Showing papers on "Physiologically based pharmacokinetic modelling published in 2022"

Pharmacokinetics of PEGylated Gold Nanoparticles: In Vitro—In Vivo Correlation

Abstract: Data suitable for assembling a physiologically-based pharmacokinetic (PBPK) model for nanoparticles (NPs) remain relatively scarce. Therefore, there is a trend in extrapolating the results of in vitro and in silico studies to in vivo nanoparticle hazard and risk assessment. To evaluate the reliability of such approach, a pharmacokinetic study was performed using the same polyethylene glycol-coated gold nanoparticles (PEG-AuNPs) in vitro and in vivo. As in vitro models, human cell lines TH1, A549, Hep G2, and 16HBE were employed. The in vivo PEG-AuNP biodistribution was assessed in rats. The internalization and exclusion of PEG-AuNPs in vitro were modeled as first-order rate processes with the partition coefficient describing the equilibrium distribution. The pharmacokinetic parameters were obtained by fitting the model to the in vitro data and subsequently used for PBPK simulation in vivo. Notable differences were observed in the internalized amount of Au in individual cell lines compared to the corresponding tissues in vivo, with the highest found for renal TH1 cells and kidneys. The main reason for these discrepancies is the absence of natural barriers in the in vitro conditions. Therefore, caution should be exercised when extrapolating in vitro data to predict the in vivo NP burden and response to exposure.

Increasing application of pediatric physiologically based pharmacokinetic models across academic and industry organizations

Abstract: There has been a significant increase in the use of physiologically based pharmacokinetic (PBPK) models during the past 20 years, especially for pediatrics. The aim of this study was to give a detailed overview of the growth and areas of application of pediatric PBPK (P‐PBPK) models. A total of 181 publications and publicly available regulatory reviews were identified and categorized according to year, author affiliation, platform, and primary application of the P‐PBPK model (in clinical settings, drug development or to advance pediatric model development in general). Secondary application areas, including dose selection, biologics, and drug interactions, were also assessed. The growth rate for P‐PBPK modeling increased 33‐fold between 2005 and 2020; this was mainly attributed to growth in clinical and drug development applications. For primary applications, 50% of articles were classified under clinical, 18% under drug development, and 33% under model development. The most common secondary applications were dose selection (75% drug development), pharmacokinetic prediction and covariate identification (47% clinical), and model parameter identification (68% model development), respectively. Although population PK modeling remains the mainstay of approaches supporting pediatric drug development, the data presented here demonstrate the widespread application of P‐PBPK models in both drug development and clinical settings. Although applications for pharmacokinetic and drug–drug interaction predictions in pediatrics is advocated, this approach remains underused in areas such as assessment of pediatric formulations, toxicology, and trial design. The increasing number of publications supporting the development and refinement of the pediatric model parameters can only serve to enhance optimal use of P‐PBPK models.

Pharmacokinetics of Caffeine: A Systematic Analysis of Reported Data for Application in Metabolic Phenotyping and Liver Function Testing

Abstract: Caffeine is by far the most ubiquitous psychostimulant worldwide found in tea, coffee, cocoa, energy drinks, and many other beverages and food. Caffeine is almost exclusively metabolized in the liver by the cytochrome P-450 enzyme system to the main product paraxanthine and the additional products theobromine and theophylline. Besides its stimulating properties, two important applications of caffeine are metabolic phenotyping of cytochrome P450 1A2 (CYP1A2) and liver function testing. An open challenge in this context is to identify underlying causes of the large inter-individual variability in caffeine pharmacokinetics. Data is urgently needed to understand and quantify confounding factors such as lifestyle (e.g., smoking), the effects of drug-caffeine interactions (e.g., medication metabolized via CYP1A2), and the effect of disease. Here we report the first integrative and systematic analysis of data on caffeine pharmacokinetics from 141 publications and provide a comprehensive high-quality data set on the pharmacokinetics of caffeine, caffeine metabolites, and their metabolic ratios in human adults. The data set is enriched by meta-data on the characteristics of studied patient cohorts and subjects (e.g., age, body weight, smoking status, health status), the applied interventions (e.g., dosing, substance, route of application), measured pharmacokinetic time-courses, and pharmacokinetic parameters (e.g., clearance, half-life, area under the curve). We demonstrate via multiple applications how the data set can be used to solidify existing knowledge and gain new insights relevant for metabolic phenotyping and liver function testing based on caffeine. Specifically, we analyzed 1) the alteration of caffeine pharmacokinetics with smoking and use of oral contraceptives; 2) drug-drug interactions with caffeine as possible confounding factors of caffeine pharmacokinetics or source of adverse effects; 3) alteration of caffeine pharmacokinetics in disease; and 4) the applicability of caffeine as a salivary test substance by comparison of plasma and saliva data. In conclusion, our data set and analyses provide important resources which could enable more accurate caffeine-based metabolic phenotyping and liver function testing.

Exploration and application of a liver-on-a-chip device in combination with modelling and simulation for quantitative drug metabolism studies.

Abstract: Microphysiological systems (MPS) are complex and more physiologically realistic cellular in vitro tools that aim to provide more relevant human in vitro data for quantitative prediction of clinical pharmacokinetics while also reducing the need for animal testing. The PhysioMimix liver-on-a-chip integrates medium flow with hepatocyte culture and has the potential to be adopted for in vitro studies investigating the hepatic disposition characteristics of drug candidates. The current study focusses on liver-on-a-chip system exploration for multiple drug metabolism applications. Characterization of cytochrome P450 (CYP), UDP-glucuronosyl transferase (UGT) and aldehyde oxidase (AO) activities was performed using 15 drugs and in vitro to in vivo extrapolation (IVIVE) was assessed for 12 of them. Next, the utility of the liver-on-a-chip for estimation of the fraction metabolized (fm) via specific biotransformation pathways of quinidine and diclofenac was established. Finally, the metabolite identification opportunities were also explored using efavirenz as an example drug with complex primary and secondary metabolism involving a combination of CYP, UGT and sulfotransferase enzymes. A key aspect of these investigations was the application of mathematical modelling for improved parameter calculation. Such approaches will be required for quantitative assessment of metabolism and/or transporter processes in systems where medium flow and system compartments result in non-homogeneous drug concentrations. In particular, modelling was used to explore the effect of evaporation from the medium and it was found that the intrinsic clearance (CLint) might be underestimated by up to 40% for low clearance compounds if evaporation is not accounted for. Modelling of liver-on-a-chip in vitro data also enhanced the approach to fm estimation allowing objective assessment of metabolism models of different complexity. The resultant diclofenac fm,UGT of 0.64 was highly comparable with values reported previously in the literature. The current study demonstrates the integration of mathematical modelling with experimental liver-on-a-chip studies and illustrates how this approach supports generation of high quality of data from complex in vitro cellular systems.

Integration of Toxicogenomics and Physiologically Based Pharmacokinetic Modeling in Human Health Risk Assessment of Perfluorooctane Sulfonate.

Abstract: Toxicogenomics and physiologically based pharmacokinetic (PBPK) models are useful approaches in chemical risk assessment, but the methodology to incorporate toxicogenomic data into a PBPK model to inform risk assessment remains to be developed. This study aimed to develop a probabilistic human health risk assessment approach by integrating toxicogenomic dose-response data and PBPK modeling using perfluorooctane sulfonate (PFOS) as a case study. Based on the available human in vitro and mouse in vivo toxicogenomic data, we identified the differentially expressed genes (DEGs) at each exposure paradigm/duration. Kyoto Encyclopedia of Genes and Genomes and disease ontology enrichment analyses were conducted on the DEGs to identify significantly enriched pathways and diseases. The dose-response data of DEGs were analyzed using the Bayesian benchmark dose (BMD) method. Using a previously published PBPK model, the gene BMDs were converted to human equivalent doses (HEDs), which were summarized to pathway and disease HEDs and then extrapolated to reference doses (RfDs) by considering an uncertainty factor of 30 for mouse in vivo data and 10 for human in vitro data. The results suggested that the median RfDs at different exposure paradigms were similar to the 2016 U.S. Environmental Protection Agency's recommended RfD, while the RfDs for the most sensitive pathways and diseases were closer to the recent European Food Safety Authority's guidance values. In conclusion, genomic dose-response data and PBPK modeling can be integrated to become a useful alternative approach in risk assessment of environmental chemicals. This approach considers multiple endpoints, provides toxicity mechanistic insights, and does not rely on apical toxicity endpoints.

Prediction of Maternal and Fetoplacental Concentrations of Cefazolin, Cefuroxime, and Amoxicillin during Pregnancy Using Bottom-Up Physiologically Based Pharmacokinetic Models

Abstract: Concerns over maternal and fetal drug exposures highlight the need for a better understanding of drug distribution into the fetus through the placental barrier. This study aimed to predict maternal and fetal drug disposition using physiologically based pharmacokinetic (PBPK) modeling. The detailed maternal-placental-fetal PBPK model within the Simcyp Simulator V20 was used to predict the maternal and fetoplacental exposure of cefazolin, cefuroxime, and amoxicillin during pregnancy and at delivery. The mechanistic dynamic model includes physiologic changes of the maternal, fetal, and placental parameters over the course of pregnancy. Placental kinetics were parametrized using permeability parameters determined from the physicochemical properties of these compounds. Then, the PBPK predictions were compared with the observed data. Fully bottom-up fetoplacental PBPK models were developed for cefuroxime, cefazolin, and amoxicillin without any parameter fitting. Predictions in nonpregnant subjects and in pregnant subjects fall within 2-fold of the observed values. Predictions matched observed pharmacokinetic data reported in nine maternal (five fetoplacental) studies for cefuroxime, 10 maternal (five fetoplacental) studies for cefazolin, and six maternal (two fetoplacental) studies for amoxicillin. Integration of the fetal and maternal system parameters within PBPK models, together with compound-related parameters used to calculate placental permeability, facilitates and extends the applications of the maternal-placental-fetal PBPK model. The developed model can also be used for designing clinical trials and prospectively used for maternal-fetal risk assessment after maternally administered drugs or unintended exposure to environmental toxicants. SIGNIFICANCE STATEMENT This study investigates the performance of an integrated maternal-placental-fetal PBPK model to predict maternal and fetal tissue exposure of renally eliminated antibiotics that cross the placenta through a passive diffusion mechanism. The transplacental permeability clearance was predicted from the drug physicochemical properties. Results demonstrate that the PBPK approach can facilitate the prediction of maternal and fetal drug exposure simultaneously at any gestational age to support its use in the maternal-fetal exposure assessments.

Clinical Implications of Altered Drug Transporter Abundance/Function and PBPK Modeling in Specific Populations: An ITC Perspective

Abstract: The role of membrane transporters on pharmacokinetics (PKs), drug–drug interactions (DDIs), pharmacodynamics (PDs), and toxicity of drugs has been broadly recognized. However, our knowledge of modulation of transporter expression and/or function in the diseased patient population or specific populations, such as pediatrics or pregnancy, is still emerging. This white paper highlights recent advances in studying the changes in transporter expression and activity in various diseases (i.e., renal and hepatic impairment and cancer) and some specific populations (i.e., pediatrics and pregnancy) with the focus on clinical implications. Proposed alterations in transporter abundance and/or activity in diseased and specific populations are based on (i) quantitative transporter proteomic data and relative abundance in specific populations vs. healthy adults, (ii) clinical PKs, and emerging transporter biomarker and/or pharmacogenomic data, and (iii) physiologically‐based pharmacokinetic modeling and simulation. The potential for altered PK, PD, and toxicity in these populations needs to be considered for drugs and their active metabolites in which transporter‐mediated uptake/efflux is a major contributor to their absorption, distribution, and elimination pathways and/or associated DDI risk. In addition to best practices, this white paper discusses current challenges and knowledge gaps to study and quantitatively predict the effects of modulation in transporter activity in these populations, together with the perspectives from the International Transporter Consortium (ITC) on future directions.

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

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

Physiologically‐based pharmacokinetic modeling of dextromethorphan to investigate interindividual variability within CYP2D6 activity score groups

Abstract: This study provides a whole‐body physiologically‐based pharmacokinetic (PBPK) model of dextromethorphan and its metabolites dextrorphan and dextrorphan O‐glucuronide for predicting the effects of cytochrome P450 2D6 (CYP2D6) drug‐gene interactions (DGIs) on dextromethorphan pharmacokinetics (PK). Moreover, the effect of interindividual variability (IIV) within CYP2D6 activity score groups on the PK of dextromethorphan and its metabolites was investigated. A parent‐metabolite‐metabolite PBPK model of dextromethorphan, dextrorphan, and dextrorphan O‐glucuronide was developed in PK‐Sim and MoBi. Drug‐dependent parameters were obtained from the literature or optimized. Plasma concentration‐time profiles of all three analytes were gathered from published studies and used for model development and model evaluation. The model was evaluated comparing simulated plasma concentration‐time profiles, area under the concentration‐time curve from the time of the first measurement to the time of the last measurement (AUClast) and maximum concentration (Cmax) values to observed study data. The final PBPK model accurately describes 28 population plasma concentration‐time profiles and plasma concentration‐time profiles of 72 individuals from four cocktail studies. Moreover, the model predicts CYP2D6 DGI scenarios with six of seven DGI AUClast and seven of seven DGI Cmax ratios within the acceptance criteria. The high IIV in plasma concentrations was analyzed by characterizing the distribution of individually optimized CYP2D6 kcat values stratified by activity score group. Population simulations with sampling from the resulting distributions with calculated log‐normal dispersion and mean parameters could explain a large extent of the observed IIV. The model is publicly available alongside comprehensive documentation of model building and model evaluation.

Quality Assurance of PBPK Modeling Platforms and Guidance on Building, Evaluating, Verifying and Applying PBPK Models Prudently under the Umbrella of Qualification: Why, When, What, How and By Whom?

Abstract: Abstract Modeling and simulation emerges as a fundamental asset of drug development. Mechanistic modeling builds upon its strength to integrate various data to represent a detailed structural knowledge of a physiological and biological system and is capable of informing numerous drug development and regulatory decisions via extrapolations outside clinically studied scenarios. Herein, physiologically based pharmacokinetic (PBPK) modeling is the fastest growing branch, and its use for particular applications is already expected or explicitly recommended by regulatory agencies. Therefore, appropriate applications of PBPK necessitates trust in the predictive capability of the tool, the underlying software platform, and related models. That has triggered a discussion on concepts of ensuring credibility of model-based derived conclusions. Questions like ‘why’, ‘when’, ‘what’, ‘how’ and ‘by whom’ remain open. We seek for harmonization of recent ideas, perceptions, and related terminology. First, we provide an overview on quality assurance of PBPK platforms with the two following concepts. Platform validation: ensuring software integrity, security, traceability, correctness of mathematical models and accuracy of algorithms. Platform qualification: demonstrating the predictive capability of a PBPK platform within a particular context of use. Second, we provide guidance on executing dedicated PBPK studies. A step-by-step framework focuses on the definition of the question of interest, the context of use, the assessment of impact and risk, the definition of the modeling strategy, the evaluation of the platform, performing model development including model building, evaluation and verification, the evaluation of applicability to address the question, and the model application under the umbrella of a qualified platform.

Assessment of the Kochak-Benet Equation for Hepatic Clearance for the Parallel-Tube Model: Relevance of Classic Clearance Concepts in PK and PBPK.

Abstract: This report reviews concepts related to operation of the classic parallel-tube model (PTM) for hepatic disposition and examines two recent proposals of a newly derived equation to describe hepatic clearance (CLH). It is demonstrated that the proposed equation is identical to a re-arrangement of an earlier relationship from Pang and Rowland and provides a means of calculation of intrinsic clearance (CLint,PTM) rather than CLH as posed. We further demonstrate how classic hepatic clearance models with an assumed CLint, while subject to numerous limitations, remain highly useful and necessary in both traditional pharmacokinetics (PK) and physiologically based pharmacokinetic (PBPK) modeling.

Physiologically Based Modeling to Predict Monoclonal Antibody Pharmacokinetics in Humans from in vitro Physiochemical Properties

Abstract: ABSTRACT A model-based framework is presented to predict monoclonal antibody (mAb) pharmacokinetics (PK) in humans based on in vitro measures of antibody physiochemical properties. A physiologically based pharmacokinetic (PBPK) model is used to explore the predictive potential of 14 in vitro assays designed to measure various antibody physiochemical properties, including nonspecific cell-surface interactions, FcRn binding, thermal stability, hydrophobicity, and self-association. Based on the mean plasma PK time course data of 22 mAbs from humans reported in the literature, we found a significant positive correlation (R = 0.64, p = .0013) between the model parameter representing antibody-specific vascular to endothelial clearance and heparin relative retention time, an in vitro measure of nonspecific binding. We also found that antibody-specific differences in paracellular transport due to convection and diffusion could be partially explained by antibody heparin relative retention time (R = 0.52, p = .012). Other physiochemical properties, including antibody thermal stability, hydrophobicity, cross-interaction and self-association, in and of themselves were not predictive of model-based transport parameters. In contrast to other studies that have reported empirically derived expressions relating in vitro measures of antibody physiochemical properties directly to antibody clearance, the proposed PBPK model-based approach for predicting mAb PK incorporates fundamental mechanisms governing antibody transport and processing, informed by in vitro measures of antibody physiochemical properties, and can be expanded to include more descriptive representations of each of the antibody processing subsystems, as well as other antibody-specific information.

Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: a comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats

Abstract: Abstract Background Physiologically based pharmacokinetic (PBPK) modeling is an important tool in predicting target organ dosimetry and risk assessment of nanoparticles (NPs). The methodology of building a multi-route PBPK model for NPs has not been established, nor systematically evaluated. In this study, we hypothesized that the traditional route-to-route extrapolation approach of PBPK modeling that is typically used for small molecules may not be appropriate for NPs. To test this hypothesis, the objective of this study was to develop a multi-route PBPK model for different sizes (1.4–200 nm) of gold nanoparticles (AuNPs) in adult rats following different routes of administration (i.e., intravenous (IV), oral gavage, intratracheal instillation, and endotracheal inhalation) using two approaches: a traditional route-to-route extrapolation approach for small molecules and a new approach that is based on route-specific data that we propose to be applied generally to NPs. Results We found that the PBPK model using this new approach had superior performance than the traditional approach. The final PBPK model was optimized rigorously using a Bayesian hierarchical approach with Markov chain Monte Carlo simulations, and then converted to a web-based interface using R Shiny. In addition, quantitative structure–activity relationships (QSAR) based multivariate linear regressions were established to predict the route-specific key biodistribution parameters (e.g., maximum uptake rate) based on the physicochemical properties of AuNPs (e.g., size, surface area, dose, Zeta potential, and NP numbers). These results showed the size and surface area of AuNPs were the main determinants for endocytic/phagocytic uptake rates regardless of the route of administration, while Zeta potential was an important parameter for the estimation of the exocytic release rates following IV administration. Conclusions This study suggests that traditional route-to-route extrapolation approaches for PBPK modeling of small molecules are not applicable to NPs. Therefore, multi-route PBPK models for NPs should be developed using route-specific data. This novel PBPK-based web interface serves as a foundation for extrapolating to other NPs and to humans to facilitate biodistribution estimation, safety, and risk assessment of NPs.

Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: a comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats

Abstract: Abstract Background Physiologically based pharmacokinetic (PBPK) modeling is an important tool in predicting target organ dosimetry and risk assessment of nanoparticles (NPs). The methodology of building a multi-route PBPK model for NPs has not been established, nor systematically evaluated. In this study, we hypothesized that the traditional route-to-route extrapolation approach of PBPK modeling that is typically used for small molecules may not be appropriate for NPs. To test this hypothesis, the objective of this study was to develop a multi-route PBPK model for different sizes (1.4–200 nm) of gold nanoparticles (AuNPs) in adult rats following different routes of administration (i.e., intravenous (IV), oral gavage, intratracheal instillation, and endotracheal inhalation) using two approaches: a traditional route-to-route extrapolation approach for small molecules and a new approach that is based on route-specific data that we propose to be applied generally to NPs. Results We found that the PBPK model using this new approach had superior performance than the traditional approach. The final PBPK model was optimized rigorously using a Bayesian hierarchical approach with Markov chain Monte Carlo simulations, and then converted to a web-based interface using R Shiny. In addition, quantitative structure–activity relationships (QSAR) based multivariate linear regressions were established to predict the route-specific key biodistribution parameters (e.g., maximum uptake rate) based on the physicochemical properties of AuNPs (e.g., size, surface area, dose, Zeta potential, and NP numbers). These results showed the size and surface area of AuNPs were the main determinants for endocytic/phagocytic uptake rates regardless of the route of administration, while Zeta potential was an important parameter for the estimation of the exocytic release rates following IV administration. Conclusions This study suggests that traditional route-to-route extrapolation approaches for PBPK modeling of small molecules are not applicable to NPs. Therefore, multi-route PBPK models for NPs should be developed using route-specific data. This novel PBPK-based web interface serves as a foundation for extrapolating to other NPs and to humans to facilitate biodistribution estimation, safety, and risk assessment of NPs.

Machine Learning Prediction of the Three Main Input Parameters of a Simplified Physiologically Based Pharmacokinetic Model Subsequently Used to Generate Time-Dependent Plasma Concentration Data in Humans after Oral Doses of 212 Disparate Chemicals

Abstract: Physiologically based pharmacokinetic (PBPK) modeling has the potential to play significant roles in estimating internal chemical exposures. The three major PBPK model input parameters (i.e., absorption rate constants, volumes of the systemic circulation, and hepatic intrinsic clearances) were generated in silico for 212 chemicals using machine learning algorithms. These input parameters were calculated based on sets of between 17 and 65 chemical properties that were generated by in silico prediction tools before being processed by machine learning algorithms. The resulting simplified PBPK models were used to estimate plasma concentrations after virtual oral administrations in humans. The estimated absorption rate constants, volumes of the systemic circulation, and hepatic intrinsic clearance values for the 212 test compounds determined traditionally (i.e., based on fitting to measured concentration profiles) and newly estimated had correlation coefficients of 0.65, 0.68, and 0.77 (p < 0.01, n = 212), respectively. When human plasma concentrations were modeled using traditionally determined input parameters and again using in silico estimated input parameters, the two sets of maximum plasma concentrations (r = 0.85, p < 0.01, n = 212) and areas under the curve (r = 0.80, p < 0.01, n = 212) were correlated. Virtual chemical exposure levels in liver and kidney were also estimated using these simplified PBPK models along with human plasma levels. These results indicate that the PBPK model input parameters for humans of a diverse set of compounds can be reliability estimated using chemical descriptors calculated using in silico tools.

Robust physiologically based pharmacokinetic model of rifampicin for predicting drug–drug interactions via P‐glycoprotein induction and inhibition in the intestine, liver, and kidney

Abstract: P‐glycoprotein (P‐gp) is an efflux transporter that plays an important role in the pharmacokinetics of its substrate, and P‐gp activities can be altered by induction and inhibition effects of rifampicin. This study aimed to establish a physiologically based pharmacokinetic (PBPK) model of rifampicin to predict the P‐gp‐mediated drug–drug interactions (DDIs) and assess the DDI impact in the intestine, liver, and kidney. The induction and inhibition parameters of rifampicin for P‐gp were estimated using two of seven DDI cases of rifampicin and digoxin and incorporated into our previously constructed PBPK model of rifampicin. The constructed rifampicin model was verified using the remaining five DDI cases with digoxin and five DDI cases with other P‐gp substrates (talinolol and quinidine). Based on the established PBPK model, following repeated dosing of 600 mg rifampicin, the deduced net effect was an approximately threefold induction in P‐gp activities in the intestine, liver, and kidney. Furthermore, in all 12 cases the predicted area under the plasma concentration–time curve ratios of the P‐gp substrates were within the predefined acceptance criteria with various dosing regimens. Intestinal effects of P‐gp–mediated DDIs had their greatest impact on the pharmacokinetics of digoxin and talinolol, with a minimal impact on the liver and kidney. For quinidine, predicted intestinal P‐gp/cytochrome P450 3A–mediated DDIs were slightly underestimated because of the complexity of nonlinearity and transporter‐enzyme interplay. These findings demonstrate that our rifampicin model can be applicable to quantitatively predict the net impact of P‐gp induction and/or inhibition on diverse P‐gp substrates and investigate the magnitude of DDIs in each tissue.

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

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

Toxicokinetics, dose-response, and risk assessment of nanomaterials: Methodology, challenges, and future perspectives.

Abstract: The rapid growth of nanomaterial applications has raised safety concerns for human health. A number of studies have been conducted to assess the toxicokinetics, toxicology, dose-response, and risk assessment of different nanomaterials using in vitro and in vivo animal and human models. However, current studies cannot meet the demand for efficient assessment of toxicokinetics, dose-response relationships, or the toxicological risk arising from the rapidly increasing number of newly synthesized nanomaterials. In this article, we review the methods for conducting toxicokinetics, hazard identification, dose-response, exposure, and risk assessment studies of nanomaterials, identify the knowledge gaps, and discuss the challenges remaining. We provide the rationale behind the appropriate design of nanomaterial plasma toxicokinetic and tissue distribution studies, including caveats on the interpretation and correlation of in vitro and in vivo toxicology studies. The potential of using physiologically based pharmacokinetic (PBPK) models to extrapolate toxicokinetic and toxicity findings from in vitro to in vivo and from animals to humans is discussed, and the knowledge gaps of PBPK modeling for nanomaterials are identified. While challenges still exist, there has been progress in the toxicokinetics, hazard identification, and risk assessment of nanomaterials in the past two decades. Recent advancements in the field are highlighted with relevant examples. We also share latest guidelines as well as our perspectives on future studies needed to characterize the toxicokinetics, toxicity, and dose-response relationship in support of nanomaterial risk assessment. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine.

Physiologically‐Based Pharmacokinetic Modelling to Investigate Baricitinib and Tofacitinib Dosing Recommendations for COVID‐19 in Geriatrics

Abstract: Janus kinase (JAK) inhibitors baricitinib and tofacitinib are recommended by the US National Institutes of Health as immunomodulatory drugs for coronavirus disease 2019 (COVID‐19) treatment. In addition, baricitinib has recently received Emergency Use Authorization from the US Food and Drug Administration, although the instruction provided dosing information only for adults. Geriatrics with organ dysfunction are one of the most vulnerable cohorts when combating the pandemic. The aim of the present work was to evaluate current dosing strategies of baricitinib and tofacitinib for potential COVID‐19 treatment for White and Chinese geriatric patients with chronic renal impairment. An established physiologically‐based pharmacokinetic (PBPK) modeling framework for age‐dependent simulations was utilized. PBPK drug models adopted from literature were first verified. Several population models representing different age groups, ethnicities, and stages of renal impairment were used for prospective simulations. Notwithstanding the increase in systemic exposure of both drugs resulting from renal dysfunction was more pronounced for geriatrics than general White populations, our simulations confirmed their current dosage adjustments based on renal functions are broadly adequate. The exception being White older subjects with mild renal impairment where current recommendation of 4 mg baricitinib yielded a 2.31‐fold increase in systemic exposure, and reduction to 2 mg could mitigate the potential risk to an acceptable 1.15‐fold. Comparable relationships between systemic exposure and renal dysfunction were observed for both drugs in the Chinese population. In summary, PBPK modeling of both JAK inhibitors supports the rational and prudent dose adjustments of these COVID‐19 therapeutics among adult patients of different age groups and renal functions.

PBPK Modeling on Organs-on-Chips: An Overview of Recent Advancements

Abstract: Organ-on-a-chip (OoC) is a new and promising technology, which aims to improve the efficiency of drug development and realize personalized medicine by simulating in vivo environment in vitro. Physiologically based pharmacokinetic (PBPK) modeling is believed to have the advantage of better reflecting the absorption, distribution, metabolism and excretion process of drugs in vivo than traditional compartmental or non-compartmental pharmacokinetic models. The combination of PBPK modeling and organ-on-a-chip is believed to provide a strong new tool for new drug development and have the potential to replace animal testing. This article provides the recent development of organ-on-a-chip technology and PBPK modeling including model construction, parameter estimation and validation strategies. Application of PBPK modeling on Organ-on-a-Chip (OoC) has been emphasized, and considerable progress has been made. PBPK modeling on OoC would become an essential part of new drug development, personalized medicine and other fields.

Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery

Abstract: Minimizing in vitro and in vivo testing in early drug discovery with the use of physiologically based pharmacokinetic (PBPK) modeling and machine learning (ML) approaches has the potential to reduce discovery cycle times and animal experimentation. However, the prediction success of such an approach has not been shown for a larger and diverse set of compounds representative of a lead optimization pipeline. In this study, the prediction success of the oral (PO) and intravenous (IV) pharmacokinetics (PK) parameters in rats was assessed using a “bottom-up” approach, combining in vitro and ML inputs with a PBPK model. More than 240 compounds for which all of the necessary inputs and PK data were available were used for this assessment. Different clearance scaling approaches were assessed, using hepatocyte intrinsic clearance and protein binding as inputs. In addition, a novel high-throughput PBPK (HT-PBPK) approach was evaluated to assess the scalability of PBPK predictions for a larger number of compounds in drug discovery. The results showed that bottom-up PBPK modeling was able to predict the rat IV and PO PK parameters for the majority of compounds within a 2- to 3-fold error range, using both direct scaling and dilution methods for clearance predictions. The use of only ML-predicted inputs from the structure did not perform well when using in vitro inputs, likely due to clearance miss predictions. The HT-PBPK approach produced comparable results to the full PBPK modeling approach but reduced the simulation time from hours to seconds. In conclusion, a bottom-up PBPK and HT-PBPK approach can successfully predict the PK parameters and guide early discovery by informing compound prioritization, provided that good in vitro assays are in place for key parameters such as clearance.