Model-based approaches to predict drug-drug interactions associated with hepatic uptake transporters: preclinical, clinical and beyond.

INTRODUCTION: Membrane transporters have been recognized to play a key role in determining the absorption, distribution and elimination processes of drugs. The organic anion-transporting polypeptide (OATP)1B1 and OATP1B3 isoforms are selectively expressed in the human liver and are known to cause significant drug-drug interactions (DDIs), as observed with an increasing number of drugs. It is evident that DDIs involving hepatic transporters are capable of altering systemic, as well as tissue-specific, exposure of drug substrates resulting in marked differences in drug safety and/or efficacy. It is therefore essential to quantitatively predict such interactions early in the drug development to mitigate clinical risks. AREAS COVERED: The role of hepatic uptake transporters in drug disposition and clinical DDIs has been reviewed with an emphasis on the current state of the models applicable for quantitative predictions. The readers will also gain insight into the in vitro experimental tools available to characterize transport kinetics, while appreciating the knowledge gaps in the in vitro-in vivo extrapolation (IVIVE), which warrant further investigation. EXPERT OPINION: Static and dynamic models can be convincingly applied to quantitatively predict drug interactions, early in drug discovery, to mitigate clinical risks as well as to avoid unnecessary clinical studies. Compared to basic models, which focus on individual processes, mechanistic models provide the ability to assess DDI potential for compounds with systemic disposition determined by both transporters and metabolic enzymes. However, complexities in the experimental tools and an apparent disconnect in the IVIVE of transport kinetics have limited the physiologically based pharmacokinetic modeling strategies. Emerging data on the expression of transporter proteins and tissue drug concentrations are expected to help bridge these gaps. In addition, detailed characterization of substrate kinetics can facilitate building comprehensive mechanistic models.

In vitro evaluation of hepatic transporter-mediated clinical drug-drug interactions: hepatocyte model optimization and retrospective investigation.

To assess the feasibility of using sandwich-cultured human hepatocytes (SCHHs) as a model to characterize transport kinetics for in vivo pharmacokinetic prediction, the expression of organic anion-transporting polypeptide (OATP) proteins in SCHHs, along with biliary efflux transporters, was confirmed quantitatively by liquid chromatography-tandem mass spectrometry. Rifamycin SV (Rif SV), which was shown to completely block the function of OATP transporters, was selected as an inhibitor to assess the initial rates of active uptake. The optimized SCHH model was applied in a retrospective investigation of compounds with known clinically significant OATP-mediated uptake and was applied further to explore drug-drug interactions (DDIs). Greater than 50% inhibition of active uptake by Rif SV was found to be associated with clinically significant OATP-mediated DDIs. We propose that the in vitro active uptake value therefore could serve as a cutoff for class 3 and 4 compounds of the Biopharmaceutics Drug Disposition Classification System, which could be integrated into the International Transporter Consortium decision tree recommendations to trigger clinical evaluations for potential DDI risks. Furthermore, the kinetics of in vitro hepatobiliary transport obtained from SCHHs, along with protein expression scaling factors, offer an opportunity to predict complex in vivo processes using mathematical models, such as physiologically based pharmacokinetics models.

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.

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.

Preclinical and clinical pharmacokinetics of PF-02413873, a nonsteroidal progesterone receptor antagonist.

The recently discovered selective nonsteroidal progesterone receptor (PR) antagonist 4-[3-cyclopropyl-1-(methylsulfonylmethyl)-5-methyl-1H-pyrazol-4-yl]oxy-2,6-dimethylbenzonitrile (PF-02413873) was characterized in metabolism studies in vitro, in preclinical pharmacokinetics in rat and dog, and in an initial pharmacokinetic study in human volunteers. Clearance (CL) of PF-02413873 was found to be high in rat (84 ml · min(-1) · kg(-1)) and low in dog (3.8 ml · min(-1) · kg(-1)), consistent with metabolic stability determined in liver microsomes and hepatocytes in these species. In human, CL was low in relation to hepatic blood flow, consistent with metabolic stability in human in vitro systems, where identified metabolites suggested predominant cytochrome P450 (P450)-catalyzed oxidative metabolism. Prediction of CL using intrinsic CL determined in human liver microsomes (HLM), recombinant human P450 enzymes, and single species scaling (SSS) from pharmacokinetic studies showed that dog SSS and HLM scaling provided the closest estimates of CL of PF-02413873 in human. These CL estimates were combined with a physiologically based pharmacokinetic (PBPK) model to predict pharmacokinetic profiles after oral suspension administration of PF-02413873 in fasted and fed states in human. Predicted plasma concentration versus time profiles were found to be similar to those observed in human over the PF-02413873 dose range 50 to 500 mg and captured the enhanced exposure in fed subjects. This case study of a novel nonsteroidal PR antagonist underlines the utility of PBPK modeling techniques in guiding prediction confidence and design of early clinical trials of novel chemical agents.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 1: goals, properties of the PhRMA dataset, and comparison with literature datasets.

This study is part of the Pharmaceutical Research and Manufacturers of America (PhRMA) initiative on predictive models of efficacy, safety, and compound properties. The overall goal of this part was to assess the predictability of human pharmacokinetics (PK) from preclinical data and to provide comparisons of available prediction methods from the literature, as appropriate, using a representative blinded dataset of drug candidates. The key objectives were to (i) appropriately assemble and blind a diverse dataset of in vitro, preclinical in vivo, and clinical data for multiple drug candidates, (ii) evaluate the dataset with empirical and physiological methodologies from the literature used to predict human PK properties and plasma concentration-time profiles, (iii) compare the predicted properties with the observed clinical data to assess the prediction accuracy using routine statistical techniques and to evaluate prediction method(s) based on the degree of accuracy of each prediction method, and (iv) compile and summarize results for publication. Another objective was to provide a mechanistic understanding as to why one methodology provided better predictions than another, after analyzing the poor predictions. A total of 108 clinical lead compounds were collected from 12 PhRMA member companies. This dataset contains intravenous (n = 19) and oral pharmacokinetic data (n = 107) in humans as well as the corresponding preclinical in vitro, in vivo, and physicochemical data. All data were blinded to protect the anonymity of both the data and the company submitting the data. This manuscript, which is the first of a series of manuscripts, summarizes the PhRMA initiative and the 108 compound dataset. More details on the predictability of each method are reported in companion manuscripts.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: comparative assessment of prediction methods of human volume of distribution.

The objective of this study was to evaluate the performance of various empirical, semimechanistic and mechanistic methodologies with and without protein binding corrections for the prediction of human volume of distribution at steady state (Vss ). PhRMA member companies contributed a set of blinded data from preclinical and clinical studies, and 18 drugs with intravenous clinical pharmacokinetics (PK) data were available for the analysis. In vivo and in vitro preclinical data were used to predict Vss by 24 different methods. Various statistical and outlier techniques were employed to assess the predictability of each method. There was not simply one method that predicts Vss accurately for all compounds. Across methods, the maximum success rate in predicting human Vss was 100%, 94%, and 78% of the compounds with predictions falling within tenfold, threefold, and twofold error, respectively, of the observed Vss . Generally, the methods that made use of in vivo preclinical data were more predictive than those methods that relied solely on in vitro data. However, 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. It is recommended to initially use the in vitro tissue composition-based equations to predict Vss in preclinical species and humans, putting the assumptions and compound properties into context. As in vivo data become available, these predictions should be reassessed and rationalized to indicate the level of confidence (uncertainty) in the human Vss prediction. The top three methods that perform strongly at integrating in vivo data in this way were the Øie-Tozer, the rat -dog-human proportionality equation, and the lumped-PBPK approach. Overall, the scientific benefit of this study was to obtain greater characterization of predictions of human Vss from several methods available in the literature.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 4: prediction of plasma concentration-time profiles in human from in vivo preclinical data by using the Wajima approach.

The objective of this study was to evaluate the performance of the Wajima allometry (Css -MRT) approach published in the literature, which is used to predict the human plasma concentration-time profiles from a scaling of preclinical species data. A diverse and blinded dataset of 108 compounds from PhRMA member companies was used in this evaluation. The human intravenous (i.v.) and oral (p.o.) pharmacokinetics (PK) data were available for 18 and 107 drugs, respectively. Three different scenarios were adopted for prediction of human PK profiles. In the first scenario, human clearance (CL) and steady-state volume of distribution (Vss ) were predicted by unbound fraction corrected intercept method (FCIM) and Øie-Tozer (OT) approaches, respectively. Quantitative structure activity relationship (QSAR)-based approaches (TSrat-dog ) based on compound descriptors together with rat and dog data were utilized in the second scenario. Finally, in the third scenario, CL and Vss were predicted using the FCIM and Jansson approaches, respectively. For the prediction of oral pharmacokinetics, the human bioavailability and absorption rate constant were assumed as the average of preclinical species. Various statistical techniques were used for assessing the accuracy of the simulation scenarios. The human CL and Vss were predicted within a threefold error range for about 75% of the i.v. drugs. However, the accuracy in predicting key p.o. PK parameters appeared to be lower with only 58% of simulations falling within threefold of observed parameters. The overall ability of the Css -MRT approach to predict the curve shape of the profile was in general poor and ranged between low to medium level of confidence for most of the predictions based on the selected criteria.

Prediction of oral pharmacokinetics of cMet kinase inhibitors in humans: physiologically based pharmacokinetic model versus traditional one-compartment model.

The objective of this study was to assess the physiologically based pharmacokinetic (PBPK) model for predicting plasma concentration-time profiles of orally available cMet kinase inhibitors, (R)-3-[1-(2,6-dichloro-3-fluoro-phenyl)-ethoxy]-5-(1-piperidin-4-yl-1H-pyrazol-4-yl)-pyridin-2-ylamine (PF02341066) and 2-[4-(3-quinolin-6-ylmethyl-3H-[1,2,3]triazolo[4,5-b]pyrazin-5-yl)-pyrazol-1-yl]-ethanol (PF04217903), in humans. The prediction accuracy of pharmacokinetics (PK) by PBPK modeling was compared with that of a traditional one-compartment PK model based on allometric scaling. The predicted clearance values from allometric scaling with the correction for the interspecies differences in protein binding were used as a representative comparison, which showed more accurate PK prediction in humans than the other methods. Overall PBPK modeling provided better prediction of the area under the plasma concentration-time curves for both PF02341066 (1.2-fold error) and PF04217903 (1.3-fold error) compared with the one-compartment PK model (1.8- and 1.9-fold errors, respectively). Of more importance, the simulated plasma concentration-time profiles of PF02341066 and PF04217903 by PBPK modeling seemed to be consistent with the observed profiles showing multiexponential declines, resulting in more accurate prediction of the apparent half-lives (t(1/2)): the observed and predicted t(1/2) values were, respectively, 10 and 12 h for PF02341066 and 6.6 and 6.3 h for PF04217903. The predicted t(1/2) values by the one-compartment PK model were 17 h for PF02341066 and 1.9 h for PF04217903. Therefore, PBPK modeling has the potential to be more useful and reliable for the PK prediction of PF02341066 and PF04217903 in humans than the traditional one-compartment PK model. In summary, the present study has shown examples to indicate that the PBPK model can be used to predict PK profiles in humans.

Substrate depletion approach for determining in vitro metabolic clearance: time dependencies in hepatocyte and microsomal incubations.

The substrate depletion method is a popular approach used for the measurement of in vitro intrinsic clearance (CL(int)). However, the incubation conditions used in these studies can vary, the consequences of which have not been systematically explored. Initial substrate depletion incubations using rat microsomes and hepatocytes were performed for eight benzodiazepines: alprazolam, clobazam, clonazepam, chlordiazepoxide, diazepam, flunitrazepam, midazolam, and triazolam. Subsequent predictions of in vivo CL(int) (ranging from 3 to 200 ml/min) and hepatic clearance (CL(H)) (ranging from 0.3 to 15 ml/min) demonstrated that the general predictive ability of this approach was similar to that of the traditional metabolite formation method. A more detailed study of the substrate depletion profiles and CL(int) estimates indicated that the concentration of enzyme used is of particular importance. The metabolism of triazolam, clonazepam, and diazepam was monoexponential at all cell densities using hepatocytes; however, with microsomes, biphasic depletion was apparent, particularly at higher microsomal protein concentrations (2-5 mg/ml). Enzyme activity studies indicated that enzyme loss was more pronounced in the microsomal system (ranged from 8 to 65% activity after a 1-h incubation) compared with the hepatocyte system (approximately 100% activity after a 1-h incubation). For clonazepam (a low clearance substrate), these biphasic profiles could be explained by loss of enzyme activity. To ensure accurate predictions of in vivo CL(int) and CL(H) when using the substrate depletion approach, based on the results obtained for this class of drugs, it is recommended that low enzyme concentrations and short incubation times are used whenever possible.