Impact of physiologically based pharmacokinetic modeling and simulation in drug development

Utility of physiologically based pharmacokinetic modeling in predicting and characterizing clinical drug interactions

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions (DDIs). With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late-stage drug development and is often used to support regulatory packages for new drug applications. This Minireview will briefly describe the approaches to predicting DDI using PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models, and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations, and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules. SIGNIFICANCE STATEMENT: Physiologically based pharmacokinetic (PBPK) models have been a key tool in the characterization of various pharmacokinetic phenomena, including drug-drug interactions. This Minireview will highlight recent advancements and publications around physiologically based pharmacokinetic drug-drug interaction modeling, an important area of drug discovery and development research in light of the increasing prevalence of polypharmacology in clinical settings.

PBPK Modeling of Azithromycin Systemic Exposure in a Roux-en-Y Gastric Bypass Surgery Patient Population

In this investigation, PBPK modeling using the Simcyp® Simulator was performed to evaluate whether Roux-en-Y gastric bypass (RYGB) surgery impacts the oral absorption and bioavailability of azithromycin. An RYGB surgery patient population was adapted from the published literature and verified using the same probe medications, atorvastatin and midazolam. Next, a PBPK model of azithromycin was constructed to simulate changes in systemic drug exposure after the administration of different oral formulations (tablet, suspension) to patients pre- and post-RYGB surgery using the developed and verified population model. Clinically observed changes in azithromycin systemic exposure post-surgery following oral administration (single-dose tablet formulation) were captured using PBPK modeling based on the comparison of model-predicted exposure metrics (Cmax, AUC) to published clinical data. Model simulations predicted a 30% reduction in steady-state AUC after surgery for three- and five-day multiple dose regimens of an azithromycin tablet formulation. The relative bioavailability of a suspension formulation was 1.5-fold higher than the tablet formulation after multiple dosing. The changes in systemic exposure observed after surgery were used to evaluate the clinical efficacy of azithromycin against two of the most common pathogens causing community acquired pneumonia based on the corresponding AUC24/MIC pharmacodynamic endpoint. The results suggest lower bioavailability of the tablet formulation post-surgery may impact clinical efficacy. Overall, the research demonstrates the potential of a PBPK modeling approach as a framework to optimize oral drug therapy in patients post-RYGB surgery.

Development of In Vitro Dissolution Testing Methods to Simulate Fed Conditions for Immediate Release Solid Oral Dosage Forms

In vitro dissolution testing is widely used to mimic and predict in vivo performance of oral drug products in the gastrointestinal (GI) tract. This literature review assesses the current in vitro dissolution methodologies being employed to simulate and predict in vivo drug dissolution under fasted and fed conditions, with emphasis on immediate release (IR) solid oral dosage forms. Notable human GI physiological conditions under fasted and fed states have been reviewed and summarized. Literature results showed that dissolution media, mechanical forces, and transit times are key dissolution test parameters for simulating specific postprandial conditions. A number of biorelevant systems, including the fed stomach model (FSM), GastroDuo device, dynamic gastric model (DGM), simulated gastrointestinal tract models (TIM), and the human gastric simulator (HGS), have been developed to mimic the postprandial state of the stomach. While these models have assisted in expanding physiological relevance of in vitro dissolution tests, in general, these models lack the ability to fully replicate physiological conditions/processes. Furthermore, the translatability of in vitro data to an in vivo system remains challenging. Additionally, physiologically based pharmacokinetic (PBPK) modeling has been employed to evaluate the effect of food on drug bioavailability and bioequivalence. Here, we assess the current status of in vitro dissolution methodologies and absorption PBPK modeling approaches to identify knowledge gaps and facilitate further development of in vitro dissolution methods that factor in fasted and fed states. Prediction of in vivo drug performance under fasted and fed conditions via in vitro dissolution testing and modeling may potentially help efforts in harmonizing global regulatory recommendations regarding in vivo fasted and fed bioequivalence studies for solid oral IR products.

Prediction of drug-drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling

Purpose

Recent in vitro studies demonstrated that dasatinib inhibits organic cation transporter 2 (OCT2), multidrug and toxin extrusion proteins (MATEs), and organic anion transporting polypeptide 1B1/1B3 (OATP1B1/1B3). We developed a physiologically based pharmacokinetic (PBPK) model to assess drug–drug interaction (DDI) potential between dasatinib and known substrates for these transporters in a virtual population.

Methods

The dasatinib PBPK model was constructed using Simcyp^® Simulator by combining its physicochemical properties, in vitro data, in silico predictions, and pharmacokinetic (PK) results from clinical studies. Model validation against three independent clinical trials not used for model development included dasatinib DDI studies with ketoconazole, rifampin, and simvastatin. The validated model was used to simulate DDIs of dasatinib and known substrates for OCT2 and MATEs (metformin) and OATP1B1/1B3 (pravastatin and rosuvastatin).

Results

Simulations of metformin PK in the presence and absence of dasatinib, using inhibitor constant (K_i) values measured in vitro, produced estimated geometric mean ratios (GMRs) of the maximum observed concentration (C_max) and area under the concentration–time curve (AUC) of 1.05 and 1.06, respectively. Sensitivity analysis showed metformin exposure increased < 30% in both AUC and C_max when dasatinib K_i was reduced by tenfold for OCT2 and MATEs simultaneously, and < 40% with a 20-fold K_i reduction. The estimated GMRs of C_max and AUC for pravastatin and rosuvastatin with co-administration of dasatinib were unity (1.00).

Conclusions

This PBPK model accurately described the observed PK profiles of dasatinib. The validated PBPK model predicts low risk of clinically significant DDIs between dasatinib and metformin, pravastatin, or rosuvastatin.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-021-04394-z.

Drug Exposure to Establish Pharmacokinetic-Response Relationships in Oncology

In the oncology field, understanding the relationship between the dose administered and the exerted effect is particularly important because of the narrow therapeutic index associated with anti-cancer drugs and the high interpatient variability. Therefore, in this review, we provide a critical perspective of the different methods of characterising treatment exposure in the oncology setting. The increasing number of modelling applications in oncology reflects the applicability and the impact of pharmacometrics on all phases of the drug development process and patient management as well. Pharmacometric modelling is a worthy component within the current paradigm of model-based drug development, but pharmacometric modelling techniques are also accessible for the clinician in the optimisation of current oncology therapies. Consequently, the application of population models in a hospital setting by generating close collaborations between physicians and pharmacometricians is highly recommended, providing a systematic means of developing and assessing model-based metrics as 'drivers' for various responses to treatments, which can then be evaluated as predictors for treatment success. Characterising the key determinants of variability in exposure is of particular importance for anticancer agents, as efficacy and toxicity are associated with exposure. We present the different strategies to describe and predict drug exposure that can be applied depending on the data available, with the objective of obtaining the most useful information in the patients' favour throughout the full drug cycle. Therefore, the objective of the present article is to review the different approaches used to characterise a patient's exposure to oncology drugs, which will result in a better understanding of the time course of the response and the magnitude of interpatient variability.

Qualification and application of liquid chromatography-quadrupole time-of-flight mass spectrometric method for the determination of carisbamate in rat plasma and prediction of its human pharmacokinetics using physiologically based pharmacokinetic modeling

Carisbamate is an antiepileptic drug and it also has broad neuroprotective activity and anticonvulsant reaction. In this study, a liquid chromatography-quadrupole time-of-flight mass spectrometric (LC-qTOF-MS) method was developed and applied for the determination of carisbamate in rat plasma to support in vitro and in vivo studies. A quadratic regression (weighted 1/concentration²), with an equation y = ax² + bx + c, was used to fit calibration curves over the concentration range from 9.05 to 6,600 ng/mL for carisbamate in rat plasma. Preclinical in vitro and in vivo studies of carisbamate have been studied through the developed bioanalytical method. Based on these study results, human pharmacokinetic (PK) profile has been predicted using physiologically based pharmacokinetic (PBPK) modeling. The PBPK model was optimized and validated by using the in vitro and in vivo data. The human PK of carisbamate after oral dosing of 750 mg was simulated by using this validated PBPK model. The human PK parameters and profiles predicted from the validated PBPK model were similar to the clinical data. This PBPK model developed from the preclinical data for carisbamate would be useful for predicting the PK of carisbamate in various clinical settings.

Development and Evaluation of Physiologically Based Pharmacokinetic Drug-Disease Models for Predicting Rifampicin Exposure in Tuberculosis and Cirrhosis Populations

The physiologically based pharmacokinetic (PBPK) approach facilitates the construction of novel drug–disease models by allowing incorporation of relevant pathophysiological changes. The aim of the present work was to explore and identify the differences in rifampicin pharmacokinetics (PK) after the application of its single dose in healthy and diseased populations by using PBPK drug–disease models. The Simcyp^® simulator was used as a platform for modeling and simulation. The model development process was initiated by predicting rifampicin PK in healthy population after intravenous (i.v) and oral administration. Subsequent to successful evaluation in healthy population, the pathophysiological changes in tuberculosis and cirrhosis population were incorporated into the developed model for predicting rifampicin PK in these populations. The model evaluation was performed by using visual predictive checks and the comparison of mean observed/predicted ratios (ratio_(Obs/pred)) of the PK parameters. The predicted PK parameters in the healthy population were in adequate harmony with the reported clinical data. The incorporation of pathophysiological changes in albumin concentration in the tuberculosis population revealed improved prediction of clearance. The developed PBPK drug–disease models have efficiently described rifampicin PK in tuberculosis and cirrhosis populations after administering single drug dose, as the ratio_(Obs/pred) for all the PK parameters were within a two-fold error range. The mechanistic nature of the developed PBPK models may facilitate their extension to other diseases and drugs.

Quantitative Analysis of Tozadenant Using Liquid Chromatography-Mass Spectrometric Method in Rat Plasma and Its Human Pharmacokinetics Prediction Using Physiologically Based Pharmacokinetic Modeling

Tozadenant is one of the selective adenosine A2a receptor antagonists with a potential to be a new Parkinson's disease (PD) therapeutic drug. In this study, a liquid chromatography-mass spectrometry based bioanalytical method was qualified and applied for the quantitative analysis of tozadenant in rat plasma. A good calibration curve was observed in the range from 1.01 to 2200 ng/mL for tozadenant using a quadratic regression. In vitro and preclinical in vivo pharmacokinetic (PK) properties of tozadenant were studied through the developed bioanalytical methods, and human PK profiles were predicted using physiologically based pharmacokinetic (PBPK) modeling based on these values. The PBPK model was initially optimized using in vitro and in vivo PK data obtained by intravenous administration at a dose of 1 mg/kg in rats. Other in vivo PK data in rats were used to validate the PBPK model. The human PK of tozadenant after oral administration at a dose of 240 mg was simulated by using an optimized and validated PBPK model. The predicted human PK parameters and profiles were similar to the observed clinical data. As a result, optimized PBPK model could reasonably predict the PK in human.

Open Source Pharmacokinetic/Pharmacodynamic Framework: Tutorial on the BioGears Engine

BioGears is an open-source, lumped parameter, full-body human physiology engine. Its purpose is to provide realistic and comprehensive simulations for medical training, research, and education. BioGears incorporates a physiologically based pharmacokinetic/pharmacodynamic (PK/PD) model that is designed to be applicable to a diversity of drug classes and patients and is extensible to future drugs. In addition, BioGears also supports drug interactions with various patient insults and interventions allowing for a realistic research framework and accurate dose-patient responses. This tutorial will demonstrate how the generic BioGears PK/PD model can be extended to a new substance for prediction of drug administration outcomes.

Application of Physiologically Based Pharmacokinetic Modeling to Predict Pharmacokinetics in Healthy Japanese Subjects

Pharmacokinetics (PKs) in Japanese healthy subjects were simulated for nine compounds using physiologically based PK (PBPK) models parameterized with physicochemical properties, preclinical absorption, distribution, metabolism, and excretion (ADME) data, and clinical PK data from non‐Japanese subjects. For each dosing regimen, 100 virtual trials were simulated and predicted/observed ratios for peak plasma concentration (C_max) and area under the curve (AUC) were calculated. As qualification criteria, it was prespecified that >80% of simulated trials should demonstrate ratios to observed data ranging from 0.5–2.0. Across all compounds and dose regimens studied, 93% of simulated C_max values in Japanese subjects fulfilled the criteria. Similarly, for AUC, 77% of single‐dosing regimens and 100% of multiple‐dosing regimens fulfilled the criteria. In summary, mechanistically incorporating the appropriate ADME properties into PBPK models, followed by qualification using non‐Japanese clinical data, can predict PKs in the Japanese population and lead to efficient trial design and conduct of Japanese phase I studies.

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.

Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review

PURPOSE

Physiologically based pharmacokinetic (PBPK) modeling, a mathematical modeling approach which uses a pharmacokinetic model to mimick human physiology to predict drug concentration-time profiles, has been used for the discover and development of drugs in various fields, including oncology, since 2000. There have been a few general review articles on the utilization of PBPK in the development of oncology drugs, but these do not include an evaluation of model prediction accuracy. We therefore conducted a systematic review to define the accuracy of PBPK model prediction and its utility throughout all the developmental phases of oncology drugs.

METHODS

A systematic search was performed in the PubMed, PubMed Central and Cochrane Library databases from 1980 to February 2017 for articles (1) written in English, (2) focused on oncology or antineoplastic or anticancer drugs, tumor or cancer or anticancer drugs listed in the U.S. National Institutes of Health and (3) involving a PBPK model. The absolute-average-folding-errors (AAFEs) of the area under the curve (AUC) between predicted and observed values in each article were calculated to assess model prediction accuracy.

RESULTS

Of the 2341 articles initially identified by our search of the databases, 40 were included in the review analysis. These articles reported on six types of studies, i.e. in vivo (n = 4), first-in-human (n = 5), phase II/III clinical trials (n = 9), organ impairment (n = 3), pediatrics (n = 4) and drug-drug interactions (n = 15). AAFEs of the predicted AUC for all groups of studies were within 1.3-fold of each other despite variations in experimental methodologies.

CONCLUSION

PBPK modeling is a potential tool which can be effectively applied throughout all phases of oncology drug development. The number of experimental animals and human participants enrolled in the studies can be reduced using PBPK modeling and PBPK-population-PK modeling. The limited number of publications of unsuccessful model application to date may contribute to bias toward the usefulness of modeling.

Prediction of a Therapeutic Dose for Buagafuran, a Potent Anxiolytic Agent by Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling Starting from Pharmacokinetics in Rats and Human

Physiologically based pharmacokinetic (PBPK)/pharmacodynamic (PD) models can contribute to animal-to-human extrapolation and therapeutic dose predictions. Buagafuran is a novel anxiolytic agent and phase I clinical trials of buagafuran have been completed. In this paper, a potentially effective dose for buagafuran of 30 mg t.i.d. in human was estimated based on the human brain concentration predicted by a PBPK/PD modeling. The software GastroPlus^TM was used to build the PBPK/PD model for buagafuran in rat which related the brain tissue concentrations of buagafuran and the times of animals entering the open arms in the pharmacological model of elevated plus-maze. Buagafuran concentrations in human plasma were fitted and brain tissue concentrations were predicted by using a human PBPK model in which the predicted plasma profiles were in good agreement with observations. The results provided supportive data for the rational use of buagafuran in clinic.

Advances in Engineered Liver Models for Investigating Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of drug attrition. Testing drugs on human liver models is essential to mitigate the risk of clinical DILI since animal studies do not always suffice due to species-specific differences in liver pathways. While primary human hepatocytes (PHHs) can be cultured on extracellular matrix proteins, a rapid decline in functions leads to low sensitivity (<50%) in DILI prediction. Semiconductor-driven engineering tools now allow precise control over the hepatocyte microenvironment to enhance and stabilize phenotypic functions. The latest platforms coculture PHHs with stromal cells to achieve hepatic stability and enable crosstalk between the various liver cell types towards capturing complex cellular mechanisms in DILI. The recent introduction of induced pluripotent stem cell-derived human hepatocyte-like cells can potentially allow a better understanding of interindividual differences in idiosyncratic DILI. Liver models are also being coupled to other tissue models via microfluidic perfusion to study the intertissue crosstalk upon drug exposure as in a live organism. Here, we review the major advances being made in the engineering of liver models and readouts as they pertain to DILI investigations. We anticipate that engineered human liver models will reduce drug attrition, animal usage, and cases of DILI in humans.

PBPK modeling and simulation in drug research and development

Physiologically based pharmacokinetic (PBPK) modeling and simulation can be used to predict the pharmacokinetic behavior of drugs in humans using preclinical data. It can also explore the effects of various physiologic parameters such as age, ethnicity, or disease status on human pharmacokinetics, as well as guide dose and dose regiment selection and aid drug-drug interaction risk assessment. PBPK modeling has developed rapidly in the last decade within both the field of academia and the pharmaceutical industry, and has become an integral tool in drug discovery and development. In this mini-review, the concept and methodology of PBPK modeling are briefly introduced. Several case studies were discussed on how PBPK modeling and simulation can be utilized through various stages of drug discovery and development. These case studies are from our own work and the literature for better understanding of the absorption, distribution, metabolism and excretion (ADME) of a drug candidate, and the applications to increase efficiency, reduce the need for animal studies, and perhaps to replace clinical trials. The regulatory acceptance and industrial practices around PBPK modeling and simulation is also discussed.

Evaluation of a New Molecular Entity as a Victim of Metabolic Drug-Drug Interactions-an Industry Perspective

Under the guidance of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ), scientists from 20 pharmaceutical companies formed a Victim Drug-Drug Interactions Working Group. This working group has conducted a review of the literature and the practices of each company on the approaches to clearance pathway identification (fCL), estimation of fractional contribution of metabolizing enzyme toward metabolism (fm), along with modeling and simulation-aided strategy in predicting the victim drug-drug interaction (DDI) liability due to modulation of drug metabolizing enzymes. Presented in this perspective are the recommendations from this working group on: 1) strategic and experimental approaches to identify fCL and fm, 2) whether those assessments may be quantitative for certain enzymes (e.g., cytochrome P450, P450, and limited uridine diphosphoglucuronosyltransferase, UGT enzymes) or qualitative (for most of other drug metabolism enzymes), and the impact due to the lack of quantitative information on the latter. Multiple decision trees are presented with stepwise approaches to identify specific enzymes that are involved in the metabolism of a given drug and to aid the prediction and risk assessment of drug as a victim in DDI. Modeling and simulation approaches are also discussed to better predict DDI risk in humans. Variability and parameter sensitivity analysis were emphasized when applying modeling and simulation to capture the differences within the population used and to characterize the parameters that have the most influence on the prediction outcome.

Ontogeny of plasma proteins, albumin and binding of diazepam, cyclosporine, and deltamethrin

BACKGROUND

To characterize the ontogeny of plasma albumin and total proteins, due to the lack of a comprehensive pediatric database. Secondly, to establish the magnitude and duration of maturational changes in binding of highly-bound drugs/chemicals.

METHODS

Anonymized plasma samples from 296 donors were pooled in 6 age brackets from birth to adolescence. Total protein and albumin levels were measured in each age group, as was the age-dependency of plasma binding of diazepam (DZP), cyclosporine (CYC), and deltamethrin (DLM), a pyrethroid insecticide.

RESULTS

Plasma levels of albumin and total proteins steadily increased for the first 1-3 y of life. Unbound DZP and CYC fractions were elevated three- to fourfold in neonates, but decreased to adult levels after 1 and 3 y, respectively. Unbound DLM levels exceeded those in adults for just 1 mo.

CONCLUSION

Neonates and infants under 1-3 y may be at risk from increased amounts of free drug, when given standard doses of some highly-bound drugs. Pyrethroid insecticides might be anticipated to pose increased risk for 1 mo.

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.

Development of a Multicompartment Permeability-Limited Lung PBPK Model and Its Application in Predicting Pulmonary Pharmacokinetics of Antituberculosis Drugs

Achieving sufficient concentrations of antituberculosis (TB) drugs in pulmonary tissue at the optimum time is still a challenge in developing therapeutic regimens for TB. A physiologically based pharmacokinetic model incorporating a multicompartment permeability-limited lung model was developed and used to simulate plasma and pulmonary concentrations of seven drugs. Passive permeability of drugs within the lung was predicted using an in vitro-in vivo extrapolation approach. Simulated epithelial lining fluid (ELF):plasma concentration ratios showed reasonable agreement with observed clinical data for rifampicin, isoniazid, ethambutol, and erythromycin. For clarithromycin, itraconazole and pyrazinamide the observed ELF:plasma ratios were significantly underpredicted. Sensitivity analyses showed that changing ELF pH or introducing efflux transporter activity between lung tissue and ELF can alter the ELF:plasma concentration ratios. The described model has shown utility in predicting the lung pharmacokinetics of anti-TB drugs and provides a framework for predicting pulmonary concentrations of novel anti-TB drugs.

Application of physiologically based pharmacokinetic modeling in the prediction of pharmacokinetics of bicyclol controlled-release formulation in human

Physiologically based pharmacokinetic (PBPK) modeling can assist in formulation development. Bicyclol is a novel anti-hepatitis drug. A bilayer osmotic pump table of bicyclol is being developed. PBPK models for bicyclol immediate-release (IR) and controlled-release (CR) tablets in beagle dog, as well as PBPK model for IR tablets in human were constructed. These models incorporated physicochemical properties and in vitro preclinical data. Parameter sensitivity analysis was performed for the effects of solubility and dissolution on pharmacokinetic (PK) parameters. Models were refined by comparing simulated results to experimental measurements. Furthermore, the clinical PK for bicyclol CR tablets was predicted using the in vivo dissolution profile by deconvolution of the mean PK profile of CR tablets in dogs. In summary, the present study described a strategy employing PBPK models to evaluate the effects of formulation factors on PK profiles and predict the performance of bicyclol CR tablets in human.

Integration of Physiologically-Based Pharmacokinetic Modeling into Early Clinical Development: An Investigation of the Pharmacokinetic Nonlinearity

BMS-911543, a promising anticancer agent, exhibited time-dependent and dose-dependent nonlinear pharmacokinetics (PKs) in its first-in-human (FIH) study. Initial physiologically based pharmacokinetic (PBPK) modeling efforts using CYP1A2-mediated clearance kinetics were unsuccessful; however, further model analysis revealed that CYP1A2 time-dependent inhibition (TDI) and perhaps other factors could be keys to the nonlinearity. Subsequent experiments in human liver microsomes showed that the compound was a time-dependent inhibitor of CYP1A2 and were used to determine the enzyme inactivation parameter values. In addition, a rat tissue distribution study was conducted and human plasma samples were profiled to support the refinement of the PBPK model. It was concluded that the interplay between four BMS-911543 properties, namely, low solubility, saturation of the metabolizing enzyme CYP1A2, CYP1A2 TDI, and CYP1A2 induction likely resulted in the time-dependent and dose-dependent nonlinear PKs. The methodology of PBPK model-guided unmasking of compound properties can serve as a general practice for mechanistic understanding of a new compound's disposition.

Guanfu base A, an antiarrhythmic alkaloid of Aconitum coreanum, Is a CYP2D6 inhibitor of human, monkey, and dog isoforms

Guanfu base A (GFA) is a novel heterocyclic antiarrhythmic drug isolated from Aconitum coreanum (Lèvl.) rapaics and is currently in a phase IV clinical trial in China. However, no study has investigated the influence of GFA on cytochrome P450 (P450) drug metabolism. We characterized the potency and specificity of GFA CYP2D inhibition based on dextromethorphan O-demethylation, a CYP2D6 probe substrate of activity in human, mouse, rat, dog, and monkey liver microsomes. In addition, (+)-bufuralol 1'-hydroxylation was used as a CYP2D6 probe for the recombinant form (rCYP2D6), 2D1 (rCYP2D1), and 2D2 (rCYP2D2) activities. Results show that GFA is a potent noncompetitive inhibitor of CYP2D6, with inhibition constant Ki = 1.20 ± 0.33 μM in human liver microsomes (HLMs) and Ki = 0.37 ± 0.16 μM for the human recombinant form (rCYP2D6). GFA is also a potent competitive inhibitor of CYP2D in monkey (Ki = 0.38 ± 0.12 μM) and dog (Ki = 2.4 ± 1.3 μM) microsomes. However, GFA has no inhibitory activity on mouse or rat CYP2Ds. GFA did not exhibit any inhibition activity on human recombinant CYP1A2, 2A6, 2C8, 2C19, 3A4, or 3A5, but showed slight inhibition of 2B6 and 2E1. Preincubation of HLMs and rCYP2D6 resulted in the inactivation of the enzyme, which was attenuated by GFA or quinidine. Beagle dogs treated intravenously with dextromethorphan (2 mg/ml) after pretreatment with GFA injection showed reduced CYP2D metabolic activity, with the Cmax of dextrorphan being one-third that of the saline-treated group and area under the plasma concentration-time curve half that of the saline-treated group. This study suggests that GFA is a specific CYP2D6 inhibitor that might play a role in CYP2D6 medicated drug-drug interaction.

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.

An adaptive design to investigate the effect of ketoconazole on pharmacokinetics of GSK239512 in healthy male volunteers

This open label drug-drug interaction (DDI) study investigated the effect of a strong CYP3A inhibitor ketoconazole on the PK and safety profile of GSK239512. To mitigate the tolerability concerns of high GSK239512 exposures resulting from CYP3A inhibition, a 2-cohort adaptive design was used to facilitate a stepwise selection of dose levels and subject numbers. In Cohort 1, 6 subjects received a single dose of 20 μg GSK239512 alone and then 10 μg GSK239512 in combination with repeated once daily doses of 400 mg ketoconazole. The results from Cohort 1 demonstrated an approximately 1.5-fold increase in GSK239512 exposure with a good tolerability profile. This led to the adoption of a 3-session option in Cohort 2, in which 16 subjects received sequential single doses of 20 μg GSK239512 alone, 40 μg GSK239512 alone, and a single dose of 40 μg GSK239512 in combination with repeated once daily doses of 400 mg ketoconazole. The 2-cohort adaptive design proved effective in mitigating any potentially significant DDI risk to healthy subjects. Final results showed a 1.3-fold increase in GSK239512 exposure with ketoconazole, suggesting that in vivo metabolism of GSK239512 by CYP3A is unlikely to be the primary route of GSK239512 elimination.

Prediction of human pharmacokinetics in 2013 and beyond

The utility of in vitro generated kinetic data to provide quantitative prediction of in vivo pharmacokinetic behavior is well established and forms a cornerstone of many research projects in drug metabolism and disposition, particularly within the pharmaceutical industry. This issue provides several excellent examples of the use of in vitro techniques for prediction of human pharmacokinetics (PK). The general area of in vitro-in vivo extrapolation (IVIVE) is broad and hence the spectrum of topics covers various aspects drug clearance and distribution and drug-drug interactions. Some articles were commissioned whereas others were identified during the reviewing process. Overall they provide a snapshot of activity at the end of 2013. They document that whereas the translation of some in vitro approaches is now established, other areas are in their infancy and need more development.