Quantitative Cytochrome P450 3A4 Induction Risk Assessment Using Human Hepatocytes Complemented with Pregnane X Receptor-Activating Profiles.

Reliable in vitro to in vivo translation of cytochrome P450 (CYP) 3A4 induction potential is essential to support risk mitigation for compounds during pharmaceutical discovery and development. In this study, a linear correlation of CYP3A4 mRNA induction potential in human hepatocytes with the respective pregnane-X receptor (PXR) activation in a reporter gene assay using DPX2 cells was successfully demonstrated for 13 clinically used drugs. Based on this correlation, using rifampicin as a positive control, the magnitude of CYP3A4 mRNA induction for 71 internal compounds at several concentrations up to 10 µM (n = 90) was predicted within 2-fold error for 64% of cases with only a few false positives (19%). Furthermore, the in vivo area under the curve reduction of probe CYP substrates was reasonably predicted for eight marketed drugs (carbamazepine, dexamethasone, enzalutamide, nevirapine, phenobarbital, phenytoin, rifampicin, and rufinamide) using the static net effect model using both the PXR activation and CYP3A4 mRNA induction data. The liver exit concentrations were used for the model in place of the inlet concentrations to avoid false positive predictions and the concentration achieving twofold induction (F2) was used to compensate for the lack of full induction kinetics due to cytotoxicity and solubility limitations in vitro. These findings can complement the currently available induction risk mitigation strategy and potentially influence the drug interaction modeling work conducted at clinical stages. SIGNIFICANCE STATEMENT: The established correlation of CYP3A4 mRNA in human hepatocytes to PXR activation provides a clear cut-off to identify a compound showing an in vitro induction risk, complementing current regulatory guidance. Also, the demonstrated in vitro-in vivo translation of induction data strongly supports a clinical development program although limitations remain for drug candidates showing complex disposition pathways, such as involvement of auto-inhibition/induction, active transport and high protein binding.

Estimation of Fraction Metabolized by Cytochrome P450 Enzymes Using Long-Term Cocultured Human Hepatocytes.

Estimation of the fraction of a drug metabolized by individual hepatic CYP enzymes relative to hepatic metabolism (fm,CYP) or total clearance h as been challenging for low turnover compounds due to insufficient resolution of the intrinsic clearance (CLint) measurement in vitro and difficulties in quantifying the formation of low abundance metabolites. To overcome this gap, inhibition of drug depletion or selective metabolite formation for 7 marker CYP substrates was investigated using chemical inhibitors and a micro-patterned hepatocyte coculture system (HepatoPac). The use of 3 µM itraconazole was successfully validated for estimation of fm,CYP3A4 by demonstration of fm values within a 2-fold of in vivo estimates for 10 out of 13 CYP3A4 substrates in a reference set of marketed drugs. Other CYP3A4 inhibitors (ketoconazole and posaconazole) were not optimal for estimation of fm,CYP3A4 for low turnover compounds due to their high CLint. The current study also demonstrated that selective inhibition sufficient for fm calculation was achieved by inhibitors of CYP1A2 (20 µM furafylline), CYP2C8 (40 µM montelukast), CYP2C9 (40 µM sulfaphenazole), CYP2C19 [3 µM (-)N-3-benzyl-phenobarbital], and CYP2D6 (5 µM quinidine). Good estimation of fm,CYP2B6 was not possible in this study due to the poor selectivity of the tested inhibitor (20 µM ticlopidine). The approach verified in this study can result in an improved fm estimation that is aligned with the regulatory agencies' guidance and can support a victim drug-drug interaction risk assessment strategy for low clearance discovery and development drug candidates. SIGNIFICANCE STATEMENT: Successful qualification of a chemical inhibition assay for estimation of fraction metabolized requires chemical inhibitors that retain sufficient unbound concentrations over time in the incubates. The current cocultured hepatocyte assay enabled estimation of fraction metabolized, especially by CYP3A4, during the drug discovery phase where metabolite quantification methods may not be available. The method enables the assessment of pharmacokinetic variability and victim drug-drug interaction risks due to enzyme polymorphism or inhibition/induction with more confidence, especially for low clearance drug candidates.

Correction to: In Vitro to In Vivo Extrapolation of Metabolic Clearance for UGT Substrates Using Short-Term Suspension and Long-Term Co-cultured Human Hepatocytes.

During production, the figure captions for Fig. 1 and Fig. 2 were inadvertently switched in the proofing stage.

In Vitro to In Vivo Extrapolation of Metabolic Clearance for UGT Substrates Using Short-Term Suspension and Long-Term Co-cultured Human Hepatocytes.

The use of micro-patterned co-cultured hepatocytes for human hepatic clearance predictions has previously been demonstrated using drugs metabolized by cytochrome P450 enzymes. The present study evaluates the in vitro to in vivo extrapolation (IVIVE) performance for UDP-glucuronosyltransferase (UGT) substrates. In vitro intrinsic clearances for 13 drugs mainly cleared by UGTs were determined using HepatoPac and suspended hepatocytes. The in vivo intrinsic clearance was predicted from in vitro intrinsic clearance and compared with weighted mean in vivo intrinsic clearance estimated from several clinical studies. A conventional scaling methodology accounting for protein binding in plasma and incubation medium was used for the IVIVE assuming that only free drug is accessible at the site of metabolism. The in vivo hepatic intrinsic clearance was predicted within threefold error for six and nine out of thirteen drugs using suspended hepatocytes and HepatoPac, respectively. A reduced under-estimation of hepatic intrinsic clearance was observed in the average fold error (AFE) in HepatoPac (AFE, 0.69) compared with the suspended hepatocytes (AFE, 0.37). The current study shows reasonable performance of hepatic clearance prediction of drugs mainly metabolized by UGT enzymes using HepatoPac with a similar under-prediction bias as obtained in the reported IVIVEs for cytochrome P450 substrates. This study provides a validation of the approach for drugs cleared via UGT conjugation mechanisms and discusses potential causes for outlier behavior considering pharmacokinetic or physicochemical properties.

Application of the Extended Clearance Classification System (ECCS) in Drug Discovery and Development: Selection of Appropriate In Vitro Tools and Clearance Prediction.

In vitro to in vivo extrapolation (IVIVE) to predict human hepatic clearance, including metabolism and transport, requires extensive experimental resources. In addition, there may be technical challenges to measure low clearance values. Therefore, prospective identification of rate-determining step(s) in hepatic clearance through application of the Extended Clearance Classification System (ECCS) could be beneficial for optimal compound characterization. IVIVE for hepatic intrinsic clearance (CLint,h) prediction is conducted for a set of 36 marketed drugs with low-to-high in vivo clearance, which are substrates of metabolic enzymes and active uptake transporters in the liver. The compounds were assigned to the ECCS classes, and CLint,h, estimated with HepatoPac (a micropatterned hepatocyte coculture system), was compared with values calculated based on suspended hepatocyte incubates. An apparent permeability threshold (apical to basal) of 50 nm/s in LLC-PK1 cells proved optimal for ECCS classification. A reasonable performance of the IVIVE for compounds across multiple classes using HepatoPac was achieved (with 2-3-fold error), except for substrates of uptake transporters (class 3b), for which scaling of uptake clearance using plated hepatocytes is more appropriate. Irrespective of the ECCS assignment, metabolic clearance can be estimated well using HepatoPac. The validation and approach elaborated in the present study can result in proposed decision trees for the selection of the optimal in vitro assays guided by ECCS class assignment, to support compound optimization and candidate selection. SIGNIFICANCE STATEMENT: Characterization of the rate-determining step(s) in hepatic elimination could be on the critical path of compound optimization during drug discovery. This study demonstrated that HepatoPac and plated hepatocytes are suitable tools for the estimation of metabolic and active uptake clearance, respectively, for a larger set of marketed drugs, supporting a comprehensive strategy to select optimal in vitro tools and to achieve Extended Clearance Classification System-dependent in vitro to in vivo extrapolation for human clearance prediction.

The In Vitro Biotransformation of the Fusion Protein Tetranectin-Apolipoprotein A1.

As more and more protein biotherapeutics enter the drug discovery pipelines, there is an increasing interest in tools for mechanistic drug metabolism investigations of biologics in order to identify and prioritize the most promising candidates. Understanding or even predicting the in vivo clearance of biologics and to support translational pharmacokinetic modeling activities is essential, however there is a lack of effective and validated in vitro cellular tools. Although different mechanisms have to be adressed in the context of biologics disposition, the scope is not comparable to the nowadays widely established tools for early characterization of small molecule disposition. Here, we describe a biotransformation study of the fusion protein tetranectin apolipoprotein A1 by cellular systems. The in vivo biotransformation of tetranectin apolipoprotein A1 has been described previously, and the same major biotransformation product could also be detected in vitro, by a targeted and highly sensitive detection method based on chymotrypsin digest. In addition, the protease responsible for the formation of this biotransformation product could be elucidated to be DPP4. To our knowledge, this is one of the first reports of an in vitro biotransformation study by cells of a therapeutic protein.

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.

Metabolic Profiling of Human Long-Term Liver Models and Hepatic Clearance Predictions from In Vitro Data Using Nonlinear Mixed-Effects Modeling.

Early prediction of human clearance is often challenging, in particular for the growing number of low-clearance compounds. Long-term in vitro models have been developed which enable sophisticated hepatic drug disposition studies and improved clearance predictions. Here, the cell line HepG2, iPSC-derived hepatocytes (iCell®), the hepatic stem cell line HepaRG™, and human hepatocyte co-cultures (HµREL™ and HepatoPac®) were compared to primary hepatocyte suspension cultures with respect to their key metabolic activities. Similar metabolic activities were found for the long-term models HepaRG™, HµREL™, and HepatoPac® and the short-term suspension cultures when averaged across all 11 enzyme markers, although differences were seen in the activities of CYP2D6 and non-CYP enzymes. For iCell® and HepG2, the metabolic activity was more than tenfold lower. The micropatterned HepatoPac® model was further evaluated with respect to clearance prediction. To assess the in vitro parameters, pharmacokinetic modeling was applied. The determination of intrinsic clearance by nonlinear mixed-effects modeling in a long-term model significantly increased the confidence in the parameter estimation and extended the sensitive range towards 3% of liver blood flow, i.e., >10-fold lower as compared to suspension cultures. For in vitro to in vivo extrapolation, the well-stirred model was used. The micropatterned model gave rise to clearance prediction in man within a twofold error for the majority of low-clearance compounds. Further research is needed to understand whether transporter activity and drug metabolism by non-CYP enzymes, such as UGTs, SULTs, AO, and FMO, is comparable to the in vivo situation in these long-term culture models.