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.

Development and applications of a physiologically-based model of paediatric oral drug absorption.

There is increasing interest in paediatric drug absorption and the development of biopharmaceutics tools to facilitate the development of oral formulations for neonates, infants and children. We describe the development and application of a physiologically-based model of paediatric drug absorption applicable from full term birth onwards. Paediatric age-specific parameters were included for salivary flow, gastric pH, gastric emptying (and associated food effects) and duodenal bile salt concentrations and the associated algorithms were integrated into a dissolution, absorption and metabolism model as part of a PBPK platform. For other parameters, there was either evidence for no age-related changes or a lack of data, so that adult values were applied. An initial assessment of the model was carried out by simulating the oral absorption of theophylline, paracetamol and ketoconazole over a range of paediatric ages. The absorption of the first two drugs, both BCS class 1 compounds, was predicted to be slower in early neonates compared to older age groups (median tmax values of 3 vs 2h, respectively), but with invariant fraction absorbed (fa). This is in agreement with clinical observations. The tmax of ketoconazole, a BCS class 2 compound, was predicted to be about 1h in both neonates and adults, but the fa value was higher in the former (0.87 vs 0.69). There is clearly a need to expand the components of the model as new information on the ontogeny of GI tract parameters becomes available, and to assess it against more in vivo data with evidence of specific age-related changes in oral drug absorption.

Representation of [Formula: see text]-Bernstein polynomials in terms of [Formula: see text]-Jacobi polynomials.

A representation of [Formula: see text]-Bernstein polynomials in terms of [Formula: see text]-Jacobi polynomials is obtained.

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." 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.

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 (Peff ) 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 Peff in rat and mouse have not been published. This work evaluates the predictive performance of the 'MechPeff' model to predict Peff 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 Peff . The jejunal and/or ileal Peff 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 logPo:w , and compared with experimental Peff obtained using SPIP. A key input is the intrinsic transcellular permeability (Ptrans,0 ) which can be derived from modelling of appropriate in vitro permeability experiments or predicted from physicochemical properties. The Peff predictions were reasonably good when experimentally derived Ptrans,0 was used; from 42 Peff,rat values, 24 (57%) were within 3-fold, and of 19 Peff,mouse values, 12 (63%) were within 3-fold, of observed Peff . Considering the lack of alternative models to predict Peff 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.

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.

Application of a Physiologically Based Pharmacokinetic Model to Predict OATP1B1-Related Variability in Pharmacodynamics of Rosuvastatin.

Typically, pharmacokinetic-pharmacodynamic (PK/PD) models use plasma concentration as the input that drives the PD model. However, interindividual variability in uptake transporter activity can lead to variable drug concentrations in plasma without discernible impact on the effect site organ concentration. A physiologically based PK/PD model for rosuvastatin was developed that linked the predicted liver concentration to the PD response model. The model was then applied to predict the effect of genotype-dependent uptake by the organic anion-transporting polypeptide 1B1 (OATP1B1) transporter on the pharmacological response. The area under the plasma concentration-time curve (AUC0-∞) was increased by 63 and 111% for the c.521TC and c.521CC genotypes vs. the c.521TT genotype, while the PD response remained relatively unchanged (3.1 and 5.8% reduction). Using local concentration at the effect site to drive the PD response enabled us to explain the observed disconnect between the effect of the OATP1B1 c521T>C polymorphism on rosuvastatin plasma concentration and the cholesterol synthesis response.

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.CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e96; doi:10.1038/psp.2013.73; published online 29 January 2014.

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.

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.

A physiologically based pharmacokinetic modeling approach to predict disease-drug interactions: suppression of CYP3A by IL-6.

Elevated cytokine levels are known to downregulate expression and suppress activity of cytochrome P450 enzymes (CYPs). Cytokine-modulating therapeutic proteins (TPs) used in the treatment of inflammation or infection could reverse suppression, manifesting as TP-drug-drug interactions (TP-DDIs). A physiologically based pharmacokinetic model was used to quantitatively predict the impact of interleukin-6 (IL-6) on sensitive CYP3A4 substrates. Elevated simvastatin area under the plasma concentration-time curve (AUC) in virtual rheumatoid arthritis (RA) patients, following 100 pg/ml of IL-6, was comparable to observed clinical data (59 vs. 58%). In virtual bone marrow transplant (BMT) patients, 500 pg/ml of IL-6 resulted in an increase in cyclosporine AUC, also in good agreement with the observed data (45 vs. 39%). In a different group of BMT patients treated with cyclosporine, the magnitude of interaction with IL-6 was underpredicted by threefold. The complexity of TP-DDIs highlights underlying pathophysiological factors to be considered, but these simulations provide valuable first steps toward predicting TP-DDI risk.

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.

Physiologically-based pharmacokinetic (PBPK) models for assessing the kinetics of xenobiotics during pregnancy: achievements and shortcomings.

The physiological changes that occur in the maternal body and the placental-foetal unit during pregnancy influence the absorption, distribution, metabolism, and excretion (ADME) of xenobiotics. These include drugs that are prescribed for therapeutic reasons or chemicals to which women are exposed unintentionally from the surrounding environment. The pregnancy physiologically-based pharmacokinetic (p-PBPK) models developed for theoretical assessment of the kinetics of xenobiotics during pregnancy should take into account all the dynamic changes of the maternal and embryonic/foetal physiological functions. A number of p-PBPK models have been reported for pregnant animals and humans in the past 3 decades which have mainly been applied in the risk assessment of various environmental chemicals. The purpose of this review is to critically evaluate the current state of the art in p-PBPK modelling and to recommend potential steps that could be taken to improve model development and its application particularly in drug discovery and development for pregnant women, with potential implications for optimal drug treatment in pregnancy. The pregnancy-induced changes in physiology and pharmacokinetics, including metabolism, are reviewed to illustrate the basic alterations essential for pregnancy model development. A systemic search of the literature for existing p-PBPK models is carried out and the model structures, governing equations, methods of modelling growth, model validation/verification as well as model applications are highlighted. This review discusses benefits and limitations of the reported p-PBPK models so far and suggests areas for model improvement. The need for establishing databases on the system-related (biological, anatomical and physiological) and drug-related (physiochemical, affinity to enzymes and transpoorters) parameters for healthy and unhealthy pregnancies is particularly emphasized.

Prediction of time-dependent CYP3A4 drug-drug interactions by physiologically based pharmacokinetic modelling: impact of inactivation parameters and enzyme turnover.

Predicting the magnitude of time-dependent metabolic drug-drug (mDDIs) interactions involving cytochrome P-450 3A4 (CYP3A4) from in vitro data requires accurate knowledge of the inactivation parameters of the inhibitor (K(I), k(inact)) and of the turnover of the enzyme (k(deg)) in both the gut and the liver. We have predicted the magnitude of mDDIs observed in 29 in vivo studies involving six CYP3A4 probe substrates and five mechanism based inhibitors of CYP3A4 of variable potency (azithromycin, clarithromycin, diltiazem, erythromycin and verapamil). Inactivation parameters determined anew in a single laboratory under standardised conditions together with data from substrate and inhibitor files within the Simcyp Simulator (Version 9.3) were used to determine a value of the hepatic k(deg) (0.0193 or 0.0077h(-1)) most appropriate for the prediction of mDDIs involving time-dependent inhibition of CYP3A4. The higher value resulted in decreased bias (geometric mean fold error - 1.05 versus 1.30) and increased precision (root mean squared error - 1.29 versus 2.30) of predictions of mean ratios of AUC in the absence and presence of inhibitor. Depending on the k(deg) value used (0.0193 versus 0.0077h(-1)), predicted mean ratios of AUC were within 2-fold of the observed values for all (100%) and 27 (93%) of the 29 studies, respectively and within 1.5-fold for 24 (83%) and 17 (59%) of the 29 studies, respectively. Comprehensive PBPK models were applied for accurate assessment of the potential for mDDIs involving time-dependent inhibition of CYP3A4 using a hepatic k(deg) value of 0.0193h(-1) in conjunction with inactivation parameters determined by the conventional experimental approach.

Interplay of metabolism and transport in determining oral drug absorption and gut wall metabolism: a simulation assessment using the "Advanced Dissolution, Absorption, Metabolism (ADAM)" model.

Bioavailability of orally administered drugs can be influenced by a number of factors including release from the formulation, dissolution, stability in the gastrointestinal (GI) environment, permeability through the gut wall and first-pass gut wall and hepatic metabolism. Although there are various enzymes in the gut wall which may contribute to gut first pass metabolism, Cytochrome P450 (CYP) 3A has been shown to play a major role. The efflux transporter P-glycoprotein (P-gp; MDR1/ABCB1) is the most extensively studied drug efflux transporter in the gut and might have a significant role in the regulation of GI absorption. Although not every CYP3A substrate will have a high extent of gut wall first-pass extraction, being a substrate for the enzyme increases the likelihood of a higher first-pass extraction. Similarly, being a P-gp substrate does not necessarily pose a problem with the gut wall absorption however it may reduce bioavailability in some cases (e.g. when drug has low passive permeability). An on-going debate has focused on the issue of the interplay between CYP3A and P-gp such that high affinity to P-gp increases the exposure of drug to CYP3A through repeated cycling via passive diffusion and active efflux, decreasing the fraction of drug that escapes first pass gut metabolism (F(G)). The presence of P-gp in the gut wall and the high affinity of some CYP3A substrates to this transporter are postulated to reduce the potential for saturating the enzymes, thus increasing gut wall first-pass metabolism for compounds which otherwise would have saturated CYP3A. Such inferences are based on assumptions in the modelling of oral drug absorption. These models should be as mechanistic as possible and tractable using available in vitro and in vivo information. We review, through simulation, this subject and examine the interplay between gut wall metabolism and efflux transporters by studying the fraction of dose absorbed into enterocytes (F(a)) and F(G) via systematic variation of drug characteristics, in accordance with the Biopharmaceutics Classification System (BCS) within one of the most physiological models of oral drug absorption currently available, respectively ADAM. Variables studied included the intrinsic clearance (CLint) and the Michaelis-Menten Constant (Km) for CYP3A4 and P-gp (C(Lint-CYP3A4) and K(m-CYP3A4), CL(int-P-gp) and K(m-P-gp)). The impact of CYP3A4 and P-gp intracellular topography were not investigated since a well-stirred enterocyte is assumed within ADAM. An increased CLint-CYP3A4 resulted in a reduced F(G) whereas an increase in C(Lint-P-gp) resulted in a reduced F(a), but interestingly decreased F(G) too. The reduction in FG was limited to certain conditions and was modest. Non-linear relationships between various parameters determining the permeability (e.g. P(app), C(Lint-P-gp,) and K(m-P-gp)) and gut wall metabolism (e.g. C(Lint-CYP3A4,) K(m-CYP3A4)) resulted in disproportionate changes in F(G) compared to the magnitude of singular effects. The results suggest that P-gp efflux decreases enterocytic drug concentration for drugs given at reasonably high dose which possess adequate passive permeability (high P(app)), by de-saturating CYP3A4 in the gut resulting in a lower F(G). However, these findings were observed only in a very limited area of the parameters space matching very few therapeutic drugs (a group with very high metabolism, high turn-over by efflux transporters and low F(a)). The systematic approach in this study enabled us to recognise the combination of parameters values where the potential interplay between metabolising enzymes and efflux transporters is expected to be highest, using a realistic range of parameter values taken from an intensive literature search.

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.