Physiologically Based Pharmacokinetic Modeling to Predict the Impact of Liver Cirrhosis on Glucuronidation via UGT1A4 and UGT2B7/2B4-A Case Study with Midazolam.

Hepatic impairment, due to liver cirrhosis, decreases the activity of cytochrome P450 enzymes (CYPs). The use of physiologically based pharmacokinetic (PBPK) modeling to predict this effect for CYP substrates has been well-established, but the effect of cirrhosis on uridine-glucuronosyltransferase (UGT) activities is less studied and few PBPK models have been reported. UGT enzymes are involved in primary N-glucuronidation of midazolam and glucuronidation of 1'-OH-midazolam following CYP3A hydroxylation. In this study, Simcyp was used to establish PBPK models for midazolam, its primary metabolites midazolam-N-glucuronide (UGT1A4) and 1'-OH midazolam (CYP3A4/3A5), and the secondary metabolite 1'-OH-midazolam-O-glucuronide (UGT2B7/2B4), allowing to simulate the impact of liver cirrhosis on the primary and secondary glucuronidation of midazolam. The model was verified in noncirrhotic subjects before extrapolation to cirrhotic patients of Child-Pugh (CP) classes A, B, and C. Our model successfully predicted the exposures of midazolam and its metabolites in noncirrhotic and cirrhotic patients, with 86% of observed plasma concentrations within 5th-95th percentiles of predictions and observed geometrical mean of area under the plasma concentration curve between 0 hours to infinity and maximal plasma concentration within 0.7- to 1.43-fold of predictions. The simulated metabolic ratio defined as the ratio of the glucuronide metabolite AUC over the parent compound AUC (AUCglucuronide/AUCparent, metabolic ratio [MR]), was calculated for midazolam-N-glucuronide to midazolam (indicative of UGT1A4 activity) and decreased by 40% (CP A), 48% (CP B), and 75% (CP C). For 1'-OH-midazolam-O-glucuronide to 1'-OH-midazolam, the MR (indicative of UGT2B7/2B4 activity) dropped by 35% (CP A), 51% (CP B), and 64% (CP C). These predicted MRs were corroborated by the observed data. This work thus increases confidence in Simcyp predictions of the effect of liver cirrhosis on the pharmacokinetics of UGT1A4 and UGT2B7/UGT2B4 substrates. SIGNIFICANCE STATEMENT: This article presents a physiologically based pharmacokinetic model for midazolam and its metabolites and verifies the accurate simulation of pharmacokinetic profiles when using the Simcyp hepatic impairment population models. Exposure changes of midazolam-N-glucuronide and 1'-OH-midazolam-O-glucuronide reflect the impact of decreases in UGT1A4 and UGT2B7/2B4 glucuronidation activity in cirrhotic patients. The approach used in this study may be extended to verify the modeling of other uridine glucuronosyltransferase enzymes affected by liver cirrhosis.

PBPK Modelling for Drugs Cleared by Non-CYP Enzymes: State-of-the-Art and Future Perspectives.

Physiologically-based pharmacokinetic (PBPK) modeling has become the established method for predicting human pharmacokinetics (PK) and drug-drug interactions (DDI). The number of drugs cleared by non-CYP enzyme metabolism has increased steadily and to date, there is no consolidated overview of PBPK modeling for drugs cleared by non-CYP enzymes. This review aims to describe the state-of-the-art for PBPK modeling for drugs cleared via non-CYP enzymes, to identify successful strategies, to describe gaps and to provide suggestion to overcome them. To this end, we conducted a detailed literature search and found 58 articles published before the 1st of January 2023 containing 95 examples of clinical PBPK models for 62 non-CYP enzyme substrates. Reviewed articles covered the drug clearance by uridine 5'-diphospho-glucuronosyltransferases (UGTs), aldehyde oxidase (AO), flavin-containing monooxygenases (FMOs), sulfotransferases (SULTs) and carboxylesterases (CES), with UGT2B7, UGT1A9, CES1, FMO3 and AO being the enzymes most frequently involved. In vitro-in vivo extrapolation (IVIVE) of intrinsic clearance and the bottom-up PBPK modeling involving non-CYP enzymes remains challenging. We observed that the middle-out modeling approach was applied in 80% of the cases, with metabolism parameters optimized in 73% of the models. Our review could not identify a standardized approach used for model optimization based on clinical data, with manual optimization employed most frequently. Successful development of models for UGT2B7, UGT1A9, CES1, and FMO3 substrates provides a foundation for other drugs metabolized by these enzymes and guides the way forward in creating PBPK models for other enzymes in these families. Significance Statement Our review charts the rise of PBPK modeling for drugs cleared by non-CYP enzymes. Analyzing 58 articles and 62 non-CYP enzyme substrates, we found that UGTs, AO, FMOs, SULTs, and CES were the main enzyme families involved and that UGT2B7, UGT1A9, CES1, FMO3 and AO are the individual enzymes with the strongest PBPK modeling precedents. Approaches established for these enzymes can now be extended to additional substrates and to drugs metabolized by enzymes that are similarly well characterized.

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.

Drug Design and Success of Prospective Mouse In Vitro-In Vivo Extrapolation (IVIVE) for Predictions of Plasma Clearance (CLp) from Hepatocyte Intrinsic Clearance (CLint).

Hepatocyte intrinsic clearance (CLint) and methods of in vitro-in vivo extrapolation (IVIVE) are often used to predict plasma clearance (CLp) in drug discovery. While the prediction success of this approach is dependent on the chemotype, specific molecular properties and drug design features that govern these outcomes are poorly understood. To address this challenge, we investigated the success of prospective mouse CLp IVIVE across 2142 chemically diverse compounds. Dilution scaling, which assumes that the free fraction in hepatocyte incubations (fu,inc) is governed by binding to the 10% of serum in the incubation medium, was used as our default CLp IVIVE approach. Results show that predictions of CLp are better for smaller (molecular weight (MW) < 500 Da), less polar (total polar surface area (TPSA) < 100 Å2, hydrogen bond donor (HBD) ≤1, hydrogen bond acceptor (HBA) ≤ 6), lipophilic (log D > 3), and neutral compounds, with low HBD count playing the key role. If compounds are classified according to their chemical space, predictions were good for compounds resembling central nervous system (CNS) drugs [average absolute fold error (AAFE) of 2.05, average fold error (AFE) of 0.90], moderate for classical druglike compounds (according to Lipinski, Veber, and Ghose guidelines; AAFE of 2.55; AFE of 0.68), and poor for nonclassical "beyond the rule of 5" compounds (AAFE of 3.31; AFE of 0.41). From the perspective of measured druglike properties, predictions of CLp were better for compounds with moderate-to-high hepatocyte CLint (>10 µL/min/106 cells), high passive cellular permeability (Papp > 100 nm/s), and moderate observed CLp (5-50 mL/min/kg). Influences of plasma protein binding (fu,p) and P-glycoprotein (Pgp) apical efflux ratio (AP-ER) were less pronounced. If the extended clearance classification system (ECCS) is applied, predictions were good for class 2 (Papp > 50 nm/s; neutral or basic; AAFE of 2.35; AFE of 0.70) and acceptable for class 1A compounds (AAFE of 2.98; AFE of 0.70). Classes 1B, 3 A/B, and 4 showed poor outcomes (AAFE > 3.80; AFE < 0.60). Functional groups trending toward weaker CLp IVIVE were esters, carbamates, sulfonamides, carboxylic acids, ketones, primary and secondary amines, primary alcohols, oxetanes, and compounds liable to aldehyde oxidase metabolism, likely due to multifactorial reasons. Multivariate analysis showed that multiple properties are relevant, combining together to define the overall success of CLp IVIVE. Our results indicate that the current practice of prospective CLp IVIVE is suitable only for CNS-like compounds and well-behaved classical druglike space (e.g., high permeability or ECCS class 2) without challenging functional groups. Unfortunately, based on existing mouse data, prospective CLp IVIVE for complex and nonclassical chemotypes is poor and hardly better than random guessing. This is likely due to complexities such as extrahepatic metabolism and transporter-mediated disposition which are poorly captured by this methodology. With small-molecule drug discovery increasingly evolving toward nonclassical and complex chemotypes, existing CLp IVIVE methodology will require improvement. While empirical correction factors may bridge the gap in the near future, improved and new in vitro assays, data integration models, and machine learning (ML) methods are increasingly needed to address this challenge and reduce the number of nonclinical pharmacokinetic (PK) studies.

A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling.

During drug discovery and development, achieving appropriate pharmacokinetics is key to establishment of the efficacy and safety of new drugs. Physiologically based pharmacokinetic (PBPK) models integrating in vitro-to-in vivo extrapolation have become an essential in silico tool to achieve this goal. In this context, the most important and probably most challenging pharmacokinetic parameter to estimate is the clearance. Recent work on high-throughput PBPK modeling during drug discovery has shown that a good estimate of the unbound intrinsic clearance (CLint,u,) is the key factor for useful PBPK application. In this work, three different machine learning-based strategies were explored to predict the rat CLint,u as the input into PBPK. Therefore, in vivo and in vitro data was collected for a total of 2639 proprietary compounds. The strategies were compared to the standard in vitro bottom-up approach. Using the well-stirred liver model to back-calculate in vivo CLint,u from in vivo rat clearance and then training a machine learning model on this CLint,u led to more accurate clearance predictions (absolute average fold error (AAFE) 3.1 in temporal cross-validation) than the bottom-up approach (AAFE 3.6-16, depending on the scaling method) and has the advantage that no experimental in vitro data is needed. However, building a machine learning model on the bias between the back-calculated in vivo CLint,u and the bottom-up scaled in vitro CLint,u also performed well. For example, using unbound hepatocyte scaling, adding the bias prediction improved the AAFE in the temporal cross-validation from 16 for bottom-up to 2.9 together with the bias prediction. Similarly, the log Pearson r2 improved from 0.1 to 0.29. Although it would still require in vitro measurement of CLint,u., using unbound scaling for the bottom-up approach, the need for correction of the fu,inc by fu,p data is circumvented. While the above-described ML models were built on all data points available per approach, it is discussed that evaluation comparison across all approaches could only be performed on a subset because ca. 75% of the molecules had missing or unquantifiable measurements of the fraction unbound in plasma or in vitro unbound intrinsic clearance, or they dropped out due to the blood-flow limitation assumed by the well-stirred model. Advantageously, by predicting CLint,u as the input into PBPK, existing workflows can be reused and the prediction of the in vivo clearance and other PK parameters can be improved.

Accelerating Clinical Development of Idasanutlin through a Physiologically Based Pharmacokinetic Modeling Risk Assessment for CYP450 Isoenzyme-Related Drug-Drug Interactions.

Idasanutlin is a potent inhibitor of the p53-MDM2 interaction that enables reactivation of the p53 pathway, which induces cell cycle arrest and/or apoptosis in tumor cells expressing functional p53. It was investigated for the treatment of solid tumors and several hematologic indications such as relapsed/refractory acute myeloid leukemia, polycythemia vera, or non-Hodgkin lymphoma. For safety reasons, it cannot be given in healthy volunteers for drug-drug interaction (DDI) explorations. This triggered the need for in silico explorations on top of the one available CYP3A clinical DDI study with posaconazole in solid tumor patients. Idasanutlin's clearance is dependent on CYP3A4/2C8 forming its major circulating metabolite M4, with contributions from UGT1A3 and biliary excretion. Idasanutlin and M4 have low permeability, very low clearance, and extremely low unbound fraction in plasma (<0.001), which makes in vitro data showing inhibition on CYP3A4/2C8 enzymes challenging to translate to clinical relevance. Physiologically-based pharmacokinetic models of idasanutlin and M4 have been established to simulate perpetrator and victim DDI scenarios and to evaluate whether further DDI studies in oncology patients are necessary. Modeling indicated that idasanutlin and M4 would show no or weak clinical inhibition of selective CYP3A4/2C8 substrates. Co-administered strong CYP3A and CYP2C8 inhibitors might lead to weak or moderate idasanutlin exposure increases, and the strong inducer rifampicin might cause moderate exposure reduction. As the simulated idasanutlin systemic exposure changes would be within the range of observed intrinsic variability, the target population can take co-medications that are either CYP2C8/3A4 inhibitors or weak/moderate CYP2C8/3A4 inducers without dose adjustment. SIGNIFICANCE STATEMENT: Clinical trials for idasanutlin are restricted to cancer patients, which imposes practical, scientific, and ethical challenges on drug-drug interaction investigations. Furthermore, idasanutlin and its major circulating metabolite have very challenging profiles of absorption, distribution, metabolism and excretion including high protein binding, low permeability and a combination of different elimination pathways each with extremely low clearance. Nonetheless, physiologically-based pharmacokinetic models could be established and applied for drug-drug interaction risk assessment and were especially useful to provide guidance on concomitant medications in patients.

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.

Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds.

While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) machine learning (ML)-predicted properties or (ii) discovery stage in vitro data. Our test set was composed of 12 challenging development compounds with high lipophilicity (mean calculated log P 4.2), low plasma-free fraction (50% of compounds with fu,p < 1%), and low aqueous solubility. Predictions focused on key human PK parameters, including plasma clearance (CL), volume of distribution at steady state (Vss), and oral bioavailability (%F). For predictions of CL, the ML inputs showed acceptable accuracy and slight underprediction bias [an average absolute fold error (AAFE) of 3.55; an average fold error (AFE) of 0.95]. Surprisingly, use of measured data only slightly improved accuracy but introduced an overprediction bias (AAFE = 3.35; AFE = 2.63). Predictions of Vss were more successful, with both ML (AAFE = 2.21; AFE = 0.90) and in vitro (AAFE = 2.24; AFE = 1.72) inputs showing good accuracy and moderate bias. The %F was poorly predicted using ML inputs [average absolute prediction error (AAPE) of 45%], and use of measured data for solubility and permeability improved this to 34%. Sensitivity analysis showed that predictions of CL limited the overall accuracy of human PK predictions, partly due to high nonspecific binding of lipophilic compounds, leading to uncertainty of unbound clearance. For accurate predictions of %F, solubility was the key factor. Despite current limitations, this work encourages further development of ML models and integration of their results within PBPK models to enable human PK prediction at the drug design stage, even before compounds are synthesized. Further evaluation of this approach with more diverse chemical types is warranted.

Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery.

Minimizing in vitro and in vivo testing in early drug discovery with the use of physiologically based pharmacokinetic (PBPK) modeling and machine learning (ML) approaches has the potential to reduce discovery cycle times and animal experimentation. However, the prediction success of such an approach has not been shown for a larger and diverse set of compounds representative of a lead optimization pipeline. In this study, the prediction success of the oral (PO) and intravenous (IV) pharmacokinetics (PK) parameters in rats was assessed using a "bottom-up" approach, combining in vitro and ML inputs with a PBPK model. More than 240 compounds for which all of the necessary inputs and PK data were available were used for this assessment. Different clearance scaling approaches were assessed, using hepatocyte intrinsic clearance and protein binding as inputs. In addition, a novel high-throughput PBPK (HT-PBPK) approach was evaluated to assess the scalability of PBPK predictions for a larger number of compounds in drug discovery. The results showed that bottom-up PBPK modeling was able to predict the rat IV and PO PK parameters for the majority of compounds within a 2- to 3-fold error range, using both direct scaling and dilution methods for clearance predictions. The use of only ML-predicted inputs from the structure did not perform well when using in vitro inputs, likely due to clearance miss predictions. The HT-PBPK approach produced comparable results to the full PBPK modeling approach but reduced the simulation time from hours to seconds. In conclusion, a bottom-up PBPK and HT-PBPK approach can successfully predict the PK parameters and guide early discovery by informing compound prioritization, provided that good in vitro assays are in place for key parameters such as clearance.

A translational strategy employing physiologically based modelling to predict the pharmacological active dose of RO7119929, an oral prodrug of a targeted cancer immunotherapy TLR7 agonist.

RO7119929 is being developed as an orally administered prodrug of the TLR7-specific agonist and active drug, RO7117418, for the treatment of patients with solid tumours.In this publication, we present a case study wherein the human pharmacokinetics and pharmacological active dose were prospectively predicted following oral administration of the prodrug.A simple translational pharmacokinetic-pharmacodynamic strategy was applied to predict the pharmacological active dose of the prodrug in human. In vivo studies in monkey showed that an unbound plasma exposure of active drug of 1.5 ng/mL elicited secretion of key serum pharmacodynamic cytokine and chemokine biomarkers in monkey. This threshold of 1.5 ng/mL was close to the minimum effective concentration of active drug required to induce cytokine secretion in human peripheral blood mononuclear cells (3 ng/mL).Measured in vitro physicochemical and biochemical properties of the prodrug and active drug were applied as input parameters in physiologically based pharmacokinetic models to predict the pharmacokinetics of active drug after oral dosing of the prodrug in humans. Then, using the PBPK model, a dose which delivered an unbound plasma Cmax in line with the target pharmacodynamic threshold of 1.5 ng/mL was found. This defined the lowest pharmacologically active dose as 3 mg.The prodrug entered the clinic in 2020 in patients with primary or secondary liver cancers. Clear pharmacodynamic, transient, and dose-dependent cytokine induction was observed at prodrug doses > 1 mg.

Characterization of Hepatic UDP-Glucuronosyltransferase Enzyme Abundance-Activity Correlations and Population Variability Using a Proteomics Approach and Comparison with Cytochrome P450 Enzymes.

The expression of ten major drug-metabolizing UDP-glucuronosyltransferase (UGT) enzymes in a panel of 130 human hepatic microsomal samples was measured using a liquid chromatography-tandem mass spectrometry-based approach. Simultaneously, ten cytochromes P450 and P450 reductase were also measured, and activity-expression relationships were assessed for comparison. The resulting data sets demonstrated that, with the exception of UGT2B17, 10th to 90th percentiles of UGT expression spanned 3- to 8-fold ranges. These ranges were small relative to ranges of reported mean UGT enzyme expression across different laboratories. We tested correlation of UGT expression with enzymatic activities using selective probe substrates. A high degree of abundance-activity correlation (Spearman's rank correlation coefficient > 0.6) was observed for UGT1As (1A1, 3, 4, 6) and cytochromes P450. In contrast, protein abundance and activity did not correlate strongly for UGT1A9 and UGT2B enzymes (2B4, 7, 10, 15, and 17). Protein abundance was strongly correlated for UGTs 2B7, 2B10, and 2B15. We suggest a number of factors may contribute to these differences including incomplete selectivity of probe substrates, correlated expression of these UGT2B isoforms, and the impact of splice and polymorphic variants on the peptides used in proteomics analysis, and exemplify this in the case of UGT2B10. Extensive correlation analyses identified important criteria for validating the fidelity of proteomics and enzymatic activity approaches for assessing UGT variability, population differences, and ontogenetic changes. SIGNIFICANCE STATEMENT: Protein expression data allow detailed assessment of interindividual variability and enzyme ontogeny. This study has observed that expression and enzyme activity are well correlated for hepatic UGT1A enzymes and cytochromes P450. However, for the UGT2B family, caution is advised when assuming correlation of expression and activity as is often done in physiologically based pharmacokinetic modeling. This can be due to incomplete probe substrate specificities, but may also be related to presence of inactive UGT protein materials and the effect of splicing variations.

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.

Optimization of Experimental Conditions of Automated Glucuronidation Assays in Human Liver Microsomes Using a Cocktail Approach and Ultra-High Performance Liquid Chromatography-Tandem Mass Spectrometry.

UDP-glucuronosyltransferase (UGT)-mediated metabolism is possibly the most important conjugation reaction for marketed drugs. However, there are currently no generally accepted standard incubation conditions for UGT microsomal assays, and substantial differences in experimental design and methodology between laboratories hinder cross-study comparison of in vitro activities. This study aimed to define optimal experimental conditions to determine glucuronidation activity of multiple UGT isoforms simultaneously using human liver microsomes. Hepatic glucuronidation activities of UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A9, UGT2B4, UGT2B7, UGT2B10, UGT2B15, and UGT2B17 were determined using cocktail incubations of 10 UGT probe substrates. Buffer components and cosubstrates were assessed over a range of concentrations including magnesium chloride (MgCl2; 0-10 mM) and uridine 5'-diphosphoglucuronic acid (UDPGA; 1-25 mM) with either Tris-HCl or potassium phosphate buffer (100 mM, pH 7.4). Greater microsomal glucuronidation activity by different hepatic UGT isoforms was obtained using 10 mM MgCl2 and 5 mM UDPGA with 100 mM Tris-HCl buffer. The influence of bovine serum albumin (BSA; 0.1%-2% w/v) on glucuronidation activity was also assessed. Enzyme- and substrate-dependent effects of BSA were observed, resulting in decreased total activity of UGT1A1, UGT1A3, and UGT2B17 and increased total UGT1A9 and UGT2B7 activity. The inclusion of BSA did not significantly reduce the between-subject variability of UGT activity. Future in vitro UGT profiling studies under the proposed optimized experimental conditions would allow high-quality positive control data to be generated across laboratories, with effective control of a high degree of between-donor variability for UGT activity and for chemical optimization toward lower-clearance drug molecules in a pharmaceutical drug discovery setting.

Perspectives from the Innovation and Quality Consortium Induction Working Group on Factors Impacting Clinical Drug-Drug Interactions Resulting from Induction: Focus on Cytochrome 3A Substrates.

A recent publication from the Innovation and Quality Consortium Induction Working Group collated a large clinical data set with the goal of evaluating the accuracy of drug-drug interaction (DDI) prediction from in vitro data. Somewhat surprisingly, comparison across studies of the mean- or median-reported area under the curve ratio showed appreciable variability in the magnitude of outcome. This commentary explores the possible drivers of this range of outcomes observed in clinical induction studies. While recommendations on clinical study design are not being proposed, some key observations were informative during the aggregate analysis of clinical data. Although DDI data are often presented using median data, individual data would enable evaluation of how differences in study design, baseline expression, and the number of subjects contribute. Since variability in perpetrator pharmacokinetics (PK) could impact the overall DDI interpretation, should this be routinely captured? Maximal induction was typically observed after 5-7 days of dosing. Thus, when the half-life of the inducer is less than 30 hours, are there benefits to a more standardized study design? A large proportion of CYP3A4 inducers were also CYP3A4 inhibitors and/or inactivators based on in vitro data. In these cases, using CYP3A selective substrates has limitations. More intensive monitoring of changes in area under the curve over time is warranted. With selective CYP3A substrates, the net effect was often inhibition, whereas less selective substrates could discern induction through mechanisms not susceptible to inhibition. The latter included oral contraceptives, which raise concerns of reduced efficacy following induction. Alternative approaches for modeling induction, such as applying biomarkers and physiologically based pharmacokinetic modeling (PBPK), are also considered. SIGNIFICANCE STATEMENT: The goal of this commentary is to stimulate discussion on whether there are opportunities to optimize clinical drug-drug interaction study design. The overall aim is to reduce, understand and contextualize the variability observed in the magnitude of induction across reported clinical studies. A large clinical CYP3A induction dataset was collected and further analyzed to identify trends and gaps. Reporting individual victim PK data, characterizing perpetrator PK and including additional PK assessments for mixed-mechanism perpetrators may provide insights into how these factors impact differences observed in clinical outcomes. The potential utility of biomarkers and PBPK modeling are discussed in considering future directions.

Development of a physiologically based pharmacokinetic model for mefloquine and its application alongside a clinical effectiveness model to select an optimal dose for prevention of malaria in young Caucasian children.

AIMS: To predict the optimal chemoprophylactic dose of mefloquine in infants of 5-10 kg using physiologically based pharmacokinetic (PBPK) and clinical effectiveness models. METHODS: The PBPK model was developed in Simcyp version 14.1 and verified against clinical pharmacokinetic data in adults; the final model, accounting for developmental physiology and enzyme ontogeny was then applied in the paediatric population. The clinical effectiveness model utilized real-world chemoprophylaxis data with stratification of output by age and including infant data from the UK population. RESULTS: PBPK simulations in infant populations depend on the assumed fraction of mefloquine metabolized by CYP3A4 (0.47, 0.95) and on the associated CYP3A4 ontogeny (Salem, Upreti). However, all scenarios suggest that a dose of 62.5 mg weekly achieves or exceeds the exposure in adults following a 250 mg weekly dose and results in a minimum plasma concentration of 620 ng ml-1 , which is considered necessary to achieve 95% prophylactic efficacy. The clinical effectiveness model predicts a 96% protective efficacy from mefloquine chemoprophylaxis at 62.5 mg weekly. CONCLUSIONS: The PBPK and clinical effectiveness models are mutually supportive and suggest a prophylactic dose of 62.5 mg weekly in the Caucasian 5-10 kg infant population travelling to endemic countries. This dual approach offers a novel route to dose selection in a vulnerable population, where clinical trials would be difficult to conduct.

A novel orally available small molecule that inhibits hepatitis B virus expression.

BACKGROUND & AIMS: The hallmarks of chronic HBV infection are a high viral load (HBV DNA) and even higher levels (>100-fold in excess of virions) of non-infectious membranous particles containing the tolerogenic viral S antigen (HBsAg). Currently, standard treatment effectively reduces viremia but only rarely results in a functional cure (defined as sustained HBsAg loss). There is an urgent need to identify novel therapies that reduce HBsAg levels and restore virus-specific immune responsiveness in patients. We report the discovery of a novel, potent and orally bioavailable small molecule inhibitor of HBV gene expression (RG7834). METHODS: RG7834 antiviral characteristics and selectivity against HBV were evaluated in HBV natural infection assays and in a urokinase-type plasminogen activator/severe combined immunodeficiency humanized mouse model of HBV infection, either alone or in combination with entecavir. RESULTS: Unlike nucleos(t)ide therapies, which reduce viremia but do not lead to an effective reduction in HBV antigen expression, RG7834 significantly reduced the levels of viral proteins (including HBsAg), as well as lowering viremia. Consistent with its proposed mechanism of action, time course RNA-seq analysis revealed a fast and selective reduction in HBV mRNAs in response to RG7834 treatment. Furthermore, oral treatment of HBV-infected humanized mice with RG7834 led to a mean HBsAg reduction of 1.09 log10 compared to entecavir, which had no significant effect on HBsAg levels. Combination of RG7834, entecavir and pegylated interferon α-2a led to significant reductions of both HBV DNA and HBsAg levels in humanized mice. CONCLUSION: We have identified a novel oral HBV viral gene expression inhibitor that blocks viral antigen and virion production, that is highly selective for HBV, and has a unique antiviral profile that is clearly differentiated from nucleos(t)ide analogues. LAY SUMMARY: We discovered a novel small molecule viral expression inhibitor that is highly selective for HBV and unlike current therapy inhibits the expression of viral proteins by specifically reducing HBV mRNAs. RG7834 can therefore potentially provide anti-HBV benefits and increase HBV cure rates, by direct reduction of viral agents needed to complete the viral life cycle, as well as a reduction of viral agents involved in evasion of the host immune responses.

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.

Low Potential of Basimglurant to Be Involved in Drug-Drug Interactions: Influence of Non-Michaelis-Menten P450 Kinetics on Fraction Metabolized.

Basimglurant, a novel mGlu5-negative allosteric modulator under development for the treatment of major depressive disorder, is cleared via cytochrome P450 (P450)-mediated oxidative metabolism. Initial enzyme phenotyping studies indicated that CYP3A4/5 dominates basimglurant metabolism and highlights a risk for drug-drug interactions when it is comedicated with strong CYP3A4/5 inhibitors or inactivators; however, a clinical drug-drug interaction (DDI) study using the potent and selective CYP3A4/5 inhibitor ketoconazole resulted in an area under the curve (AUC) AUCi/AUC ratio of only 1.24. A further study using the CYP3A4 inducer carbamazepine resulted in an AUCi/AUC ratio of 0.69. More detailed in vitro enzyme phenotyping and kinetics studies showed that, at the low concentrations attained clinically, basimglurant metabolic clearance is catalyzed mainly by CYP1A2. The relative contributions of the enzymes were estimated as 70:30 CYP1A2:CYP3A4/5. Using this information, a clinical study using the CYP1A2 inhibitor fluvoxamine was performed, resulting in an AUCi/AUC ratio of 1.60, confirming the role of CYP1A2 and indicating a balanced DDI risk profile. Basimglurant metabolism kinetics show enzyme dependency: CYP1A2-mediated metabolism follows Michaelis-Menten kinetics, whereas CYP3A4 and CYP3A5 follow sigmoidal kinetics [with similar constant (KM) and S50 values]. The interplay of the different enzyme kinetics leads to changing fractional enzyme contributions to metabolism with substrate concentration, even though none of the metabolic enzymes is saturated. This example demonstrates the relevance of non-Michaelis-Menten P450 enzyme kinetics and highlights the need for a thorough understanding of metabolism enzymology to make accurate predictions for human metabolism in vivo.

Characterization of Pharmacokinetics in the Göttingen Minipig with Reference Human Drugs: An In Vitro and In Vivo Approach.

PURPOSE: This study aims to expand our understanding of the mechanisms of drug absorption, distribution, metabolism and excretion in the Göttingen minipig to aid a knowledge-driven selection of the optimal species for preclinical pharmaceutical research. METHODS: The pharmacokinetics of seven reference compounds (antipyrine, atenolol, cimetidine, diazepam, hydrochlorothiazide, midazolam and theophylline) was investigated after intravenous and oral dosing in minipigs. Supportive in vitro data were generated on hepatocellularity, metabolic clearance in hepatocytes, blood cell and plasma protein binding and metabolism routes. RESULTS: Systemic plasma clearance for the seven drugs ranged from low (1.1 ml/min/kg, theophylline) to close to liver blood flow (37.4 ml/min/kg, cimetidine). Volume of distribution in minipigs ranged from 0.7 L/kg for antipyrine to 3.2 L/kg for hydrochlorothiazide. A gender-related difference of in vivo metabolic clearance was observed for antipyrine. The hepatocellularity for minipig was determined as 124 Mcells/g liver, similar to the values reported for human. Based on these data a preliminary in vitro to in vivo correlation (IVIVC) for metabolic clearance measured in hepatocytes was investigated. Metabolite profiles of diazepam and midazolam compared well between minipig and human. CONCLUSIONS: The results of the present study support the use of in vitro metabolism data for the evaluation of minipig in preclinical research and safety testing.

Simulation-Based Evaluation of PK/PD Indices for Meropenem Across Patient Groups and Experimental Designs.

PURPOSE: Antibiotic dose predictions based on PK/PD indices rely on that the index type and magnitude is insensitive to the pharmacokinetics (PK), the dosing regimen, and bacterial susceptibility. In this work we perform simulations to challenge these assumptions for meropenem and Pseudomonas aeruginosa. METHODS: A published murine dose fractionation study was replicated in silico. The sensitivity of the PK/PD index towards experimental design, drug susceptibility, uncertainty in MIC and different PK profiles was evaluated. RESULTS: The previous murine study data were well replicated with fT > MIC selected as the best predictor. However, for increased dosing frequencies fAUC/MIC was found to be more predictive and the magnitude of the index was sensitive to drug susceptibility. With human PK fT > MIC and fAUC/MIC had similar predictive capacities with preference for fT > MIC when short t1/2 and fAUC/MIC when long t1/2. CONCLUSIONS: A longitudinal PKPD model based on in vitro data successfully predicted a previous in vivo study of meropenem. The type and magnitude of the PK/PD index were sensitive to the experimental design, the MIC and the PK. Therefore, it may be preferable to perform simulations for dose selection based on an integrated PK-PKPD model rather than using a fixed PK/PD index target.