Thierry Lavé

A Novel Strategy for Physiologically Based Predictions of Human Pharmacokinetics

BackgroundThe major aim of this study was to develop a strategy for predicting human pharmacokinetics using physiologically based pharmacokinetic (PBPK) modelling. This was compared with allometry (of plasma concentration-time profiles using the Dedrick approach), in order to determine the best approaches and strategies for the prediction of human pharmacokinetics.MethodsPBPK and Dedrick predictions were made for 19 F. Hoffmann-La Roche compounds. A strategy for the prediction of human pharmacokinetics using PBPK modelling was proposed in this study. Predicted values (pharmacokinetic parameters, plasma concentrations) were compared with observed values obtained after intravenous and oral administration in order to assess the accuracy of the prediction methods.ResultsBy following the proposed strategy for PBPK, a prediction would have been made prospectively for approximately 70% of the compounds. The prediction accuracy for these compounds in terms of the percentage of compounds with an average-fold error of <2-fold was 83%, 50%, 75%, 67%, 92% and 100% for apparent oral clearance (CL/F), apparent volume of distribution during terminal phase after oral administration (Vz/F), terminal elimination half-life (t½), peak plasma concentration (Cmax), area under the plasma concentration-time curve (AUC) and time to reach Cmax (tmax), respectively. For the other 30% compounds, unacceptable prediction accuracy was obtained in animals; therefore, a prospective prediction of human pharmacokinetics would not have been made using PBPK. For these compounds, prediction accuracy was also poor using the Dedrick approach. In the majority of cases, PBPK gave more accurate predictions of pharmacokinetic parameters and plasma concentration-time profiles than the Dedrick approach.ConclusionsBased on the dataset evaluated in this study, PBPK gave reasonable predictions of human pharmacokinetics using preclinical data and is the recommended approach in the majority of cases. In addition, PBPK modelling is a useful tool to gain insights into the properties of a compound. Thus, PBPK can guide experimental efforts to obtain the relevant information necessary to understand the compound’s properties before entry into human, ultimately resulting in a higher level of prediction accuracy.

Prediction of Hepatic Metabolic Clearance Based on Interspecies Allometric Scaling Techniques and In Vitro-In Vivo Correlations

This article reviews the methods available for predicting hepatic metabolic clearance in humans, and discusses their application to the processes of drug discovery and development. The application of these techniques has increased markedly during the past few years because of the improved availability of human liver samples, which has increased the opportunities to use in vitro studies to predict human clearance. The techniques available involve both empirical and physiologically based approaches. Allometric scaling using in vitro data from animals and humans combines certain aspects of both approaches.An evaluation of data retrieved from the literature indicates that, together with in vitro human data, allometric scaling based on a combination of in vitro and in vivo preclinical data can accurately predict clearance in humans. With this approach, 80% of the predictions were within a 2-fold factor of actual human clearance values, with an overall accuracy of 1.6-fold.The uncertainties and inaccuracies in predicting human clearance are related to: (i) the specific method that is used to make the prediction; (ii) the experimental design and the model used to determine the in vitro clearance; (iii) protein binding within the in vitro test system; and (iv) various in vivo factors such as the involvement of extrahepatic metabolism and active transport processes, interindividual variability and nonlinearity in pharmacokinetics.In contrast to purely empirical approaches, the physiological approach to predicting clearance gives an opportunity to integrate some of these complexities and, therefore, should provide more confidence in the prediction of clearance in humans.

Prediction of intestinal absorption: comparative assessment of gastroplus™ and idea™

We have assessed two commercial software tools employing physiologically based models for prediction of intestinal absorption in human. IDEA 2.0 and GASTROPLUS 3.1.0 were compared both in their ability to predict fraction absorbed for a set of 28 drugs and in terms of the functionality offered. The emphasis was placed on the practical usefulness to pharmaceutical drug discovery. Predictions were assessed for three levels of input data (i) pure in silico input, (ii) thermodynamic solubility and in silico permeability, (iii) thermodynamic solubility and human colon carcinoma cell line (CACO-2) permeability. We found the pure in silico prediction ability of the tools to be comparable with 70% correct classification rate. With measured input data the IDEA prediction rate improved to 79% while GASTROPLUS stayed at 70%. In terms of functionality GASTROPLUS is a powerful system for the trained user. Open access to model parameters, diagnostic tools and the ability to integrate data make it particularly suitable for the later stages of discovery and development. IDEA is web based and presents a simple interface suitable for widespread use with minimal training. However the limited functionality and inconvenient handling of multiple compound batches currently restrict the usefulness of version 2.0 for drug discovery.

PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: Prediction of plasma concentration–time profiles in human by using the physiologically‐based pharmacokinetic modeling approach

The objective of this study is to assess the effectiveness of physiologically based pharmacokinetic (PBPK) models for simulating human plasma concentration-time profiles for the unique drug dataset of blinded data that has been assembled as part of a Pharmaceutical Research and Manufacturers of America initiative. Combinations of absorption, distribution, and clearance models were tested with a PBPK approach that has been developed from published equations. An assessment of the quality of the model predictions was made on the basis of the shape of the plasma time courses and related parameters. Up to 69% of the simulations of plasma time courses made in human demonstrated a medium to high degree of accuracy for intravenous pharmacokinetics, whereas this number decreased to 23% after oral administration based on the selected criteria. The simulations resulted in a general underestimation of drug exposure (Cmax and AUC0- t ). The explanations for this underestimation are diverse. Therefore, in general it may be due to underprediction of absorption parameters and/or overprediction of distribution or oral first-pass. The implications of compound properties are demonstrated. The PBPK approach based on in vitro-input data was as accurate as the approach based on in vivo data. Overall, the scientific benefit of this modeling study was to obtain more extensive characterization of predictions of human PK from PBPK methods.

Predicting Pharmacokinetic Food Effects Using Biorelevant Solubility Media and Physiologically Based Modelling

AbstractBackground: Food-induced changes in gastric emptying time, gastric pH and/or intestinal fluid composition may have an impact on the pharmacokinetics of drugs. The aim of this work was to use mathematical models describing physiology in fed and fasted states together with biorelevant solubility and degradation data to simulate food effects for six compounds from recent Roche projects. Methods: The solubility of each compound was measured in different biorelevant media: simulated human gastric fluid for the fasted and fed state, simulated human intestinal fluid for the fasted, fed and high-fat state, and simulated human colonic fluid for the upper and the lower colon. A physiologically based absorption model was developed in GastroPlus™ for each compound using permeability, solubility, metabolism and distribution data. By incorporating the appropriate physiological parameters and solubility data into the model, the oral pharmacokinetics of each drug was simulated under fasted, fed and/or high-fat conditions. Predicted and observed plasma concentration-time profiles and food effects were compared for a range of doses to assess the accuracy of the simulations. Results: The models were able to distinguish between minor and significant food effects. The simulation captured well the magnitude of the food effects and for the six compounds correctly predicted the observed plasma exposure in fasted, fed and high-fat conditions. Conclusion: Biorelevant solubility tests can be used together with physiologically based absorption models to predict clinical food effects caused by solubility and/or dissolution rate limitations.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: Comparative assessement of prediction methods of human clearance

The objective of this study was to evaluate the performance of various allometric and in vitro-in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o. ) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability (F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL.

Applications of physiologically based absorption models in drug discovery and development.

This article describes the use of physiologically based models of intestinal drug absorption to guide the research and development of new drugs. Applications range from lead optimization in the drug discovery phase through clinical candidate selection and extrapolation to human to phase 2 formulation development. Early simulations in preclinical species integrate multiple screening data and add value by transforming these individual properties into a prediction of in vivo absorption. Comparison of simulations to plasma levels measured after oral dosing in animals highlights unexpected behavior, and parameter sensitivity analysis can explore the impact of uncertainties in key properties, point toward factors which are limiting absorption and contribute to assessment of compound developability. Physiological models provide reliable prediction of human absorption and with refinement based on phase 1 data are useful guides to further market formulation development. Improvements in the accuracy of simulations are expected as better in vitro methods generate more in vivo relevant solubility and permeability data, and modeling will play a central role in the development of more predictive methods for transporter-related effects on drug absorption.

Design, Data Analysis, and Simulation of in Vitro Drug Transport Kinetic Experiments Using a Mechanistic in Vitro Model

The use of in vitro data for quantitative predictions of transporter-mediated elimination in vivo requires an accurate estimation of the transporter Michaelis-Menten parameters, Vmax and Km, as a first step. Therefore, the experimental conditions of in vitro studies used to assess hepatic uptake transport were optimized regarding active transport processes, nonspecific binding, and passive diffusion (Pdif). A mechanistic model was developed to analyze and accurately describe these active and passive processes. This two-compartmental model was parameterized to account for nonspecific binding, bidirectional passive diffusion, and active uptake processes based on the physiology of the cells. The model was used to estimate kinetic parameters of in vitro transport data from organic anion-transporting peptide model substrates (e.g., cholecystokinin octapeptide deltorphin II, fexofenadine, and pitavastatin). Data analysis by this mechanistic model significantly improved the accuracy and precision in all derived parameters [mean coefficient of variations (CVs) for Vmax and Km were 19 and 23%, respectively] compared with the conventional kinetic method of transport data analysis (mean CVs were 58 and 115%, respectively, using this method). Furthermore, permeability was found to be highly temperature-dependent in Chinese hamster ovary (CHO) control cells and artificial membranes (parallel artificial membrane permeability assay). Whereas for some compounds (taurocholate, estrone-3-sulfate, and propranolol) the effect was moderate (1.5–6-fold higher permeability at 37°C compared with that at 4°C), for fexofenadine a 16-fold higher passive permeability was seen at 37°C. Therefore, Pdif was better predicted if it was evaluated under the same experimental conditions as Vmax and Km, i.e., in a single incubation of CHO overexpressed cells or rat hepatocytes at 37°C, instead of a parallel control evaluation at 4°C.

Challenges and opportunities with modelling and simulation in drug discovery and drug development

The benefits of modelling and simulation at the pre-clinical stage of drug development can be realized through formal and realistic integration of data on physicochemical properties, pharmacokinetics, pharmacodynamics, formulation and safety. Such data integration and the powerful combination of physiologically based pharmacokinetic (PBPK) with pharmacokinetic–pharmacodynamic relationship (PK/PD) models provides the basis for quantitative outputs allowing comparisons across compounds and resulting in improved decision-making during the selection process. Such PBPK/PD evaluations provide crucial information on the potency and safety of drug candidates in vivo and the bridging of the PK/PD concept established during the pre-clinical phase to clinical studies. Modelling and simulation is required to address a number of key questions at the various stages of the drug-discovery and -development process. Such questions include the following. (1) What is the expected human PK profile for potential clinical candidate(s)? (2) Is this profile and its associated PD adequate for the given indication? (3) What is the optimal dosing schedule with respect to safety and efficacy? (4) Is a food effect expected? (5) How can formulation be improved and what is the potential benefit? (6) What is the expected variability and uncertainty in the predictions?

Influence of isolation procedure, extracellular matrix and dexamethasone on the regulation of membrane transporters gene expression in rat hepatocytes

The influence of the isolation procedure of hepatocytes, extracellular matrix (ECM) configuration and incubation medium supplementation by dexamethasone (DEX) on the cell morphology and on the gene expression of membrane transporters was examined in rat hepatocytes. The mRNA levels were determined using oligonucleotide microarrays, in liver, in suspension and in primary culture in monolayer (CPC), and in collagen gels sandwich (SPC) in absence and presence of DEX (100 and 1000 nM). The results indicated pronounced morphological differences between CPC and SPC in response to DEX demonstrating that the hepatocytes re-formed, as in vivo, multicellular arrays with extensive bile canalicular network only in SPC in presence of DEX. The mRNA levels of membrane transporters were not affected significantly during isolation procedure. However, plating hepatocytes in CPC resulted in a decrease of major basolateral transporters mRNA level whereas mRNA levels of mdr1b and mrp3 were increased (>100-fold). Similar observations were made in SPC in the absence of DEX demonstrating that the ECM configuration alone did not play a critical role in the regulation of membrane transporters. However, adding DEX to the incubation medium in SPC resulted in an up-regulation of mdr2, oatp2 and mrp2 in a concentration-dependent way for the two latter genes, whereas mdr1b and mrp3 expression were maintained to their baseline liver levels. These data suggested therefore that the combination of ECM and DEX supplementation is essential for the formation of the bile canalicular network and is a determinant factor in the regulation of membrane transporters in cultured rat hepatocytes.