Verified Physiologically Based Pharmacokinetic Model Research Articles

Integrating Clinical Variability into PBPK Models for Virtual Bioequivalence of Single and Multiple Doses of Tofacitinib Modified-Release Dosage Form.

Tofacitinib is a potent, selective inhibitor of the Janus kinase (JAK) family of kinases with a high degree of selectivity within the human genome's set of protein kinases. Currently approved formulations for tofacitinib citrate are immediate-release (IR) tablets, modified-release (MR) tablets, and IR solution. A once daily MR microsphere formulation was developed for use in pediatric patients. Demonstration of bioequivalence (BE) between the 10 mg once daily (q.d.) MR microsphere formulation and 5 mg twice daily (b.i.d.) IR solution is needed to enable the exposure-response analyses-based bridging to support regulatory approval. To assess BE between MR microsphere and IR solution, an innovative approach was utilized with physiologically-based pharmacokinetic (PBPK) virtual BE trials (VBE) in lieu of a clinical BE trial. A PBPK model was developed to characterize the absorption of different formulations of tofacitinib using Simcyp ADAM module. VBE trials were conducted by simulating PK profiles using the verified PBPK model and integrating the clinically observed intrasubject coefficient of variation (ICV) where BE was assessed with a predetermined sample size and prespecified criteria. The VBE trials demonstrated BE between IR solution 5 mg b.i.d. and MR microsphere 10 mg q.d. after a single dose on day 1 and after multiple doses on day 5. This research presents an innovative approach that incorporates clinically observed ICV in PBPK model-based VBE trials, which could reduce unnecessary drug exposure to healthy volunteers and streamline new formulation development strategies.

Development and Verification of a Full Physiologically Based Pharmacokinetic Model for Sublingual Buprenorphine in Healthy Adult Volunteers that Accounts for Nonlinear Bioavailability.

Sublingual buprenorphine is used for opioid use disorder and neonatal opioid withdrawal syndrome. The study aimed to develop a full physiologically based pharmacokinetic (PBPK) model that can adequately describe dose- and formulation-dependent bioavailability of buprenorphine. Simcyp (v21.0) was used for model construction. Four linear regression models (i.e., untransformed or log transformed for dose or proportion sublingually absorbed) were explored to describe sublingual absorption of buprenorphine across dose. Published clinical trial data not used in model development were used for verification. The PBPK model's predictive performance was deemed adequate if the geometric means of ratios between predicted and observed (P/O) area under the curve (AUC), peak concentration (Cmax), and time to reach Cmax (Tmax) fell within the 1.25-fold prediction error range. Sublingual buprenorphine absorption was best described by a regression model with logarithmically transformed dose. By integrating this nonlinear absorption profile, the PBPK model adequately predicted buprenorphine pharmacokinetics (PK) following administration of sublingual tablets and solution across a dose range of 2-32 mg, with geometric mean (95% confidence interval) P/O ratios for AUC and Cmax equaling 0.99 (0.86-1.12) and 1.24 (1.09-1.40), respectively, and median (5th to 95th percentile) for Tmax equaling 1.11 (0.69-1.57). A verified PBPK model was developed that adequately predicts dose- and formulation-dependent buprenorphine PK following sublingual administration. SIGNIFICANCE STATEMENT: The physiologically based pharmacokinetic (PBPK) model developed in this study is the first to adequately predict dose- and formulation-dependent sublingual buprenorphine pharmacokinetics. Accurate prediction was facilitated by the incorporation of a novel nonlinear absorption model. The developed model will serve as the foundation for maternal-fetal PBPK modeling to predict maternal and fetal buprenorphine exposures to optimize buprenorphine treatment for neonatal opioid withdrawal syndrome.

In Vitro In Vivo Extrapolation and Bioequivalence Prediction for Immediate-Release Capsules of Cefadroxil Based on a Physiologically-Based Pharmacokinetic ACAT Model.

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic concept, which helps to judge the effects of biopharmceutical properties of drug product such as in vitro dissolution on its pharmacokinetic and in vivo performance. With the application of virtual bioequivalence (VBE) study, the drug product development using model-based approach can help in evaluating the possibility of extending BCS-based biowaiver. Therefore, the current study was intended to develop PBPK model as well as in vitro in vivo extrapolation (IVIVE) for BCS class III drug i.e. cefadroxil.A PBPK model was created in GastroPlus™ 9.8.3 utilizing clinical data of immediate-release cefadroxil formulations. By the examination of simulated and observed plasma drug concentration profiles, the predictability of the proposed model was assessed for the prediction errors. Furthermore, mechanistic deconvolution was used to create IVIVE, and the plasma drug concentration profiles and pharmacokinetic parameters were predicted for different virtual formulations with variable cefadroxil in vitro release. Virtual bioequivalence study was also executed to assess the bioequivalence of the generic verses the reference drug product (Duricef®).The developed PBPK model satisfactorily predicted Cmax and AUC0-t after cefadroxil single and multiple oral dose administrations, with all individual prediction errors within the limits except in a few cases. Second order polynomial correlation function obtained accurately predict in vivo drug release and plasma concentration profile of cefadroxil test and reference (Duricef®) formulation. The VBE study also proved test formulation bioequivalent to reference formulation and the statistical analysis on pharmacokinetic parameters reported 90% confidence interval for Cmax and AUC0-t in the FDA acceptable limits.The analysis found that a validated and verified PBPK model with a mechanistic background is as a suitable approach to accelerate generic drug development.

Physiologically Based Pharmacokinetic Modeling for Maribavir to Inform Dosing in Drug-Drug Interaction Scenarios with CYP3A4 Inducers and Inhibitors.

Maribavir, an orally available antiviral agent, has been approved in multiple countries for the treatment of patients with refractory post-transplant cytomegalovirus (CMV) infection and/or disease. Maribavir is primarily metabolized by CYP3A4; coadministration with CYP3A4 inducers and inhibitors may significantly alter maribavir exposure, thereby affecting its efficacy and safety. The effect of CYP3A4 inducers and inhibitors on maribavir exposure was evaluated based on a drug-drug interaction (DDI) study and physiologically-based pharmacokinetic (PBPK) modeling. The effect of rifampin (a strong inducer of CYP3A4 and moderate inducer of CYP1A2), administered at a 600mg dose once daily, on maribavir pharmacokinetics was assessed in a clinical phase1 DDI study in healthy participants. A full PBPK model for maribavir was developed and verified using invitro and clinical pharmacokinetic data from phase1 studies. The verified PBPK model was then used to simulate maribavir DDI interactions with various CYP3A4 inducers and inhibitors. The DDI study results showed that coadministration with rifampin decreased the maribavir maximum plasma concentration (Cmax), area under the plasma concentration-time curve (AUC), and trough concentration (Ctrough) by 39%, 60%, and 82%, respectively. Based on the results from the clinical DDI study, the coadministration of maribavir with rifampin is not recommended. The PBPK model did not predict a clinically significant effect of CYP3A4 inhibitors on maribavir exposure; however, it predicted that strong or moderate CYP3A4 inducers, including carbamazepine, efavirenz, phenobarbital, and phenytoin, may reduce maribavir exposure to a clinically significant extent, and may prompt the consideration of a maribavir dosing increase, in accordance with local approved labels and/or regulations.

Application of a physiologically based pharmacokinetic model to predict isoniazid disposition during pregnancy.

Pregnancy can increase the risk of latent tuberculosis infection (LTBI) progression to tuberculosis (TB) disease. Isoniazid (INH) is the preferred preventative treatment for LTBI in pregnancy. INH is mainly cleared by N-acetyltransferase 2 (NAT2) but the pharmacokinetics (PK) of INH in different NAT2 phenotypes during pregnancy is not well characterized. To address this knowledge gap, we used physiologically based pharmacokinetic (PBPK) modeling to evaluate NAT2 phenotype-specific effects of pregnancy on INH disposition. A whole-body PBPK model for INH was developed and verified for non-pregnant NAT2 fast (FA), intermediate (IA), and slow (SA) acetylators. Model predictive performance was assessed using a drug-specific model acceptance criterion for mean plasma area under the curve (AUC) and peak plasma concentration (Cmax ), and the absolute average fold error (AAFE) for individual plasma concentrations. The verified model was extended to simulate INH disposition during pregnancy in NAT2 SA, IA, and FA populations. A sensitivity analysis was conducted using the verified PBPK model and known changes in INH disposition during pregnancy to determine whether NAT2 activity changes during pregnancy or other INH clearance pathways are altered. This analysis suggested that NAT2 activity is unchanged while other INH clearance pathways increase by ~80% during pregnancy. The model was applied to explore the effect of pregnancy on INH disposition in two ethnic populations with different NAT2 phenotype distributions and with high TB burden. Our PBPK model can be used to predict INH disposition during pregnancy in diverse populations and expanded to other drugs cleared by NAT2 during pregnancy.

Virtual Bioequivalence Assessment of Elagolix Formulations Using Physiologically Based Pharmacokinetic Modeling.

In lieu of large bioequivalence studies and exposing healthy postmenopausal women to additional drug exposure for elagolix coadministered with hormonal add-back therapy, physiologically based pharmacokinetic (PBPK) modeling was used with in vitro dissolution data to test for virtual bioequivalence. For endometriosis, elagolix is approved at doses of 150mg once daily and 200mg twice daily as a tablet. As a combination therapy, two individual tablets, consisting of an elagolix tablet and an estradiol/norethindrone acetate 1/0.5mg (E2/NETA) tablet, were utilized in Phase 3 endometriosis trials. However, the commercial combination drug products consist of a morning capsule (containing an elagolix tablet and E2/NETA tablet as a fixed-dose combination capsule, AM capsule) and an evening capsule (consisting of an elagolix tablet, PM capsule). In vitro dissolution profiles were dissimilar for the tablet and capsule formulations; thus, in vivo bioequivalence studies or a bioequivalence waiver would have been required. To simulate virtual cross-over, bioequivalence trials, in vitro dissolution data was incorporated into a previously verified PBPK model. The updated PBPK model was externally validated using relevant bioequivalence study data. Based on results of the virtual bioequivalence simulations, the commercial drug product capsules met the bioequivalence criteria of 0.80-1.25 when compared to the reference tablets. This was a novel example where PBPK modeling was utilized along with in vitro dissolution data to demonstrate virtual bioequivalence in support of a regulatory bioequivalence waiver.

PBPK modeling of ivermectin-Considerations for the purpose of developing alternative routes to optimize its safety profile.

Although single-dose ivermectin has been widely used in mass-drug administration programs for onchocerciasis and lymphatic filariasis for many years, ivermectin may have utility as an endectocide with mosquito-lethal effects at dosages greater and longer than those used to treat helminths. The final physiologically-based pharmacokinetic (PBPK) model for ivermectin described here was able to capture, with reasonable accuracy, observed plasma drug concentration-time profiles and exposures of ivermectin after a single oral dose of the drug in healthy male (dose range 6-30 mg) and female subjects, in both fasted and fed states, in African patients with onchocerciasis (150 μg/kg) and in African children. The PBPK model can be used for further work on lactation, pediatric dosing (considering CYP3A4 and Pg-p ontogenies), and pregnancy, especially if nonstandard doses will be used. The key findings of our study indicate that absorption of ivermectin may be highly dependent on bile micelle-mediated solubility. The drug is highly lipophilic and permeable, and its plasma exposure appears to be associated with the body mass index of an individual. These are all factors that need to be considered when extrapolating to more complex oral formulations or alternative routes of administration. Administering lower doses over a longer period may attenuate the dependence on bile micelle-mediated solubility. With relevant inputs, the verified PBPK model developed here could be used to simulate plasma exposures following administration of ivermectin by complex generics in development.

Tiered approach to evaluate the CYP3A victim and perpetrator drug-drug interaction potential for vonoprazan using PBPK modeling and clinical data to inform labeling.

Vonoprazan is metabolized extensively through CYP3A and is an in vitro time-dependent inhibitor of CYP3A. A tiered approach was applied to understand the CYP3A victim and perpetrator drug-drug interaction (DDI) potential for vonoprazan. Mechanistic static modeling suggested vonoprazan is a potential clinically relevant CYP3A inhibitor. Thus, a clinical study was conducted to evaluate the impact of vonoprazan on the exposure of oral midazolam, an index substrate for CYP3A. A physiologically-based pharmacokinetic (PBPK) model for vonoprazan was also developed using in vitro data, drug- and system-specific parameters, and clinical data and observations from a [14 C] human absorption, distribution, metabolism, and excretion study. The PBPK model was refined and verified using data from a clinical DDI study with the strong CYP3A inhibitor, clarithromycin, to confirm the fraction metabolized by CYP3A, and the oral midazolam clinical DDI data assessing vonoprazan as a time-dependent inhibitor of CYP3A. The verified PBPK model was applied to simulate the anticipated changes in vonoprazan exposure due to moderate and strong CYP3A inducers (efavirenz and rifampin, respectively). The clinical midazolam DDI study indicated weak inhibition of CYP3A, with a less than twofold increase in midazolam exposure. PBPK simulations projected a 50% to 80% reduction in vonoprazan exposure when administered concomitantly with moderate or strong CYP3A inducers. Based on these results, the vonoprazan label was revised and states that lower doses of sensitive CYP3A substrates with a narrow therapeutic index should be used when administered concomitantly with vonoprazan, and co-administration with moderate and strong CYP3A inducers should be avoided.

Evaluation of drug-drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling.

Pemigatinib is a potent inhibitor of fibroblast growth factor receptor being developed for oncology indications. It is primarily metabolized by cytochrome P450 (CYP) 3A4, and the ratio of estimated concentration over concentration required for 50% inhibition ratio for pemigatinib as an inhibitor of P‐glycoprotein (P‐gp), organic cation transporter‐2 (OCT2), and multidrug and toxin extrusion protein‐1 (MATE1) exceeds the cutoff values established in regulatory guidance. A Simcyp minimal physiologically based pharmacokinetic (PBPK) with advanced dissolution, absorption, and metabolism absorption model for pemigatinib was developed and validated using observed clinical pharmacokinetic (PK) data and itraconazole/rifampin drug–drug interaction (DDI) data. The model accurately predicted itraconazole DDI (approximate 90% area under the plasma drug concentration–time curve [AUC] and approximate 20% maximum plasma drug concentration [Cmax] increase). The model underpredicted rifampin induction by 100% (approximate 6.7‐fold decrease in AUC and approximate 2.6‐fold decrease in Cmax in the DDI study), presumably reflecting non‐CYP3A4 mechanisms being impacted. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors/inducers on pemigatinib PK and pemigatinib as an inhibitor of P‐gp or OCT2/MATE1 substrates. The worst‐case scenario DDI simulation for pemigatinib as an inhibitor of P‐gp or OCT2/MATE1 substrates showed only a modest DDI effect. The recommendation based on this simulation and clinical data is to reduce pemigatinib dose for coadministration with strong and moderate CYP3A4 inhibitors. No dose adjustment is required for weak CYP3A4 inhibitors. The coadministration of strong and moderate CYP3A4 inducers with pemigatinib should be avoided. PBPK modeling suggested no dose adjustment with P‐gp or OCT2/MATE1 substrates.

Physiologically based pharmacokinetic modeling and simulations to inform dissolution specifications and clinical relevance of release rates on elagolix exposure

The aim of this analysis was to use a physiologically based pharmacokinetic (PBPK) model to predict the impact of changes in dissolution rates on elagolix exposures and define clinically relevant acceptance criteria for dissolution. Varying in vitro dissolution profiles were utilized in a PBPK model to describe the absorption profiles of elagolix formulations used in Phase 3 clinical trials and for the to be marketed commercial formulations. Single dose studies of 200 mg elagolix formulations were used for model verification under fasted conditions. Additional dissolution scenarios were evaluated to assess the impact of dissolution rates on elagolix exposures. Compared to the Phase 3 clinical trial formulation, sensitivity analysis on dissolution rates suggested that a hypothetical scenario of ∼75% slower dissolution rate would result in 14% lower predicted elagolix plasma exposures, however, the predicted exposures are still within the bioequivalence boundaries of 0.8–1.25 for both Cmax and AUC. A clinically verified PBPK model of elagolix was utilized to evaluate the impact of wider dissolution specifications on elagolix plasma exposures. The simulation results indicated that a slower in vitro dissolution profile, would not have a clinically significant impact on elagolix exposures. These model results informed the setting of wider dissolution specifications without requiring in vivo studies.

Physiologically Based Pharmacokinetic (PBPK) Modeling for Betrixaban and the Impact of P-Glycoprotein Inhibition on Betrixaban Pharmacokinetics

Introduction: Betrixaban is a factor Xa inhibitor indicated for the prophylaxis of venous thromboembolism (VTE) in adult patients hospitalized for an acute medical illness. A physiologically based pharmacokinetic (PBPK) model for betrixaban provided the mechanistic understanding of the contribution of P-glycoprotein (P-gp), as well as CYP3A4, to the disposition of betrixaban, and predictions of the effects of drug-drug interactions mediated by P-gp and/or CYP3A4. The PBPK model specifically evaluated contribution of the interaction with the P-gp transporter to the disposition of betrixaban, as well as the effects of co-administration of P-gp inhibitors (ketoconazole, verapamil, clarithromycin) on betrixaban pharmacokinetics (PK). Further, in vitro and clinical studies confirmed the lack of a significant interaction of betrixaban with CYP3A4.Methods: The base PBPK model was developed using physiochemical data, in vitro metabolism andtransport data generated using recombinant CYP enzymes and human hepatocytes, and relevant clinical data (eg, observed renal clearance). The model was verified by comparing model predictions with PK data derived from single oral doses of betrixaban (from 80 to 550 mg) in healthy volunteers. The combined contribution of intestinal and hepatic P-gp transport to the disposition of betrixaban was verified by comparing model predictions to the observed ketoconazole and verapamil clinical drug-drug interaction data from healthy volunteers. The verified PBPK model was prospectively employed to predict the impact of a strong P-gp inhibitor (clarithromycin) and a moderate P-gp inhibitor (diltiazem) on betrixaban PK, and to predict the impact of food intake on betrixaban PK and compare it against the observed data in phase 3 studies. The Simcyp Population-Based Simulator (v15 release 1) was used for all simulations.Results: The major clearance pathway for betrixaban was biliary clearance (assigned to hepatic P-gp) as scaled from uptake data generated in human hepatocytes, accounting for ~80% of clearance after IV administration (CLIV). CYP3A4 played virtually no role in the disposition of betrixaban. The base PBPK model incorporating an intestinal P-gp component within the ADAM model resulted in simulations that predicted concentrations very close to the clinically observed pharmacokinetic non-linearity arising from multiple oral dosing of betrixaban in the dose range of 80 to 550 mg in healthy volunteers. The model predicted that the inhibition of intestinal and hepatic P-gp transport of betrixaban would result in the geometric mean area under the curve (AUC) and maximal concentration (Cmax) ratios due to concurrent ketoconazole treatment of 2.4 and 2.5, respectively, which were consistent with the observed ratios of 2.1 and 2.3. The predicted betrixaban geometric mean AUC and Cmax ratios due to concurrent verapamil treatment were 2.3 and 3.1, respectively, compared with the observed ratios of 3.0 and 4.7. The model qualitatively predicted the negative impact of food intake on betrixaban exposure (Figure). The predicted AUC and Cmax for patients treated with betrixaban in the fed state in the phase 3 study were within 1.5-fold of the observed values. In the final application of the PBPK model to predict the impact of the strong and moderate P-gp inhibitors, the predicted AUC and Cmax ratios due to clarithromycin treatment were 3.76 and 3.11, respectively, and due to diltiazem treatment were 1.1 and 1.1, respectively.Conclusions: The PBPK model for betrixaban adequately predicted the clinically observed PK non-linearity and accounted for the observed drug-drug interaction effects due to co-administration of betrixaban with ketoconazole and verapamil in healthy volunteers. The PBPK model did not predict a role for CYP3A4 in the clearance of betrixaban and accurately predicted a negative food effect. Intestinal and hepatic P-gp were identified as major contributors to the observed non-linearity in the kinetics of betrixaban and observed drug-drug interaction effects with P-gp inhibitors. [Display omitted] DisclosuresLeeds:Portola Pharmaceuticals, Inc.: Employment, Equity Ownership. Ke:Portola Pharmaceuticals, Inc.: Consultancy. Rowland Yeo:Portola Pharmaceuticals, Inc.: Consultancy. Curnutte:Portola Pharmaceuticals, Inc.: Employment, Equity Ownership. Conley:Portola Pharmaceuticals, Inc.: Employment, Equity Ownership, Patents & Royalties.

Predicting Resolvin D1 Pharmacokinetics in Humans with Physiologically-Based Pharmacokinetic Modeling.

Sjögren’s syndrome (SS) is an autoimmune disease with no effective treatment options. Resolvin D1 (RvD1) belongs to a class of lipid‐based specialized pro‐resolving mediators that showed efficacy in preclinical models of SS. We developed a physiologically‐based pharmacokinetic (PBPK) model of RvD1 in mice and optimized the model using plasma and salivary gland pharmacokinetic (PK) studies performed in NOD/ShiLtJ mice with SS‐like features. The predictive performance of the PBPK model was also evaluated with two external datasets from the literature reporting RvD1 PKs. The PBPK model adequately captured the observed concentrations of RvD1 administered at different doses and in different species. The PKs of RvD1 in virtual humans were predicted using the verified PBPK model at various doses (0.01–10 mg/kg). The first‐in‐human predictions of RvD1 will be useful for the clinical trial design and translation of RvD1 as an effective treatment strategy for SS.

Physiologically Based Pharmacokinetic Modeling Approach to Identify the Drug–Drug Interaction Mechanism of Nifedipine and a Proton Pump Inhibitor, Omeprazole

Proton pump inhibitors (PPIs) can affect the intragastric release of other drugs from their dosage forms by elevating the gastric pH. They may also influence drug absorption and metabolism by interacting with P-glycoprotein or with the cytochrome P450 (CYP) enzyme system. Nifedipine is a Biopharmaceutics Classification System (BCS) class II drug with low solubility across physiologic pH and high permeability. Previous studies have demonstrated that drug-drug interaction (DDI) existed between omeprazole and nifedipine with significantly increased systemic exposure of nifedipine in subjects after pre-treatment for 7days with omeprazole compared to the subjects without omeprazole treatment. It was shown that omeprazole not only induced an increase in intragastric pH, but also inhibited the CYP3A4 activity, while CYP3A4-mediated oxidation is the main metabolic pathway of nifedipine. The purpose of this study is to apply a physiologically based pharmacokinetic (PBPK) modeling approach to investigate the DDI mechanism for an immediate release formulation of nifedipine with omeprazole. A previously published model for omeprazole was modified to integrate metabolites and to update CYP inhibition based on the most updated published in vitro data. We simulated the nifedipine pharmacokinetics in healthy subjects with or without the multiple-dose pretreatment of omeprazole (20mg) following oral administrations of immediate-release (IR) (10mg) nifedipine. Nifedipine solubility at different pHs was used to simulate the nifedipine pharmacokinetics for both clinical arms. Multiple sensitivity analyses were performed to understand the impact of gastric pH and the CYP3A4-mediated gut and liver first pass metabolism on the overall nifedipine pharmacokinetics. The developed PBPK model properly described the pharmacokinetics of nifedipine and predicted the inhibitory effect of multiple-dose omeprazole on CYP3A4 activity. With the incorporation of the physiologic effect of omeprazole on both gastric pH and CYP3A4 to the PBPK model, the verified PBPK model allows evaluating the impact of the increase in gastric pH and/or CYP3A4 inhibition. The simulated results show that the nifedipine metabolic inhibition by omeprazole may play an important role in the DDI between nifedipine and omeprazole for IR nifedipine formulation. The developed full PBPK model with the capability to simulate DDI by considering gastric pH change and metabolic inhibition provides a mechanistic understanding of the observed DDI of nifedipine with a PPI, omeprazole.

Effect of ageing on antiretroviral drug pharmacokinetics using clinical data combined with modelling and simulation.

The impact of ageing on antiretroviral pharmacokinetics remains uncertain, leading to missing dosing recommendations for elderly people living with human immunodeficiency virus (HIV: PLWH). The objective of this study was to investigate whether ageing leads to clinically relevant pharmacokinetic changes of antiretrovirals that would support a dose adjustment based on the age of the treated PLWH. Plasma concentrations for 10 first-line antiretrovirals were obtained in PLWH ≥55 years, participating in the Swiss HIV Cohort Study, and used to proof the predictive performance of our physiologically based pharmacokinetic (PBPK) model. The verified PBPK model predicted the continuous effect of ageing on HIV drug pharmacokinetics across adulthood (20-99 years). The impact of ethnicity on age-related pharmacokinetic changes between whites and other races was statistically analysed. Clinically observed concentration-time profiles of all investigated antiretrovirals were generally within the 95% confidence interval of the PBPK simulations, demonstrating the predictive power of the modelling approach used. The predicted decline in drug clearance drove age-related pharmacokinetic changes of antiretrovirals, resulting in a maximal 70% [95% confidence interval: 40%, 120%] increase in antiretrovirals exposure across adulthood. Peak concentration, time to peak concentration and apparent volume of distribution were predicted to be unaltered by ageing. There was no statistically significant difference of age-related pharmacokinetic changes between studied ethnicities. Dose adjustment for antiretrovirals based on the age of male and female PLWH is a priori not necessary in the absence of severe comorbidities considering the large safety margin of the current first-line HIV treatments.

Assessing the impact of cystic fibrosis on the antipyretic response of ibuprofen in children: Physiologically-based modeling as a candle in the dark.

The goal of this study is to present the utility of quantitative modelling for extrapolation of drug safety and efficacy to underrepresented populations in controlled clinical trials. To illustrate this, the stepwise development of an integrated disease/pharmacokinetics/pharmacodynamics model of antipyretic efficacy of ibuprofen in children with cystic fibrosis (CF) is presented along with therapy optimization suggestions. Published clinical trials, in vitro data, and drug physiochemical properties were used to develop an ibuprofen-mediated antipyresis model for febrile children also having CF. Workflow included first developing a mechanistic absorption model using in vitro-in vivo extrapolation followed by physiologically-based pharmacokinetic (PBPK) modelling. The verified PBPK model was then scaled to paediatric patients with CF. Once verified, the PBPK model was linked to an indirect response model of antipyresis for simulation of the overall antipyretic efficacy of ibuprofen in CF children. Model simulations showed therapeutic inequivalence between healthy children and paediatric patients with CF; Cmax and AUC decreased by 39% (32-46%) and 44% (36-52%), respectively, in patients. Further, and in agreement with literature reports, predicted pharmacodynamics time courses suggest a slower onset and faster offset of action in patients compared to healthy children, 30 and 60 minutes, respectively. Exploratory simulations suggest an increase in dosing frequency for CF children as a better therapeutic strategy. Model-informed approaches to leveraging knowledge obtained throughout the life cycle of drug development may play a key role in extrapolating drug efficacy and safety to underrepresented populations.

Establishment of a clinically relevant specification for dissolution testing using physiologically based pharmacokinetic (PBPK) modeling approaches

This paper presented how to establish a clinically relevant specification (CRS) using in silico physiologically based pharmacokinetic (PBPK) modeling. Three different formulations of model drug products were used in the clinical studies in order to distinguish between bioequivalent (BE) batches from non-BE batches. A human PBPK model was constructed by integrating the clinical and non-clinical observations by using GastroPlusTM software. The developed model was verified by the comparison between human PK behavior observed in clinical studies and human PK behavior predicted from the software. The simulation results were obtained by using the dissolution profiles showing clinically relevant discriminatory power as input variables for the developed PBPK model. For three investigated formulations, the simulated PK behavior was comparable to the PK behavior observed in clinical studies. In addition, in silico BE simulation studies confirmed that the verified PBPK model can successfully reproduce the clinical study results. In conclusion, a CRS was established with the BE simulation by using the verified PBPK model, in order to detect and reject non-BE batches of drug products. The establishment of the CRS is useful for a quality control and finding optimal formulation to accomplish target PK behavior, safety, and efficacy.

Guidance for Rifampin and Midazolam Dosing Protocols To Study Intestinal and Hepatic Cytochrome P450 (CYP) 3A4 Induction and De-induction.

Cytochrome P450 3A4 (CYP3A4) catalyses the metabolism of > 30% of clinically used small molecule drugs. Induction of CYP3A4 is often associated with clinically important metabolic drug-drug interactions (DDIs). To collate published data regarding induction of CYP3A4 expression by rifampin and identify an optimal protocol to study DDIs using physiologically based pharmacokinetic (PBPK) modelling. The University of Washington Drug Interaction Database was searched for published data regarding induction of CYP3A4 by rifampin. A verified PBPK model was used to define the optimal dose, duration, timing and route of administration of rifampin and midazolam to assess induction of intestinal and hepatic CYP3A4 by rifampin. Sensitivity analysis was performed to evaluate the impact of participant characteristics including sex, race and age. The maximal induction of intestinal CYP3A4 (9.5-fold) was almost double that of hepatic CYP3A4 (5.5-fold). Maximal induction of intestinal and hepatic CYP3A4 was achieved in > 90% of participants within 5 and 10days, respectively. Intestinal CYP3A4 expression returned to baseline in > 90% of participants within 7days of rifampin cessation, whereas induction of hepatic CYP3A4 persisted for greater than 7days in > 50% of participants. There was a significant difference in magnitude, but not time course, of CYP3A4 induction between males and females. Age and race did not significantly affect either the magnitude or time course of CYP3A4 induction. Maximal induction of intestinal CYP3A4 is achieved faster than hepatic CYP3A4. To assess maximal hepatic CYP3A4 induction, oral rifampin (600mg daily) should be dosed for > 10days.

Physiologically Based Pharmacokinetic Modelling of Hyperforin to Predict Drug Interactions with St John's Wort.

Herb-drug interactions with St John's wort (SJW) have been widely studied in numerous clinical studies. The objective of this study was to develop and evaluate a physiologically based pharmacokinetic (PBPK) model for hyperforin (the constituent of SJW responsible for interactions), which has the potential to provide unique insights into SJW interactions and allow prediction of the likely extent of interactions with SJW compared to published interaction reports. A PBPK model of hyperforin accounting for the induction of cytochrome P450 (CYP) 3A, CYP2C9 and CYP2C19 was developed in the Simcyp® Simulator (version 17) and verified using published, clinically observed pharmacokinetic data. The predictive performance of this model based on the prediction fold-difference (expressed as the ratio of predicted and clinically observed change in systemic exposure of drug) was evaluated across a range of CYP substrates. The verified PBPK model predicted the change in victim drug exposure due to the induction by SJW (expressed as area under the plasma concentration-time curve (AUC) ratio) within 1.25-fold (0.80-1.25) of that reported in clinical studies. The PBPK simulation indicated that the unbound concentration of hyperforin in the liver was far lower than in the gut (enterocytes). Simulations revealed that induction of intestinal CYP enzymes by hyperforin was found to be more pronounced than the corresponding increase in liver CYP activity (15.5- vs. 1.1-fold, respectively, at a hyperforin dose of 45mg/day). In the current study, a PBPK model for hyperforin was successfully developed, with a predictive capability for the interactions of SJW with different CYP3A, CYP2C9 and CYP2C19 substrates. This PBPK model is valuable to predict the extent of herb-drug interactions with SJW and help design the clinical interaction studies, particularly for new drugs and previously unstudied clinical scenarios.

Application of Physiologically Based Pharmacokinetic Modeling in Understanding Bosutinib Drug-Drug Interactions: Importance of Intestinal P-Glycoprotein

Bosutinib is an orally available Src/Abl tyrosine kinase inhibitor indicated for the treatment of patients with Ph+ chronic myelogenous leukemia at a clinically recommended dose of 500 mg once daily. Clinical results indicated that increases in bosutinib oral exposures were supraproportional at the lower doses (50-200 mg) and approximately dose-proportional at the higher doses (200-600 mg). Bosutinib is a substrate of CYP3A4 and P-glycoprotein and exhibits pH-dependent solubility with moderate intestinal permeability. These findings led us to investigate the factors influencing the underlying pharmacokinetic mechanisms of bosutinib with physiologically based pharmacokinetic (PBPK) models. Our primary objectives were to: 1) refine the previously developed bosutinib PBPK model on the basis of the latest oral bioavailability data and 2) verify the refined PBPK model with P-glycoprotein kinetics on the basis of the bosutinib drug-drug interaction (DDI) results with ketoconazole and rifampin. Additionally, the verified PBPK model was applied to predict bosutinib DDIs with dual CYP3A/P-glycoprotein inhibitors. The results indicated that 1) the refined PBPK model adequately described the observed plasma concentration-time profiles of bosutinib and 2) the verified PBPK model reasonably predicted the effects of ketoconazole and rifampin on bosutinib exposures by accounting for intestinal P-glycoprotein inhibition/induction. These results suggested that bosutinib DDI mechanism could involve not only CYP3A4-mediated metabolism but also P-glycoprotein-mediated efflux on absorption. In summary, P-glycoprotein kinetics could constitute an element in the PBPK models critical to understanding the pharmacokinetic mechanism of dual CYP3A/P-glycoprotein substrates, such as bosutinib, that exhibit nonlinear pharmacokinetics owing largely to a saturation of intestinal P-glycoprotein-mediated efflux.

Physiologically Based Pharmacokinetic Modeling to Identify Physiological and Molecular Characteristics Driving Variability in Drug Exposure.

Prospectively defining the physiological and molecular characteristics most likely driving between-subject variability (BSV) in drug exposure provides the opportunity to inform the assessment of biomarkers to account for this variability. A physiologically based pharmacokinetic (PBPK) model was constructed and verified for dabrafenib. This model was then used to evaluate the physiological and molecular characteristics driving BSV in dabrafenib exposure. The capacity to discriminate a steady-state dabrafenib trough concentration >48 ng/mL was also evaluated. The mean simulated/observed ratios for the parameters describing dabrafenib exposure in single-dose, multiple-dose, and drug-drug interaction studies were between 0.78 and 1.23. Multivariable analysis indicated that consideration of baseline weight, body mass index, and CYP2C8, CYP3A4, and P-gp abundance strongly predicts steady-state dabrafenib trough concentration above 48 ng/mL (ROC AUC = 0.94; accuracy = 86%). This is the first study to use a verified PBPK model to identify baseline physiological and molecular characteristics driving BSV in drug exposure.