Prediction of time-dependent CYP3A4 drug–drug interactions by physiologically based pharmacokinetic modelling: Impact of inactivation parameters and enzyme turnover

In vitro assessment of inhibitory effects of kinase inhibitors on CYP2C9, 3A and 1A2: Prediction of drug-drug interaction risk with warfarin and direct oral anticoagulants

OBJECTIVE

This study aimed to evaluate the cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) potential of kinase inhibitors with warfarin and direct oral anticoagulants (DOACs).

METHODS

An in vitro CYP probe substrate cocktail assay was used to study the inhibitory effects of fifteen kinase inhibitors on CYP2C9, 3A, and 1A2. Then, DDI predictions were performed using both mechanistic static and physiologically-based pharmacokinetic (PBPK) models.

RESULTS

Linsitinib, masitinib, regorafenib, tozasertib, trametinib, and vatalanib were identified as competitive CYP2C9 inhibitors (Ki = 1.4, 1.0, 1.1, 3.8, 0.5, and 0.1 μM, respectively). Masitinib and vatalanib were competitive CYP3A inhibitors (Ki = 1.3 and 0.2 μM), and vatalanib noncompetitively inhibited CYP1A2 (Ki = 2.0 μM). Moreover, linsitinib and tozasertib were CYP3A time-dependent inhibitors (KI = 26.5 and 400.3 μM, kinact = 0.060 and 0.026 min-1, respectively). Only linsitinib showed time-dependent inhibition of CYP1A2 (KI = 13.9 μM, kinact = 0.018 min-1). Mechanistic static models identified possible DDI risks for linsitinib and vatalanib with (S)-/(R)-warfarin, and for masitinib with (S)-warfarin. PBPK simulations further confirmed that vatalanib may increase (S)- and (R)-warfarin exposure by 4.37- and 1.80-fold, respectively, and that linsitinib may increase (R)-warfarin exposure by 3.10-fold. Mechanistic static models predicted a smaller risk of DDIs between kinase inhibitors and apixaban or rivaroxaban. The greatest AUC increases (1.50-1.74) were predicted for erlotinib in combination with apixaban and rivaroxaban. Linsitinib, masitinib, and vatalanib were predicted to have a smaller effect on apixaban and rivaroxaban AUCs (AUCR 1.22-1.53). No kinase inhibitor was predicted to increase edoxaban exposure.

CONCLUSIONS

Our results suggest that several kinase inhibitors, including vatalanib and linsitinib, can cause CYP-mediated drug-drug interactions with warfarin and, to a lesser extent, with apixaban and rivaroxaban. The work provides mechanistic insights into the risk of DDIs between kinase inhibitors and anticoagulants, which can be used to avoid preventable DDIs in the clinic.

Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective

Physiologically-based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug-drug interactions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made with sufficient confidence. In the current work, the ability to predict the effect of rifampin, a well-characterized strong CYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow with optimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, fm,CYP3A4, and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of fm,CYP3A4 (0.086-1.0), Fg (0.11-1.0) and hepatic availability (0.09-0.96) were included. Predictions were most often accurate for compounds that are not P-gp substrates or that are P-gp substrates but that have high permeability. Case studies for three challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parameter sensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures should be considered, for example, a mechanistic absorption model instead of a first-order absorption model might be more appropriate for a P-gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate that rifampin might impact (e.g., induction of other enzymes or P-gp) should be considered for inclusion in the model. PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates with limited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused on rifampin, the learnings may apply to other inducers.

Quantitatively Predicting Effects of Exercise on Pharmacokinetics of Drugs Using a Physiologically Based Pharmacokinetic Model

Exercise significantly alters human physiological functions, such as increasing cardiac output and muscle blood flow and decreasing glomerular filtration rate (GFR) and liver blood flow, thereby altering the absorption, distribution, metabolism, and excretion of drugs. In this study, we aimed to establish a database of human physiological parameters during exercise and to construct equations for the relationship between changes in each physiological parameter and exercise intensity, including cardiac output, organ blood flow (e.g., muscle blood flow and kidney blood flow), oxygen uptake, plasma pH and GFR, etc. The polynomial equation P = ΣaiHRi was used for illustrating the relationship between the physiological parameters (P) and heart rate (HR), which served as an index of exercise intensity. The pharmacokinetics of midazolam, quinidine, digoxin, and lidocaine during exercise were predicted by a whole-body physiologically based pharmacokinetic (WB-PBPK) model and the developed database of physiological parameters following administration to 100 virtual subjects. The WB-PBPK model simulation results showed that most of the observed plasma drug concentrations fell within the 5th-95th percentiles of the simulations, and the estimated peak concentrations (Cmax) and area under the curve (AUC) of drugs were also within 0.5-2.0 folds of observations. Sensitivity analysis showed that exercise intensity, exercise duration, medication time, and alterations in physiological parameters significantly affected drug pharmacokinetics and the net effect depending on drug characteristics and exercise conditions. In conclusion, the pharmacokinetics of drugs during exercise could be quantitatively predicted using the developed WB-PBPK model and database of physiological parameters. SIGNIFICANCE STATEMENT: This study simulated real-time changes of human physiological parameters during exercise in the WB-PBPK model and comprehensively investigated pharmacokinetic changes during exercise following oral and intravenous administration. Furthermore, the factors affecting pharmacokinetics during exercise were also revealed.

A guide to developing population files for physiologically-based pharmacokinetic modeling in the Simcyp Simulator

The Simcyp Simulator is a software platform widely used in the pharmaceutical industry to conduct stochastic physiologically-based pharmacokinetic (PBPK) modeling. This approach has the advantage of combining routinely generated in vitro data on drugs and drug products with knowledge of biology and physiology parameters to predict a priori potential pharmacokinetic changes in absorption, distribution, metabolism, and excretion for populations of interest. Combining such information with pharmacodynamic knowledge of drugs enables planning for potential dosage adjustment when clinical studies are feasible. Although the conduct of dedicated clinical studies in some patient groups (e.g., with hepatic or renal diseases) is part of the regulatory path for drug development, clinical studies for all permutations of covariates potentially affecting pharmacokinetics are impossible to perform. The role of PBPK in filling the latter gap is becoming more appreciated. This tutorial describes the different input parameters required for the creation of a virtual population giving robust predictions of likely changes in pharmacokinetics. It also highlights the considerations needed to qualify the models for such contexts of use. Two case studies showing the step-by-step development and application of population files for obese or morbidly obese patients and individuals with Crohn's disease are provided as the backbone of our tutorial to give some hands-on and real-world examples.

Docetaxel, cyclophosphamide, and epirubicin: application of PBPK modeling to gain new insights for drug-drug interactions

The new adjuvant chemotherapy of docetaxel, epirubicin, and cyclophosphamide has been recommended for treating breast cancer. It is necessary to investigate the potential drug-drug Interactions (DDIs) since they have a narrow therapeutic window in which slight differences in exposure might result in significant differences in treatment efficacy and tolerability. To guide clinical rational drug use, this study aimed to evaluate the DDI potentials of docetaxel, cyclophosphamide, and epirubicin in cancer patients using physiologically based pharmacokinetic (PBPK) models. The GastroPlus™ was used to develop the PBPK models, which were refined and validated with observed data. The established PBPK models accurately described the pharmacokinetics (PKs) of three drugs in cancer patients, and the predicted-to-observed ratios of all the PK parameters met the acceptance criterion. The PBPK model predicted no significant changes in plasma concentrations of these drugs during co-administration, which was consistent with the observed clinical phenomenon. Besides, the verified PBPK models were then used to predict the effect of other Cytochrome P450 3A4 (CYP3A4) inhibitors/inducers on these drug exposures. In the DDI simulation, strong CYP3A4 modulators changed the exposure of three drugs by 0.71-1.61 fold. Therefore, patients receiving these drugs in combination with strong CYP3A4 inhibitors should be monitored regularly to prevent adverse reactions. Furthermore, co-administration of docetaxel, cyclophosphamide, or epirubicin with strong CYP3A4 inducers should be avoided. In conclusion, the PBPK models can be used to further investigate the DDI potential of each drug and to develop dosage recommendations for concurrent usage by additional perpetrators or victims.

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.

Mechanistic Account of Distinct Change in Organic Anion Transporting Polypeptide 1B (OATP1B) Substrate Pharmacokinetics during OATP1B-Mediated Drug-Drug Interactions Using Physiologically Based Pharmacokinetic Modeling

The role of transporters in drug clearance is widely acknowledged, directly and indirectly by facilitating tissue/enzyme exposure. Through the latter, transporters also affect volume of distribution. Drug-drug interactions (DDIs) involving organic anion transporting polypeptides (OATPs) 1B1/1B3 and SLCO1B1 pharmacogenetics lead to altered pharmacokinetics of OATP1B substrates; however, several factors may confound direct interpretation of pharmacokinetic parameters from these clinical studies using noncompartmental analysis (NCA). A review of clinical data herein indicates a single dose of OATP1B inhibitor rifampin almost never leads to increased substrate half-life but often a decrease and that most clinical OATP1B substrates are CYP3A4 substrates and/or undergo enterohepatic cycling (EHC). Using hypothetically simple OATP1B substrate physiologically based pharmacokinetic (PBPK) models, simulated effect of rifampin differed from specific OATP1B inhibition due to short rifampin half-life causing dissipation of OATP1B inhibition over time combined with CYP3A4 induction. Calculated using simulated tissue data, volume of distribution indeed decreased with OATP1B inhibition and was expectedly limited to the contribution of liver volume. However, an apparent and counterintuitive effect of rifampin on volume greater than that on clearance resulted for CYP3A4 substrates using NCA. The effect of OATP1B inhibition and rifampin on OATP1B substrate models incorporating EHC plus or minus renal clearance was distinct compared with simpler models. Using PBPK models incorporating reversible lactone metabolism for clinical OATP1B substrates atorvastatin and pitavastatin, DDIs reporting decreased half-life with rifampin were reproduced. These simulations provide an explanation for the distinct change in OATP1B substrate pharmacokinetics observed in clinical studies, including changes in volume of distribution and additional mechanisms. SIGNIFICANCE STATEMENT: Transporters are involved in drug clearance and volume of distribution, and distinct changes in OATP1B substrate pharmacokinetics are observed with OATP1B inhibitor rifampin. Using hypothetical and validated PBPK models and simulations, this study addresses the limitations of single-dose rifampin and complicated clinical OATP1B substrate disposition in evaluating the pharmacokinetic parameters of OATP1B substrates during rifampin drug-drug interactions (DDIs). These models account for change in volume of distribution and identify additional mechanisms underlying apparent pharmacokinetic changes in OATP1B DDIs.

Assessment of the CYP3A4 Induction Potential by Carbamazepine: Insights from Two Clinical DDI Studies and PBPK Modeling

In the past, rifampicin was well-established as strong index CYP3A inducer in clinical drug-drug interaction (DDI) studies. However, due to identified potentially genotoxic nitrosamine impurities, it should not any longer be used in healthy volunteer studies. Available clinical data suggest carbamazepine as an alternative to rifampicin as strong index CYP3A4 inducer in clinical DDI studies. Further, physiologically-based pharmacokinetic (PBPK) modeling is a tool with increasing importance to support the DDI risk assessment of drugs during drug development. CYP3A4 induction properties and the safety profile of carbamazepine were investigated in two open-label, fixed sequence, crossover clinical pharmacology studies in healthy volunteers using midazolam as a sensitive index CYP3A4 substrate. Carbamazepine was up-titrated from 100 mg twice daily (b.i.d.) to 200 mg b.i.d., and to a final dose of 300 mg b.i.d. for 10 consecutive days. Mean area under plasma concentration-time curve from zero to infinity (AUC(0-∞)) of midazolam consistently decreased by 71.8% (ratio: 0.282, 90% confidence interval (CI): 0.235-0.340) and 67.7% (ratio: 0.323, 90% CI: 0.256-0.407) in study 1 and study 2, respectively. The effect was adequately described by an internally developed PBPK model for carbamazepine which has been made freely available to the scientific community. Further, carbamazepine was safe and well-tolerated in the investigated dosing regimen in healthy participants. The results demonstrated that the presented design is appropriate for the use of carbamazepine as alternative inducer to rifampicin in DDI studies acknowledging its CYP3A4 inductive potency and safety profile.

Evaluation of the potential impact on pharmacokinetics of various cytochrome P450 substrates of increasing IL-6 levels following administration of the T-cell bispecific engager glofitamab

Glofitamab is a novel T cell bispecific antibody developed for treatment of relapsed-refractory diffuse large B cell lymphoma and other non-Hodgkin's lymphoma indications. By simultaneously binding human CD20-expressing tumor cells and CD3 on T cells, glofitamab induces tumor cell lysis, in addition to T-cell activation, proliferation, and cytokine release. Here, we describe physiologically-based pharmacokinetic (PBPK) modeling performed to assess the impact of glofitamab-associated transient increases in interleukin 6 (IL-6) on the pharmacokinetics of several cytochrome P450 (CYP) substrates. By refinement of a previously described IL-6 model and inclusion of in vitro CYP suppression data for CYP3A4, CYP1A2, and 2C9, a PBPK model was established in Simcyp to capture the induced IL-6 levels seen when glofitamab is administered at the intended dose and dosing regimen. Following model qualification, the PBPK model was used to predict the potential impact of CYP suppression on exposures of various CYP probe substrates. PBPK analysis predicted that, in the worst-case, the transient elevation of IL-6 would increase exposures of CYP3A4, CYP2C9, and CYP1A2 substrates by less than or equal to twofold. Increases for CYP3A4, CYP2C9, and CYP1A2 substrates were projected to be 1.75, 1.19, and 1.09-fold following the first administration and 2.08, 1.28, and 1.49-fold following repeated administrations. It is recommended that there are no restrictions on concomitant treatment with any other drugs. Consideration may be given for potential drug-drug interaction during the first cycle in patients who are receiving concomitant CYP substrates with a narrow therapeutic index via monitoring for toxicity or for drug concentrations.

Prediction of drug-drug interactions between roflumilast and CYP3A4/1A2 perpetrators using a physiologically-based pharmacokinetic (PBPK) approach

This study aimed to develop a physiologically-based pharmacokinetic (PBPK) model to predict changes in the pharmacokinetics (PK) and pharmacodynamics (PD, PDE4 inhibition) of roflumilast (ROF) and ROF N-oxide when co-administered with eight CYP3A4/1A2 perpetrators. The population PBPK model of ROF and ROF N-oxide has been successfully developed and validated based on the four clinical PK studies and five clinical drug-drug interactions (DDIs) studies. In PK simulations, every ratio of prediction to observation for PK parameters fell within the range 0.7 to 1.5. In DDI simulations, except for tow peak concentration ratios (Cmax) of ROF with rifampicin (prediction: 0.63 vs. observation: 0.19) and with cimetidine (prediction: 1.07 vs. observation: 1.85), the remaining predicted ratios closely matched the observed ratios. Additionally, the PBPK model suggested that co-administration with the three perpetrators (cimetidine, enoxacin, and fluconazole) may use with caution, with CYP3A4 strong inhibitor (ketoconazole and itraconazole) or with dual CYP3A41A2 inhibitor (fluvoxamine) may reduce to half-dosage or use with caution, while co-administration with CYP3A4 strong or moderate inducer (rifampicin, efavirenz) should avoid. Overall, the present PBPK model can provide recommendations for adjusting dosing regimens in the presence of DDIs.

Design, synthesis and biological evaluation of chalcone derivatives as potent and orally active hCYP3A4 inhibitors

Human cytochrome P450 3A4 (hCYP3A4), one of the most important drug-metabolizing enzymes, catalyze the metabolic clearance of ∼50% therapeutic drugs. CYP3A4 inhibitors have been used for improving the in vivo efficacy of hCYP3A4-substrate drugs. However, most of existing hCYP3A4 inhibitors may trigger serious adverse effects or undesirable effects on endogenous metabolism. This study aimed to discover potent and orally active hCYP3A4 inhibitors from chalcone derivatives and to test their anti-hCYP3A4 effects both in vitro and in vivo. Following three rounds of screening and structural optimization, the isoquinoline chalcones were found with excellently anti-hCYP3A4 effects. SAR studies showed that introducing an isoquinoline ring on the A-ring significantly enhanced anti-CYP3A4 effect, generating A10 (IC50 = 102.10 nM) as a promising lead compound. The 2nd round of SAR studies showed that introducing a substituent group at the para position of the carbonyl group on B-ring strongly improved the anti-CYP3A4 effect. As a result, C6 was identified as the most potent hCYP3A4 inhibitor (IC50 = 43.93 nM) in human liver microsomes (HLMs). C6 also displayed potent anti-hCYP3A4 effect in living cells (IC50 = 153.00 nM), which was superior to the positive inhibitor ketoconazole (IC50 = 251.00 nM). Mechanistic studies revealed that C6 could potently inhibit CYP3A4-catalyzed N-ethyl-1,8-naphthalimide (NEN) hydroxylation in a competitive manner (Ki = 30.00 nM). Moreover, C6 exhibited suitable metabolic stability in HLMs and showed good safety profiles in mice. In vivo tests demonstrated that C6 (100 mg/kg, orally administration) significantly increased the AUC(0-inf) of midazolam by 3.63-fold, and strongly prolonged its half-life by 1.66-fold compared with the vehicle group in mice. Collectively, our findings revealed the SARs of chalcone derivatives as hCYP3A4 inhibitors and offered several potent chalcone-type hCYP3A4 inhibitors, while C6 could serve as a good lead compound for developing novel, orally active CYP3A4 inhibitors with improved druglikeness properties.

Power of Integrating PBPK with PBBM (PBPK-BM): A Single Model Predicting Food Effect, Gender Impact, Drug-Drug Interactions and Bioequivalence in Fasting & Fed Conditions

Over the past few years, PBPK and PBBM modeling have proven their significance in drug development. PBPK modeling is traditionally used to predict drug-drug interactions, exposures in special populations whereas PBBM modeling is a part of PBPK modeling that is used for a range of biopharmaceutics applications.Because of these differences in utilities, often PBPK and PBBM models are developed separately. When both models are combined, they serve multiple purposes through unified model. In the present case, an integrated PBPK-PBBM model for an IR product has been utilized for bioequivalence prediction in fasting & fed conditions, evaluating gender impact and food effect, prediction of drug-drug interactions.Model was built using physicochemical properties, enzymes and transporter kinetics, bio-predictive dissolution and has been validated with passing and failed pilot BE studies. The validated model predicted pivotal bioequivalence outcome in fasting & fed conditions accurately, predicted gender impact and food effect in line with literature. Drug-drug interactions arising from transporter and metabolizing enzymes were predicted accurately.Overall, this work demonstrates utility of combining PBPK and PBBM model that can yield a single model which can be used for multiple purposes, regulatory justifications and can reduce regulatory review timelines.

Impact of Obesity on the Drug-Drug Interaction Between Dolutegravir and Rifampicin or Any Other Strong Inducers

Background

Obesity is increasingly prevalent among people with HIV. Obesity can impact drug pharmacokinetics and consequently the magnitude of drug-drug interactions (DDIs) and, thus, the related recommendations for dose adjustment. Virtual clinical DDI studies were conducted using physiologically based pharmacokinetic (PBPK) modeling to compare the magnitude of the DDI between dolutegravir and rifampicin in nonobese, obese, and morbidly obese individuals.

Methods

Each DDI scenario included a cohort of virtual individuals (50% female) between 20 and 50 years of age. Drug models for dolutegravir and rifampicin were verified against clinical observed data. The verified models were used to simulate the concurrent administration of rifampicin (600 mg) at steady state with dolutegravir (50 mg) administered twice daily in normal-weight (BMI 18.5-30 kg/m²), obese (BMI 30-40 kg/m²), and morbidly obese (BMI 40-50 kg/m²) individuals.

Results

Rifampicin was predicted to decrease dolutegravir area under the curve (AUC) by 72% in obese and 77% in morbidly obese vs 68% in nonobese individuals; however, dolutegravir trough concentrations were reduced to a similar extent (83% and 85% vs 85%). Twice-daily dolutegravir with rifampicin resulted in trough concentrations always above the protein-adjusted 90% inhibitory concentration for all BMI groups and above the 300 ng/mL threshold in a similar proportion for all BMI groups.

Conclusions

The combined effect of obesity and induction by rifampicin was predicted to further decrease dolutegravir exposure but not the minimal concentration at the end of the dosing interval. Thus, dolutegravir 50 mg twice daily with rifampicin can be used in individuals with a high BMI up to 50 kg/m².

Prediction of Omeprazole Pharmacokinetics and its Inhibition on Gastric Acid Secretion in Humans Using Physiologically Based Pharmacokinetic-Pharmacodynamic Model Characterizing CYP2C19 Polymorphisms

PURPOSE

To develop a whole physiologically based pharmacokinetic-pharmacodynamic (PBPK-PD) model to describe the pharmacokinetics and anti-gastric acid secretion of omeprazole in CYP2C19 extensive metabolizers (EMs), intermediate metabolizers (IMs), poor metabolizers (PMs) and ultrarapid metabolizers (UMs) following oral or intravenous administration.

METHODS

A PBPK/PD model was built using Phoenix WinNolin software. Omeprazole was mainly metabolized by CYP2C19 and CYP3A4 and the CYP2C19 polymorphism was incorporated using in vitro data. We described the PD by using a turn-over model with parameter estimates from dogs and the effect of a meal on the acid secretion was also implemented. The model predictions were compared to 53 sets of clinical data.

RESULTS

Predictions of omeprazole plasma concentration (72.2%) and 24 h stomach pH after administration (85%) were within 0.5-2.0-fold of the observed values, indicating that the PBPK-PD model was successfully developed. Sensitivity analysis demonstrated that the contributions of the tested factors to the plasma concentration of omeprazole were Vmax,2C19 ≈ Papp > Vmax,3A4 > Kti, and contributions to its pharmacodynamic were Vmax,2C19 > kome > kms > Papp > Vmax,3A4. The simulations showed that while the initial omeprazole dose in UMs, EMs, and IMs increased 7.5-, 3- and 1.25-fold compared to those of PMs, the therapeutic effect was similar.

CONCLUSIONS

The successful establishment of this PBPK-PD model highlights that pharmacokinetic and pharmacodynamic profiles of drugs can be predicted using preclinical data. The PBPK-PD model also provided a feasible alternative to empirical guidance for the recommended doses of omeprazole.

Physiologically based pharmacokinetic modeling (PBPK) to predict drug-drug interactions for encorafenib. Part II. Prospective predictions in hepatic and renal impaired populations with clinical inhibitors and inducers

Encorafenib, a potent BRAF kinase inhibitor gets significantly metabolised by CYP3A4 (83%) and CYP2C19 (16%) and is a substrate for P-glycoprotein (P-gp). Due to significant metabolism by CYP3A4, encorafenib exposure can increase in hepatic and renal impairment and may lead to altered magnitude of drug-drug interactions (DDI). Hence, it is necessary to assess the exposures & DDI's in impaired population.Physiologically based pharmacokinetic modelling (PBPK) was utilised to determine the exposures of encorafenib in hepatic and renal impairment along with altered DDI's. Prospective DDI's were predicted with USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors and CYP3A4 inducers.PBPK models for encorafenib, perpetrators simulated PK parameters within 2-folds error. Encorafenib exposures significantly increased in hepatic as compared to renal impairment because of reduced CYP3A4 levels.Hepatic impairment caused changes in inhibition and induction DDI's, when compared to healthy population. Renal impairment did not cause significant changes in DDIs except for itraconazole. P-gp, CYP2C19 inhibitors did not result in altered DDI's. The DDI's were found to have insignificant correlation with relative exposure increase of perpetrators in case of impairment. Overall, this work signifies use of PBPK modelling for DDI's evaluations in hepatic and renal impairment populations.

Physiologically based pharmacokinetic modelling to predict drug-drug interactions for encorafenib. Part I. Model building, validation, and prospective predictions with enzyme inhibitors, inducers, and transporter inhibitors

Encorafenib, a potent BRAF kinase inhibitor undergoes significant metabolism by CYP3A4 (83%) and CYP2C19 (16%) and also a substrate of P-glycoprotein (P-gp). Because of this, encorafenib possesses potential for enzyme-transporter related interactions. Clinically, its drug-drug interactions (DDIs) with CYP3A4 inhibitors (posaconazole, diltiazem) were reported and hence there is a necessity to study DDIs with multiple enzyme inhibitors, inducers, and P-gp inhibitors.USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors, CYP3A4 inducers were selected and prospective DDIs were simulated using physiologically based pharmacokinetic modelling (PBPK). Impact of dose (50 mg vs. 300 mg) and staggering of administrations (0-10 h) on the DDIs were predicted.PBPK models for encorafenib, perpetrators simulated PK parameters within twofold prediction error. Clinically reported DDIs with posaconazole and diltiazem were successfully predicted.CYP2C19 inhibitors did not result in significant DDI whereas strong CYP3A4 inhibitors resulted in DDI ratio up to 4.5. Combining CYP3A4, CYP2C19 inhibitors yielded DDI equivalent CYP3A4 alone. Strong CYP3A4 inducers yielded DDI ratio up to 0.3 and no impact of P-gp inhibitors on DDIs was observed. The DDIs were not impacted by dose and staggering of administration. Overall, this work indicated significance of PBPK modelling for evaluating clinical DDIs with enzymes, transporters and interplay.

Treating asthma in the time of COVID

The Precision Interventions for Severe and/or Exacerbation-Prone Asthma clinical trials network is actively assessing novel treatments for severe asthma during the coronavirus disease (COVID-19) pandemic and has needed to adapt to various clinical dilemmas posed by the COVID-19 pandemic. Pharmacologic interactions between established asthma therapies and novel drug interventions for COVID-19 infection, including antivirals, biologics, and vaccines, have emerged as a critical and unanticipated issue in the clinical care of asthma. In particular, impaired metabolism of some long-acting beta-2 agonists by the cytochrome P4503A4 enzyme in the setting of antiviral treatment using ritonavir-boosted nirmatrelvir (NVM/r, brand name Paxlovid) may increase risk for adverse cardiovascular events. Although available data have documented the potential for such interactions, these issues are largely unappreciated by clinicians who treat asthma, or those dispensing COVID-19 interventions in patients who happen to have asthma. Because these drug-drug interactions have not previously been relevant to patient care, clinicians have had no guidance on management strategies to reduce potentially serious interactions between treatments for asthma and COVID-19. The Precision Interventions for Severe and/or Exacerbation-Prone Asthma network considered the available literature and product information, and herein share our considerations and plans for treating asthma within the context of these novel COVID-19-related therapies.

Physiologically Based Pharmacokinetic Modelling to Identify Physiological and Drug Parameters Driving Pharmacokinetics in Obese Individuals

BACKGROUND

Obese individuals are often underrepresented in clinical trials, leading to a lack of dosing guidance.

OBJECTIVE

This study aimed to investigate which physiological parameters and drug properties determine drug disposition changes in obese using our physiologically based pharmacokinetic (PBPK) framework, informed with obese population characteristics.

METHODS

Simulations were performed for ten drugs with clinical data in obese (i.e., midazolam, triazolam, caffeine, chlorzoxazone, acetaminophen, lorazepam, propranolol, amikacin, tobramycin, and glimepiride). PBPK drug models were developed and verified first against clinical data in non-obese (body mass index (BMI) ≤ 30 kg/m²) and subsequently in obese (BMI ≥ 30 kg/m²) without changing any drug parameters. Additionally, the PBPK model was used to study the effect of obesity on the pharmacokinetic parameters by simulating drug disposition across BMI, starting from 20 up to 60 kg/m².

RESULTS

Predicted pharmacokinetic parameters were within 1.25-fold (71.5%), 1.5-fold (21.5%) and twofold (7%) of clinical data. On average, clearance increased by 1.6% per BMI unit up to 64% for a BMI of 60 kg/m², which was explained by the increased hepatic and renal blood flows. Volume of distribution increased for all drugs up to threefold for a BMI of 60 kg/m²; this change was driven by pK_a for ionized drugs and logP for neutral and unionized drugs. C_max decreased similarly across all drugs while t_max remained unchanged.

CONCLUSION

Both physiological changes and drug properties impact drug pharmacokinetics in obese subjects. Clearance increases due to enhanced hepatic and renal blood flows. Volume of distribution is higher for all drugs, with differences among drugs depending on their pK_a/logP.

Prediction of Drug-Drug-Gene Interaction Scenarios of (E)-Clomiphene and Its Metabolites Using Physiologically Based Pharmacokinetic Modeling

Clomiphene, a selective estrogen receptor modulator (SERM), has been used for the treatment of anovulation for more than 50 years. However, since (E)-clomiphene ((E)-Clom) and its metabolites are eliminated primarily via Cytochrome P450 (CYP) 2D6 and CYP3A4, exposure can be affected by CYP2D6 polymorphisms and concomitant use with CYP inhibitors. Thus, clomiphene therapy may be susceptible to drug-gene interactions (DGIs), drug-drug interactions (DDIs) and drug-drug-gene interactions (DDGIs). Physiologically based pharmacokinetic (PBPK) modeling is a tool to quantify such DGI and DD(G)I scenarios. This study aimed to develop a whole-body PBPK model of (E)-Clom including three important metabolites to describe and predict DGI and DD(G)I effects. Model performance was evaluated both graphically and by calculating quantitative measures. Here, 90% of predicted C_max and 80% of AUC_last values were within two-fold of the corresponding observed value for DGIs and DD(G)Is with clarithromycin and paroxetine. The model also revealed quantitative contributions of different CYP enzymes to the involved metabolic pathways of (E)-Clom and its metabolites. The developed PBPK model can be employed to assess the exposure of (E)-Clom and its active metabolites in as-yet unexplored DD(G)I scenarios in future studies.

Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone

The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug–gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim^®, using a total of 57 plasma concentration–time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUC_last ratios and 9/12, 5/5 and 7/7 of DGI C_max ratios were within the prediction success limits. Finally, the models were used to predict their compound’s exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUC_ss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively.

Guide to development of compound files for PBPK modeling in the Simcyp population-based simulator

The Simcyp Simulator is a software platform for population physiologically‐based pharmacokinetic (PBPK) modeling and simulation. It links in vitro data to in vivo absorption, distribution, metabolism, excretion and pharmacokinetic/pharmacodynamic outcomes to explore clinical scenarios and support drug development decisions, including regulatory submissions and drug labels. This tutorial describes the different input parameters required, as well as the considerations needed when developing a PBPK model within the Simulator, for a small molecule intended for oral administration. A case study showing the development and application of a PBPK model for ondansetron is herein used to aid the understanding of different PBPK model development concepts.

Prediction of CYP‐mediated DDIs involving inhibition: Approaches to address the requirements for system qualification of the Simcyp Simulator

Physiologically‐based pharmacokinetic (PBPK) modeling is being increasingly used in drug development to avoid unnecessary clinical drug–drug interaction (DDI) studies and inform drug labels. Thus, regulatory agencies are recommending, or indeed requesting, more rigorous demonstration of the prediction accuracy of PBPK platforms in the area of their intended use. We describe a framework for qualification of the Simcyp Simulator with respect to competitive and mechanism‐based inhibition (MBI) of CYP1A2, CYP2D6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4/5. Initially, a DDI matrix, consisting of a range of weak, moderate, and strong inhibitors and substrates with varying fraction metabolized by specific CYP enzymes that were susceptible to different degrees of inhibition, were identified. Simulations were run with 123 clinical DDI studies involving competitive inhibition and 78 clinical DDI studies involving MBI. For competitive inhibition, the overall prediction accuracy was good with an average fold error (AFE) of 0.91 and 0.92 for changes in the maximum plasma concentration (C_max) and area under the plasma concentration (AUC) time profile, respectively, as a consequence of the DDI. For MBI, an AFE of 1.03 was determined for both C_max and AUC. The prediction accuracy was generally comparable across all CYP enzymes, irrespective of the isozyme and mechanism of inhibition. These findings provide confidence in application of the Simcyp Simulator (V19 R1) for assessment of the DDI potential of drugs in development either as inhibitors or victim drugs of CYP‐mediated interactions. The approach described herein and the identified DDI matrix can be used to qualify subsequent versions of the platform.

Effect of CYP3A4 Inhibitors and Inducers on Pharmacokinetics and Pharmacodynamics of Saxagliptin and Active Metabolite M2 in Humans Using Physiological-Based Pharmacokinetic Combined DPP-4 Occupancy

We aimed to develop a physiological-based pharmacokinetic and dipepidyl peptidase 4 (DPP-4) occupancy model (PBPK-DO) characterized by two simultaneous simulations to predict pharmacokinetic (PK) and pharmacodynamic changes of saxagliptin and metabolite M2 in humans when coadministered with CYP3A4 inhibitors or inducers. Ketoconazole, delavirdine, and rifampicin were selected as a CYP3A4 competitive inhibitor, a time-dependent inhibitor, and an inducer, respectively. Here, we have successfully simulated PK profiles and DPP-4 occupancy profiles of saxagliptin in humans using the PBPK-DO model. Additionally, under the circumstance of actually measured values, predicted results were good and in line with observations, and all fold errors were below 2. The prediction results demonstrated that the oral dose of saxagliptin should be reduced to 2.5 mg when coadministrated with ketoconazole. The predictions also showed that although PK profiles of saxagliptin showed significant changes with delavirdine (AUC 1.5-fold increase) or rifampicin (AUC: a decrease to 0.19-fold) compared to those without inhibitors or inducers, occupancies of DPP-4 by saxagliptin were nearly unchanged, that is, the administration dose of saxagliptin need not adjust when there is coadministration with delavirdine or rifampicin.

Evidence-Based Guidelines for Drug Interaction Studies: Model-Informed Time Course of Intestinal and Hepatic CYP3A4 Inhibition by Clarithromycin

Drug-drug interaction (DDI) studies are mandated in drug development; however, protocols for evaluating the impact of cytochrome P450 (CYP) inhibition on new molecular entities are currently inconsistent. This study utilised validated physiologically based pharmacokinetic (PBPK) software to define the optimal dose, frequency, and duration of clarithromycin to achieve optimal characterisation of CYP3A4 inhibition in a study population. The Simcyp® Simulator (Version 19.0) was used to simulate clarithromycin-mediated CYP3A4 inhibition in healthy virtual cohorts. Between trial variability in magnitude and time course of CYP3A4 activity was assessed following clarithromycin dosing strategies obtained from the University of Washington Drug Interaction Database. Heterogeneity in CYP3A4 inhibition was evaluated across sex, race, and age. Literature review identified 500 mg twice daily for 5 days as the most common clarithromycin dosing protocol for CYP3A4 inhibition studies. On simulation, clarithromycin 500 mg twice daily resulted in the largest steady-state inhibition of hepatic (percent mean inhibition [95%CI] = 80 [77-83]) and small intestine (94 [94-95]) CYP3A4 activity (as compared to 500 mg once daily, 400 mg once/twice daily, or 250 mg once/twice daily). Additionally, 500 mg twice daily was associated with the shortest time for 90% of individuals to reach 90% of their minimum hepatic (4 days) and small intestine (1 days) CYP3A4 activity. The study presented herein supports that clarithromycin dosing protocol of 500 mg twice daily for 5 days is sufficient to achieve maximal hepatic and small intestine CYP3A4 inhibition. These findings were consistent between sex, race, and age differences.

Physiologically Based Pharmacokinetic Modeling and Allometric Scaling in Pediatric Drug Development: Where Do We Draw the Line?

Developing medicines for children is now established in legislation in both the United States and Europe; new drugs require pediatric study or investigation plans as part of their development. Particularly in early age groups, many developmental processes are not reflected by simple scalars such as body weight or body surface area, and even projecting doses based on simple allometric scaling can lead to significant overdoses in certain age groups. Modeling and simulation methodology, including physiologically based modeling, has evolved as part of the drug development toolkit and is being increasingly applied to various aspects of pediatric drug development. Pediatric physiologically based pharmacokinetic (PBPK) models account for the development of organs and the ontogeny of specific enzymes and transporters that determine the age-related pharmacokinetic profiles. However, when should this approach be used, and when will simpler methods such as allometric scaling suffice in answering specific problems? The aim of this review article is to illustrate the application of allometric scaling and PBPK in pediatric drug development and explore the optimal application of the latter approach with reference to case examples. In reality, allometric scaling included as part of population pharmacokinetic and PBPK approaches are all part of a model-informed drug development toolkit helping with decision making during the process of drug discovery and development; to that end, they should be viewed as complementary.

Physiologically Based Pharmacokinetic Modelling to Identify Pharmacokinetic Parameters Driving Drug Exposure Changes in the Elderly

BACKGROUND

Medication use is highly prevalent with advanced age, but clinical studies are rarely conducted in the elderly, leading to limited knowledge regarding age-related pharmacokinetic changes.

OBJECTIVE

The objective of this study was to investigate which pharmacokinetic parameters determine drug exposure changes in the elderly by conducting virtual clinical trials for ten drugs (midazolam, metoprolol, lisinopril, amlodipine, rivaroxaban, repaglinide, atorvastatin, rosuvastatin, clarithromycin and rifampicin) using our physiologically based pharmacokinetic (PBPK) framework.

METHODS

PBPK models for all ten drugs were developed in young adults (20-50 years) following the best practice approach, before predicting pharmacokinetics in the elderly (≥ 65 years) without any modification of drug parameters. A descriptive relationship between age and each investigated pharmacokinetic parameter (peak concentration [C_max], time to C_max [t_max], area under the curve [AUC], clearance, volume of distribution, elimination-half-life) was derived using the final PBPK models, and verified with independent clinically observed data from 52 drugs.

RESULTS

The age-related changes in drug exposure were successfully simulated for all ten drugs. Pharmacokinetic parameters were predicted within 1.25-fold (70%), 1.5-fold (86%) and 2-fold (100%) of clinical data. AUC increased progressively by 0.9% per year throughout adulthood from the age of 20 years, which was explained by decreased clearance, while C_max, t_max and volume of distribution were not affected by human aging. Additional clinical data of 52 drugs were contained within the estimated variability of the established age-dependent correlations for each pharmacokinetic parameter.

CONCLUSION

The progressive decrease in hepatic and renal blood flow, as well as glomerular filtration, rate led to a reduced clearance driving exposure changes in the healthy elderly, independent of the drug.

Recent advances in computational metabolite structure predictions and altered metabolic pathways assessment to inform drug development processes

Many drug candidates fail during preclinical and clinical trials due to variable or unexpected metabolism which may lead to variability in drug efficacy or adverse drug reactions. The drug metabolism field aims to address this important issue from many angles which range from the study of drug-drug interactions, pharmacogenomics, computational metabolic modeling, and others. This manuscript aims to provide brief but comprehensive manuscript summaries highlighting the conclusions and scientific importance of seven exceptional manuscripts published in recent years within the field of drug metabolism. Two main topics within the field are reviewed: novel computational metabolic modeling approaches which provide complex outputs beyond site of metabolism predictions, and experimental approaches designed to discern the impacts of interindividual variability and species differences on drug metabolism. The computational approaches discussed provide novel outputs in metabolite structure and formation likelihood and/or extend beyond the saturated field of drug phase I metabolism, while the experimental metabolic pathways assessments aim to highlight the impacts of genetic polymorphisms and clinical animal model metabolic differences on human metabolism and subsequent health outcomes.

PBPK modeling to predict drug-drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction

Ivosidenib is a potent, targeted, orally active, small-molecule inhibitor of mutant isocitrate dehydrogenase 1 (IDH1) that has been approved in the United States for the treatment of adults with newly diagnosed acute myeloid leukemia (AML) who are greater than or equal to 75 years of age or ineligible for intensive chemotherapy, and those with relapsed or refractory AML, with a susceptible IDH1 mutation. Ivosidenib is an inducer of the CYP2B6, CYP2C8, CYP2C9, and CYP3A4 and an inhibitor of P-glycoprotein (P-gp), organic anion transporting polypeptide-1B1/1B3 (OATP1B1/1B3), and organic anion transporter-3 (OAT3) in vitro. A physiologically-based pharmacokinetic (PK) model was developed to predict drug-drug interactions (DDIs) of ivosidenib in patients with AML. The in vivo CYP3A4 induction effect of ivosidenib was quantified using 4β-hydroxycholesterol and was subsequently verified with the PK data from an ivosidenib and venetoclax combination study. The verified model was prospectively applied to assess the effect of multiple doses of ivosidenib on a sensitive CYP3A4 substrate, midazolam. The simulated midazolam geometric mean area under the curve (AUC) and maximum plasma concentration (C_max ) ratios were 0.18 and 0.27, respectively, suggesting ivosidenib is a strong inducer. The model was also used to predict the DDIs of ivosidenib with CYP2B6, CYP2C8, CYP2C9, P-gp, OATP1B1/1B3, and OAT3 substrates. The AUC ratios following multiple doses of ivosidenib and a single dose of CYP2B6 (bupropion), CYP2C8 (repaglinide), CYP2C9 (warfarin), P-gp (digoxin), OATP1B1/1B3 (rosuvastatin), and OAT3 (methotrexate) substrates were 0.90, 0.52, 0.84, 1.01, 1.02, and 1.27, respectively. Finally, in accordance with regulatory guidelines, the Simcyp modeling platform was qualified to predict CYP3A4 induction using known inducers and sensitive substrates.

Cytochrome P450 3A Time-Dependent Inhibition Assays Are Too Sensitive for Identification of Drugs Causing Clinically Significant Drug-Drug Interactions: A Comparison of Human Liver Microsomes and Hepatocytes and Definition of Boundaries for Inactivation Rate Constants

Time-dependent inhibition (TDI) of CYP3A is an important mechanism underlying numerous drug-drug interactions (DDIs), and assays to measure this are done to support early drug research efforts. However, measuring TDI of CYP3A in human liver microsomes (HLMs) frequently yields overestimations of clinical DDIs and thus can lead to the erroneous elimination of many viable drug candidates from further development. In this investigation, 50 drugs were evaluated for TDI in HLMs and suspended human hepatocytes (HHEPs) to define appropriate boundary lines for the TDI parameter rate constant for inhibition (k_obs) at a concentration of 30 µM. In HLMs, a k_obs value of 0.002 minute^-1 was statistically distinguishable from control; however, many drugs show k_obs greater than this but do not cause DDI. A boundary line defined by the drug with the lowest k_obs that causes a DDI (diltiazem) was established at 0.01 minute^-1 Even with this boundary, of the 33 drugs above this value, only 61% cause a DDI (true positive rate). A corresponding analysis was done using HHEPs; k_obs of 0.0015 minute^-1 was statistically distinguishable from control, and the boundary was established at 0.006 minute^-1 Values of k_obs in HHEPs were almost always lower than those in HLMs. These findings offer a practical guide to the use of TDI data for CYP3A in early drug-discovery research. SIGNIFICANCE STATEMENT: Time-dependent inhibition of CYP3A is responsible for many drug interactions. In vitro assays are employed in early drug research to identify and remove CYP3A time-dependent inhibitors from further consideration. This analysis demonstrates suitable boundaries for inactivation rates to better delineate drug candidates for their potential to cause clinically significant drug interactions.

Inhibition of drug-metabolizing enzymes by Qingfei Paidu decoction: Implication of herb-drug interactions in COVID-19 pharmacotherapy

Corona Virus Disease 2019 (COVID-19) has spread all over the world and brings significantly negative effects on human health. To fight against COVID-19 in a more efficient way, drug-drug or drug-herb combinations are frequently used in clinical settings. The concomitant use of multiple medications may trigger clinically relevant drug/herb-drug interactions. This study aims to assay the inhibitory potentials of Qingfei Paidu decoction (QPD, a Chinese medicine compound formula recommended for combating COVID-19 in China) against human drug-metabolizing enzymes and to assess the pharmacokinetic interactions in vivo. The results demonstrated that QPD dose-dependently inhibited CYPs1A, 2A6, 2C8, 2C9, 2C19, 2D6 and 2E1 but inhibited CYP3A in a time- and NADPH-dependent manner. In vivo test showed that QPD prolonged the half-life of lopinavir (a CYP3A substrate-drug) by 1.40-fold and increased the AUC of lopinavir by 2.04-fold, when QPD (6 g/kg) was co-administrated with lopinavir (160 mg/kg) to rats. Further investigation revealed that Fructus Aurantii Immaturus (Zhishi) in QPD caused significant loss of CYP3A activity in NADPH-generating system. Collectively, our findings revealed that QPD potently inactivated CYP3A and significantly modulated the pharmacokinetics of CYP3A substrate-drugs, which would be very helpful for the patients and clinicians to avoid potential drug-interaction risks in COVID-19 treatment.

Physiologically-Based Pharmacokinetic Modeling to Support the Clinical Management of Drug-Drug Interactions With Bictegravir

Bictegravir is equally metabolized by cytochrome P450 (CYP)3A and uridine diphosphate‐glucuronosyltransferase (UGT)1A1. Drug–drug interaction (DDI) studies were only conducted for strong inhibitors and inducers, leading to some uncertainty whether moderate perpetrators or multiple drug associations can be safely coadministered with bictegravir. We used physiologically‐based pharmacokinetic (PBPK) modeling to simulate DDI magnitudes of various scenarios to guide the clinical DDI management of bictegravir. Clinically observed DDI data for bictegravir coadministered with voriconazole, darunavir/cobicistat, atazanavir/cobicistat, and rifampicin were predicted within the 95% confidence interval of the PBPK model simulations. The area under the curve (AUC) ratio of the DDI divided by the control scenario was always predicted within 1.25‐fold of the clinically observed data, demonstrating the predictive capability of the used modeling approach. After the successful verification, various DDI scenarios with drug pairs and multiple concomitant drugs were simulated to analyze their effect on bictegravir exposure. Generally, our simulation results suggest that bictegravir should not be coadministered with strong CYP3A and UGT1A1 inhibitors and inducers (e.g., atazanavir, nilotinib, and rifampicin), but based on the present modeling results, bictegravir could be administered with moderate dual perpetrators (e.g., efavirenz). Importantly, the inducing effect of rifampicin on bictegravir was predicted to be reversed with the concomitant administration of a strong inhibitor such as ritonavir, resulting in a DDI magnitude within the efficacy and safety margin for bictegravir (0.5–2.4‐fold). In conclusion, the PBPK modeling strategy can effectively be used to guide the clinical management of DDIs for novel drugs with limited clinical experience, such as bictegravir.

Physiologically Based Pharmacokinetic Modeling of Doravirine and Its Major Metabolite to Support Dose Adjustment With Rifabutin

Doravirine, a novel nonnucleoside reverse transcriptase inhibitor for the treatment of human immunodeficiency virus 1 (HIV-1), is predominantly cleared by cytochrome P450 (CYP) 3A4 and metabolized to an oxidative metabolite (M9). Coadministration with rifabutin, a moderate CYP3A4 inducer, decreased doravirine exposure. Based on nonparametric superposition modeling, a doravirine dose adjustment from 100 mg once daily to 100 mg twice daily during rifabutin coadministration was proposed. However, M9 exposure may also be impacted by induction, in addition to the dose adjustment. As M9 concentrations have not been quantified in previous clinical studies, a physiologically based pharmacokinetic model was developed to investigate the change in M9 exposure when doravirine is coadministered with CYP3A inducers. Simulations demonstrated that although CYP3A induction increases doravirine clearance by up to 4.4-fold, M9 exposure is increased by only 1.2-fold relative to exposures for doravirine 100 mg once daily in the absence of CYP3A induction. Thus, a 2.4-fold increase in M9 exposure relative to the clinical dose of doravirine is anticipated when doravirine 100 mg twice daily is coadministered with rifabutin. In a subsequent clinical trial, doravirine and M9 exposures, when doravirine 100 mg twice daily was coadministered with rifabutin, were found to be consistent with model predictions using rifampin and efavirenz as representative inducers. These findings support the dose adjustment to doravirine 100 mg twice daily when coadministered with rifabutin.

Changes in perampanel levels during de-induction: Simulations following carbamazepine discontinuation

Objective

To evaluate the time course of changes in perampanel levels when co‐administered with carbamazepine, and following carbamazepine discontinuation, using a physiologically based pharmacokinetic (PBPK) model.

Methods

The PBPK model was developed, verified using clinical PK data, and used to simulate the effect of abrupt discontinuation and down‐titration (75 mg twice daily [bid]/wk) of co‐administered carbamazepine 300 mg bid on the PK of perampanel once daily (qd). Perampanel dose tapering (8‐4 mg) and up‐titration (2‐6 mg) were simulated during abrupt carbamazepine 300 mg bid discontinuation to identify a titration schedule that minimizes changes in perampanel plasma concentrations.

Results

The PBPK model accurately reproduced perampanel plasma concentration‐time profiles from clinical studies in single‐ and multiple‐dose regimen simulations, including multiple‐dose carbamazepine co‐administration. The time course of return to pre‐induced perampanel levels occurred more slowly following carbamazepine down‐titration (~48 days after first down‐titration) vs abrupt discontinuation (~25 days). Perampanel dose tapering (8‐4 mg) at abrupt carbamazepine discontinuation produced minimal changes in steady‐state concentrations, which returned to the levels observed during carbamazepine co‐administration in ~15 days from the time of carbamazepine discontinuation. When perampanel was up‐titrated in the presence of carbamazepine, return to steady state occurred more slowly when carbamazepine was down‐titrated weekly (~45 days) vs abrupt discontinuation (~24 days).

Conclusion

This PBPK model simulated and predicted optimal perampanel dose tapering and up‐titration schedules for maintaining perampanel levels during conversion to monotherapy. These results may guide physicians when managing conversion from perampanel polytherapy with concomitant enzyme‐inducing anti‐seizure medications to monotherapy.

Challenging the Relevance of Unbound Tissue-to-Blood Partition Coefficient (Kpuu) on Prediction of Drug-Drug Interactions

PURPOSE

To examine the theoretical/practical utility of the liver-to-blood partition coefficient (Kp_uu) for predicting drug-drug interactions (DDIs), and compare the Kp_uu-approach to the extended clearance concept AUC_R-approach.

METHODS

The Kp_uu relationship was derived from first principles. Theoretical simulations investigated the impact of changes in a single hepatic-disposition process on unbound systemic (AUC_B,u) and hepatic exposure (AUC_H,u) versus Kp_uu. Practical aspects regarding Kp_uu utilization were examined by predicting the magnitude of DDI between ketoconazole and midazolam employing published ketoconazole Kp_uu values.

RESULTS

The Kp_uu hepatic-disposition relationship is based on the well-stirred model. Simulations emphasize that changes in influx/efflux intrinsic clearances result in Kp_uu changes, however AUC_H,u remains unchanged. Although incorporation of Kp_uu is believed to improve DDI-predictions, utilizing published ketoconazole Kp_uu values resulted in prediction errors for a midazolam DDI.

CONCLUSIONS

There is limited benefit in using Kp_uu for DDI-predictions as the AUC_R-based approach can reasonably predict DDIs without measurement of intracellular drug concentrations, a difficult task hindered by experimental variability. Further, Kp_uu changes can mislead as they may not correlate with changes in AUC_B,u or AUC_H,u. The well-stirred model basis of Kp_uu when applied to hepatic-disposition implies that nuances of intracellular drug distribution are not considered by the Kp_uu model.

Dose Predictions for Drug Design

The efficacious dose of a drug is perhaps the most holistic metric reflecting its therapeutic potential. Dose is predicted at many stages in drug discovery and development. Prior to the 1990s, dose prediction was limited to the drug "working" at a reasonable dose and dose regimen in an animal model. Through the early 2000s, dose predictions were generated at candidate nomination and then refined during clinical development. Currently, dose predictions can be made early in drug discovery to enable drug design. Dose predictions at this stage can identify critical drug properties for a viable dose regimen and provide clinically relevant context to lead optimization. In this paper, we give an overview of the opportunities and challenges associated with dose prediction for drug design. A number of general considerations, approaches, and case examples are discussed.

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.

Simultaneously predict pharmacokinetic interaction of rifampicin with oral versus intravenous substrates of cytochrome P450 3A/P‑glycoprotein to healthy human using a semi-physiologically based pharmacokinetic model involving both enzyme and transporter turnover

Several reports demonstrated that rifampicin affected pharmacokinetics of victim drugs following oral more than intravenous administration. We aimed to establish a semi-physiologically based pharmacokinetic (semi-PBPK) model involving both enzyme and transporter turnover to simultaneously predict pharmacokinetic interaction of rifampicin with oral versus intravenous substrates of cytochrome P450 (CYP) 3A4/P‑glycoprotein (P-GP) in human. Rifampicin was chosen as the CYP3A /P-GP inducer. Thirteen victim drugs including P-GP substrates (digoxin and talinolol), CYP3A substrates (alfentanil, midazolam, nifedipine, ondansetron and oxycodone), dual substrates of CYP3A/P-GP (quinidine, cyclosporine A, tacrolimus and verapamil) and complex substrates (S-ketamine and tramadol) were chosen to investigate drug-drug interactions (DDIs) with rifampicin. Corresponding parameters were cited from literatures. Before and after multi-dose of oral rifampicin, the pharmacokinetic profiles of victim drugs for oral or intravenous administration to human were predicted using the semi-PBPK model and compared with the observed values. Contribution of both CYP3A and P-GP induction in intestine and liver by rifampicin to pharmacokinetic profiles of victim drugs was investigated. The predicted pharmacokinetic profiles of drugs before and after rifampicin administration accorded with the observations. The predicted pharmacokinetic parameters and DDIs were successful, whose fold-errors were within 2. It was consistent with observations that the DDIs of rifampicin with oral victim drugs were larger than those with intravenous victim drugs. DDIs of rifampicin with CYP3A or P-GP substrates following oral versus intravenous administration to human were successfully predicted using the developed semi-PBPK model.

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.

The nested enzyme-within-enterocyte (NEWE) turnover model for predicting dynamic drug and disease effects on the gut wall

Physiologically-based pharmacokinetic (PBPK) models provide a framework for in vitro-in vivo extrapolation of metabolic drug clearance. Many of the concepts in PBPK can have consequential impact on more mechanistic systems pharmacology models. In the gut wall, turnover of enzymes and enterocytes are typically lumped into one rate constant that describes the time dependent enzyme activity. This assumption may influence predictability of any sustained and dynamic effects such as mechanism-based inhibition (MBI), particularly when considering translation from healthy to gut disease. A novel multi-level systems PBPK model was developed. This model comprised a 'nested enzyme-within enterocyte' (NEWE) turnover model to describe levels of drug-metabolising enzymes. The ability of the model to predict gut metabolism following MBI and gut disease was investigated and compared to the conventional modelling approach. For MBI, the default NEWE model performed comparably to the conventional model. However, when drug-specific spatial crypt-villous absorption was considered, up to approximately 50% lower impact of MBI was simulated for substrates highly metabolised by cytochrome P450 (CYP) 3A4, interacting with potent inhibitors. Further, the model showed potential in predicting the disease effect of gastrointestinal mucositis and untreated coeliac disease when compared to indirect clinical pharmacokinetic parameters. Considering the added complexity of the NEWE model, it does not provide an attractive solution for improving upon MBI predictions in healthy individuals. However, nesting turnover may enable extrapolation to gut disease-drug interactions. The principle detailed herein may be useful for modelling drug interactions with cellular targets where turnover is significant enough to affect this process.

PBPK Modeling Strategy for Predicting Complex Drug Interactions of Letermovir as a Perpetrator in Support of Product Labeling

Letermovir is a human cytomegalovirus (CMV) terminase inhibitor for the prophylaxis of CMV infection in allogeneic hematopoietic stem-cell transplant (HSCT) recipients. In vitro, letermovir is a time-dependent inhibitor and an inducer of cytochrome P450 (CYP)3A, and an inhibitor of CYP2C8 and organic anion transporting polypeptide (OATP)1B. A stepwise approach was taken to qualify the interaction model of an existing letermovir physiologically based pharmacokinetic model to predict letermovir interactions with CYP3A and OATP1B. The model was then used to prospectively predict the interaction between letermovir and CYP2C8 substrates such as repaglinide, a substrate of CYP2C8, CYP3A, and OATP1B. The results showed that letermovir modestly increased the exposure of CYP2C8 substrates. These results were used to inform the US prescribing information in the absence of clinical drug-drug interaction studies. In addition, midazolam interactions with letermovir at therapeutic doses were also simulated to confirm that letermovir is a moderate CYP3A inhibitor.

PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin

According to current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidance documents, physiologically based pharmacokinetic (PBPK) modeling is a powerful tool to explore and quantitatively predict drug‐drug interactions (DDIs) and may offer an alternative to dedicated clinical trials. This study provides whole‐body PBPK models of rifampicin, itraconazole, clarithromycin, midazolam, alfentanil, and digoxin within the Open Systems Pharmacology (OSP) Suite. All models were built independently, coupled using reported interaction parameters, and mutually evaluated to verify their predictive performance by simulating published clinical DDI studies. In total, 112 studies were used for model development and 57 studies for DDI prediction. 93% of the predicted area under the plasma concentration‐time curve (AUC) ratios and 94% of the peak plasma concentration (C_max) ratios are within twofold of the observed values. This study lays a cornerstone for the qualification of the OSP platform with regard to reliable PBPK predictions of enzyme‐mediated and transporter‐mediated DDIs during model‐informed drug development. All presented models are provided open‐source and transparently documented.

Incompatibility of chemical protein synthesis inhibitors with accurate measurement of extended protein degradation rates

Protein synthesis inhibitors are commonly used for measuring protein degradation rates, but may cause cytotoxicity via direct or indirect mechanisms. This study aimed to identify concentrations providing optimal inhibition in the absence of overt cytotoxicity. Actinomycin D, cycloheximide, emetine, and puromycin were assessed individually, and in two-, three-, and four-drug combinations for protein synthesis inhibition (IC₅₀ ) and cytotoxicity (CC₅₀ ) over 72 h. Experiments were conducted in HepG2 cells and primary rat hepatocytes (PRH). IC₅₀ for actinomycin D, cycloheximide, emetine, and puromycin were 39 ± 7.4, 6600 ± 2500, 2200 ± 1400, and 1600 ± 1200 nmol/L; with corresponding CC₅₀ values of 6.2 ± 7.3, 570 ± 510, 81 ± 9, and 1300 ± 64 nmol/L, respectively, in HepG2 cells. The IC₅₀ were 1.7 ± 1.8, 290 ± 90, 620 ± 920, and 2000 ± 2000 nmol/L, with corresponding CC₅₀ values of 0.98 ± 1.8, 680 ± 1300, 180 ± 700, and 1600 ± 1000 (SD) nmol/L, respectively, in PRH. CC₅₀ were also lower than the IC₅₀ for all drug combinations in HepG2 cells. These data indicate that using pharmacological interference is inappropriate for measuring protein degradation over a protracted period, because inhibitory effects cannot be extricated from cytotoxicity.

Derivation of CYP3A4 and CYP2B6 degradation rate constants in primary human hepatocytes: A siRNA-silencing-based approach

The first-order degradation rate constant (k_deg) of cytochrome P450 (CYP) enzymes is a known source of uncertainty in the prediction of time-dependent drug-drug interactions (DDIs) in physiologically-based pharmacokinetic (PBPK) modelling. This study aimed to measure CYP k_deg using siRNA to suppress CYP expression in primary human hepatocytes followed by incubation over a time-course and tracking of protein expression and activity to observe degradation. The magnitude of gene knockdown was determined by qPCR and activity was measured by probe substrate metabolite formation and CYP2B6-Glo™ assay. Protein disappearance was determined by Western blotting. During a time-course of 96 and 60 h of incubation, over 60% and 76% mRNA knockdown was observed for CYP3A4 and CYP2B6, respectively. The k_deg of CYP3A4 and CYP2B6 protein was 0.0138 h^-1 (±0.0023) and 0.0375 h^-1 (±0.025), respectively. The k_deg derived from probe substrate metabolism activity was 0.0171 h^-1 (±0.0025) for CYP3A4 and 0.0258 h^-1 (±0.0093) for CYP2B6. The CYP3A4 k_deg values derived from protein disappearance and metabolic activity were in relatively good agreement with each other and similar to published values. This novel approach can now be used for other less well-characterised CYPs.

Reverse Translation in PBPK and QSP: Going Backwards in Order to Go Forward With Confidence

Significant events have taken place shaping the recent industrialization of physiologically based pharmacokinetic in vitro-in vivo extrapolation (PBPK-IVIVE) use in drug development. Due to our knowledge gaps about drug-independent systems parameters, there are limitations in the use of purely IVIVE-based (bottom-up) approaches. This has encouraged combining the classical data analysis (top-down) with PBPK-IVIVE-linked models in order to optimize model parameters by taking advantage of observed clinical data. This concept, when initiated after clinical observations, can be viewed as "reverse translation," since it refers back to available systems information preclinical data before trying to describe the observations. This review demonstrates the advantages of such strategies in filling knowledge gaps and discusses the perceived hurdles in widening applications. It is paramount that no clinical data are assessed on their own, but in conjunction with other studies for that drug in different populations and/or other similar drugs in the same population.