Prediction of fraction metabolized via CYP3A in humans utilizing cryopreserved human hepatocytes from a set of 12 single donors

Primary Human Hepatocyte Spheroid Model as a 3D In Vitro Platform for Metabolism Studies

3D cultures of primary human hepatocytes (PHH) are emerging as a more in vivo-like culture system than previously available hepatic models. This work describes the characterisation of drug metabolism in 3D PHH spheroids. Spheroids were formed from three different donors of PHH and the expression and activities of important cytochrome P450 enzymes (CYP1A2, 2B6, 2C9, 2D6, and 3A4) were maintained for up to 21 days after seeding. The activity of CYP2B6 and 3A4 decreased, while the activity of CYP2C9 and 2D6 increased over time (P < 0.05). For six test compounds, that are metabolised by multiple enzymes, intrinsic clearance (CL_int) values were comparable to standard in vitro hepatic models and successfully predicted in vivo CL_int within 3-fold from observed values for low clearance compounds. Remarkably, the metabolic turnover of these low clearance compounds was reproducibly measured using only 1-3 spheroids, each composed of 2000 cells. Importantly, metabolites identified in the spheroid cultures reproduced the major metabolites observed in vivo, both primary and secondary metabolites were captured. In summary, the 3D PHH spheroid model shows promise to be used in drug discovery projects to study drug metabolism, including unknown mechanisms, over an extended period of time.

Application of Azamulin to Determine the Contribution of CYP3A4/5 to Drug Metabolic Clearance Using Human Hepatocytes

Early determination of CYP3A4/5 contribution to the clearance of new chemical entities is critical to inform on the risk of drug-drug interactions with CYP3A inhibitors and inducers. Several in vitro approaches (recombinant P450 enzymes, correlation analysis, chemical and antibody inhibition in human liver microsomes) are available, but they are usually labor-intensive and/or suffer from specific limitations. In the present study, we have validated the use of azamulin as a specific CYP3A inhibitor in human hepatocytes. Azamulin (3 µM) was found to significantly inhibit CYP3A4/5 (>90%), whereas other P450 enzymes were not affected (less than 20% inhibition). Because human hepatocytes were used as a test system, the effect of azamulin on other key drug-metabolizing enzymes (aldehyde oxidase, carboxylesterase, UGT, flavin monooxygenase, and sulfotransferase) was also investigated. Apart from some UGTs showing minor inhibition (∼20%-30%), none of these non-P450 enzymes were inhibited by azamulin. Use of CYP3A5-genotyped human hepatocyte batches in combination with CYP3cide demonstrated that azamulin (at 3 µM) inhibits both CYP3A4 and CYP3A5 enzymes. Finally, 11 compounds with known in vivo CYP3A4/5 contribution have been evaluated in this human hepatocyte assay. Results showed that the effect of azamulin on the in vitro intrinsic clearance of these known CYP3A4/5 substrates was predictive of the in vivo CYP3A4/5 contribution. Overall, the study showed that human hepatocytes treated with azamulin provide a fast and accurate estimation of CYP3A4/5 contribution in metabolic clearance of new chemical entities. SIGNIFICANCE STATEMENT: Accurate estimation of CYP3A4/5 contribution in drug clearance is essential to anticipate risk of drug-drug interactions and select the appropriate candidate for clinical development. The present study validated the use of azamulin as selective CYP3A4/5 inhibitor in suspended human hepatocytes and demonstrated that this novel approach provides a direct and accurate determination of the contribution of CYP3A4/5 (fraction metabolized by CYP3A4/5) in the metabolic clearance of new chemical entities.

The Cancer Drug Fraction of Metabolism Database

This study aims to create a database for quantifying the fraction of metabolism of cytochrome P450 isozymes for cancer drugs approved by the US Food and Drug Administration. A reproducible data collection protocol was developed to extract essential information, including both substrate-depletion and metabolite-formation data from publicly available in vitro selective cytochrome P450 enzyme inhibition studies. We estimated the fraction of metabolism from the curated data. To demonstrate the utility of this database, we conducted an in vitro drug interaction prediction for the 42 cancer drugs. In the drug-drug interaction prediction, we identified 31 drug pairs with at least one cancer drug in each pair that had predicted area under concentration ratios > 2. We further found clinical drug interaction pieces of evidence in the literature to support 20 of these 31 drug-drug interaction pairs.

Human hepatocytes and cytochrome P450-selective inhibitors predict variability in human drug exposure more accurately than human recombinant P450s

BACKGROUND AND PURPOSE

Drugs metabolically eliminated by several enzymes are less vulnerable to variable compound exposure in patients due to drug-drug interactions (DDI) or if a polymorphic enzyme is involved in their elimination. Therefore, it is vital in drug discovery to accurately and efficiently estimate and optimize the metabolic elimination profile.

EXPERIMENTAL APPROACH

CYP3A and/or CYP2D6 substrates with well described variability in vivo in humans due to CYP3A DDI and CYP2D6 polymorphism were selected for assessment of fraction metabolized by each enzyme (fmCYP ) in two in vitro systems: (i) human recombinant P450s (hrP450s) and (ii) human hepatocytes combined with selective P450 inhibitors. Increases in compound exposure in poor versus extensive CYP2D6 metabolizers and by the strong CYP3A inhibitor ketoconazole were mathematically modelled and predicted changes in exposure were compared with in vivo data.

KEY RESULTS

Predicted changes in exposure were within twofold of reported in vivo values using fmCYP estimated in human hepatocytes and there was a strong linear correlation between predicted and observed changes in exposure (r2  = 0.83 for CYP3A, r2  = 0.82 for CYP2D6). Predictions using fmCYP in hrP450s were not as accurate (r2  = 0.55 for CYP3A, r2  = 0.20 for CYP2D6).

CONCLUSIONS AND IMPLICATIONS

The results suggest that variability in human drug exposure due to DDI and enzyme polymorphism can be accurately predicted using fmCYP from human hepatocytes and CYP-selective inhibitors. This approach can be efficiently applied in drug discovery to aid optimization of candidate drugs with a favourable metabolic elimination profile and limited variability in patients.

Simultaneous Assessment of Clearance, Metabolism, Induction, and Drug-Drug Interaction Potential Using a Long-Term In Vitro Liver Model for a Novel Hepatitis B Virus Inhibitor

Long-term in vitro liver models are now widely explored for human hepatic metabolic clearance prediction, enzyme phenotyping, cross-species metabolism, comparison of low clearance drugs, and induction studies. Here, we present studies using a long-term liver model, which show how metabolism and active transport, drug-drug interactions, and enzyme induction in healthy and diseased states, such as hepatitis B virus (HBV) infection, may be assessed in a single test system to enable effective data integration for physiologically based pharmacokinetic (PBPK) modeling. The approach is exemplified in the case of (3S)-4-[[(4R)-4-(2-Chloro-4-fluorophenyl)-5-methoxycarbonyl-2-thiazol-2-yl-1,4-dihydropyrimidin-6-yl]methyl]morpholine-3-carboxylic acid RO6889678, a novel inhibitor of HBV with a complex absorption, distribution, metabolism, and excretion (ADME) profile. RO6889678 showed an intracellular enrichment of 78-fold in hepatocytes, with an apparent intrinsic clearance of 5.2 µl/min per mg protein and uptake and biliary clearances of 2.6 and 1.6 µl/min per mg protein, respectively. When apparent intrinsic clearance was incorporated into a PBPK model, the simulated oral human profiles were in good agreement with observed data at low doses but were underestimated at high doses due to unexpected overproportional increases in exposure with dose. In addition, the induction potential of RO6889678 on cytochrome P450 (P450) enzymes and transporters at steady state was assessed and cotreatment with ritonavir revealed a complex drug-drug interaction with concurrent P450 inhibition and moderate UDP-glucuronosyltransferase induction. Furthermore, we report on the first evaluation of in vitro pharmacokinetics studies using HBV-infected HepatoPac cocultures. Thus, long-term liver models have great potential as translational research tools exploring pharmacokinetics of novel drugs in vitro in health and disease.

Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research

Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.

Clinical Exposure Boost Predictions by Integrating Cytochrome P450 3A4-Humanized Mouse Studies With PBPK Modeling

NVS123 is a poorly water-soluble protease 56 inhibitor in clinical development. Data from in vitro hepatocyte studies suggested that NVS123 is mainly metabolized by CYP3A4. As a consequence of limited solubility, NVS123 therapeutic plasma exposures could not be achieved even with high doses and optimized formulations. One approach to overcome NVS123 developability issues was to increase plasma exposure by coadministrating it with an inhibitor of CYP3A4 such as ritonavir. A clinical boost effect was predicted by using physiologically based pharmacokinetic (PBPK) modeling. However, initial boost predictions lacked sufficient confidence because a key parameter, fraction of drug metabolized by CYP3A4 (fmCYP3A4), could not be estimated with accuracy on account of disconnects between in vitro and in vivo preclinical data. To accurately estimate fmCYP3A4 in human, an in vivo boost effect study was conducted using CYP3A4-humanized mouse model which showed a 33- to 56-fold exposure boost effect. Using a top-down approach, human fmCYP3A4 for NVS123 was estimated to be very high and included in the human PBPK modeling to support subsequent clinical study design. The combined use of the in vivo boost study in CYP3A4-humanized mouse model mice along with PBPK modeling accurately predicted the clinical outcome and identified a significant NVS123 exposure boost (∼42-fold increase) with ritonavir.

Cytotoxicity evaluation using cryopreserved primary human hepatocytes in various culture formats

Sixteen training compounds selected in the IMI MIP-DILI consortium, 12 drug-induced liver injury (DILI) positive compounds and 4 non-DILI compounds, were assessed in cryopreserved primary human hepatocytes. When a ten-fold safety margin threshold was applied, the non-DILI-compounds were correctly identified 2h following a single exposure to pooled human hepatocytes (n=13 donors) in suspension and 14-days following repeat dose exposure (3 treatments) to an established 3D-microtissue co-culture (3D-MT co-culture, n=1 donor) consisting of human hepatocytes co-cultured with non-parenchymal cells (NPC). In contrast, only 5/12 DILI-compounds were correctly identified 2h following a single exposure to pooled human hepatocytes in suspension. Exposure of the 2D-sandwich culture human hepatocyte monocultures (2D-sw) for 3days resulted in the correct identification of 11/12 DILI-positive compounds, whereas exposure of the human 3D-MT co-cultures for 14days resulted in identification of 9/12 DILI-compounds; in addition to ximelagatran (also not identified by 2D-sw monocultures, Sison-Young et al., 2016), the 3D-MT co-cultures failed to detect amiodarone and bosentan. The sensitivity of the 2D human hepatocytes co-cultured with NPC to ximelagatran was increased in the presence of lipopolysaccharide (LPS), but only at high concentrations, therefore preventing its classification as a DILI positive compound. In conclusion (1) despite suspension human hepatocytes having the greatest metabolic capacity in the short term, they are the least predictive of clinical DILI across the MIP-DILI test compounds, (2) longer exposure periods than 72h of human hepatocytes do not allow to increase DILI-prediction rate, (3) co-cultures of human hepatocytes with NPC, in the presence of LPS during the 72h exposure period allow the assessment of innate immune system involvement of a given drug.

Drug biokinetic and toxicity assessments in rat and human primary hepatocytes and HepaRG cells within the EU-funded Predict-IV project

The overall aim of Predict-IV (EU-funded collaborative project #202222) was to develop improved testing strategies for drug safety in the late discovery phase. One major focus was the prediction of hepatotoxicity as liver remains one of the major organ leading to failure in drug development, drug withdrawal and has a poor predictivity from animal experiments. In this overview we describe the use and applicability of the three cell models employed, i.e., primary rat hepatocytes, primary human hepatocytes and the human HepaRG cell line, using four model compounds, chlorpromazine, ibuprofen, cyclosporine A and amiodarone. This overview described the data generated on mode of action of liver toxicity after long-term repeat-dosing. Moreover we have quantified parent compound and its distribution in various in vitro compartments, which allowed us to develop biokinetic models where we could derive real exposure concentrations in vitro. In conclusion, the complex data set enables quantitative measurements that proved the concept that we can define human relevant free and toxic exposure levels in vitro. Further compounds have to be analyzed in a broader concentration range to fully exploit these promising results for improved prediction of hepatotoxicity and hazard assessment for humans.