Hugh A. Barton

Mechanistic pharmacokinetic modeling for the prediction of transporter-mediated disposition in humans from sandwich culture human hepatocyte data

With efforts to reduce cytochrome P450-mediated clearance (CL) during the early stages of drug discovery, transporter-mediated CL mechanisms are becoming more prevalent. However, the prediction of plasma concentration-time profiles for such compounds using physiologically based pharmacokinetic (PBPK) modeling is far less established in comparison with that for compounds with passively mediated pharmacokinetics (PK). In this study, we have assessed the predictability of human PK for seven organic anion-transporting polypeptide (OATP) substrates (pravastatin, cerivastatin, bosentan, fluvastatin, rosuvastatin, valsartan, and repaglinide) for which clinical intravenous data were available. In vitro data generated from the sandwich culture human hepatocyte system were simultaneously fit to estimate parameters describing both uptake and biliary efflux. Use of scaled active uptake, passive distribution, and biliary efflux parameters as inputs into a PBPK model resulted in the overprediction of exposure for all seven drugs investigated, with the exception of pravastatin. Therefore, fitting of in vivo data for each individual drug in the dataset was performed to establish empirical scaling factors to accurately capture their plasma concentration-time profiles. Overall, active uptake and biliary efflux were under- and overpredicted, leading to average empirical scaling factors of 58 and 0.061, respectively; passive diffusion required no scaling factor. This study illustrates the mechanistic and model-driven application of in vitro uptake and efflux data for human PK prediction for OATP substrates. A particular advantage is the ability to capture the multiphasic plasma concentration-time profiles for such compounds using only preclinical data. A prediction strategy for novel OATP substrates is discussed.

Database for Physiologically Based Pharmacokinetic (PBPK) Modeling: Physiological Data for Healthy and Health-Impaired Elderly

Physiologically based pharmacokinetic (PBPK) models have increasingly been employed in chemical health risk assessments. By incorporating individual variability conferred by genetic polymorphisms, health conditions, and physiological changes during development and aging, PBPK models are ideal for predicting chemical disposition in various subpopulations of interest. In order to improve the parameterization of PBPK models for healthy and health-impaired elderly (herein defined as those aged 65 yr and older), physiological parameter values were obtained from the peer-reviewed literature, evaluated, and entered into a Microsoft ACCESS database. Database records include values for key age-specific model inputs such as ventilation rates, organ volumes and blood flows, glomerular filtration rates, and other clearance-related processes. In total, 528 publications were screened for relevant data, resulting in the inclusion of 155 publications comprising 1051 data records for healthy elderly adults and 115 data records for elderly with conditions such as diabetes, chronic obstructive pulmonary disease (COPD), obesity, heart disease, and renal disease. There are no consistent trends across parameters or their associated variance with age; the gross variance in body weight decreased with advancing age, whereas there was no change in variance for brain weight. The database contains some information to inform ethnic and gender differences in parameters; however, the majority of the published data pertain to Asian (mostly Japanese) and Caucasian males. As expected, the number of records tends to decrease with advancing age. In addition to a general lack of data for parameters in the elderly with various health conditions, there is also a dearth of information on blood and tissue composition in all elderly groups. Importantly, there are relatively few records for alveolar ventilation rate; therefore, the relationship between this parameter and cardiac output (usually assumed to be 1:1) in the elderly is not well informed by the database. Despite these limitations, the database represents a potentially useful resource for parameterizing PBPK models for the elderly to facilitate the prediction of dose metrics in older populations for application in risk assessment.

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.

Prediction of Pharmacokinetics and Drug–Drug Interactions When Hepatic Transporters are Involved

Hepatobiliary transport mechanisms have been identified to play a significant role in determining the systemic clearance for a number of widely prescribed drugs and an increasing number of new molecular entities (NMEs). While determining the pharmacokinetics, drug transporters also regulate the target tissue exposure and play a key role in regulating the pharmacological and/or toxicological responses. Consequently, it is of great relevance in drug discovery and development to assess hepatic transporter activity in regard to pharmacokinetic and dose predictions and to evaluate pharmacokinetic variability associated with drug–drug interactions (DDIs) and genetic variants. Mechanistic predictions utilizing physiological-based pharmacokinetic modeling are increasingly used to evaluate transporter contribution and delineate the transporter–enzyme interplay on the basis of hypothesis-driven functional in vitro findings. Significant strides were made in the development of in vitro techniques to facilitate characterization of hepatobiliary transport. However, challenges exist in the quantitative in vitro–in vivo extrapolation of transporter kinetics due to the lack of information on absolute abundance of the transporter in both in vitro and in vivo situations, and/or differential function in the holistic in vitro reagents such as suspended and plated hepatocytes systems, and lack of complete mechanistic understanding of liver model structure. On the other hand, models to predict transporter-mediated DDIs range from basic models to mechanistic static and dynamic models. While basic models provide conservative estimates and are useful upfront in avoiding false negative predictions, mechanistic models integrate multiple victim and perpetrator drugs parameters and are expected to provide quantitative predictions. The aim of this paper is to review the current state of the model-based approaches to predict clinical pharmacokinetics and DDIs of drugs or NMEs that are substrates of hepatic transporters.

The evolution of the OATP hepatic uptake transport protein family in DMPK sciences: from obscure liver transporters to key determinants of hepatobiliary clearance

Over the last two decades the impact on drug pharmacokinetics of the organic anion transporting polypeptides (OATPs: OATP-1B1, 1B3 and 2B1), expressed on the sinusoidal membrane of the hepatocyte, has been increasingly recognized. OATP-mediated uptake into the hepatocyte coupled with subsequent excretion into bile via efflux proteins, such as MRP2, is often referred to as hepatobiliary excretion. OATP transporter proteins can impact some drugs in several ways including pharmacokinetic variability, pharmacodynamic response and drug-drug interactions (DDIs). The impact of transporter mediated hepatic clearance is illustrated with case examples, from the literature and also from the Pfizer portfolio. The currently available in vitro techniques to study the hepatic transporter proteins involved in the hepatobiliary clearance of drugs are reviewed herein along with recent advances in using these in vitro data to predict the human clearance of compounds recognized by hepatic uptake transporters.

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.

Exploring BSEP inhibition-mediated toxicity with a mechanistic model of drug-induced liver injury

Inhibition of the bile salt export pump (BSEP) has been linked to incidence of drug-induced liver injury (DILI), presumably by the accumulation of toxic bile acids in the liver. We have previously constructed and validated a model of bile acid disposition within DILIsym®, a mechanistic model of DILI. In this paper, we use DILIsym® to simulate the DILI response of the hepatotoxic BSEP inhibitors bosentan and CP-724,714 and the non-hepatotoxic BSEP inhibitor telmisartan in humans in order to explore whether we can predict that hepatotoxic BSEP inhibitors can cause bile acid accumulation to reach toxic levels. We also simulate bosentan in rats in order to illuminate potential reasons behind the lack of toxicity in rats compared to the toxicity observed in humans. DILIsym® predicts that bosentan, but not telmisartan, will cause mild hepatocellular ATP decline and serum ALT elevation in a simulated population of humans. The difference in hepatotoxic potential between bosentan and telmisartan is consistent with clinical observations. However, DILIsym® underpredicts the incidence of bosentan toxicity. DILIsym® also predicts that bosentan will not cause toxicity in a simulated population of rats, and that the difference between the response to bosentan in rats and in humans is primarily due to the less toxic bile acid pool in rats. Our simulations also suggest a potential synergistic role for bile acid accumulation and mitochondrial electron transport chain (ETC) inhibition in producing the observed toxicity in CP-724,714, and suggest that CP-724,714 metabolites may also play a role in the observed toxicity. Our work also compares the impact of competitive and noncompetitive BSEP inhibition for CP-724,714 and demonstrates that noncompetitive inhibition leads to much greater bile acid accumulation and potential toxicity. Our research demonstrates the potential for mechanistic modeling to contribute to the understanding of how bile acid transport inhibitors cause DILI.

A “middle-out” approach to human pharmacokinetic predictions for OATP substrates using physiologically-based pharmacokinetic modeling

Physiologically based pharmacokinetic (PBPK) models provide a framework useful for generating credible human pharmacokinetic predictions from data available at the earliest, preclinical stages of pharmaceutical research. With this approach, the pharmacokinetic implications of in vitro data are contextualized via scaling according to independent physiological information. However, in many cases these models also require model-based estimation of additional empirical scaling factors (SFs) in order to accurately recapitulate known human pharmacokinetic behavior. While this practice clearly improves data characterization, the introduction of empirically derived SFs may belie the extrapolative power commonly attributed to PBPK. This is particularly true when such SFs are compound dependent and/or when there are issues with regard to identifiability. As such, when empirically-derived SFs are necessary, a critical evaluation of parameter estimation and model structure are prudent. In this study, we applied a global optimization method to support model-based estimation of a single set of empirical SFs from intravenous clinical data on seven OATP substrates within the context of a previously published PBPK model as well as a revised PBPK model. The revised model with experimentally measured unbound fraction in liver, permeability between liver compartments, and permeability limited distribution to selected tissues improved data characterization. We utilized large-sample approximation and resampling approaches to estimate confidence intervals for the revised model in support of forward predictions that reflect the derived uncertainty. This work illustrates an objective approach to estimating empirically-derived SFs, systematically refining PBPK model performance and conveying the associated confidence in subsequent forward predictions.

Physiologically Based Pharmacokinetic Prediction of Telmisartan in Human

A previously developed physiologically based pharmacokinetic model for hepatic transporter substrates was extended to an organic anion transporting polypeptide substrate, telmisartan. Predictions used in vitro data from sandwich culture human hepatocyte and human liver microsome assays. We have developed a novel method to calibrate partition coefficients (Kps) between nonliver tissues and plasma on the basis of published human positron emission tomography (PET) data to decrease the uncertainty in tissue distribution introduced by in silico–predicted Kps. With in vitro data–predicted hepatic clearances, published empirical scaling factors, and PET-calibrated Kps, the model could accurately recapitulate telmisartan pharmacokinetic (PK) behavior before 2.5 hours. Reasonable predictions also depend on having a model structure that can adequately describe the drug disposition pathways. We showed that the elimination phase (2.5–12 hours) of telmisartan PK could be more accurately recapitulated when enterohepatic recirculation of parent compound derived from intestinal deconjugation of glucuronide metabolite was incorporated into the model. This study demonstrated the usefulness of the previously proposed physiologically based modeling approach for purely predictive intravenous PK simulation and identified additional biologic processes that can be important in prediction.

Cerebrospinal fluid β-Amyloid turnover in the mouse, dog, monkey and human evaluated by systematic quantitative analyses.

Background: Reducing brain β-amyloid (Aβ) via inhibition of β-secretase, or inhibition/modulation of γ-secretase, has been widely pursued as a potential disease-modifying treatment for Alzheimers disease. Compounds that act through these mechanisms have been screened and characterized with Aβ lowering in the brain and/or cerebrospinal fluid (CSF) as the primary pharmacological end point. Interpretation and translation of the pharmacokinetic (PK)/pharmacodynamic (PD) relationship for these compounds is complicated by the relatively slow Aβ turnover process in these compartments. Objective: To understand Aβ turnover kinetics in preclinical species and humans. Methods: We collected CSF Aβ dynamic data after β- or γ-secretase inhibitor treatment from in-house experiments and the public domain, and analyzed the data using PK/PD modeling to obtain CSF Aβ turnover rates (kout) in the mouse, dog, monkey and human. Results: The kout for CSF Aβ40 follows allometry (kout = 0.395 × body weight-0.351). The kout for CSF Aβ40 is approximately 2-fold higher than the turnover of CSF in rodents, but in higher species, the two are comparable. Conclusion: The turnover of CSF Aβ40 was systematically examined, for the first time, in multiple species through quantitative modeling of multiple data sets. Our result suggests that the clearance mechanisms for CSF Aβ in rodents may be different from those in the higher species. The understanding of Aβ turnover has considerable implications for the discovery and development of Aβ-lowering therapeutics, as illustrated from the perspectives of preclinical PK/PD characterization and preclinical-to-clinical translation.