VIVD: Virtual in vitro distribution model for the mechanistic prediction of intracellular concentrations of chemicals in in vitro toxicity assays

In vitro toxicity testing routinely uses nominal treatment concentrations as the driver for measured toxicity endpoints. However, test compounds can bind to the plastic of culture vessels or interact with culture media components, such as lipids and albumin. Additionally, volatile compounds may partition into the air above culture media. These processes reduce the free concentrations of compound to which cells are exposed. Models predicting the freely dissolved concentrations by accounting for these interactions have been published. However, these have only been applied to neutral compounds or assume no differential ionisation of test compounds between the media and cell cytoplasm. Herein, we describe an in vitro distribution model, based on the Fick-Nernst Planck equation accounting for differential compound ionisation in culture medium and intracellular water. The model considers permeability of ionised and unionised species and accounts for membrane potential in the partitioning of ionised moieties. By accounting for lipid and protein binding in culture medium, binding to cell culture plastic, air-partitioning, and lipid binding in the cell, the model can predict chemical concentrations (free and total) in medium and cells. The model can improve in vitro in vivo extrapolation of toxicity endpoint by determining intracellular concentrations for translation to in vivo.

Mechanistic Oral Absorption Modeling and Simulation for Formulation Development and Bioequivalence Evaluation: Report of an FDA Public Workshop

On May 19, 2016, the US Food and Drug Administration (FDA) hosted a public workshop, entitled “Mechanistic Oral Absorption Modeling and Simulation for Formulation Development and Bioequivalence Evaluation.”1 The topic of mechanistic oral absorption modeling, which is one of the major applications of physiologically based pharmacokinetic (PBPK) modeling and simulation, focuses on predicting oral absorption by mechanistically integrating gastrointestinal transit, dissolution, and permeation processes, incorporating systems, active pharmaceutical ingredient (API), and the drug product information, into a systemic mathematical whole‐body framework.2

Application of the MechPeff model to predict passive effective intestinal permeability in the different regions of the rodent small intestine and colon

A major component of physiologically based pharmacokinetic (PBPK) models is the prediction of the rate and extent of absorption of orally dosed drugs for which knowledge of effective passive intestinal permeability (P_eff ) is essential. Single-pass intestinal perfusion (SPIP) studies are used to establish effective permeability in vivo but are difficult to perform in rodents, while mechanistic models to predict drug P_eff in rat and mouse have not been published. This work evaluates the predictive performance of the 'MechPeff' model to predict P_eff in the rodent intestine based upon knowledge of regional gut physiology and drug-specific physicochemical parameters. The 'MechPeff' model, built-in to the Simcyp Rat and Mouse Simulators, predicts transcellular, paracellular and mucus layer permeabilities and combines these to give the overall P_eff . The jejunal and/or ileal P_eff was predicted for 12 (4) acidic, 13 (12) basic, 10 (8) neutral and 2 (0) ampholytic drugs in the rat (mouse), spanning a wide range of MW and logP_o:w , and compared with experimental P_eff obtained using SPIP. A key input is the intrinsic transcellular permeability (P_trans,0 ) which can be derived from modelling of appropriate in vitro permeability experiments or predicted from physicochemical properties. The P_eff predictions were reasonably good when experimentally derived P_trans,0 was used; from 42 P_eff,rat values, 24 (57%) were within 3-fold, and of 19 P_eff,mouse values, 12 (63%) were within 3-fold, of observed P_eff . Considering the lack of alternative models to predict P_eff in preclinical species, and the minimal drug-specific inputs required, this model provides a valuable tool within drug discovery and development programmes. Copyright © 2017 John Wiley & Sons, Ltd.

Metformin and cimetidine: Physiologically based pharmacokinetic modelling to investigate transporter mediated drug-drug interactions

Metformin is used as a probe for OCT2 mediated transport when investigating possible DDIs with new chemical entities. The aim of the current study was to investigate the ability of physiologically-based pharmacokinetic (PBPK) models to simulate the effects of OCT and MATE inhibition by cimetidine on metformin kinetics. PBPK models were developed, incorporating mechanistic kidney and liver sub-models for metformin (OCT and MATE substrate) and a mechanistic kidney sub-model for cimetidine. The models were used to simulate inhibition of the MATE1, MATE2-K, OCT1 and OCT2 mediated transport of metformin by cimetidine. Assuming competitive inhibition and using cimetidine Ki values determined in vitro, the predicted metformin AUC ratio was 1.0 compared to an observed value of 1.46. The observed AUC ratio could only be recovered with this model when the cimetidine Ki for OCT2 was decreased 1000-fold or the Ki's for both OCT1 and OCT2 were decreased 500-fold. An alternative description of metformin renal transport by OCT1 and OCT2, incorporating electrochemical modulation of the rate of metformin uptake together with 8-18-fold decreases in cimetidine Ki's for OCTs and MATEs, allowed recovery of the extent of the observed effect of cimetidine on metformin AUC. While the final PBPK model has limitations, it demonstrates the benefit of allowing for the complexities of passive permeability combined with active cellular uptake modulated by an electrochemical gradient and active efflux.

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

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

A mechanistic framework for in vitro-in vivo extrapolation of liver membrane transporters: prediction of drug-drug interaction between rosuvastatin and cyclosporine

Background and Objectives

The interplay between liver metabolising enzymes and transporters is a complex process involving system-related parameters such as liver blood perfusion as well as drug attributes including protein and lipid binding, ionisation, relative magnitude of passive and active permeation. Metabolism- and/or transporter-mediated drug–drug interactions (mDDIs and tDDIs) add to the complexity of this interplay. Thus, gaining meaningful insight into the impact of each element on the disposition of a drug and accurately predicting drug–drug interactions becomes very challenging. To address this, an in vitro–in vivo extrapolation (IVIVE)-linked mechanistic physiologically based pharmacokinetic (PBPK) framework for modelling liver transporters and their interplay with liver metabolising enzymes has been developed and implemented within the Simcyp Simulator^®.

Methods

In this article an IVIVE technique for liver transporters is described and a full-body PBPK model is developed. Passive and active (saturable) transport at both liver sinusoidal and canalicular membranes are accounted for and the impact of binding and ionisation processes is considered. The model also accommodates tDDIs involving inhibition of multiple transporters. Integrating prior in vitro information on the metabolism and transporter kinetics of rosuvastatin (organic-anion transporting polypeptides OATP1B1, OAT1B3 and OATP2B1, sodium-dependent taurocholate co-transporting polypeptide [NTCP] and breast cancer resistance protein [BCRP]) with one clinical dataset, the PBPK model was used to simulate the drug disposition of rosuvastatin for 11 reported studies that had not been used for development of the rosuvastatin model.

Results

The simulated area under the plasma concentration–time curve (AUC), maximum concentration (C_max) and the time to reach C_max (t_max) values of rosuvastatin over the dose range of 10–80 mg, were within 2-fold of the observed data. Subsequently, the validated model was used to investigate the impact of coadministration of cyclosporine (ciclosporin), an inhibitor of OATPs, BCRP and NTCP, on the exposure of rosuvastatin in healthy volunteers.

Conclusion

The results show the utility of the model to integrate a wide range of in vitro and in vivo data and simulate the outcome of clinical studies, with implications for their design.

Electronic supplementary material

The online version of this article (doi:10.1007/s40262-013-0097-y) contains supplementary material, which is available to authorized users.

Interaction Between Domperidone and Ketoconazole: Toward Prediction of Consequent QTc Prolongation Using Purely In Vitro Information

We aimed to investigate the application of combined mechanistic pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation in predicting the domperidone (DOM) triggered pseudo-electrocardiogram modification in the presence of a CYP3A inhibitor, ketoconazole (KETO), using in vitro-in vivo extrapolation. In vitro metabolic and inhibitory data were incorporated into physiologically based pharmacokinetic (PBPK) models within Simcyp to simulate time course of plasma DOM and KETO concentrations when administered alone or in combination with KETO (DOM+KETO). Simulated DOM concentrations in plasma were used to predict changes in gender-specific QTcF (Fridericia correction) intervals within the Cardiac Safety Simulator platform taking into consideration DOM, KETO, and DOM+KETO triggered inhibition of multiple ionic currents in population. Combination of in vitro-in vivo extrapolation, PBPK, and systems pharmacology of electric currents in the heart was able to predict the direction and magnitude of PK and PD changes under coadministration of the two drugs although some disparities were detected.

Incorporating Target Shedding Into a Minimal PBPK-TMDD Model for Monoclonal Antibodies

Shedding of a pharmacological target from cells, giving rise to a soluble target that can also bind therapeutic proteins, is a common phenomenon. In this study, a minimal physiologically based pharmacokinetic model was used to simulate the pharmacokinetics of trastuzumab and the simultaneous binding of the compound to soluble (in blood and tissue interstitial space) and membrane-bound (in the tissue interstitial space) forms of human epidermal growth factor receptor 2 (HER2). The parameter values describing binding of trastuzumab to HER2 were largely derived from in vitro data, and the effects of varying HER2 levels, the affinity difference between membrane-bound HER2 and shed antigen, and slow binding kinetics were investigated. The model simulates a sharp decrease in trough drug concentrations at concentrations of soluble target between 500 and 1,000 ng/ml in plasma. This corresponds with the clinical concentration range of soluble target wherein changes in half-life of trastuzumab have been observed.

Evaluation of an In Silico PBPK Post-Bariatric Surgery Model through Simulating Oral Drug Bioavailability of Atorvastatin and Cyclosporine

An increasing prevalence of morbid obesity has led to dramatic increases in the number of bariatric surgeries performed. Altered gastrointestinal physiology following surgery can be associated with modified oral drug bioavailability (Foral). In the absence of clinical data, an indication of changes to Foral via systems pharmacology models would be of value in adjusting dose levels after surgery. A previously developed virtual "post-bariatric surgery" population was evaluated through mimicking clinical investigations on cyclosporine and atorvastatin after bariatric surgery. Cyclosporine simulations displayed a reduced fraction absorbed through gut wall (fa) and Foral after surgery, consistent with reported observations. Simulated atorvastatin Foral postsurgery was broadly reflective of observed data with indications of counteracting interplay between reduced fa and an increased fraction escaping gut wall metabolism (FG). Inability to fully recover observed atorvastatin exposure after biliopancreatic diversion with duodenal switch highlights the current gap regarding the knowledge of associated biological changes.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e47; doi:10.1038/psp.2013.23; advance online publication 12 June 2013.

ITC recommendations for transporter kinetic parameter estimation and translational modeling of transport-mediated PK and DDIs in humans

This white paper provides a critical analysis of methods for estimating transporter kinetics and recommendations on proper parameter calculation in various experimental systems. Rational interpretation of transporter-knockout animal findings and application of static and dynamic physiologically based modeling approaches for prediction of human transporter-mediated pharmacokinetics and drug-drug interactions (DDIs) are presented. The objective is to provide appropriate guidance for the use of in vitro, in vivo, and modeling tools in translational transporter science.

Towards a quantitative framework for the prediction of DDIs arising from cytochrome P450 induction

Although CYP induction is not generally considered to be as clinically relevant as CYP inhibition, there are important examples where induction has caused both therapeutic failure, due to insufficient exposure to parent drug, and toxicity, mediated by increased formation of reactive metabolites. Furthermore, while there has been considerable progress in the extrapolation of in vitro data to predict the in vivo consequences of enzyme inhibition, less attention has been given to the quantitative impact of enzyme induction as a mechanism of drug-drug interaction (DDI) and as a component of compound selection and early drug development. We discuss current approaches in the context of a mechanistic framework for the prediction of the extent and time-course of enzyme induction in vivo based on in vitro experimentation. Factors influencing the extent of DDI due to CYP induction are summarised, and areas deficient in information that would allow more accurate prediction within target populations are highlighted.