBPK) model‐based strategy was proposed to extrapolate DDI risk from healthy adults to children with SMA in an iterative manner. A clinical DDI study was performed in healthy adults at relevant risdiplam exposures observed in children. Risdiplam caused an 1.11‐fold increase in the ratio of midazolam area under the curve with and without risdiplam (AUCR)), suggesting an 18‐fold lower in vivo CYP3A inactivation constant compared with the in vitro value. A pediatric PBPK model for risdiplam was validated with independent data and combined with a validated midazolam pediatric PBPK model to extrapolate DDI from adults to pediatric patients with SMA. The impact of selected intestinal and hepatic CYP3A ontogenies on the DDI susceptibility in children relative to adults was investigated. The PBPK analysis suggests that primary CYP3A inhibition by risdiplam occurs in the intestine rather than the liver. The PBPK‐predicted risdiplam CYP3A inhibition risk in pediatric patients with SMA aged 2 months–18 years was negligible (midazolam AUCR of 1.09–1.18) and included in the US prescribing information of risdiplam. Comprehensive evaluation of the sensitivity of predicted CYP3A DDI on selected intestinal and hepatic CYP3A ontogeny functions, together with PBPK model‐based strategy proposed here, aim to guide and facilitate DDI extrapolations in pediatric populations.

Do Inhibitory Metabolites Impact DDI Risk Assessment? Analysis of in vitro and in vivo Data from NDA Reviews Between 2013 and 2018

Evaluating the potential of new drugs and their metabolites to cause drug-drug interactions (DDIs) is critical for understanding drug safety and efficacy. Although multiple analyses of proprietary metabolite testing data have been published, no systematic analyses of metabolite data collected according to current testing criteria have been conducted. To address this knowledge gap, 120 new molecular entities approved between 2013 and 2018 were reviewed. Comprehensive data on metabolite-to-parent area under the curve ratios (AUC_M /AUC_P ), inhibitory potency of parent and metabolites, and clinical DDIs were collected. Sixty-four percent of the metabolites quantified in vivo had AUC_M /AUC_P  ≥ 0.25 and 75% of these metabolites were tested for cytochrome P450 (CYP) inhibition in vitro, resulting in 15 metabolites with potential DDI risk identification. Although 50% of the metabolites with AUC_M /AUC_P  < 0.25 were also tested in vitro, none of them showed meaningful CYP inhibition potential. The metabolite percentage of plasma total radioactivity cutoff of ≥ 10% did not appear to add value to metabolite testing strategies. No relationship between metabolite versus parent drug polarity and inhibition potency was observed. Comparison of metabolite and parent maximum concentration (C_max ) divided by inhibition constant (K_i ) values suggested that metabolites can contribute to in vivo DDIs and, hence, quantitative prediction of clinical DDI magnitude may require both parent and metabolite data. This systematic analysis of metabolite data for newly approved drugs supports an AUC_M /AUC_P cutoff of ≥ 0.25 to warrant metabolite in vitro CYP screening to adequately characterize metabolite inhibitory DDI potential and support quantitative DDI predictions.

The Necessity of Using Changes in Absorption Time to Implicate Intestinal Transporter Involvement in Oral Drug-Drug Interactions

INTRODUCTION

In drug discovery and development, it is of high interest to characterize the potential for intestinal drug-drug interactions to alter bioavailability of a victim drug. For drugs that are substrates of both intestinal transporters and enzymes, estimating the relative contribution of each process has proved challenging, especially since the susceptibility of drug to uptake or efflux transporters in vitro does not always translate to clinically significant in vivo involvement. Here we introduce a powerful methodology to implicate intestinal transporters in drug-drug interactions based on the theory that clinically relevant intestinal transporter interactions will result in altered rate of absorption of victim drugs.

METHODS AND MATERIALS

We present exemplary clinical drug-drug interaction studies that utilize well-characterized clinical substrates and perpetrators to demonstrate how mean absorption time (MAT) and time to maximum concentration (t_max) are expected to change (or remain unchanged) when either intestinal transporters or metabolic enzymes were/are altered. Apixaban was also selected to demonstrate the utility of the methodology, as the purported involvement of both intestinal enzymes and transporters has been suggested in its FDA package insert.

RESULTS AND DISCUSSION

Acute inhibition of gut efflux transporters resulted in decreased MAT and t_maxvalues, induction increased these values, while inhibition of intestinal metabolic enzymes did not result in altered MAT or t_max. Involvement of intestinal efflux transporters in apixaban disposition is unlikely.

CONCLUSION

Utilization of this simple but powerful methodology to implicate intestinal transporter involvement will have significant impact on how drug-drug interactions are interpreted.

Drug-Drug Interaction Risk Assessment of Esaxerenone as a Perpetrator by In Vitro Studies and Static and Physiologically Based Pharmacokinetic Models

Esaxerenone (CS-3150) is a novel, oral, nonsteroidal, selective mineralocorticoid receptor blocker approved for the treatment of hypertension in Japan. Here, the drug-drug interaction (DDI) potential of esaxerenone was evaluated in vitro, and its impact in clinical practice was estimated. Esaxerenone exhibited time-dependent inhibition and induction of CYP3A. When the clinical impacts of esaxerenone on the inhibition and induction of CYP3A were estimated separately by using a mechanistic static model, the predicted area under the curve ratios (AUCRs) of midazolam, a typical CYP3A substrate, were 1.80 and 0.31, respectively, suggesting that the DDI potential of esaxerenone cannot be neglected. Because it was suggested that DDIs mainly occur in the intestine, predictions using concentration-time profiles in each segment of the gastrointestinal tract were performed with GastroPlus, a physiologically based pharmacokinetic (PBPK) modeling software. The predicted AUCR of midazolam was approximately 1.2, which is close to that in a clinical study, despite the difficulty of predicting DDIs for compounds with both inhibition and induction effects. When only inhibition or induction was incorporated into a model, the AUCR of midazolam changed depending on the dosing period and dose level of esaxerenone and the timing of midazolam administration. However, the AUCR calculated by incorporating both effects remained almost constant. This study shows the ability of PBPK models to simulate weak DDIs via intestinal CYP3A and that esaxerenone has low DDI potential as a perpetrator because of the offset of inhibition and induction. SIGNIFICANCE STATEMENT: Weak CYP3A inhibition and/or induction sometimes cause DDIs in the intestine but not the liver. Because strong inhibitors maximally inhibit intestinal CYP3A, the predictability of weak DDIs in the intestine should be evaluated further. Here, we simulate the DDIs of esaxerenone as a perpetrator by using physiologically based pharmacokinetic modeling focusing on the intestine and offset of inhibition and induction.

Assessment of Inhibition of Bovine Hepatic Cytochrome P450 by 43 Commercial Bovine Medicines Using a Combination of In Vitro Assays and Pharmacokinetic Data from the Literature

BACKGROUND

There has been a lack of information about the inhibition of bovine medicines on bovine hepatic CYP450 at their commercial doses and dosing routes.

OBJECTIVE

The aim of this work was to assess the inhibition of 43 bovine medicines on bovine hepatic CYP450 using a combination of in vitro assay and Cmax values from pharmacokinetic studies with their commercial doses and dosing routes in the literature.

METHODS

Those drugs were first evaluated through a single point inhibitory assay at 3 μM in bovine liver microsomes for six specific CYP450 metabolisms, phenacetin o-deethylation, coumarin 7- hydroxylation, tolbutamide 4-hydroxylation, bufuralol 1-hydroxylation, chlorzoxazone 6-hydroxylation and midazolam 1'-hydroxylation. When the inhibition was greater than 20% in the assay, IC50 values were then determined. The potential in vivo bovine hepatic CYP450 inhibition by those drugs was assessed using a combination of the IC50 values and in vivo Cmax values from pharmacokinetic studies at their commercial doses and administration routes in the literature.

RESULTS

Fifteen bovine medicines or metabolites showed in vitro inhibition on one or more bovine hepatic CYP450 metabolisms with different IC50 values. Desfuroylceftiour (active metabolite of ceftiofur), nitroxinil and flunixin have the potential to inhibit one of the bovine hepatic CYP450 isoforms in vivo at their commercial doses and administration routes. The rest of the bovine medicines had low risks of in vivo bovine hepatic CYP450 inhibition.

CONCLUSION

This combination of in vitro assay and in vivo Cmax data provides a good approach to assess the inhibition of bovine medicines on bovine hepatic CYP450.

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.

Different Roles of Human Cytochrome P450 2C9 and 3A Enzymes in Diclofenac 4'- and 5-Hydroxylations Mediated by Metabolically Inactivated Human Hepatocytes in Previously Transplanted Chimeric Mice

To investigate the respective roles of cytochromes P450 2C9 and 3A in drug oxidation in human livers, the in vivo pharmacokinetics of S-warfarin and diclofenac were analyzed after intravenous administrations in chimeric mice that had been transplanted with human hepatocytes. P450 2C9 was metabolically inactivated in the humanized mice by orally pretreating them with tienilic acid. After intravenous administration of S-warfarin, a significant difference in the concentration-time profiles of the primary metabolite 7-hydroxywarfarin between untreated mice and mice treated with tienilic acid was observed. In contrast, there were no apparent differences in the profiles for S-warfarin between the treated and untreated groups. The mean values of the maximum concentrations (C_max) and the areas under the plasma concentration versus time curves (AUC_infinity) for 7-hydroxywarfarin were significantly lower (22 and 16% of the untreated values, respectively) in the treated group. This presumably resulted from suppressed P450 2C9 activity in the primary oxidative metabolism in vivo in the treated group. After diclofenac administration, plasma levels of diclofenac, 5-hydroxydiclofenac, and diclofenac acylglucuronide were roughly similar in pretreated and untreated mice. However, the mean C_max and AUC_infinity values for 4'-hydroxydiclofenac were significantly lower (38 and 53% of the untreated group, respectively) in the treated group. The reported value of ∼0.8 for the fraction of S-warfarin metabolized to 7-hydroxywarfarin mediated by P450 2C9 in in vitro systems was similar to the value implied by the present humanized-liver mouse model pretreated with tienilic acid in which the AUC of 7-hydroxywarfarin was reduced by 84%. In contrast, the fractions of diclofenac metabolized to 4'-hydroxydiclofenac in in vitro and in vivo experiments were inconsistent. These results suggested that humanized-liver mice orally treated with tienilic acid might constitute an in vivo model for metabolically inactivated P450 2C9 in human hepatocytes transplanted into chimeric mice. Moreover, diclofenac, a typical in vitro P450 2C9 probe substrate, was cleared differently in vitro and in humanized-liver mice in vivo.

A review of the antimicrobial side of antidepressants and its putative implications on the gut microbiome

OBJECTIVE

Serotonin reuptake inhibitors are the predominant treatment for major depressive disorder. In recent years, the diversity of the gut microbiota has emerged to play a significant role in the occurrence of major depressive disorder and other mood and anxiety disorders. Importantly, the role of the gut microbiota in the treatment of such disorders remains to be elucidated. Here, we provide a review of the literature regarding the effects of physiologically relevant concentrations of serotonin reuptake inhibitors on the gut microbiota and the implications this might have on their efficacy in the treatment of mood disorders.

METHODS

First, an estimation of gut serotonin reuptake inhibitor concentrations was computed based on pharmacokinetic and gastrointestinal transit properties of serotonin reuptake inhibitors. Literature regarding the in vivo and in vitro antimicrobial properties of serotonin reuptake inhibitors was gathered, and the estimated gut concentrations were examined in the context of these data. Computer-based investigation revealed putative mechanisms for the antimicrobial effects of serotonin reuptake inhibitors.

RESULTS

In vivo evidence using animal models shows an antimicrobial effect of serotonin reuptake inhibitors on the gut microbiota. Examination of the estimated physiological concentrations of serotonin reuptake inhibitors in the gastrointestinal tract collected from in vitro studies suggests that the microbial community of both the small intestine and the colon are exposed to serotonin reuptake inhibitors for at least 4 hours per day at concentrations that are likely to exert an antimicrobial effect. The potential mechanisms of the effect of serotonin reuptake inhibitors on the gut microbiota were postulated to include inhibition of efflux pumps and/or amino acid transporters.

CONCLUSION

This review raises important issues regarding the role that gut microbiota play in the treatment of mood-related behaviours, which holds substantial potential clinical outcomes for patients suffering from major depressive disorder and other mood-related disorders.

Mechanisms of Metabolism Interaction Between p-Cresol and Mycophenolic Acid

Mycophenolic acid (MPA) is commonly prescribed for preventing graft rejection after kidney transplantation. The primary metabolic pathways of MPA are hepatic glucuronidation through UDP-glucuronosyltransferase (UGT) enzymes in the formation of MPA-glucuronide (MPAG, major pathway) and MPA-acyl glucuronide (AcMPAG). p-Cresol, a potent uremic toxin known to accumulate in patients with renal dysfunction, can potentially interact with MPA via the inhibition of glucuronidation. We hypothesized that the interaction between MPA and p-cresol is clinically relevant and that the estimated exposure changes in the clinic are of toxicological significance. Using in vitro approaches (ie, human liver microsomes and recombinant enzymes), the potency and mechanisms of inhibition by p-cresol towards MPA glucuronidation were characterized. Inter-individual variabilities, effects of clinical co-variates, in vitro-in vivo prediction of likely changes in MPA exposure, and comparison to other toxins were determined for clinical relevance. p-Cresol inhibited MPAG formation in a potent and competitive manner (Ki=5.2 µM in pooled human liver microsomes) and the interaction was primarily mediated by UGT1A9. This interaction was estimated to increase plasma MPA exposure in patients by approximately 1.8-fold, which may result in MPA toxicity. The mechanism of inhibition for AcMPAG formation was noncompetitive (Ki=127.5 µM) and less likely to be clinically significant. p-Cresol was the most potent inhibitor of MPA-glucuronidation compared with other commonly studied uremic toxins (eg, indole-3-acetic acid, indoxyl sulfate, hippuric acid, kynurenic acid, and 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid) and its metabolites (ie, p-cresol sulfate and p-cresol glucuronide). Our findings indicate that the interaction between p-cresol and MPA is of toxicological significance and warrants clinical investigation.

Leveraging genetic interactions for adverse drug-drug interaction prediction

In light of increased co-prescription of multiple drugs, the ability to discern and predict drug-drug interactions (DDI) has become crucial to guarantee the safety of patients undergoing treatment with multiple drugs. However, information on DDI profiles is incomplete and the experimental determination of DDIs is labor-intensive and time-consuming. Although previous studies have explored various feature spaces for in silico screening of interacting drug pairs, their use of conventional cross-validation prevents them from achieving generalizable performance on drug pairs where neither drug is seen during training. Here we demonstrate for the first time targets of adversely interacting drug pairs are significantly more likely to have synergistic genetic interactions than non-interacting drug pairs. Leveraging genetic interaction features and a novel training scheme, we construct a gradient boosting-based classifier that achieves robust DDI prediction even for drugs whose interaction profiles are completely unseen during training. We demonstrate that in addition to classification power—including the prediction of 432 novel DDIs—our genetic interaction approach offers interpretability by providing plausible mechanistic insights into the mode of action of DDIs.

Adverse drug-drug interactions are adverse side effects caused by taking two or more drugs together. As co-prescription of multiple drugs becomes an increasingly prevalent practice, affecting 42.2% of Americans over 65 years old, adverse drug-drug interactions have become a serious safety concern, accounting for over 74,000 emergency room visits and 195,000 hospitalizations each year in the United States alone. Since experimental determination of adverse drug-drug interactions is labor-intensive and time-consuming, various machine learning-based computational approaches have been developed for predicting drug-drug interactions. Considering the fact that drugs effect through binding and modulating the function of their targets, we have explored whether drug-drug interactions can be predicted from the genetic interaction between the gene targets of two drugs, which characterizes the unexpected fitness effect when two genes are simultaneously knocked out. Furthermore, we have built a fast and robust classifier that achieves accurate prediction of adverse drug-drug interactions by incorporating genetic interaction and several other types of widely used features. Our analyses suggest that genetic interaction is an important feature for our prediction model, and that it provides mechanistic insight into the mode of action of drugs leading to drug-drug interactions.

Physiologically Based Pharmacokinetic Modelling of Cytochrome P450 2C9-Related Tolbutamide Drug Interactions with Sulfaphenazole and Tasisulam

Background and Objectives

Cytochrome P450 2C9 (CYP2C9) is involved in the biotransformation of many commonly used drugs, and significant drug interactions have been reported for CYP2C9 substrates. Previously published physiologically based pharmacokinetic (PBPK) models of tolbutamide are based on an assumption that its metabolic clearance is exclusively through CYP2C9; however, many studies indicate that CYP2C9 metabolism is only responsible for 80–90% of the total clearance. Therefore, these models are not useful for predicting the magnitude of CYP2C9 drug–drug interactions (DDIs). This paper describes the development and verification of SimCYP^®-based PBPK models that accurately describe the human pharmacokinetics of tolbutamide when dosed alone or in combination with the CYP2C9 inhibitors sulfaphenazole and tasisulam.

Methods

A PBPK model was optimized in SimCYP^® for tolbutamide as a CYP2C9 substrate, based on published in vitro and clinical data. This model was verified to replicate the magnitude of DDI reported with sulfaphenazole and was further applied to simulate the DDI with tasisulam, a small molecule investigated for the treatment of cancer. A clinical study (CT registration # NCT01185548) was conducted in patients with cancer to assess the pharmacokinetic interaction of tasisulum with tolbutamide. A PBPK model was built for tasisulam, and the clinical study design was replicated using the optimized tolbutamide model.

Results

The optimized tolbutamide model accurately predicted the magnitude of tolbutamide AUC increase (5.3–6.2-fold) reported for sulfaphenazole. Furthermore, the PBPK simulations in a healthy volunteer population adequately predicted the increase in plasma exposure of tolbutamide in patients with cancer (predicted AUC ratio = 4.7–5.4; measured mean AUC ratio = 5.7).

Conclusions

This optimized tolbutamide PBPK model was verified with two strong CYP2C9 inhibitors and can be applied to the prediction of CYP2C9 interactions for novel inhibitors. Furthermore, this work highlights the utility of mechanistic models in navigating the challenges in conducting clinical pharmacology studies in cancer patients.

Suitable albumin concentrations for enhanced drug oxidation activities mediated by human liver microsomal cytochrome P450 2C9 and other forms predicted with unbound fractions and partition/distribution coefficients of model substrates

Albumin has reportedly enhanced cytochrome P450 (P450)-mediated drug oxidation rates in human liver microsomes. Consequently, measurements of clearances and fractions metabolized could vary depending on the experimental albumin concentrations used. In this study, the oxidation rates of diclofenac and warfarin by human liver microsomes were significantly enhanced in the presence of 0.10% (w/v) bovine serum albumin, whereas those of tolbutamide and phenytoin required 1.0% and 2.0% of albumin for significant enhancement. Values of the fractions metabolized by P450 2C9 for four substrates did not markedly change in the presence of albumin at the above-mentioned concentrations. The oxidation rates of bupropion, omeprazole, chlorzoxazone and phenacetin in human liver microsomes were reportedly enhanced by 0.5%, 1%, 2% and 2% of albumin, respectively. Analysis of reported intrinsic clearance values and suitable albumin concentrations for the currently analyzed substrates and the reported substrates revealed an inverse correlation, with warfarin as an outlier. Suitable albumin concentrations were multivariately correlated with physicochemical properties, that is, the plasma unbound fractions, octanol-water partition coefficient and acid dissociation constant (r = 0.98, p<.0001, n = 10). Therefore, multiple physicochemical properties may be determinants of suitable albumin concentrations for substrate oxidations in human liver microsomes.

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.

Notable Drug-Drug Interaction Between Etizolam and Itraconazole in Poor Metabolizers of Cytochrome P450 2C19

In this study, impact of a polymorphism of CYP2C19 on drug-drug interaction (DDI) was examined for etizolam. The effect of itraconazole (a strong CYP3A inhibitor) on the pharmacokinetics of etizolam (a substrate of CYP2C19 and CYP3A) was assessed in both extensive metabolizers (EMs) and poor metabolizers (PMs) of CYP2C19. Sixteen participants (8 EMs and 8 PMs) received a single oral dose of etizolam (0.25 mg) on day 1. The participants ingested itraconazole (200 mg twice a day) on days 2-5. On day 5, participants received an oral dose of etizolam (0.25 mg) again. Before coadministration of itraconazole (day 1), the area under the time-plasma concentration curve from time zero to infinity (AUC_∞ ) of etizolam was higher in PMs than in EMs (2.65-fold, P < .01). Coadministration of itraconazole increased the AUC_∞ of etizolam 1.66-fold and 2.34-fold in EMs and PMs, respectively (day 5). Consequently, AUC_∞ was 6.18-fold higher in PMs with itraconazole than that in EMs without itraconazole. The increase by itraconazole was larger in PMs (P < .01). In heterozygous EMs (hEMs), AUC_∞ was simulated to be 2.56-fold higher with itraconazole than that in EMs without itraconazole. We found that in vitro measurements of fraction metabolized (f_m ) using the liver microsome prepared from PM donors would be helpful to predict polymorphism-dependent DDIs. These results suggest that the PMs and hEMs of a polymorphic CYP would be at higher risk of DDIs relative to EMs for drugs metabolized by both polymorphic and nonpolymorphic CYPs such as etizolam.

Mechanisms and Predictions of Drug-Drug Interactions of the Hepatitis C Virus Three Direct-Acting Antiviral Regimen: Paritaprevir/Ritonavir, Ombitasvir, and Dasabuvir

To assess drug-drug interaction (DDI) potential for the three direct-acting antiviral (3D) regimen of ombitasvir, dasabuvir, and paritaprevir, in vitro studies profiled drug-metabolizing enzyme and transporter interactions. Using mechanistic static and dynamic models, DDI potential was predicted for CYP3A, CYP2C8, UDP-glucuronosyltransferase (UGT) 1A1, organic anion-transporting polypeptide (OATP) 1B1/1B3, breast cancer resistance protein (BCRP), and P-glycoprotein (P-gp). Perpetrator static model DDI predictions for metabolizing enzymes were within 2-fold of the clinical observations, but additional physiologically based pharmacokinetic modeling was necessary to achieve the same for drug transporters. When perpetrator interactions were assessed, ritonavir was responsible for the strong increase in exposure of sensitive CYP3A substrates, whereas paritaprevir (an OATP1B1/1B3 inhibitor) greatly increased the exposure of sensitive OATP1B1/1B3 substrates. The 3D regimen drugs are UGT1A1 inhibitors and are predicted to moderately increase plasma exposure of sensitive UGT1A1 substrates. Paritaprevir, ritonavir, and dasabuvir are BCRP inhibitors. Victim DDI predictions were qualitatively in line with the clinical observations. Plasma exposures of the 3D regimen were reduced by strong CYP3A inducers (paritaprevir and ritonavir; major CYP3A substrates) but were not affected by strong CYP3A4 inhibitors, since ritonavir (a CYP3A inhibitor) is already present in the regimen. Strong CYP2C8 inhibitors increased plasma exposure of dasabuvir (a major CYP2C8 substrate), OATP1B1/1B3 inhibitors increased plasma exposure of paritaprevir (an OATP1B1/1B3 substrate), and P-gp or BCRP inhibitors (all compounds are substrates of P-gp and/or BCRP) increased plasma exposure of the 3D regimen. Overall, the comprehensive mechanistic assessment of compound disposition along with mechanistic and PBPK approaches to predict victim and perpetrator DDI liability may enable better clinical management of nonstudied drug combinations with the 3D regimen.

Small structural changes of the imidazopyridine diacylglycerol acyltransferase 2 (DGAT2) inhibitors produce an improved safety profile

Small molecule DGAT2 inhibitors have shown promise for the treatment of metabolic diseases in preclinical models. Herein, we report the first toxicological evaluation of imidazopyridine-based DGAT2 inhibitors and show that the arteriopathy associated with imidazopyridine 1 can be mitigated with small structural modifications, and is thus not mechanism related.

Comparison of the static in vivo approach to a physiologically based pharmacokinetic approach for metabolic drug–drug interactions prediction

Background: The in vivo mechanistic static model (IMSM) and the physiologically based pharmacokinetic (PBPK) model are two approaches used to predict the magnitude of drug–drug interactions (DDIs). The aim of this study was to evaluate the performance of IMSM and to compare IMSM with the PBPK approach implemented in Simcyp. Methods: The predictive performances of IMSM were evaluated on a panel of 628 DDIs. Subsequently, the IMSM and PBPK approaches were compared on a set of 104 DDIs. Results: The IMSM yielded 85% of predictions within 1.5-fold of the observed value on the 628 DDIs panel. The predictive performances of IMSM were better than those of the PBPK approach (median fold error 1 vs 0.86 on 104 studies; p = 0.02). Conclusion: The IMSM approach is an alternative tool for metabolic DDIs prediction.

Assessment of inhibition of porcine hepatic cytochrome P450 enzymes by 48 commercial drugs

Drug interactions due to inhibition of hepatic cytochrome P450 (CYP450) enzymes are not well understood in veterinary medicine. Forty-eight commercial porcine medicines were selected to evaluate their potential inhibition on porcine hepatic CYP450 enzymes at their commercial doses and administration routes. Those drugs were first assessed through a single point inhibitory assay at 3 µM in porcine liver microsomes for six specific CYP450 metabolisms (phenacetin o-deethylation, coumarin 7-hydroxylation, tolbutamide 4-hydroxylation, bufuralol 1-hydroxylation, chlorozoxazone 6-hydroxylation and midazolam 1'-hydroxylation). When the inhibition was > 10% in the single point inhibitory assay, IC50 values (inhibitory concentrations that decrease biotransformation of selected substrate by 50%) were determined. Overall, 17 drugs showed in vitro inhibition on one or more porcine hepatic CYP450 metabolisms with different IC50 values. The potential in vivo porcine hepatic CYP450 inhibition by those drugs was assessed by combining the in vitro data and in vivo Cmax (maximum plasma concentrations from pharmacokinetic studies of the porcine medicines at their commercial doses and administration routes). Three drugs showed high potential inhibition to one or two porcine hepatic CYP450 isoforms at their commercial doses and administration routes, while seven drugs had medium risk and seven had low risk of such in vivo inhibition. These data are useful to prevent potential drug interactions in veterinary medical practice.

Drug-drug interactions in pharmacologic management of gastroparesis

BACKGROUND

Gastroparesis is a disorder characterized by delayed gastric emptying due to chronic abnormal gastric motility. The treatment of the disease often entails the co-administration of several classes of pharmacological agents. These agents may be metabolized via the same pathway. Inhibition or induction of a shared metabolic pathway leads to change in the systemic levels of prescribed drugs, possibly leading to undesired clinical outcomes.

PURPOSE

This review discusses different pharmacological treatment for gastroparesis patients and describes the potential for drug-drug interactions (DDIs) in some of the combinations that are currently used. Prokinetic agents such as metoclopramide and domperidone are the cornerstone in treatment of gastroparesis. Antiemetic agents such as promethazine and ondansetron are frequently administered to gastroparesis patients to reduce nausea and vomiting. Gastroparesis is prevalent in diabetic patients and therefore antidiabetic agents are also prescribed. Many of these co-administered drugs are metabolized via common drug metabolizing enzymes and this can trigger potential DDIs. The scientific literature was reviewed from the years 1975-2014 for original research articles and reviews that evaluated DDIs in gastroparesis. Many commonly prescribed combinations were predicted to cause potential DDIs in gastroparesis patients. This review will help inform about potential hazardous combinations. This information will hopefully lead to less adverse effects and more successful gastroparesis management.

Importance of ALDH1A enzymes in determining human testicular retinoic acid concentrations

Retinoic acid (RA), the active metabolite of vitamin A, is required for spermatogenesis and many other biological processes. RA formation requires irreversible oxidation of retinal to RA by aldehyde dehydrogenase enzymes of the 1A family (ALDH1A). While ALDH1A1, ALDH1A2, and ALDH1A3 all form RA, the expression pattern and relative contribution of these enzymes to RA formation in the testis is unknown. In this study, novel methods to measure ALDH1A protein levels and intrinsic RA formation were used to accurately predict RA formation velocities in individual human testis samples and an association between RA formation and intratesticular RA concentrations was observed. The distinct localization of ALDH1A in the testis suggests a specific role for each enzyme in controlling RA formation. ALDH1A1 was found in Sertoli cells, while only ALDH1A2 was found in spermatogonia, spermatids, and spermatocytes. In the absence of cellular retinol binding protein (CRBP)1, ALDH1A1 was predicted to be the main contributor to intratesticular RA formation, but when CRBP1 was present, ALDH1A2 was predicted to be equally important in RA formation as ALDH1A1. This study provides a comprehensive novel methodology to evaluate RA homeostasis in human tissues and provides insight to how the individual ALDH1A enzymes mediate RA concentrations in specific cell types.

Domperidone interacts with pioglitazone but not with ondansetron via common CYP metabolism in vitro

Domperidone (prokinetic agent) is frequently co-administered with pioglitazone (anitidiabetic) or ondansetron (antiemetic) in gastroparesis management. These drugs are metabolized via cytochome P-450 (CYP) 3A4, raising the possibility of interaction and adverse reactions. The concentration-dependent inhibitory effect of pioglitazone and ondansetron on domperidone hydroxylation was monitored in pooled human liver microsomes (HLM). Pioglitazone was further assessed as a mechanism-based inhibitor. Microsomal binding was evaluated in our assessment. In HLM, Vmax/Km estimates for monohydroxy domperidone formation decreased in presence of pioglitazone. Diagnostic plots indicated that pioglitazone inhibited domperidone in a partial mixed-type manner. The in vitro Ki was 1.52 µM. Predicted in vivo AUCi/AUC ratio was 1.98. Pioglitazone also exerted time-dependent inhibition on the metabolism of domperidone and the average remaining enzymatic activity decreased significantly upon preincubation with pioglitazone over 0-40 min. Diagnostic plots showed no inhibitory effect of ondansetron on domperidone hydroxylation. 6. In conclusion, pioglitazone inhibited domperidone metabolism in vitro through different complex mechanisms. Our in vitro data predict that the co-administration of these drugs can potentially trigger an in vivo drug-drug interaction.

Evaluation of various static in vitro-in vivo extrapolation models for risk assessment of the CYP3A inhibition potential of an investigational drug

Nine static models (seven basic and two mechanistic) and their respective cutoff values used for predicting cytochrome P450 3A (CYP3A) inhibition, as recommended by the US Food and Drug Administration and the European Medicines Agency, were evaluated using data from 119 clinical studies with orally administered midazolam as a substrate. Positive predictive error (PPE) and negative predictive error (NPE) rates were used to assess model performance, based on a cutoff of 1.25-fold change in midazolam area under the curve (AUC) by inhibitor. For reversible inhibition, basic models using total or unbound systemic inhibitor concentration [I] had high NPE rates (46-47%), whereas those using intestinal luminal ([I]gut) values had no NPE but a higher PPE. All basic models for time-dependent inhibition had no NPE and reasonable PPE rates (15-18%). Mechanistic static models that incorporate all interaction mechanisms and organ specific [I] values (enterocyte and hepatic inlet) provided a higher predictive precision, a slightly increased NPE, and a reasonable PPE. Various cutoffs for predicting the likelihood of CYP3A inhibition were evaluated for mechanistic models, and a cutoff of 1.25-fold change in midazolam AUC appears appropriate.

Characterization of inhibition kinetics of (S)-warfarin hydroxylation by noscapine: implications in warfarin therapy

Noscapine is an antitussive and potential anticancer drug. Clinically significant interactions between warfarin and noscapine have been previously reported. In this study, to provide a basis for warfarin dosage adjustment, the inhibition kinetics of noscapine against warfarin metabolism was characterized. Our enzyme kinetics data obtained from human liver microsomes and recombinant CYP2C9 proteins indicate that noscapine is a competitive inhibitor of the (S)-warfarin 7-hydroxylation reaction by CYP2C9. Interestingly, noscapine also inhibited (S)-warfarin metabolism in a NADPH- and time-dependent manner, and removal of unbound noscapine and its metabolites by ultrafiltration did not reverse inhibition of (S)-warfarin metabolism by noscapine, suggesting mechanism-based inhibition of CYP2C9 by noscapine. Spectral scanning of the reaction between CYP2C9 and noscapine revealed the formation of an absorption spectrum at 458 nm, indicating the formation of a metabolite-intermediate complex. Surprisingly, noscapine is a 2- to 3-fold more efficient inactivator of CYP2C9.2 and CYP2C9.3 variants than it is of the wild type, by unknown mechanisms. Based on the inhibitory kinetic data, (S)-warfarin exposure is predicted to increase up to 7-fold (depending on CYP2C9 genotypes) upon noscapine coadministration, mainly due to mechanism-based inactivation of CYP2C9 by noscapine. Together, these results indicate that mechanism-based inhibition of CYP2C9 by noscapine may dramatically alter pharmacokinetics of warfarin and provide a basis for warfarin dosage adjustment when noscapine is coadministered.

Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME

This review encompasses the most important advances in liver functions and hepatotoxicity and analyzes which mechanisms can be studied in vitro. In a complex architecture of nested, zonated lobules, the liver consists of approximately 80 % hepatocytes and 20 % non-parenchymal cells, the latter being involved in a secondary phase that may dramatically aggravate the initial damage. Hepatotoxicity, as well as hepatic metabolism, is controlled by a set of nuclear receptors (including PXR, CAR, HNF-4α, FXR, LXR, SHP, VDR and PPAR) and signaling pathways. When isolating liver cells, some pathways are activated, e.g., the RAS/MEK/ERK pathway, whereas others are silenced (e.g. HNF-4α), resulting in up- and downregulation of hundreds of genes. An understanding of these changes is crucial for a correct interpretation of in vitro data. The possibilities and limitations of the most useful liver in vitro systems are summarized, including three-dimensional culture techniques, co-cultures with non-parenchymal cells, hepatospheres, precision cut liver slices and the isolated perfused liver. Also discussed is how closely hepatoma, stem cell and iPS cell-derived hepatocyte-like-cells resemble real hepatocytes. Finally, a summary is given of the state of the art of liver in vitro and mathematical modeling systems that are currently used in the pharmaceutical industry with an emphasis on drug metabolism, prediction of clearance, drug interaction, transporter studies and hepatotoxicity. One key message is that despite our enthusiasm for in vitro systems, we must never lose sight of the in vivo situation. Although hepatocytes have been isolated for decades, the hunt for relevant alternative systems has only just begun.

Effect of CYP3A5 expression on the inhibition of CYP3A-catalyzed drug metabolism: impact on modeling CYP3A-mediated drug-drug interactions

The purpose of this study was to determine the impact of CYP3A5 expression on inhibitory potency (Ki or IC50 values) of CYP3A inhibitors, using recombinant CYP3A4 and CYP3A5 (rCYP3A4 and rCYP3A5) and CYP3A5 genotyped human liver microsomes (HLMs). IC50 ratios between rCYP3A4 and rCYP3A5 (rCYP3A5/rCYP3A4) of ketoconazole (KTZ) and itraconazole (ITZ) were 8.5 and 8.8 for midazolam (MDZ), 4.7 and 9.1 for testosterone (TST), 1.3 and 2.8 for terfenadine, and 0.6 and 1.7 for vincristine, respectively, suggesting substrate- and inhibitor-dependent selectivity of the two azoles. Due to the difference in the IC50 values for CYP3A4 and CYP3A5, nonconcordant expression of CYP3A4 and CYP3A5 protein can significantly affect the observed magnitude of CYP3A-mediated drug-drug interactions in humans. Indeed, the IC50 values of KTZ and ITZ for CYP3A-catalyzed MDZ and TST metabolism were significantly higher in HLMs with CYP3A5*1/*1 and CYP3A5*1/*3 genotypes than in HLMs with the CYP3A5*3/*3 genotype, showing CYP3A5 expression-dependent IC50 values. Moreover, when IC50 values of KTZ and ITZ for different HLMs were kinetically simulated based on CYP3A5 expression level and enzyme-specific IC50 values, a good correlation between the simulated and the experimentally measured IC50 values was observed. Further simulation analysis revealed that both the Ki ratio (for inhibitors) and Vmax/Km ratio (for substrates) between CYP3A4 and CYP3A5 were major factors for CYP3A5 expression-dependent IC50 values. In conclusion, the present study demonstrates that CYP3A5 genotype and expression level have a significant impact on inhibitory potency for CYP3A-catalyzed drug metabolism, but that the magnitude of its effect is inhibitor-substrate pair specific.

A physiologically based pharmacokinetic model to predict disposition of CYP2D6 and CYP1A2 metabolized drugs in pregnant women

Conducting pharmacokinetic (PK) studies in pregnant women is challenging. Therefore, we asked if a physiologically based pharmacokinetic (PBPK) model could be used to evaluate different dosing regimens for pregnant women. We refined and verified our previously published pregnancy PBPK model by incorporating cytochrome P450 CYP1A2 suppression (based on caffeine PK) and CYP2D6 induction (based on metoprolol PK) into the model. This model accounts for gestational age-dependent changes in maternal physiology and hepatic CYP3A activity. For verification, the disposition of CYP1A2-metabolized drug theophylline (THEO) and CYP2D6-metabolized drugs paroxetine (PAR), dextromethorphan (DEX), and clonidine (CLO) during pregnancy was predicted. Our PBPK model successfully predicted THEO disposition during the third trimester (T3). Predicted mean postpartum to third trimester (PP:T3) ratios of THEO area under the curve (AUC), maximum plasma concentration, and minimum plasma concentration were 0.76, 0.95, and 0.66 versus observed values 0.75, 0.89, and 0.72, respectively. The predicted mean PAR steady-state plasma concentration (Css) ratio (PP:T3) was 7.1 versus the observed value 3.7. Predicted mean DEX urinary ratio (UR) (PP:T3) was 2.9 versus the observed value 1.9. Predicted mean CLO AUC ratio (PP:T3) was 2.2 versus the observed value 1.7. Sensitivity analysis suggested that a 100% induction of CYP2D6 during T3 was required to recover the observed PP:T3 ratios of PAR Css, DEX UR, and CLO AUC. Based on these data, it is prudent to conclude that the magnitude of hepatic CYP2D6 induction during T3 ranges from 100 to 200%. Our PBPK model can predict the disposition of CYP1A2, 2D6, and 3A drugs during pregnancy.

Importance of multi-p450 inhibition in drug-drug interactions: evaluation of incidence, inhibition magnitude, and prediction from in vitro data

Drugs that are mainly cleared by a single enzyme are considered more sensitive to drug-drug interactions (DDIs) than drugs cleared by multiple pathways. However, whether this is true when a drug cleared by multiple pathways is coadministered with an inhibitor of multiple P450 enzymes (multi-P450 inhibition) is not known. Mathematically, simultaneous equipotent inhibition of two elimination pathways that each contribute half of the drug clearance is equal to equipotent inhibition of a single pathway that clears the drug. However, simultaneous strong or moderate inhibition of two pathways by a single inhibitor is perceived as an unlikely scenario. The aim of this study was (i) to identify P450 inhibitors currently in clinical use that can inhibit more than one clearance pathway of an object drug in vivo and (ii) to evaluate the magnitude and predictability of DDIs caused by these multi-P450 inhibitors. Multi-P450 inhibitors were identified using the Metabolism and Transport Drug Interaction Database. A total of 38 multi-P450 inhibitors, defined as inhibitors that increased the AUC or decreased the clearance of probes of two or more P450s, were identified. Seventeen (45%) multi-P450 inhibitors were strong inhibitors of at least one P450, and an additional 12 (32%) were moderate inhibitors of one or more P450s. Only one inhibitor (fluvoxamine) was a strong inhibitor of more than one enzyme. Fifteen of the multi-P450 inhibitors also inhibit drug transporters in vivo, but such data are lacking on many of the inhibitors. Inhibition of multiple P450 enzymes by a single inhibitor resulted in significant (>2-fold) clinical DDIs with drugs that are cleared by multiple pathways such as imipramine and diazepam, while strong P450 inhibitors resulted in only weak DDIs with these object drugs. The magnitude of the DDIs between multi-P450 inhibitors and diazepam, imipramine, and omeprazole could be predicted using in vitro data with similar accuracy as probe substrate studies with the same inhibitors. The results of this study suggest that inhibition of multiple clearance pathways in vivo is clinically relevant, and the risk of DDIs with object drugs may be best evaluated in studies using multi-P450 inhibitors.

Risk assessment of mechanism-based inactivation in drug-drug interactions

Drug-drug interactions (DDIs) that occur via mechanism-based inactivation of cytochrome P450 are of serious concern. Although several predictive models have been published, early risk assessment of MBIs is still challenging. For reversible inhibitors, the DDI risk categorization using [I]/K(i) ([I], the inhibitor concentration; K(i), the inhibition constant) is widely used in drug discovery and development. Although a simple and reliable methodology such as [I]/K(i) categorization for reversible inhibitors would be useful for mechanism-based inhibitors (MBIs), comprehensive analysis of an analogous measure reflecting in vitro potency for inactivation has not been reported. The aim of this study was to evaluate whether the term λ/k(deg) (λ, first-order inactivation rate at a given MBI concentration; k(deg), enzyme degradation rate constant) would be useful in the prediction of the in vivo DDI risk of MBIs. Twenty-one MBIs with both in vivo area under the curve (AUC) change of marker substrates and in vitro inactivation parameters were identified in the literature and analyzed. The results of this analysis show that in vivo DDIs with >2-fold change of object drug AUC can be identified with the cutoff value of λ/k(deg) = 1, where unbound steady-state C(max) is used for inhibitor concentration. However, the use of total C(max) led to great overprediction of DDI risk. The risk assessment using λ/k(deg) coupled with unbound C(max) can be useful for the DDI risk evaluation of MBIs in drug discovery and development.