Contribution of UGT Enzymes to Human Drug Metabolism Stereoselectivity: A Case Study of Medetomidine, RO5263397, Propranolol, and Testosterone.

The enantiomeric forms of chiral compounds have identical physical properties but may vary greatly in their metabolism by individual enzymes. Enantioselectivity in UDP-glucuronosyl transferase (UGT) metabolism has been reported for a number of compounds and with different UGT isoforms involved. However, the impact of such individual enzyme results on overall clearance stereoselectivity is often not clear. The enantiomers of medetomidine, RO5263397, and propranolol and the epimers testosterone and epitestosterone exhibit more than a 10-fold difference in glucuronidation rates by individual UGT enzymes. In this study, we examined the translation of human UGT stereoselectivity to hepatic drug clearance considering the combination of multiple UGTs to overall glucuronidation, the contribution of other metabolic enzymes such as cytochrome P450s (P450s), and the potential for differences in protein binding and blood/plasma partitioning. For medetomidine and RO5263397, the high individual enzyme (UGT2B10) enantioselectivity translated into ∼3- to >10-fold differences in predicted human hepatic in vivo clearance. For propranolol, the UGT enantioselectivity was irrelevant in the context of high P450 metabolism. For testosterone, a complex picture emerged due to differential epimeric selectivity of various contributing enzymes and potential for extrahepatic metabolism. Quite different patterns of P450- and UGT-mediated metabolism were observed across species, as well as differences in stereoselectivity, indicating that extrapolation from human enzyme and tissue data are essential when predicting human clearance enantioselectivity. SIGNIFICANCE STATEMENT: Individual enzyme stereoselectivity illustrates the importance of three-dimensional drug-metabolizing enzyme-substrate interactions and is essential when considering the clearance of racemic drugs. However, translation from in vitro to in vivo can be challenging as contributions from multiple enzymes and enzyme classes must be combined with protein binding and blood/plasma partitioning data to estimate the net intrinsic clearance for each enantiomer. Preclinical species may be misleading as enzyme involvement and metabolism stereoselectivity can differ substantially.

Use of Phenotypically Poor Metabolizer Individual Donor Human Liver Microsomes To Identify Selective Substrates of UGT2B10.

UDP-glucuronosyltransferase (UGT)1A4 and UGT2B10 are the human UGT isoforms most frequently involved in N-glucuronidation of drugs. UGT2B10 exhibits higher affinity than UGT1A4 for numerous substrates, making it potentially the more important enzyme for metabolism of these compounds in vivo. Clinically relevant UGT2B10 polymorphisms, including a null activity splice site mutation common in African populations, can lead to large exposure differences for UGT2B10 substrates that may limit their developability as marketed drugs. UGT phenotyping approaches using recombinantly expressed UGTs are limited by low enzyme activity and lack of validation of scaling to in vivo. In this study, we describe the use of an efficient experimental protocol for identification of UGT2B10-selective substrates (i.e., those with high fraction metabolized by UGT2B10), which exploits the activity difference between pooled human liver microsomes (HLM) and HLM from a phenotypically UGT2B10 poor metabolizer donor. Following characterization of the approach with eight known UGT2B10 substrates, we used ligand-based virtual screening and literature precedents to select 24 potential UGT2B10 substrates of 140 UGT-metabolized drugs for testing. Of these, dothiepin, cidoxepin, cyclobenzaprine, azatadine, cyproheptadine, bifonazole, and asenapine were indicated to be selective UGT2B10 substrates that have not previously been described. UGT phenotyping experiments and tests comparing conjugative and oxidative clearance were then used to confirm these findings. These approaches provide rapid and sensitive ways to evaluate whether a potential drug candidate cleared via glucuronidation will be sensitive to UGT2B10 polymorphisms in vivo. SIGNIFICANCE STATEMENT: The role of highly polymorphic UDP-glucuronosyltransferase (UGT)2B10 is likely to be underestimated currently for many compounds cleared via N-glucuronidation due to high test concentrations often used in vitro and low activity of UGT2B10 preparations. The methodology described in this study can be combined with the assessment of UGT versus oxidative in vitro metabolism to rapidly identify compounds likely to be sensitive to UGT2B10 polymorphism (high fraction metabolized by UGT2B10), enabling either chemical modification or polymorphism risk assessment before candidate selection.

Structure-metabolism relationships in human-AOX: Chemical insights from a large database of aza-aromatic and amide compounds.

Aldehyde oxidase (AOX) is a metabolic enzyme catalyzing the oxidation of aldehyde and aza-aromatic compounds and the hydrolysis of amides, moieties frequently shared by the majority of drugs. Despite its key role in human metabolism, to date only fragmentary information about the chemical features responsible for AOX susceptibility are reported and only "very local" structure-metabolism relationships based on a small number of similar compounds have been developed. This study reports a more comprehensive coverage of the chemical space of structures with a high risk of AOX phase I metabolism in humans. More than 270 compounds were studied to identify the site of metabolism and the metabolite(s). Both electronic [supported by density functional theory (DFT) calculations] and exposure effects were considered when rationalizing the structure-metabolism relationship.

In silico modeling and simulation of organ-on-a-chip systems to support data analysis and a priori experimental design.

Organ-on-a-chip (OoC) systems are a promising new class of in vitro devices that can combine various tissues, cultured in different compartments, linked by media flow. The properties of these novel in vitro systems linked to increased physiological relevance of culture conditions may lead to more in vivo-relevant cell phenotypes, enabling better in vitro pharmacology and toxicology assessment. Improved cell activities combined with longer lasting cultures offer opportunities to improve the characterization of absorption, distribution, metabolism, and excretion (ADME) processes, potentially leading to more accurate prediction of human pharmacokinetics (PKs). The inclusion of barrier tissue elements and metabolically competent tissue types results in complex concentration-time profiles (in vitro PK) for test drugs and their metabolites that require appropriate mathematical modeling of in vitro data for parameter estimation. In particular, modeling is critical to estimate in vitro ADME parameters when multiple different tissues are combined in a single device. Therefore, sophisticated in silico data analysis and a priori experimental design are highly recommended for OoC experiments in a manner not needed with standard ADME screening. The design of the experiment should be optimized based on an investigation of the structural characteristics of the in vitro system, the ADME features of the test compound and any available knowledge of cell phenotypes. This tutorial aims to provide such a modeling framework to inform experimental design and refine parameter estimation in a Gut-Liver OoC (the most studied multi-organ systems to predict the oral drug PKs) to improve translatability of data generated in such complex cellular systems.

Application of a gut-liver-on-a-chip device and mechanistic modelling to the quantitative in vitro pharmacokinetic study of mycophenolate mofetil.

Microphysiological systems (MPS) consisting of multiple linked organ-on-a-chip (OoC) components are highly promising tools with potential to provide more relevant in vitro to in vivo translation of drug disposition, efficacy and toxicity. A gut-liver OoC system was employed with Caco2 cells in co-culture with HT29 cells in the intestinal compartment and single donor primary hepatocytes in the hepatic compartment for the investigation of intestinal permeability, metabolism (intestinal and hepatic) and potential interplay of those processes. The prodrug mycophenolate mofetil was tested for quantitative evaluation of the gut-liver OoC due to the contribution of both gut and liver in its metabolism. Conversion of mycophenolate mofetil to active drug mycophenolic acid and further metabolism to a glucuronide metabolite was assessed over time in the gut apical, gut basolateral and liver compartments. Mechanistic modelling of experimental data was performed to estimate clearance and permeability parameters for the prodrug, active drug and glucuronide metabolite. Integration of gut-liver OoC data with in silico modelling allowed investigation of the complex combination of intestinal and hepatic processes, which is not possible with standard single tissue in vitro systems. A comprehensive evaluation of the mechanistic model, including structural model and parameter identifiability and global sensitivity analysis, enabled a robust experimental design and estimation of in vitro pharmacokinetic parameters. We propose that similar methodologies may be applied to other multi-organ microphysiological systems used for drug metabolism studies or wherever quantitative knowledge of changing drug concentration with time enables better understanding of biological effect.

Exploration and application of a liver-on-a-chip device in combination with modelling and simulation for quantitative drug metabolism studies.

Microphysiological systems (MPS) are complex and more physiologically realistic cellular in vitro tools that aim to provide more relevant human in vitro data for quantitative prediction of clinical pharmacokinetics while also reducing the need for animal testing. The PhysioMimix liver-on-a-chip integrates medium flow with hepatocyte culture and has the potential to be adopted for in vitro studies investigating the hepatic disposition characteristics of drug candidates. The current study focusses on liver-on-a-chip system exploration for multiple drug metabolism applications. Characterization of cytochrome P450 (CYP), UDP-glucuronosyl transferase (UGT) and aldehyde oxidase (AO) activities was performed using 15 drugs and in vitro to in vivo extrapolation (IVIVE) was assessed for 12 of them. Next, the utility of the liver-on-a-chip for estimation of the fraction metabolized (fm) via specific biotransformation pathways of quinidine and diclofenac was established. Finally, the metabolite identification opportunities were also explored using efavirenz as an example drug with complex primary and secondary metabolism involving a combination of CYP, UGT and sulfotransferase enzymes. A key aspect of these investigations was the application of mathematical modelling for improved parameter calculation. Such approaches will be required for quantitative assessment of metabolism and/or transporter processes in systems where medium flow and system compartments result in non-homogeneous drug concentrations. In particular, modelling was used to explore the effect of evaporation from the medium and it was found that the intrinsic clearance (CLint) might be underestimated by up to 40% for low clearance compounds if evaporation is not accounted for. Modelling of liver-on-a-chip in vitro data also enhanced the approach to fm estimation allowing objective assessment of metabolism models of different complexity. The resultant diclofenac fm,UGT of 0.64 was highly comparable with values reported previously in the literature. The current study demonstrates the integration of mathematical modelling with experimental liver-on-a-chip studies and illustrates how this approach supports generation of high quality of data from complex in vitro cellular systems.

From Experiments to a Fast Easy-to-Use Computational Methodology to Predict Human Aldehyde Oxidase Selectivity and Metabolic Reactions.

Aldehyde oxidase (AOX) is a molibdo-flavoenzyme that has raised great interest in recent years, since its contribution in xenobiotic metabolism has not always been identified before clinical trials, with consequent negative effects on the fate of new potential drugs. The fundamental role of AOX in metabolizing xenobiotics is also due to the attempt of medicinal chemists to stabilize candidates toward cytochrome P450 activity, which increases the risk for new compounds to be susceptible to AOX nucleophile attack. Therefore, novel strategies to predict the potential liability of new entities toward the AOX enzyme are urgently needed to increase effectiveness, reduce costs, and prioritize experimental studies. In the present work, we present the most up-to-date computational method to predict liability toward human AOX (hAOX), for applications in drug design and pharmacokinetic optimization. The method was developed using a large data set of homogeneous experimental data, which is also disclosed as Supporting Information .