PREDICTION OF TIME-DEPENDENT CYP3A4 DRUG-DRUG INTERACTIONS: IMPACT OF ENZYME DEGRADATION, PARALLEL ELIMINATION PATHWAYS, AND INTESTINAL INHIBITION

Mechanism-based inactivation of cytochromes P450: implications in drug interactions and pharmacotherapy

Cytochrome P40 (CYP) enzymes dominate the metabolism of numerous endogenous and xenobiotic substances. While it is commonly believed that CYP-catalysed reactions result in the detoxication of foreign substances, these reactions can also yield reactive intermediates that can bind to cellular macromolecules to cause cytotoxicity or irreversibly inactivate CYPs that create them.Mechanism-based inactivation (MBI) produces either irreversible or quasi-irreversible inactivation and is commonly caused by CYP metabolic bioactivation to an electrophilic reactive intermediate. Many drugs that have been known to cause MBI in CYPs have been discovered as perpetrators in drug-drug interactions throughout the last 20-30 years.This review will highlight the key findings from the recent literature about the mechanisms of CYP enzyme inhibition, with a focus on the broad mechanistic elements of MBI for widely used drugs linked to the phenomenon. There will also be a brief discussion of the clinical or pharmacokinetic consequences of CYP inactivation with regard to drug interaction and toxicity risk.Gaining knowledge about the selective inactivation of CYPs by common therapeutic drugs helps with the assessment of factors that affect the systemic clearance of co-administered drugs and improves comprehension of anticipated interactions with other drugs or xenobiotics.

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.

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.

Evaluation and Optimization of a Microcavity Plate-Based Human Hepatocyte Spheroid Model for Predicting Clearance of Slowly Metabolized Drug Candidates

In vitro clearance assays are routinely conducted in drug discovery to predict in vivo clearance, but low metabolic turnover compounds are often difficult to evaluate. Hepatocyte spheroids can be cultured for days, achieving higher drug turnover, but have been hindered by limitations on cell number per well. Corning Elplasia microcavity 96-well microplates enable the culture of 79 hepatocyte spheroids per well. In this study, microcavity spheroid properties (size, hepatocyte function, longevity, culturing techniques) were assessed and optimized for clearance assays, which were then compared with microsomes, hepatocyte suspensions, two-dimensional-plated hepatocytes, and macrowell spheroids cultured as one per well. Higher enzyme activity coupled with greater hepatocyte concentrations in microcavity spheroids enabled measurable turnover of all 17 test compounds, unlike the other models that exhibited less drug turnover. Microcavity spheroids also predicted intrinsic clearance (CLint) and blood clearance (CLb) within threefold for 53% [9/17; average absolute fold error (AAFE), 3.9] and 82% (14/17; AAFE, 2.6) of compounds using a linear regression correction model, respectively. An alternate method incorporating mechanistic modeling that accounts for mass transport (permeability and diffusion) within spheroids demonstrated improved predictivity for CLint (12/17; AAFE, 4.0) and CLb (14/17; AAFE, 2.1) without the need for empirical scaling factors. The estimated fraction of drug metabolized by cytochrome P450 3A4 (fm,CYP3A4) using 3 μM itraconazole was within 25% of observed values for 6 of 8 compounds, with 5 of 8 compounds within 10%. In sum, spheroid cultures in microcavity plates permit the ability to test and predict clearance as well as fm,CYP3A4 of low metabolic turnover compounds and represent a valuable complement to conventional in vitro clearance assays. SIGNIFICANCE STATEMENT: Culturing multiple spheroids in ultralow attachment microcavities permits accurate quantitation of metabolically stable compounds in substrate depletion assays, overcoming limitations with singly cultured spheroids. In turn, this permits robust estimates of intrinsic clearance, which is improved with the consideration of mass transport within the spheroid. Incubations with 3 μM itraconazole enabled assessments of CYP3A4 involvement in hepatic clearance.

Screening of 16 major drug glucuronides for time-dependent inhibition of nine drug-metabolizing CYP enzymes - detailed studies on CYP3A inhibitors

Time-dependent inhibition of cytochrome P450 (CYP) enzymes has been observed for a few glucuronide metabolites of clinically used drugs. Here, we investigated the inhibitory potential of 16 glucuronide metabolites towards nine major CYP enzymes in vitro. Automated substrate cocktail methods were used to screen time-dependent inhibition of CYP1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2J2 and 3A in human liver microsomes. Seven glucuronides (carvedilol β-D-glucuronide, diclofenac acyl-β-D-glucuronide, 4-hydroxyduloxetine β-D-glucuronide, ezetimibe phenoxy-β-D-glucuronide, raloxifene 4'-glucuronide, repaglinide acyl-β-D-glucuronide and valproic acid β-D-glucuronide) caused NADPH- and time-dependent inhibition of at least one of the CYPs investigated, including CYP2A6, CYP2C19 and CYP3A. In more detailed experiments, we focused on the glucuronides of carvedilol and diclofenac, which inhibited CYP3A. Carvedilol β-D-glucuronide showed weak time-dependent inhibition of CYP3A, but the parent drug carvedilol was found to be a more potent inhibitor of CYP3A, with the half-maximal inhibitor concentration (IC50) decreasing from 7.0 to 1.1 µM after a 30-min preincubation with NADPH. The maximal inactivation constant (kinact) and the inhibitor concentration causing half of kinact (KI) for CYP3A inactivation by carvedilol were 0.051 1/min and 1.8 µM, respectively. Diclofenac acyl-β-D-glucuronide caused time-dependent inactivation of CYP3A at high concentrations, with a 4-fold IC50 shift (from 400 to 98 µM after a 30-min preincubation with NADPH) and KI and kinact values of >2,000 µM and >0.16 1/min. In static predictions, carvedilol caused significant (>1.25-fold) increase in the exposure of the CYP3A substrates midazolam and simvastatin. In conclusion, we identified several glucuronide metabolites with CYP inhibitory properties. Based on detailed experiments, the inactivation of CYP3A by carvedilol may cause clinically significant drug-drug interactions.

Assessment of the Effects of Abrocitinib on the Pharmacokinetics of Probe Substrates of Cytochrome P450 1A2, 2B6 and 2C19 Enzymes and Hormonal Oral Contraceptives in Healthy Individuals

BACKGROUND AND OBJECTIVE

Abrocitinib is an oral small-molecule Janus kinase (JAK)-1 inhibitor approved for the treatment of moderate-to-severe atopic dermatitis. In vitro studies indicated that abrocitinib is a weak time-dependent inhibitor of cytochrome P450 (CYP) 2C19/3A and a weak inducer of CYP1A2/2B6/2C19/3A. To assess the potential effect of abrocitinib on concomitant medications, drug-drug interaction (DDI) studies were conducted for abrocitinib with sensitive probe substrates of these CYP enzymes. The impact of abrocitinib on hormonal oral contraceptives (ethinyl estradiol and levonorgestrel), as substrates of CYP3A and important concomitant medications for female patients, was also evaluated.

METHODS

Three Phase 1 DDI studies were performed to assess the impact of abrocitinib 200 mg once daily (QD) on the probe substrates of: (1) 1A2 (caffeine), 2B6 (efavirenz) and 2C19 (omeprazole) in a cocktail study; (2) 3A (midazolam); and (3) 3A (oral contraceptives).

RESULTS

After multiple doses of abrocitinib 200 mg QD, there is a lack of effect on the pharmacokinetics of midazolam, efavirenz and contraceptives. Abrocitinib increased the area under the concentration time curve from 0 to infinity (AUCinf) and the maximum concentration (Cmax) of omeprazole by approximately 189 and 134%, respectively. Abrocitinib increased the AUCinf of caffeine by 40% with lack of effect on Cmax.

CONCLUSIONS

Based on the study results, abrocitinib is a moderate inhibitor of CYP2C19. Caution should be exercised when using abrocitinib concomitantly with narrow therapeutic index medicines that are primarily metabolized by CYP2C19 enzyme. Abrocitinib is a mild inhibitor of CYP1A2; however, the impact is not clinically relevant, and no general dose adjustment is recommended for CYP1A2 substrates. Abrocitinib does not inhibit CYP3A or induce CYP1A2/2B6/2C19/3A and does not affect the pharmacokinetics of contraceptives.

CLINICAL TRIALS REGISTRATION

ClinicalTrials.gov registration IDs: NCT03647670, NCT05067439, NCT03662516.

Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition

Most drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential DDI issues, high-throughput in vitro assays were established to assess the TDI of CYP3A4 during the discovery and lead optimization phases. However, in silico machine learning models would enable an earlier and larger-scale assessment of TDI potential liabilities. For CYP inhibition, most modeling efforts have focused on highly imbalanced and small data sets. Moreover, assay variability is rarely considered, which is key to understand the model's quality and suitability for decision-making. In this work, machine learning models were built for the prediction of TDI of CYP3A4, evaluated prospectively, and compared to the variability of the experimental assay. Different modeling strategies were investigated to assess their influence on the model's performance. Through multitask learning, additional data sets were leveraged for model building, coming from public databases, in-house CYP-related assays, or other pharmaceutical companies (federated learning). Apart from the numerical prediction of inactivation rates of CYP3A4 TDI, three-class predictions were carried out, giving a negative (inactivation rate kobs < 0.01 min-1), weak positive (0.01 ≤ kobs ≤ 0.025 min-1), or positive (kobs > 0.025 min-1) output. The final multitask graph neural network model achieved misclassification rates of 8 and 7% for positive and negative TDI, respectively. Importantly, the presented deep learning-based predictions had a similar precision to the reproducibility of in vitro experiments and thus offered great opportunities for drug design, early derisk of DDI potential, and selection of experiments. To facilitate CYP inhibition modeling efforts in the public domain, the developed model was used to annotate ∼16 000 publicly available structures, and a surrogate data set is shared as Supporting Information.

Omeprazole Induces CYP3A4 mRNA Expression but Not CYP3A4 Protein Expression in HepaRG Cells

Unknown interactions between drugs remain the limiting factor for clinical application of drugs, and the induction and inhibition of drug-metabolizing CYP enzymes are considered the key to examining the drug-drug interaction (DDI). In this study, using human HepaRG cells as an in vitro model system, we analyzed the potential DDI based on the expression levels of CYP3A4 and CYP1A2. Rifampicin and omeprazole, the potent inducers for CYP3A4 and CYP1A2, respectively, induce expression of the corresponding CYP enzymes at both the mRNA and protein levels. We noticed that, in addition to inducing CYP1A2, omeprazole induced CYP3A4 mRNA expression in HepaRG cells. However, unexpectedly, CYP3A4 protein expression levels were not increased after omeprazole treatment. Concurrent administration of rifampicin and omeprazole showed an inhibitory effect of omeprazole on the CYP3A4 protein expression induced by rifampicin, while its mRNA induction remained intact. Cycloheximide chase assay revealed increased CYP3A4 protein degradation in the cells exposed to omeprazole. The data presented here suggest the potential importance of broadening the current DDI examination beyond conventional transcriptional induction and enzyme-activity inhibition tests to include post-translational regulation analysis of CYP enzyme expression.

Estimation of FMO3 Ontogeny by Mechanistic Population Pharmacokinetic Modelling of Risdiplam and Its Impact on Drug-Drug Interactions in Children

BACKGROUND AND OBJECTIVE

Spinal muscular atrophy (SMA) is a progressive neuromuscular disease caused by insufficient levels of survival motor neuron (SMN) protein. Risdiplam (EvrysdiTM) increases SMN protein and is approved for the treatment of SMA. Risdiplam has high oral bioavailability and is primarily eliminated through hepatic metabolism by flavin-containing monooxygenase3 (FMO3) and cytochrome P450 (CYP) 3A, by 75% and 20%, respectively. While the FMO3 ontogeny is critical input data for the prediction of risdiplam pharmacokinetics (PK) in children, it was mostly studied in vitro, and robust in vivo FMO3 ontogeny is currently lacking. We derived in vivo FMO3 ontogeny by mechanistic population PK modelling of risdiplam and investigated its impact on drug-drug interactions in children.

METHODS

Population and physiologically based PK (PPK and PBPK) modelling conducted during the development of risdiplam were integrated into a mechanistic PPK (Mech-PPK) model to estimate in vivo FMO3 ontogeny. A total of 10,205 risdiplam plasma concentration-time data from 525 subjects aged 2 months-61 years were included. Six different structural models were examined to describe the in vivo FMO3 ontogeny. Impact of the newly estimated FMO3 ontogeny on predictions of drug-drug interaction (DDI) in children was investigated by simulations for dual CYP3A-FMO3 substrates including risdiplam and theoretical substrates covering a range of metabolic fractions (fm) of CYP3A and FMO3 (fmCYP3A:fmFMO3 = 10%:90%, 50%:50%, 90%:10%).

RESULTS

All six models consistently predicted higher FMO3 expression/activity in children, reaching a maximum at the age of 2 years with an approximately threefold difference compared with adults. Different trajectories of FMO3 ontogeny in infants < 4 months of age were predicted by the six models, likely due to limited observations for this age range. Use of this  in vivo FMO3 ontogeny function improved prediction of risdiplam PK in children compared to in vitro FMO3 ontogeny functions. The simulations of theoretical dual CYP3A-FMO3 substrates predicted comparable or decreased CYP3A-victim DDI propensity in children compared to adults across the range of fm values. Refinement of FMO3 ontogeny in the risdiplam model had no impact on the previously predicted low CYP3A-victim or -perpetrator DDI risk of risdiplam in children.

CONCLUSION

Mech-PPK modelling successfully estimated in vivo FMO3 ontogeny from risdiplam data collected from 525 subjects aged 2 months-61 years. To our knowledge, this is the first investigation of in vivo FMO3 ontogeny by population approach using comprehensive data covering a wide age range. Derivation of a robust in vivo FMO3 ontogeny function has significant implications on the prospective prediction of PK and DDI in children for other FMO3 substrates in the future, as illustrated in the current study for FMO3 and/or dual CYP3A-FMO3 substrates.

CLINICAL TRIAL REGISTRY NUMBERS

NCT02633709, NCT03032172, NCT02908685, NCT02913482, NCT03988907.

Can Mechanistic Static Models for Drug-Drug Interactions Support Regulatory Filing for Study Waivers and Label Recommendations?

BACKGROUND AND OBJECTIVE

Mechanistic static and dynamic physiologically based pharmacokinetic models are used in clinical drug development to assess the risk of drug-drug interactions (DDIs). Currently, the use of mechanistic static models is restricted to screening DDI risk for an investigational drug, while dynamic physiologically based pharmacokinetic models are used for quantitative predictions of DDIs to support regulatory filing. As physiologically based pharmacokinetic model development by sponsors as well as a review of models by regulators require considerable resources, we explored the possibility of using mechanistic static models to support regulatory filing, using representative cases of successful physiologically based pharmacokinetic submissions to the US Food and Drug Administration under different classes of applications.

METHODS

Drug-drug interaction predictions with mechanistic static models were done for representative cases in the different classes of applications using the same data and modelling workflow as described in the Food and Drug Administration clinical pharmacology reviews. We investigated the hypothesis that the use of unbound average steady-state concentrations of modulators as driver concentrations in the mechanistic static models should lead to the same conclusions as those from physiologically based pharmacokinetic modelling for non-dynamic measures of DDI risk assessment such as the area under the plasma concentration-time curve ratio, provided the same input data are employed for the interacting drugs.

RESULTS

Drug-drug interaction predictions of area under the plasma concentration-time curve ratios using mechanistic static models were mostly comparable to those reported in the Food and Drug Administration reviews using physiologically based pharmacokinetic models for all representative cases in the different classes of applications.

CONCLUSIONS

The results reported in this study should encourage the use of models that best fit an intended purpose, limiting the use of physiologically based pharmacokinetic models to those applications that leverage its unique strengths, such as what-if scenario testing to understand the effect of dose staggering, evaluating the role of uptake and efflux transporters, extrapolating DDI effects from studied to unstudied populations, or assessing the impact of DDIs on the exposure of a victim drug with concurrent mechanisms. With this first step, we hope to trigger a scientific discussion on the value of a routine comparison of the two methods for regulatory submissions to potentially create a best practice that could help identify examples where the use of dynamic changes in modulator concentrations could make a difference to DDI risk assessment.

Development of In Silico Models for Predicting Potential Time-Dependent Inhibitors of Cytochrome P450 3A4

Cytochrome P450 3A4 (CYP3A4) is one of the major drug metabolizing enzymes in the human body and metabolizes ∼30-50% of clinically used drugs. Inhibition of CYP3A4 must always be considered in the development of new drugs. Time-dependent inhibition (TDI) is an important P450 inhibition type that could cause undesired drug-drug interactions. Therefore, identification of CYP3A4 TDI by a rapid convenient way is of great importance to any new drug discovery effort. Here, we report the development of in silico classification models for prediction of potential CYP3A4 time-dependent inhibitors. On the basis of the CYP3A4 TDI data set that we manually collected from literature and databases, both conventional machine learning and deep learning models were constructed. The comparisons of different sampling strategies, molecular representations, and machine-learning algorithms showed the benefits of a balanced data set and the deep-learning model featured by GraphConv. The generalization ability of the best model was tested by screening an external data set, and the prediction results were validated by biological experiments. In addition, several structural alerts that are relevant to CYP3A4 time-dependent inhibitors were identified via information gain and frequency analysis. We anticipate that our effort would be useful for identification of potential CYP3A4 time-dependent inhibitors in drug discovery and design.

Core shell stationary phase for a novel separation of some COVID-19 used drugs by UPLC-MS/MS Method: Study of grapefruit consumption impact on their pharmacokinetics in rats

A sensitive and selective UPLC-MS/MS method was developed for the synchronized determination of four drugs used in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), namely, azithromycin, apixaban, dexamethasone, and favipiravir in rat plasma. using a Poroshell 120 EC-C18 column (50 mm × 4.6 mm, 2.7 m) with a high-resolution ESI tandem mass spectrometer detection with multiple reaction monitoring. We used an Agilent Poroshell column, which is characterized by a stationary phase based on non-porous core particles. With a remarkable improvement in the number of theoretical plates and low column backpressure. In addition, the developed method was employed in studying the potential food-drug interaction of grapefruit juice (GFJ) with the selected drugs which affects their pharmacokinetics in rats. The LC-MS/MS operated in positive and negative ionization mode using two internal standards: moxifloxacin and chlorthalidone, respectively. Liquid- liquid extraction of the cited drugs from rat plasma was accomplished using diethyl ether: dichloromethane (70:30, v/v). The analytes were separated using methanol: 0.1 % formic acid in water (95: 5, v/v) as a mobile phase in isocratic mode of elution pumped at a flow rate of 0.3 mL/min. A detailed validation of the bio-analytical method was performed in accordance with US-FDA and EMA guidelines. Concerning the in vivo pharmacokinetic study, the statistical significance between the results of the test groups receiving GFJ along with the cited drugs and the control group was assessed demonstrating that GFJ increased the plasma concentration of azithromycin, apixaban, and dexamethasone. Accordingly, this food-drug interaction requires cautious ingestion of GFJ in patients using (SARS-CoV-2) medications as it can produce negative effects in the safety of the drug therapy. A potential drug-drug interaction is also suggested between those medications requiring a suitable dose adjustment.

Identification of Optimal Urinary Biomarkers of Synthetic Cannabinoids BZO-HEXOXIZID, BZO-POXIZID, 5F-BZO-POXIZID, and BZO-CHMOXIZID for Illicit Abuse Monitoring

BACKGROUND

The continuous introduction of new synthetic cannabinoid (SC) subtypes and analogues remains a major problem worldwide. Recently, a new "OXIZID" generation of SCs surfaced in seized materials across various countries. Hence, there is an impetus to identify urinary biomarkers of the OXIZIDs to detect their abuse.

METHODS

We adapted our previously reported two-pronged approach to investigate the metabolite profiles and disposition kinetics of 4 OXIZID analogues, namely, BZO-HEXOXIZID (MDA-19), BZO-POXIZID (5C-MDA-19), 5F-BZO-POXIZID (5F-MDA-19), and BZO-CHMOXIZID (CHM-MDA-19). First, bottom-up in vitro incubation experiments comprising metabolite identification, metabolic stability, and reaction phenotyping were performed using human liver microsomes and recombinant human cytochrome P450 enzymes. Second, top-down analysis of authentic urine samples from drug abusers was performed to corroborate the in vitro findings and establish a panel of urinary biomarkers.

RESULTS

A total of 42 to 51 metabolites were detected for each OXIZID, and their major metabolic pathways included N-alkyl and phenyl hydroxylation, oxidative defluorination (for 5F-BZO-POXIZID), oxidation to ketone and carboxylate, amide hydrolysis, and N-dealkylation. The OXIZIDs were metabolically unstable, mainly metabolized by cytochromes P3A4, P3A5, and P2C9, and demonstrated mechanism-based inactivation of cytochrome P3A4. Integrating with the results of 4 authentic urine samples, the parent drug and both N-alkyl and phenyl mono-hydroxylated metabolites of each OXIZID were determined as suitable urinary biomarkers.

CONCLUSIONS

Drug enforcement agencies worldwide may apply these biomarkers in routine monitoring procedures to identify abusers and counter the escalation of OXIZID abuse.

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.

Development of Physiologically Based Pharmacokinetic Model for Orally Administered Fexuprazan in Humans

Fexuprazan is a new drug candidate in the potassium-competitive acid blocker (P-CAB) family. As proton pump inhibitors (PPIs), P-CABs inhibit gastric acid secretion and can be used to treat gastric acid-related disorders such as gastroesophageal reflux disease (GERD). Physiologically based pharmacokinetic (PBPK) models predict drug interactions as pharmacokinetic profiles in biological matrices can be mechanistically simulated. Here, we propose an optimized and validated PBPK model for fexuprazan by integrating in vitro, in vivo, and in silico data. The extent of fexuprazan tissue distribution in humans was predicted using tissue-to-plasma partition coefficients in rats and the allometric relationships of fexuprazan distribution volumes (V_SS) among preclinical species. Urinary fexuprazan excretion was minimal (0.29-2.02%), and this drug was eliminated primarily by the liver and metabolite formation. The fraction absorbed (Fa) of 0.761, estimated from the PBPK modeling, was consistent with the physicochemical properties of fexuprazan, including its in vitro solubility and permeability. The predicted oral bioavailability of fexuprazan (38.4-38.6%) was within the range of the preclinical datasets. The C_max, AUC_last, and time-concentration profiles predicted by the PBPK model established by the learning set were accurately predicted for the validation sets.

Disease-drug and drug-drug interaction in COVID-19: Risk and assessment

COVID-19 is announced as a global pandemic in 2020. Its mortality and morbidity rate are rapidly increasing, with limited medications. The emergent outbreak of COVID-19 prompted by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) keeps spreading. In this infection, a patient's immune response plays pivotal role in the pathogenesis. This inflammatory factor was shown by its mediators that, in severe cases, reach the cytokine at peaks. Hyperinflammatory state may sparks significant imbalances in transporters and drug metabolic machinery, and subsequent alteration of drug pharmacokinetics may result in unexpected therapeutic response. The present scenario has accounted for the requirement for therapeutic opportunities to relive and overcome this pandemic. Despite the diminishing developments of COVID-19, there is no drug still approved to have significant effects with no side effect on the treatment for COVID-19 patients. Based on the evidence, many antiviral and anti-inflammatory drugs have been authorized by the Food and Drug Administration (FDA) to treat the COVID-19 patients even though not knowing the possible drug-drug interactions (DDI). Remdesivir, favipiravir, and molnupiravir are deemed the most hopeful antiviral agents by improving infected patient’s health. Dexamethasone is the first known steroid medicine that saved the lives of seriously ill patients. Some oligopeptides and proteins have also been using. The current review summarizes medication updates to treat COVID-19 patients in an inflammatory state and their interaction with drug transporters and drug-metabolizing enzymes. It gives an opinion on the potential DDI that may permit the individualization of these drugs, thereby enhancing the safety and efficacy.

Physiologically-based pharmacokinetic modeling to predict drug interactions of lemborexant with CYP3A inhibitors

Lemborexant, a recently approved dual orexin receptor antagonist for treatment of adults with insomnia, is eliminated primarily by cytochrome P450 (CYP)3A metabolism. The recommended dose of lemborexant is 5 mg once per night, with a maximum recommended dose of 10 mg once daily. A physiologically-based pharmacokinetic (PBPK) model for lemborexant was developed and applied to integrate data obtained from in vivo drug-drug interaction (DDI) assessments, and to further explore lemborexant interaction with CYP3A inhibitors and inducers. The model predictions were in good agreement with observed pharmacokinetic data and with DDI results from clinical studies with CYP3A inhibitors, itraconazole and fluconazole. The model further predicted that DDI effects of weak CYP3A inhibitors (fluoxetine and ranitidine) are weak, and effects of moderate inhibitors (erythromycin and verapamil) are moderate. Based on the PBPK simulations and clinical efficacy and safety data, the maximum daily recommended lemborexant dose when administered with weak CYP3A inhibitors is 5 mg; co-administration of moderate and strong inhibitors should be avoided except in countries where 2.5 mg has been approved.

Application of Azamulin to Determine the Contribution of CYP3A4/5 to Drug Metabolic Clearance Using Human Hepatocytes

Early determination of CYP3A4/5 contribution to the clearance of new chemical entities is critical to inform on the risk of drug-drug interactions with CYP3A inhibitors and inducers. Several in vitro approaches (recombinant P450 enzymes, correlation analysis, chemical and antibody inhibition in human liver microsomes) are available, but they are usually labor-intensive and/or suffer from specific limitations. In the present study, we have validated the use of azamulin as a specific CYP3A inhibitor in human hepatocytes. Azamulin (3 µM) was found to significantly inhibit CYP3A4/5 (>90%), whereas other P450 enzymes were not affected (less than 20% inhibition). Because human hepatocytes were used as a test system, the effect of azamulin on other key drug-metabolizing enzymes (aldehyde oxidase, carboxylesterase, UGT, flavin monooxygenase, and sulfotransferase) was also investigated. Apart from some UGTs showing minor inhibition (∼20%-30%), none of these non-P450 enzymes were inhibited by azamulin. Use of CYP3A5-genotyped human hepatocyte batches in combination with CYP3cide demonstrated that azamulin (at 3 µM) inhibits both CYP3A4 and CYP3A5 enzymes. Finally, 11 compounds with known in vivo CYP3A4/5 contribution have been evaluated in this human hepatocyte assay. Results showed that the effect of azamulin on the in vitro intrinsic clearance of these known CYP3A4/5 substrates was predictive of the in vivo CYP3A4/5 contribution. Overall, the study showed that human hepatocytes treated with azamulin provide a fast and accurate estimation of CYP3A4/5 contribution in metabolic clearance of new chemical entities. SIGNIFICANCE STATEMENT: Accurate estimation of CYP3A4/5 contribution in drug clearance is essential to anticipate risk of drug-drug interactions and select the appropriate candidate for clinical development. The present study validated the use of azamulin as selective CYP3A4/5 inhibitor in suspended human hepatocytes and demonstrated that this novel approach provides a direct and accurate determination of the contribution of CYP3A4/5 (fraction metabolized by CYP3A4/5) in the metabolic clearance of new chemical entities.

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.

Prediction of pharmacokinetic drug-drug interactions causing atorvastatin-induced rhabdomyolysis using physiologically based pharmacokinetic modelling

Atorvastatin and its lactone form metabolite are reported to be associated with statin-induced myopathy (SIM) such as myalgia and life-threatening rhabdomyolysis. Though the statin-induced rhabdomyolysis is not common during statin therapy, its incidence will significantly increase due to pharmacokinetic drug-drug interactions (DDIs) with inhibitor drugs which inhibit atorvastatin's and its lactone's metabolism and hepatic uptake. Thus, the quantitative analysis of DDIs of atorvastatin and its lactone with cytochrome P450 3A4 (CYP3A4) and organic anion-transporting polypeptide (OATP) inhibitors is of great importance. This study aimed to predict pharmacokinetic DDIs possibly causing atorvastatin-induced rhabdomyolysis using Physiologically Based Pharmacokinetic (PBPK) Modelling. Firstly, we refined the PBPK models of atorvastatin and atorvastatin lactone for predicting the DDIs with CYP3A4 and OATP inhibitors. Thereafter, we predicted the exposure changes of atorvastatin and atorvastatin lactone originating from the case reports of atorvastatin-induced rhabdomyolysis using the refined models. The simulation results show that pharmacokinetic DDIs of atorvastatin and its lactone with fluconazole, palbociclib diltiazem and cyclosporine are significant. Consequently, clinicians should be aware of necessary dose adjustment of atorvastatin being used with these four inhibitor drugs.

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.

Interaction Between Cyclosporine and Palbociclib in a Renal Transplant Patient: Case Report and Pharmacokinetic Perspective

Solid organ transplant recipients have increased cancer risk due in part to chronic immunosuppression and opportunistic oncogenic viral infections. The management of drug interactions in transplant recipients being treated for cancer is important both to minimize the likelihood of drug-related toxicities and to optimize therapeutic outcomes. We present a case of a 41-year-old woman with a stable living–related kidney transplant maintained on an immunosuppressive regimen of cyclosporine, mycophenolate mofetil, and prednisone, who was subsequently diagnosed with a metastatic lobular breast carcinoma and papillary thyroid cancer and started palbociclib, a time-dependent CYP3A inhibitor. After initiation of palbociclib, cyclosporine trough and peak concentrations were increased by 159% and 81%, respectively, relative to the average cyclosporine concentrations pre-palbociclib. Using the Drug Interaction Probability Scale (DIPS), the interaction between palbociclib and cyclosporine was rated as “probable.” Dose reductions of immunosuppressive agents that are CYP3A substrates are warranted if palbociclib is initiated, followed by close monitoring of blood concentrations. This report also highlights the challenges of coadministering a time-dependent inhibitor with a narrow therapeutic index drug that is metabolized by the same enzyme, particularly when the inhibitor is given in cycles with off-treatment periods.

Improved predictions of time-dependent drug-drug interactions by determination of cytosolic drug concentrations

The clinical impact of drug-drug interactions based on time-dependent inhibition of cytochrome P450 (CYP) 3A4 has often been overpredicted, likely due to use of improper inhibitor concentration estimates at the enzyme. Here, we investigated if use of cytosolic unbound inhibitor concentrations could improve predictions of time-dependent drug-drug interactions. First, we assessed the inhibitory effects of ten time-dependent CYP3A inhibitors on midazolam 1′-hydroxylation in human liver microsomes. Then, using a novel method, we determined the cytosolic bioavailability of the inhibitors in human hepatocytes, and used the obtained values to calculate their concentrations at the active site of the enzyme, i.e. the cytosolic unbound concentrations. Finally, we combined the data in mechanistic static predictions, by considering different combinations of inhibitor concentrations in intestine and liver, including hepatic concentrations corrected for cytosolic bioavailability. The results were then compared to clinical data. Compared to no correction, correction for cytosolic bioavailability resulted in higher accuracy and precision, generally in line with those obtained by more demanding modelling. The best predictions were obtained when the inhibition of hepatic CYP3A was based on unbound maximal inhibitor concentrations corrected for cytosolic bioavailability. Our findings suggest that cytosolic unbound inhibitor concentrations improves predictions of time-dependent drug-drug interactions for CYP3A.

Allosteric Interactions in Human Cytochrome P450 CYP3A4: The Role of Phenylalanine 213

The role of Phe213 in the allosteric mechanism of human cytochrome P450 CYP3A4 was studied using a combination of progesterone (PGS) and carbamazepine (CBZ) as probe substrates. We expressed, purified, and incorporated into POPC Nanodiscs three mutants, F213A, F213S, and F213Y, and compared them with wild-type (WT) CYP3A4 by monitoring spectral titration, the rate of NADPH oxidation, and steady-state product turnover rates with pure substrates and substrate mixtures. All mutants demonstrated higher activity with CBZ, lower activity with PGS, and a reduced level of activation of CBZ epoxidation by PGS, which was most pronounced in the F213A mutant. Using all-atom molecular dynamics simulations, we compared the dynamics of WT CYP3A4 and the F213A mutant incorporated into the lipid bilayer and the effect of the presence of the PGS molecule at the allosteric peripheral site and evaluated the critical role of Phe213 in mediating the heterotropic allosteric interactions in CYP3A4.

A Novel PBPK Modeling Approach to Assess Cytochrome P450 Mediated Drug-Drug Interaction Potential of the Cytotoxic Prodrug Evofosfamide

Evofosfamide is a cytotoxic small‐molecule prodrug preferentially activated under hypoxic conditions. The cytotoxicity of evofosfamide impacted the generation of in vitro drug‐drug interaction (DDI) data, especially in vitro induction results. Therefore, a novel physiologically based pharmacokinetic (PBPK) approach was used, which involved available in vitro and clinical data of evofosfamide and combined it with induction data from the prototypical cytochrome P450 (CYP)3A inducer rifampicin. The area under the concentration‐time curve (AUC) ratios of midazolam were above 0.80, indicating that induction of CYP3A by evofosfamide administered weekly is unlikely to occur in humans. Moreover, static and PBPK modeling showed no clinically relevant inhibition via CYP2B6, CYP2D6, and CYP3A4. In conclusion, PBPK models were used to supplement in vitro information of a cytotoxic compound. This approach may set a precedent for future studies of cytotoxic drugs, potentially reducing the need for clinical DDI studies and providing more confidence in the clinical use of approved cytotoxic compounds for which DDI information is sparse.

In Vitro and In Vivo Correlation of Hepatic Fraction of Metabolism by P450 in Dogs

1-Aminobenzotriazole (ABT) has been widely used as a nonspecific mechanism-based inhibitor of cytochrome P450 (P450) enzymes. It is extensively used in preclinical studies to determine the relative contribution of oxidative metabolism mediated by P450 in vitro and in vivo. The aim of present study was to understand the translation of fraction metabolized by P450 in dog hepatocytes to in vivo using ABT, for canagliflozin, known to be cleared by P450-mediated oxidation and UDP-glucuronosyltransferases-mediated glucuronidation, and 3 drug discovery project compounds mainly cleared by hepatic metabolism. In a dog hepatocyte, intrinsic clearance assay with and without preincubation of ABT, 3 Lilly compounds exhibited a wide range of fraction metabolized by P450. Subsequent metabolite profiling in dog hepatocytes demonstrated a combination of metabolism by P450 and UDP-glucuronosyltransferases. In vivo, dogs were pretreated with 50 mg/kg ABT or vehicle at 2 h before intravenous administration of canagliflozin and Lilly compounds. The areas under the concentration-time curve (AUC) were compared for the ABT-pretreated and vehicle-pretreated groups. The measured AUC_ABT/AUC_veh ratios were correlated to fraction of metabolism by P450 in dog hepatocytes, suggesting that in vitro ABT inhibition in hepatocytes is useful to rank order compounds for in vivo fraction of metabolism assessment.

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.

Assessment of inhibitory effects on major human cytochrome P450 enzymes by spasmolytics used in the treatment of overactive bladder syndrome

BACKGROUND

The objective of this study was to examine the inhibitory potential of darifenacin, fesoterodine, oxybutynin, propiverine, solifenacin, tolterodine and trospium chloride on the seven major human cytochrome P450 enzymes (CYP) by using a standardized and validated seven-in-one cytochrome P450 cocktail inhibition assay.

METHODS

An in vitro cocktail of seven highly selective probe substrates was incubated with human liver microsomes and varying concentrations of the seven test compounds. The major metabolites of the probe substrates were simultaneously analysed using a validated liquid chromatography tandem mass spectrometry (LC-MS/MS) method. Enzyme kinetics were estimated by determining IC50 and Ki values via nonlinear regression. Obtained Ki values were used for predictions of potential clinical impact of the inhibition using a static mechanistic prediction model.

RESULTS

In this study, 49 IC50 experiments were conducted. In six cases, IC50 values lower than the calculated threshold for drug-drug interactions (DDIs) in the gut wall were observed. In these cases, no increase in inhibition was determined after a 30 min preincubation. Considering a typical dosing regimen and applying the obtained Ki values of 0.72 µM (darifenacin, 15 mg daily) and 7.2 µM [propiverine, 30 mg daily, immediate release (IR)] for the inhibition of CYP2D6 yielded a predicted 1.9-fold and 1.4-fold increase in the area under the curve (AUC) of debrisoquine (CYP2D6 substrate), respectively. Due to the inhibition of the particular intestinal CYP3A4, the obtained Ki values of 14 µM of propiverine (30 mg daily, IR) resulted in a predicted doubling of the AUC for midazolam (CYP3A4 substrate).

CONCLUSIONS

In vitro/in vivo extrapolation based on pharmacokinetic data and the conducted screening experiments yielded similar effects of darifenacin on CYP2D6 and propiverine on CYP3A4 as obtained in separately conducted in vivo DDI studies. As a novel finding, propiverine was identified to potentially inhibit CYP2D6 at clinically occurring concentrations.

Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection

Drug-drug interaction (DDI) defines a situation in which one drug affects the activity of another when both are administered together. DDI is a common cause of adverse drug reactions and sometimes also leads to improved therapeutic effects. Therefore, it is of great interest to discover novel DDIs according to their molecular properties and mechanisms in a robust and rigorous way. This paper attempts to predict effective DDIs using the following properties: (1) chemical interaction between drugs; (2) protein interactions between the targets of drugs; and (3) target enrichment of KEGG pathways. The data consisted of 7323 pairs of DDIs collected from the DrugBank and 36,615 pairs of drugs constructed by randomly combining two drugs. Each drug pair was represented by 465 features derived from the aforementioned three categories of properties. The random forest algorithm was adopted to train the prediction model. Some feature selection techniques, including minimum redundancy maximum relevance and incremental feature selection, were used to extract key features as the optimal input for the prediction model. The extracted key features may help to gain insights into the mechanisms of DDIs and provide some guidelines for the relevant clinical medication developments, and the prediction model can give new clues for identification of novel DDIs.

Determination of a Degradation Constant for CYP3A4 by Direct Suppression of mRNA in a Novel Human Hepatocyte Model, HepatoPac

Accurate determination of rates of de novo synthesis and degradation of cytochrome P450s (P450s) has been challenging. There is a high degree of variability in the multiple published values of turnover for specific P450s that is likely exacerbated by differences in methodologies. For CYP3A4, reported half-life values range from 10 to 140 hours. An accurate value for kdeg has been identified as a major limitation for prediction of drug interactions involving mechanism-based inhibition and/or induction. Estimation of P450 half-life from in vitro test systems, such as human hepatocytes, is complicated by differential decreased enzyme function over culture time, attenuation of the impact of enzyme loss through inclusion of glucocorticoids in media, and viability limitations over long-term culture times. HepatoPac overcomes some of these challenges by providing extended stability of enzymes (2.5 weeks in our hands). As such it is a unique tool for studying rates of enzyme degradation achieved through modulation of enzyme levels. CYP3A4 mRNA levels were rapidly depleted by >90% using either small interfering RNA or addition of interleukin-6, which allowed an estimation of the degradation rate constant for CYP3A protein over an incubation time of 96 hours. The degradation rate constant of 0.0240 ± 0.005 hour(-1) was reproducible in hepatocytes from five different human donors. These donors also reflected the overall population with respect to CYP3A5 genotype. This methodology can be applied to additional enzymes and may provide a more accurate in vitro derived kdeg value for predicting clinical drug-drug interaction outcomes.

Ritonavir is the best alternative to ketoconazole as an index inhibitor of cytochrome P450-3A in drug-drug interaction studies

AIMS

The regulatory prohibition of ketoconazole as a CYP3A index inhibitor in drug-drug interaction (DDI) studies has compelled consideration of alternative inhibitors.

METHODS

The biomedical literature was searched to identify DDI studies in which oral midazolam (MDZ) was the victim, and the inhibitory perpetrator was either ketoconazole, itraconazole, clarithromycin, or ritonavir. The ratios (RAUC ) of total area under the curve (AUC) for MDZ with inhibitor divided by MDZ AUC in the control condition were aggregated across individual studies for each inhibitor.

RESULTS

Mean (± SE) RAUC values were: ketoconazole (15 studies, 131 subjects), 11.5 (±1.2); itraconazole (five studies, 48 subjects), 7.3 (±1.0); clarithromycin (five studies, 73 subjects), 6.5 (±10.9); and ritonavir (13 studies, 159 subjects), 14.5 (±2.0). Differences among inhibitors were significant (F = 5.31, P < 0.005). RAUC values were not significantly related to inhibitor dosage or to duration of inhibitor pre-exposure prior to administration of MDZ.

CONCLUSIONS

Ritonavir produces CYP3A inhibition equivalent to or greater than ketoconazole, and is the best index CYP3A inhibitor alternative to ketoconazole. Cobicistat closely resembles ritonavir in structure and function, and can also be considered. Itraconazole and clarithromycin are not suitable alternatives since they do not produce inhibition comparable with ketoconazole or ritonavir, and have other significant disadvantages as well.

Cytochrome P450 time-dependent inhibition and induction: advances in assays, risk analysis and modelling

INTRODUCTION

It is widely accepted that current practice of polypharmacy inevitably increases the incidence of drug-drug interactions (DDIs). Serious DDIs are a major liability for new molecular entities entering the pharmaceutical market. Various strategies are employed to avoid problematic compounds for clinical development. Progress made with reversible CYP DDIs has prompted a switch to study and model time-dependent inhibition and induction interactions.

AREAS COVERED

An overview of popular experimental practices is presented with discussion of techniques and algorithms used to analyse the clinical DDI risk. Emphasis is placed on the transition from early, simple static equations, via more complex net mechanistic, static models to dynamic approaches involving multiple perpetrators and metabolites, simultaneous inhibition and induction.

EXPERT OPINION

Inclusion of the more conservative terms for parameters required for DDI evaluation may eliminate promising chemical space, encourages poor practice and hampers innovation. Breakthroughs have originated from understanding of 'outliers' from such analyses where CYP enzyme-transporter interplay may be involved. The role of key transporters in drug disposition is firmly established as the chemistry required to address new targets deviates from traditional 'drug-like' space. Attempts to model more complex interactions for substrates of both CYP enzymes and drug transporters are still in their infancy and will benefit from dynamic modelling.

Investigation into CYP3A4-mediated drug-drug interactions on midostaurin in healthy volunteers

PURPOSE

Midostaurin (PKC412), a multitargeted tyrosine kinase inhibitor that targets FMS-related tyrosine kinase 3 and KIT, is in clinical trials for the treatment for acute myeloid leukemia and advanced systemic mastocytosis. In vitro studies showed that midostaurin is predominantly metabolized by cytochrome P450 3A4 (CYP3A4) and that midostaurin inhibits and/or induces the same enzyme. Here, we address the clinical relevance of CYP3A4-related drug-drug interactions with midostaurin as either a "victim" or "perpetrator."

METHODS

Three phase I studies in healthy volunteers evaluated the effects of a CYP3A4 inhibitor (ketoconazole 400 mg daily for 10 days) or CYP3A4 inducer (rifampicin 600 mg daily for 14 days) on concentrations of midostaurin and its metabolites following a single 50-mg dose of midostaurin and the effects of midostaurin as a single dose (100 mg) and multiple doses (50 mg twice daily) on midazolam (a sensitive CYP3A4 probe) concentration. The plasma concentrations of midostaurin and its 2 active metabolites, CGP62221 and CGP52421, were determined using a sensitive liquid chromatography/tandem mass spectrometry method.

RESULTS

Inhibition of CYP3A4 by ketoconazole increased midostaurin exposure more than tenfold, and induction of CYP3A4 by rifampicin decreased midostaurin exposure by more than tenfold. Midostaurin did not appreciably affect the concentrations of midazolam or its metabolite, 1'-hydroxymidazolam, at single or multiple doses.

CONCLUSION

The pharmacokinetics of midostaurin and its metabolites was affected substantially by ketoconazole and rifampicin, suggesting that midostaurin is a sensitive CYP3A4 substrate. Midostaurin did not appear to inhibit or induce CYP3A4 in vivo.

Modeling of rifampicin-induced CYP3A4 activation dynamics for the prediction of clinical drug-drug interactions from in vitro data

Induction of cytochrome P450 3A4 (CYP3A4) expression is often implicated in clinically relevant drug-drug interactions (DDI), as metabolism catalyzed by this enzyme is the dominant route of elimination for many drugs. Although several DDI models have been proposed, none have comprehensively considered the effects of enzyme transcription/translation dynamics on induction-based DDI. Rifampicin is a well-known CYP3A4 inducer, and is commonly used as a positive control for evaluating the CYP3A4 induction potential of test compounds. Herein, we report the compilation of in vitro induction data for CYP3A4 by rifampicin in human hepatocytes, and the transcription/translation model developed for this enzyme using an extended least squares method that can account for inherent inter-individual variability. We also developed physiologically based pharmacokinetic (PBPK) models for the CYP3A4 inducer and CYP3A4 substrates. Finally, we demonstrated that rifampicin-induced DDI can be predicted with reasonable accuracy, and that a static model can be used to simulate DDI once the blood concentration of the inducer reaches a steady state following repeated dosing. This dynamic PBPK-based DDI model was implemented on a new multi-hierarchical physiology simulation platform named PhysioDesigner.

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.

Predicting drug-drug interactions: application of physiologically based pharmacokinetic models under a systems biology approach

The development of in vitro-in vivo extrapolation (IVIVE), a 'bottom-up' approach, to predict pharmacokinetic parameters and drug-drug interactions (DDIs) has accelerated mainly due to an increase in the understanding of the multiple mechanisms involved in these interactions and the availability of appropriate in vitro systems that act as surrogates for delineating various elements of the interactions relevant to absorption, distribution, metabolism and elimination. Recent advances in the knowledge of the population variables required for IVIVE (demographic, anatomical, genetic and physiological parameters) have also contributed to the appreciation of the sources of variability and wider use of this approach for different scenarios within the pharmaceutical industry. Initially, the authors present an overview of the integration of IVIVE into 'static' and 'dynamic' models for the quantitative prediction of DDIs. The main purpose of this review is to discuss the application of IVIVE in conjunction with physiologically based pharmacokinetic modeling under a systems biology approach to characterize the potential DDIs in individual patients, including those who cannot be investigated in formal clinical trials for ethical reasons. In addition, we address the issues related to the prediction of complex DDIs involving the inhibition of cytochrome P- and transporter-mediated activities through multiple drugs.