Predicting drug interaction potential with a physiologically based pharmacokinetic model: a case study of telithromycin, a time-dependent CYP3A inhibitor

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

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

Drug-drug interactions involving CFTR modulators: a review of the evidence and clinical implications

INTRODUCTION

Cystic fibrosis (CF) is characterized by mucus accumulation impairing the lungs, gastrointestinal tract, and other organs. Cystic fibrosis transmembrane conductance regulator (CFTR) modulators (ivacaftor, tezacaftor, elexacaftor, and lumacaftor) significantly improve lung function and nutritional status; however, they are substrates, inhibitors, and/or inducers of certain CYP enzymes and transporters, raising the risk of drug-drug interactions (DDI) with common CF medications.

AREAS COVERED

A literature search was conducted for DDIs involving CFTR modulators by reviewing new drug applications, drug package inserts, clinical studies, and validated databases of substrates, inhibitors, and inducers. Clinically, CYP3A inducers and inhibitors significantly decrease and increase systemic concentrations of elexacaftor/tezacaftor/ivacaftor, respectively. Additionally, lumacaftor and ivacaftor alter concentrations of CYP3A and P-gp substrates. Potential DDIs without current clinical evidence include ivacaftor and elexacaftor's effect on CYP2C9 and OATP1B1/3 substrates, respectively, and OATP1B1/3 and P-gp inhibitors' effect on tezacaftor. A literature review was conducted using PubMed.

EXPERT OPINION

Dosing recommendations for CFTR modulators with DDIs are relatively comprehensive; however, recommendations on timing of dosing transition of CFTR modulators when CYP3A inhibitors are initiated or discontinued is incomplete. Certain drug interactions may be managed by choosing an alternative treatment to avoid/minimize DDIs. Next generation CFTR modulator therapies under development are expected to provide increased activity with reduced DDI risk.

Physiological-based pharmacokinetic modeling trends in pharmaceutical drug development over the last 20-years; in-depth analysis of applications, organizations, and platforms

We assess the advancement of physiologically based pharmacokinetic (PBPK) modeling and simulation (M&S) over the last 20 years (start of 2000 to end of 2019) focusing on the trends in each decade with the relative contributions from different organizations, areas of applications, and software tools used. Unlike many of the previous publications which focused on regulatory applications, our analysis is based on PBPK publications in peer-reviewed journals based on a large sample (>700 original articles). We estimated a rate of growth for PBPK (>40 fold/20 years) that was much steeper than the general pharmacokinetic modeling (<3 fold/20 years) or overall scientific publications (∼3 fold/20 years). The analyses demonstrated that contrary to commonly held belief, commercial specialized PBPK platforms with graphical-user interface were a much more popular choice than open-source alternatives even within academic organizations. These platforms constituted 81% of the whole set of the sample we assessed. The major PBPK applications (top 3) were associated with the study design, predicting formulation effects, and metabolic drug-drug interactions, while studying the fate of drugs in special populations, predicting kinetics in early drug development, and investigating transporter drug interactions have increased proportionally over the last decade. The proportions of application areas based on published research were distinctively different from those shown previously for the regulatory submissions and impact on labels. This may demonstrate the lag time between the research applications versus verified usage within the regulatory framework. The report showed the trend of overall PBPK publications in pharmacology drug development from the past 2 decades stratified by the organizations involved, software used, and area of applications. The analysis showed a more rapid increase in PBPK than that of the pharmacokinetic space itself with an equal contribution from academia and industry. By establishing and recording the journey of PBPK modeling in the past and looking at its current status, the analysis can be used for devising plans based on the anticipated trajectory of future regulatory applications.

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

BACKGROUND AND OBJECTIVES

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 7 days 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.

METHODS

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 (20 mg) following oral administrations of immediate-release (IR) (10 mg) 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.

RESULTS

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.

CONCLUSION

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.

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.

Differential pharmacokinetic drug-drug interaction potential of eletriptan between oral and subcutaneous routes

1. Pharmacokinetic drug-drug interaction (DDI) data is important from a label claim either in combination drug usage or in polypharmacy situation. 2. Eletriptan undergoes first pass related metabolism through CYP3A4 enzyme to form pharmacologically active N-desmethyl metabolite. 3. Differential DDI interaction of the concomitant oral dosing of ketoconazole (20.1 mg/kg), a CYP3A4 inhibitor, with oral (4.2 mg/kg) or subcutaneous dose (2.1 mg/kg) of eletriptan was evaluated in male Sprague Dawley rats. Serial pharmacokinetic samples were collected and simultaneously analysed for eletriptan/N-desmethyl eletriptan using validated assay. Non-compartmentally derived pharmacokinetic parameters for various treatments were analysed statistically. 4. After oral eletriptan in presence of ketoconazole, C_max (40 vs. 32 ng/mL alone) and AUC_inf (81 vs. 24 ng.h/mL alone) of eletriptan increased; the formation of N-desmethyl eletriptan decreased (C_max=1.1 ng/mL, 3.9%) with ketoconazole as compared to without treatment (C_max=3.7 ng/mL, 11.2%). After subcutaneous eletriptan in presence of ketoconazole, there was no change in C_max (153 vs.152 ng/mL) or AUC_inf (267 vs. 266 ng.h/mL) of eletriptan. Formation of N-desmethyl eletriptan after the subcutaneous dose was determined at few intermittent time points with/without ketoconazole. 5. Preclinical data support differential DDI of eletriptan when dosed oral vs. subcutaneous, which need to be evaluated in a clinical setting.

Assessing CYP2C19 Ontogeny in Neonates and Infants Using Physiologically Based Pharmacokinetic Models: Impact of Enzyme Maturation Versus Inhibition

The objective of this study was to develop pediatric physiologically based pharmacokinetic (PBPK) models for pantoprazole and esomeprazole. Pediatric PBPK models were developed by Simcyp version 15 by incorporating cytochrome P450 (CYP)2C19 maturation and auto-inhibition. The predicted-to-observed pantoprazole clearance (CL) ratio ranged from 0.96-1.35 in children 1-17 years of age and 0.43-0.70 in term infants. The predicted-to-observed esomeprazole CL ratio ranged from 1.08-1.50 for children 6-17 years of age, and 0.15-0.33 for infants. The prediction was markedly improved by assuming no auto-inhibition of esomeprazole in infants in the PBPK model. Our results suggested that the CYP2C19 auto-inhibition model was appropriate for esomeprazole in adults and older children but could not be directly extended to infants. A better understanding of the complex interplay of enzyme maturation, inhibition, and compensatory mechanisms for CYP2C19 is necessary for PBPK modeling in infants.

Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A

Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min-1, the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min-1. These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

Physiologically Based Pharmacokinetic Prediction of Linezolid and Emtricitabine in Neonates and Infants

INTRODUCTION

Modeling and simulation approaches are increasingly being utilized in pediatric drug development. Physiologically based pharmacokinetic (PBPK) modeling offers an enhanced ability to predict age-related changes in pharmacokinetics in the pediatric population.

METHODS

In the current study, adult PBPK models were developed for the renally excreted drugs linezolid and emtricitabine. PBPK models were then utilized to predict pharmacokinetics in pediatric patients for various age groups from the oldest to the youngest patients in a stepwise approach.

RESULTS

Pharmacokinetic predictions for these two drugs in the pediatric population, including infants and neonates, were within a twofold range of clinical observations. Based on this study, linezolid and emtricitabine pediatric PBPK models incorporating the ontogeny in renal maturation describe the pharmacokinetic differences between adult and pediatric populations, even though the contribution of renal clearance to the total clearance of two drugs was very different (30 % for linezolid vs. 86 % for emtricitabine).

CONCLUSION

These results suggest that PBPK modeling may provide one option to help predict the pharmacokinetics of renally excreted drugs in neonates and infants.

Development of an Adult Physiologically Based Pharmacokinetic Model of Solithromycin in Plasma and Epithelial Lining Fluid

Solithromycin is a fluoroketolide antibiotic under investigation for community-acquired bacterial pneumonia (CABP). We developed a whole-body physiologically based pharmacokinetic (PBPK) model for solithromycin in adults using PK-Sim and MoBi version 6.2, which incorporated time-dependent CYP3A4 auto-inhibition. The model was developed and evaluated using plasma and epithelial lining fluid (ELF) concentration data from 100 healthy subjects and 22 patients with CABP (1,966 plasma, 30 ELF samples). We performed population simulations and calculated the number of observations falling outside the 90% prediction interval. For the oral regimen (800 mg on day 1 and 400 mg daily on days 2-5) that was evaluated in phase III studies, 11% and 23% of observations from healthy adults fell outside the 90% prediction interval for plasma and ELF, respectively. This regimen should be effective because ≥97% of simulated adults achieved area under the concentration vs. time curve (AUC) to minimum inhibitory concentration ratios associated with a log₁₀ colony forming unit reduction in ELF.

The Application of Physiologically Based Pharmacokinetic Modeling to Predict the Role of Drug Transporters: Scientific and Regulatory Perspectives

Transporters play an important role in drug absorption, disposition, and drug action. The evaluation of drug transporters requires a comprehensive understanding of transporter biology and pharmacology. Physiologically based pharmacokinetic (PBPK) models may offer an integrative platform to quantitatively evaluate the role of drug transporters and its interplay with other drug disposition processes such as passive drug diffusion and elimination by metabolizing enzymes. To date, PBPK modeling and simulations integrating drug transporters lag behind that for drug-metabolizing enzymes. In addition, predictive performance of PBPK has not been well established for predicting the role of drug transporters in the pharmacokinetics of a drug. To enhance overall predictive performance of transporter-based PBPK models, it is necessary to have a detailed understanding of transporter biology for proper representation in the models and to have a quantitative understanding of the contribution of transporters in the absorption and metabolism of a drug. This article summarizes PBPK-based submissions evaluating the role of drug transporters to the Office of Clinical Pharmacology of the US Food and Drug Administration.

Autoinhibitory properties of the parent but not of the N-oxide metabolite contribute to infusion rate-dependent voriconazole pharmacokinetics

AIMS

The pharmacokinetics of voriconazole show a nonlinear dose-exposure relationship caused by inhibition of its own CYP3A-dependent metabolism. Because the magnitude of autoinhibition also depends on voriconazole concentrations, infusion rate might modulate voriconazole exposure. The impact of four different infusion rates on voriconazole pharmacokinetics was investigated.

METHODS

Twelve healthy participants received 100 mg voriconazole intravenous over 4 h, 400 mg over 6 h, 4 h, and 2 h in a crossover design. Oral midazolam (3 μg) was given at the end of infusion. Blood and urine samples were collected up to 48 h. Voriconazole and its N-oxide metabolite were quantified using high-performance liquid chromatography coupled to tandem mass spectrometry. Midazolam estimated metabolic clearance (eCLmet) was calculated using a limited sampling strategy. Voriconazole-N-oxide inhibition of cytochrome P450 (CYP) isoforms 2C19 and 3A4 were assessed with the P450-Glo luminescence assay.

RESULTS

Area under the concentration-time curve for 400 mg intravenous voriconazole was 16% (90% confidence interval: 12-20%) lower when administered over 6 h compared to 2 h infusion. Dose-corrected area under the concentration-time curve for 100 mg over 4 h was 34% lower compared to 400 mg over 4 h. Midazolam eCLmet was 516 ml min^-1 (420-640) following 100 mg 4 h^-1 voriconazole, 152 ml min^-1 (139-166) for 400 mg 6 h^-1 , 192 ml min^-1 (167-220) for 400 mg 4 h^-1 , and 202 ml min^-1 (189-217) for 400 mg 2 h^-1 . Concentration giving 50% CYP inhibition of voriconazole N-oxide was 146 ± 23 μmol l^-1 for CYP3A4, and 40.2 ± 4.2 μmol l^-1 for CYP2C19.

CONCLUSIONS

Voriconazole pharmacokinetics is modulated by infusion rate, an autoinhibitory contribution voriconazole metabolism by CYP3A and 2C19 and to a lesser extent its main N-oxide metabolite for CYP2C19. To avoid reduced exposure, the infusion rate should be 2 h.

Analysis of Nonlinear Pharmacokinetics of a Highly Albumin-Bound Compound: Contribution of Albumin-Mediated Hepatic Uptake Mechanism

The cause of nonlinear pharmacokinetics (PK) (more than dose-proportional increase in exposure) of a urea derivative under development (compound A: anionic compound [pKa: 4.4]; LogP: 6.5; and plasma protein binding: 99.95%) observed in a clinical trial was investigated. Compound A was metabolized by CYP3A4, UGT1A1, and UGT1A3 with unbound K_m of 3.3-17.8 μmol/L. OATP1B3-mediated uptake of compound A determined in the presence of human serum albumin (HSA) showed that unbound K_m and V_max decreased with increased HSA concentration. A greater decrease in unbound K_m than in V_max resulted in increased uptake clearance (V_max/unbound K_m) with increased HSA concentration, the so-called albumin-mediated uptake. At 2% HSA concentration, unbound K_m was 0.00657 μmol/L. A physiologically based PK model assuming saturable hepatic uptake nearly replicated clinical PK of compound A. Unbound K_m for hepatic uptake estimated from the model was 0.000767 μmol/L, lower than the in vitro unbound K_m at 2% HSA concentration, whereas decreased K_m with increased concentration of HSA in vitro indicated lower K_m at physiological HSA concentration (4%-5%). In addition, unbound K_m values for metabolizing enzymes were much higher than unbound K_m for OATP1B3, indicating that the nonlinear PK of compound A is primarily attributed to saturated OATP1B3-mediated hepatic uptake of compound A.

Physiologically based pharmacokinetic and pharmacodynamic modeling in cancer drug development: status, potential and gaps

INTRODUCTION

Modeling and simulation have become important means of answering questions relevant to the development of a drug, making it possible to assess risks early and to reduce costs. Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models contribute to a comprehensive understanding of the drug, covering specific questions from early discovery through lifecycle management stages. As for other disease areas, in oncology, PBPK and PD models are important topics that remain to be addressed.

AREAS COVERED

This review describes current PBPK and PD approaches, their applicability in drug development in general and specifically in the area of oncology. It discusses the current status and then focuses on key challenges and the potential for future use. It provides cases in which modeling currently cannot answer the questions and assesses the requirements to close gaps for PBPK/PD in oncology.

EXPERT OPINION

PBPK/PD models have led to improvements in identifying risks and reducing costs during the drug development process. Nevertheless, there is a lot of potential, where more rigorous integration of biological knowledge and specific experimental design would result in a more comprehensive biological picture. Ideally, such approaches would reveal the extent to which preclinical work can be extrapolated to clinical settings, thus enabling reliable prediction and, ultimately, reducing failed trials in clinical oncology.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

Physiologically based pharmacokinetic modeling to predict complex drug-drug interactions: a case study of AZD2327 and its metabolite, competitive and time-dependent CYP3A inhibitors

4-{(R)-(3-Aminophenyl)[4-(4-fluorobenzyl)-piperazin-1-yl]methyl}-N,N-diethylbenzamide (AZD2327) is a highly potent and selective agonist of the δ-opioid receptor. AZD2327 and N-deethylated AZD2327 (M1) are substrates of cytochrome P450 3A (CYP3A4) and comprise a complex multiple inhibitory system that causes competitive and time-dependent inhibition of CYP3A4. The aim of the current work was to develop a physiologically based pharmacokinetic (PBPK) model to predict quantitatively the magnitude of CYP3A4 mediated drug-drug interaction with midazolam as the substrate. Integrating in silico, in vitro and in vivo PK data, a PBPK model was successfully developed to simulate the clinical accumulation of AZD2327 and its primary metabolite. The inhibition of CYP3A4 by AZD2327, using midazolam as a probe drug, was reasonably predicted. The predicted maximum concentration (Cmax) and area under the concentration-time curve (AUC) for midazolam were increased by 1.75 and 2.45-fold, respectively, after multiple dosing of AZD2327, indicating no or low risk for clinically relevant drug-drug interactions (DDI). These results are in agreement with those obtained in a clinical trial with a 1.4 and 1.5-fold increase in Cmax and AUC of midazolam, respectively. In conclusion, this model simulated DDI with less than a two-fold error, indicating that complex clinical DDI associated with multiple mechanisms, pathways and inhibitors (parent and metabolite) can be predicted using a well-developed PBPK model.

Applications of physiologically based pharmacokinetic modeling for the optimization of anti-infective therapies

INTRODUCTION

The pharmacokinetic properties of anti-infective drugs are a determinant part of treatment success. Pathogen replication is inhibited if adequate drug levels are achieved in target sites, whereas excessive drug concentrations linked to toxicity are to be avoided. Anti-infective distribution can be predicted by integrating in vitro drug properties and mathematical descriptions of human anatomy in physiologically based pharmacokinetic models. This method reduces the need for animal and human studies and is used increasingly in drug development and simulation of clinical scenario such as, for instance, drug-drug interactions, dose optimization, novel formulations and pharmacokinetics in special populations.

AREAS COVERED

We have assessed the relevance of physiologically based pharmacokinetic modeling in the anti-infective research field, giving an overview of mechanisms involved in model design and have suggested strategies for future applications of physiologically based pharmacokinetic models.

EXPERT OPINION

Physiologically based pharmacokinetic modeling provides a powerful tool in anti-infective optimization, and there is now no doubt that both industry and regulatory bodies have recognized the importance of this technology. It should be acknowledged, however, that major challenges remain to be addressed and that information detailing disease group physiology and anti-infective pharmacodynamics is required if a personalized medicine approach is to be achieved.

Predicting nonlinear pharmacokinetics of omeprazole enantiomers and racemic drug using physiologically based pharmacokinetic modeling and simulation: application to predict drug/genetic interactions

PURPOSE

The objective of this study is to develop a physiologically-based pharmacokinetic (PBPK) model for each omeprazole enantiomer that accounts for nonlinear PK of the two enantiomers as well as omeprazole racemic drug.

METHODS

By integrating in vitro, in silico and human PK data, we first developed PBPK models for each enantiomer. Simulation of racemic omeprazole PK was accomplished by combining enantiomer models that allow mutual drug interactions to occur.

RESULTS

The established PBPK models for the first time satisfactorily predicted the nonlinear PK of esomeprazole, R-omeprazole and the racemic drug. The modeling exercises revealed that the strong time-dependent inhibition of CYP2C19 by esomeprazole greatly altered the R-omeprazole PK following administration of racemic omeprazole as in contrast to R-omeprazole given alone. When PBPK models incorporated both autoinhibition of each enantiomer and mutual interactions, the ratios between predicted and observed AUC following single and multiple dosing of omeprazole were 0.97 and 0.94, respectively.

CONCLUSIONS

PBPK models of omeprazole enantiomers and racemic drug were developed. These models can be utilized to assess CYP2C19-mediated drug and genetic interaction potential for omeprazole and esomeprazole.

PBPK model describes the effects of comedication and genetic polymorphism on systemic exposure of drugs that undergo multiple clearance pathways

An important goal in drug development is to understand the effects of intrinsic and/or extrinsic factors (IEFs) on drug pharmacokinetics. Although clinical studies investigating a given IEF can accomplish this goal, they may not be feasible for all IEFs or for situations when multiple IEFs exist concurrently. Physiologically based pharmacokinetic (PBPK) models may serve as a complementary tool for forecasting the effects of IEFs. We developed PBPK models for four drugs that are eliminated by both cytochrome P450 (CYP)3A4 and CYP2D6, and evaluated model prediction of the effects of comedications and/or genetic polymorphism on drug exposure. PBPK models predicted 100 and ≥70% of the observed results when the conventional "twofold rule" and the more conservative 25% deviation cut point were applied, respectively. These findings suggest that PBPK models can be used to infer effects of individual or combined IEFs and should be considered to optimize studies that evaluate these factors, specifically drug interactions and genetic polymorphism of drug-metabolizing enzymes.

Application of physiologically based pharmacokinetic modeling to predict acetaminophen metabolism and pharmacokinetics in children

Acetaminophen (APAP) is a widely used analgesic and antipyretic drug that undergoes extensive phase I and II metabolism. To better understand the kinetics of this process and to characterize the dynamic changes in metabolism and pharmacokinetics (PK) between children and adults, we developed a physiologically based PK (PBPK) model for APAP integrating in silico, in vitro, and in vivo PK data into a single model. The model was developed and qualified for adults and subsequently expanded for application in children by accounting for maturational changes from birth. Once developed and qualified, it was able to predict clinical PK data in neonates (0–28 days), infants (29 days to <2 years), children (2 to <12 years), and adolescents (12–17 years) following intravenous and orally administered APAP. This approach represents a general strategy for projecting drug exposure in children, in the absence of pediatric PK information, using previous drug- and system-specific information of adults and children through PBPK modeling.

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

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

Case studies for practical food effect assessments across BCS/BDDCS class compounds using in silico, in vitro, and preclinical in vivo data

Practical food effect predictions and assessments were described using in silico, in vitro, and/or in vivo preclinical data to anticipate food effects and Biopharmaceutics Classification System (BCS)/Biopharmaceutics Drug Disposition Classification System (BDDCS) class across drug development stages depending on available data: (1) limited in silico and in vitro data in early discovery; (2) preclinical in vivo pharmacokinetic, absorption, and metabolism data at candidate selection; and (3) physiologically based absorption modeling using biorelevant solubility and precipitation data to quantitatively predict human food effects, oral absorption, and pharmacokinetic profiles for early clinical studies. Early food effect predictions used calculated or measured physicochemical properties to establish a preliminary BCS/BDDCS class. A rat-based preclinical BCS/BDDCS classification used rat in vivo fraction absorbed and metabolism data. Biorelevant solubility and precipitation kinetic data were generated via animal pharmacokinetic studies using advanced compartmental absorption and transit (ACAT) models or in vitro methods. Predicted human plasma concentration-time profiles and the magnitude of the food effects were compared with observed clinical data for assessment of simulation accuracy. Simulations and analyses successfully identified potential food effects across BCS/BDDCS classes 1-4 compounds with an average fold error less than 1.6 in most cases. ACAT physiological absorption models accurately predicted positive food effects in human for poorly soluble bases after oral dosage forms. Integration of solubility, precipitation time, and metabolism data allowed confident identification of a compound's BCS/BDDCS class, its likely food effects, along with prediction of human exposure profiles under fast and fed conditions.

Understanding the critical disposition pathways of statins to assess drug-drug interaction risk during drug development: it's not just about OATP1B1

The use of statins is widespread across disease areas because many patients have comorbidities. Given that these drugs have become common as comedications, it is essential to have an understanding of the potential risks of drug-drug interactions (DDIs) between statins and candidate drugs in development. Although the hepatic uptake transporter organic anion-transporting polypeptide 1B1 (OATP1B1) is known to play a substantial role in statin-related DDI risk, other transporters and metabolizing enzymes can also be involved. Consequently, a holistic approach to risk assessment is required, tailored to each statin. Using evidence from pharmacogenetics, DDIs, and literature on absorption, distribution, metabolism, and elimination (ADME) in humans, this review identifies pathways that contribute the most to, and are therefore the most critical to, the disposition of each statin. It also provides an understanding of the expected theoretical maximum increase in systemic exposure if the disposition of a statin is inhibited. Finally, on a statin-by-statin basis, we propose in vitro inhibition studies that should be routinely conducted during drug development so as to better assess DDI risk.