SHORT COURSE DETAILS

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1 SHORT COURSE DETAILS Short Course 3: In silico Modeling of in vitro Metabolism, Transport, and Toxicity Data Chair: Iain Gardner, Certara 13:30-13:45 Why is Modeling of in vitro Data Important? Iain Gardner, Certara Over the last 25 years there has been an increased use of in vitro data to understand the metabolism, transport, interaction potential and toxicity of drugs and other xenobiotics. A number of in vitro-in vivo extrapolation approaches have been described to utilise this type of in vitro information to parameterise physiologically based pharmacokinetic (PBPK) models (Rostami-Hodjegan, 2012) and also to try and understand the in vivo toxicity potential of drugs and other xenobiotics (e.g., Hamon et al., 2014). With this increased utility of in vitro data to predict the in vivo effects of xenobiotics it has become apparent that in many cases it is necessary to use mathematical modelling approaches to accurately describe the processes that are occurring in the in vitro systems. This is particularly apparent in the more complex in vitro systems that are currently being developed such as multi organ microphysiological systems. Some of the key things to understand in the in vitro system are the unbound concentration of the xenobiotic that is available for passive and active cellular uptake and the unbound intracellular concentration available for metabolism, transport or to exert toxicological effects. The different experimental and computational approaches that can be used to determine these concentrations will be discussed in detail during this session. As an example of the impact of understanding the intracellular concentration, markedly different conclusions about the behaviour of the efflux transporter P-glycoprotein (P-gp) were drawn depending on whether the action of the transporter was related to the applied, nominal (extracellular) concentration of a xenobiotic or to the intracellular concentration (Tachibana et al., 2010). The michaelis constant for the transport of quinidine by P-gp was more than 30-fold lower when calculated based on the intracellular rather than the applied, nominal concentration. This difference would lead to very different conclusions about the saturability of quinidine transport and drug interaction potential if the in vitro data based on either the extracellular or intracellular data was used as an input into a PBPK model. There are also some challenges in trying to predict the interaction potential of compounds from in vitro data when multiple processes e.g. transport, mechanism-based inhibition and induction are occurring simultaneously. The utility of different modelling approaches to tease apart the complex interplay of these various processes will also be discussed during the session. 1. Hamon J., Jennings P. and Bois F. (2014), BMC Syst-Biol., 8, Rostami-Hodjegan A. (2012), Clin Pharmacol. Ther, 92, Tachibana T., Kitamura S., Kato M., Shirasaka Y., Yamashita S. and Sugiyama, Y. (2010), Pharm. Res., 27, 442. of drug metabolizing enzymes can result in clinically significant adverse effects due to changes in the pharmacokinetics, efficacy and safety profile of drugs. Nuclear hormone receptors such as the pregnane X receptor (PXR) and constitutive androstane receptor play a significant role in the induction of major cytochrome P450 (CYP) enzymes, such as CYP3A4 and CYP2B6, respectively. 1

2 The presentation will describe the basic mechanisms of induction with an emphasis on PXR and CARmediated induction of enzymes, the tools that are used to predict human drug-drug interactions, and the correlation between in vitro assays and in vivo induction related drug-drug interactions. Moreover, a brief description of the draft FDA guidance associated with predicting enzyme induction will be described. 13:45-14:35 Experimental Approaches to Understand Intracellular Based Drug Bioavailability Per Artursson, Uppsala University Exposure at the site of action has been identified as one of the three most important factors for success in drug discovery and the design of chemical probes. Modern drug discovery programs have, to a great extent, shifted to intracellular targets, but methods for efficient determination of intracellular drug concentrations have been lacking. We therefore developed a methodology for predicting intracellular exposure of small-molecule drugs to understand their potency toward intracellular targets and off targets (including intracellular drug metabolizing enzymes) based on measurements of the free intracellular drug concentration. For this purpose, we introduced the concept of intracellular bioavailability (Fic), i.e., the ratio between the intracellular unbound concentration that is available to elicit pharmacological effects inside the cell and the extracellularly applied free concentration. We then assessed how cellular drug disposition processes affect Fic and observed how the intracellular drug exposure is influenced by cell- and tissue-dependent expression of drug-transporting proteins and metabolizing enzymes, tissue lipid content and type, intracellular drug binding proteins and subcellular distribution as exemplified by low ph organelles such as the endo-lysosomal compartment. For the latter case, the concept of lysosomal bioavailability was developed. Finally, we determined Fic in multiple cellular assays and cell types representing different targets from a number of therapeutic areas, including cancer, inflammation and dementia. We found Fic predicts drug access to intracellular targets and hence pharmacological effect. Further, Fic gives new insights on membrane permeable compounds in terms of cellular potency and intracellular target engagement compared to biochemical potency measurements alone. We conclude that Fic provides a measurement of the net impact of all cellular drug disposition processes on bioavailable drug levels inside cells. Importantly, no prior knowledge of the involved drug distribution pathways is required, allowing for high-throughput determination of drug access to intracellular targets in complex cell systems (including primary human cells). Knowledge of the amount of drug that is locally available to bind intracellular targets provides a powerful new tool for compound selection in early drug discovery. 14:35-15:15 In silico Approaches to Modeling Transporter Data Manthena Varma, Pfizer Inc. A number of in vitro cell-based systems are used to characterize transport function and further estimate transport kinetics of drugs and metabolites. Generally, there are two types of experiments that are routinely conducted to characterize passive diffusion and transporter-mediated processes. These include permeability assessments across cell monolayers in the apical to basolateral (A-B) and/or basolateral to apical (B-A) directions, and cell or vesicle accumulation measurements. Kinetic studies may also be conducted at multiple drug concentrations to determine in vitro parameters such as the Km (Michaelis constant) or KI (inhibition constant). Several approaches can be used to estimate transporter kinetics from these in vitro experiments. Here, the importance of modelling transport data to ensure that accurate kinetic parameters (CLint, Jmax, Km and Ki) are obtained for subsequent use in in vitro-in vivo extrapolation will be discussed. Basic and mechanistic dynamic fitting approaches to characterize uptake and efflux kinetics will be presented with case examples. 2

3 15:30-16:00 Mechanistic Modeling of in vitro Assays to Improve in vitro/in vivo Extrapolation Grace Fraczkiewicz, Simulations Plus In vitro assays using cell monolayers (Caco-2, MDCK, etc.), sandwich or suspended hepatocyte cultures are routinely run to assess passive and active transport and/or metabolism of drug molecules. These are important to aid in the evaluation of critical mechanisms affecting drug absorption, clearance and metabolism pathways, and the extraction of in vitro parameters from these systems inform physiologically-based pharmacokinetic (PBPK) models to predict in vivo absorption, hepatobiliary transport, drug-drug interactions, and the relative importance between active transport and metabolism in the liver tissue. To that end, a fully mechanistic simulation of drug transport in cell monolayers (Caco-2, MDCK), sandwich and suspended hepatocytes has been developed in MembranePlus (Simulations Plus, Inc.) that allows for the simultaneous determination of kinetic parameters (Km and Vmax) for metabolic enzymes and active influx/efflux transporters. The sandwich hepatocyte model also includes the simulation of active and passive transport into the bile canaliculus. All models account for additional processes affecting the drug behavior in the in vitro assay, such as passive transcellular permeation, diffusion in the unstirred boundary layer, protein binding in both media and cytosol, lysosomal trapping, and drug partitioning into lipid bilayers. Case studies will be presented to demonstrate the model s capabilities, including active uptake and biliary excretion, the interplay between active uptake and metabolism of small drug molecules, in vitro/in vivo extrapolation of metabolism, and the evaluation of the effect of lysosomal trapping on drug absorption. Collectively, the examples will show that mechanistic simulations of in vitro assays can be valuable to aid in the extraction of critical parameters from in vitro assays for further use in PBPK models. 16:00-16:30 Simultaneous Modeling of Metabolism, Competitive and Mechanism Based Inhibition Howard Burt, Certara Mechanism-based enzyme inhibition (MBI), whereby an inhibitor causes a time-dependent and irreversible loss of enzyme activity, often leads to strong drug-drug interactions (DDIs). The currently established methods for the prediction DDIs from MBI rely on the experimental determination of k inact (the maximum inactivation rate constant) and K I (the irreversible inhibition constant) for an inhibitor. However, both static and PBPK models have a tendency to over-predict DDI magnitude. The conventional two-step in vitro assay involves pre-incubation of the inhibitor with enzyme, prior to a final incubation with a specific probe substrate. The analysis of such experiments typically assumes that the inhibitor is not significantly metabolised during the preincubation and that competitive inhibition is not significant during the final incubation, despite these often being inherent properties of the enzyme-inhibitor interaction. In order to address this, two methods were developed for the determination of k inact and K I. The mechanistic experimental protocol (MEP) involves the simultaneous analysis of substrate depletion and competitive inhibition assays alongside the two-step assay, whereas progress curve approach (PCA) aims to simultaneously model a simple experiment whereby all components (enzyme, inhibitor and substrate) are co-incubated. The PCA method has shown potential for providing an improved mechanistic insight when determining MBI parameters; however a clear improvement in DDI predictability is yet to be shown and the method has not been widely adopted (Burt et al. 2012). Conversely, a protocol based on the use of plasma suspended hepatocytes, has shown improved predictability (Mao et al. 2011). More recently, investigators have challenged the assumption of a conventional MBI reaction scheme for all such inhibitors (Nagar et al. 2014, Korzekwa et al. 2014) and a numerical approach to the analysis of conventional experiments has shown a potential improvement in prediction accuracy (Yadav et al. 2018). 3

4 This talk will provide an appraisal of these methods with respect to both in vitro data analysis and DDI prediction and will discuss the potential advantage of a combination of modelling approaches, whereby both inhibitor depletion is accounted for and the conventional MBI reaction scheme is not assumed in all cases. 1. Burt HJ, Pertinez H, Säll C, Collins C, Hyland R, Houston JB, Galetin A. Progress curve mechanistic modeling approach for assessing time-dependent inhibition of CYP3A4. Drug Metab Dispos. 2012; 40(9): Mao J, Mohutsky MA, Harrelson JP, Wrighton SA, Hall SD. Prediction of CYP3A-mediated drugdrug interactions using human hepatocytes suspended in human plasma. Drug Metab Dispos Apr; 39(4): Nagar S, Jones JP, Korzekwa K. A numerical method for analysis of in vitro time-dependent inhibition data. Part 1. Theoretical considerations. Drug Metab Dispos. 2014; 42(9): Korzekwa K, Tweedie D, Argikar UA, Whitcher-Johnstone A, Bell L, Bickford S, Nagar S. A numerical method for analysis of in vitro time-dependent inhibition data. Part 2. Application to experimental data. Drug Metab Dispos. 2014; 42(9): Yadav J, Korzekwa K, Nagar S. Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A. Mol Pharm :30-17:00 Virtual Cell Models to Predict Binding and Distribution of Chemicals in in vitro Toxicity Assays Ciaran Fisher, Certara Toxicity endpoints quantified in in vitro assays are routinely related to the nominal treatment concentration. However, this nominal treatment concentration is not necessarily equal to the operational concentration at the target site mediating the observed effect. Unless mediated by targets on the cell surface, the intracellular, or even intraorganelle, concentration will be the relevant effect concentration driving toxicity. This concentration can also be considered to be the more appropriate in extrapolating in vitro endpoints to in vivo. The processes that determine the in vitro distribution of compounds, and so the intracellular exposure, are dependent on the assay format, cell type, and physicochemical properties of the test compound. Together these will dictate, for instance, the partitioning of compound into the air (headspace) above the culture media, binding to media components, binding to the culture vessel, and partitioning into the cultured cells. Additionally, the in vitro kinetics will be impacted upon by metabolic activity of the cultured cells, the activity of transport proteins expressed in the cell membrane, cell turnover and cytotoxicity. To experimentally quantify each of these processes routinely would be resource intensive and does not represent a pragmatic approach to high-throughput safety assessment. As a result, a number of in silico modelling tools have been developed, and published, in recent years to predict the intracellular concentration accounting for the processes outlined above (Armitage, Wania and Arnot, 2014; Comenges et al., 2016; Fischer et al., 2017). Here we will discuss some of the models developed, their assumptions, applicability, and performance. Finally, we will look at how such models may be used in conjunction with physiologically based pharmacokinetic models as part of an integrated approach to testing and assessment (IATA). This synergistic use of in vitro and in silico tools will be a key component of toxicity testing in the 21 st century. 1. Armitage, J. M., Wania, F. and Arnot, J. a (2014) Application of mass balance models and the chemical activity concept to facilitate the use of in vitro toxicity data for risk assessment., Environmental science & technology, 48(16), pp doi: /es501955g. 4

5 2. Comenges, J. M. Z. et al. (2016) Theoretical and mathematical foundation of the Virtual Cell Based Assay A review, Toxicology in Vitro. Elsevier B.V. doi: /j.tiv Fischer, F. C. et al. (2017) Modeling exposure in the Tox21 in vitro bioassays, Chemical Research in Toxicology, p. acs.chemrestox.7b doi: /acs.chemrestox.7b