Preclinical evaluation of chemically reactive metabolites and mitigation of bioactivation in drug discovery

Drug metabolizing enzymes and transporters, and their roles for the development of drug-induced liver injury

INTRODUCTION

Drug-induced liver injury (DILI) poses a significant challenge to drug development and human healthcare. The complex mechanisms underlying DILI make it challenging to accurately predict its occurrence, often leading to substantial financial losses from failed drug development projects and drug withdrawals. Growing evidence suggests that drug-metabolizing enzymes and transporters (DMETs) play a critical role in the development of DILI.

AREAS COVERED

In this review, we explore findings about the contributions of DMETs to DILI, with a focus on the studies examining genetic polymorphisms and their interactions with drugs. Additionally, we highlight the roles of DMETs in the development of predictive models for assessing DILI potential and in uncovering the mechanisms involved in DILI.

EXPERT OPINION

As new approach methods (NAMs) for assessing and predicting drug toxicity gain more prominence, it is imperative to better understand the adverse outcome pathways (AOPs) that underpin these methods. DMETs largely play a pivotal role in the molecular initiating events of DILI-related AOPs. Further research is needed to characterize DILI-related AOP networks and enhance the predictive performance of NAMs for assessing DILI risk.

In-depth understanding of the structure-based reactive metabolite formation of organic functional groups

Idiosyncratic drug-induced liver injury (DILI) is a leading cause of drug attrition and/or withdrawal. The formation of reactive metabolites is widely accepted as a key factor contributing to idiosyncratic DILI. Therefore, identifying reactive metabolites has become a critical focus during lead optimization, and a combination of GSH-/cyano-trapping and cytochrome P450 inactivation studies is recommended to identify compounds with the potential to generate reactive metabolites. Daily dose, clinical indication, detoxication pathways, administration route, and treatment duration are the most considerations when deprioritizing candidates that generate reactive metabolites. Removing the structural alerts is considered a pragmatic strategy for mitigating the risk associated with reactive metabolites, although this approach may sometimes exclude otherwise potent molecules. In this context, an in-depth insight into the structure-based reactive metabolite formation of organic functional groups can significantly aid in the rational design of drug candidates with improved safety profiles. The primary goal of this review is to delve into an analysis of the bioactivation mechanisms of organic functional groups and their potential detrimental effects with recent examples to assist medicinal chemists and metabolism scientists in designing safer drug candidates with a higher likelihood of success.

The Current State of Biotransformation Science - Industry Survey of In Vitro and In Vivo Practices, Clinical Translation, and Future Trends

Embedded within the field of drug metabolism and pharmacokinetics (DMPK), biotransformation is a discipline that studies the origins, disposition, and structural identity of metabolites to provide a comprehensive safety assessment, including the assessment of exposure coverage in toxicological species. Spanning discovery and development, metabolite identification (metID) scientists employ various strategies and tools to address stage-specific questions aimed at guiding the maturation of early chemical matter into drug candidates. During this process, the identity of major (and minor) circulating human metabolites is ascertained to comply with the regulatory requirements such as the Metabolites in Safety Testing (MIST) guidance. Through the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ), the "Translatability of MetID In Vitro Systems Working Group" was created within the Translational and ADME Sciences Leadership Group. The remit of this group was to objectively determine how accurate commonly employed in vitro systems have been with respect to prediction of circulating human metabolites, both qualitatively and quantitatively. A survey composed of 34 questions was conducted across 26 pharmaceutical companies to obtain a foundational understanding of current metID practices, preclinically and clinically, as well as to provide perspective on how successful these practices have been at predicting circulating human metabolites. The results of this survey are presented as an initial snapshot of current industry-based metID practices, including our perspective on how a harmonized framework for the conduct of in vitro metID studies could be established. Future perspectives from current practices to emerging advances with greater translational capability are also provided.

Drug metabolism and transport mediated the hepatotoxicity of Pleuropterus multiflorus root: a review

Pleuropterus multiflorus root (PMR, Polygoni Multiflori Radix) is an herbal medicine widely used in East Asia, particularly China. However, the potential hepatotoxicity has hindered its rational and safe application of PMR in clinical practice. Recently, the hepatotoxic study of PMR have made great progress, especially drug metabolism and transport-mediated liver injury. In this review, we summarized the advancement of drug metabolism and transport regluated hepatic injury of PMR, pointed out the key role of drug metabolizing enzymes and transporters in regulating hepatic injury of PMR, and emphasized the main hepatotoxic substances, toxicity promoter, and hepatic toxic substance-toxicity promoter interactions in PMR. On this basis, the clinical prospect of preventing and treating hepatic injury of PMR from the perspective of metabolism and transporter was discussed, to provide a useful reference and theoretical basis for the prevention and treatment of hepatic injury of PMR.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Cyanide Trapping of Iminium Ion Reactive Metabolites: Implications for Clinical Hepatotoxicity

Reactive metabolite formation is a major mechanism of hepatotoxicity. Although reactive electrophiles can be soft or hard in nature, screening strategies have generally focused on the use of glutathione trapping assays to screen for soft electrophiles, with many data sets available to support their use. The use of a similar assay for hard electrophiles using cyanide as the trapping agent is far less common, and there is a lack of studies with sufficient supporting data. Using a set of 260 compounds with a defined hepatotoxicity status by the FDA, a comprehensive literature search yielded cyanide trapping data on an unbalanced set of 20 compounds that were all clinically hepatotoxic. Thus, a further set of 19 compounds was selected to generate cyanide trapping data, resulting in a more balanced data set of 39 compounds. Analysis of the data demonstrated that the cyanide trapping assay had high specificity (92%) and a positive predictive value (83%) such that hepatotoxic compounds would be confidently flagged. Structural analysis of the adducts formed revealed artifactual methylated cyanide adducts to also occur, highlighting the importance of full structural identification to confirm the nature of the adduct formed. The assay was demonstrated to add the most value for compounds containing typical structural alerts for hard electrophile formation: half of the severe hepatotoxins with these structural alerts formed cyanide adducts, while none of the severe hepatotoxins with no relevant structural alerts formed adducts. The assay conditions used included cytosolic enzymes (e.g., aldehyde oxidase) and an optimized cyanide concentration to minimize the inhibition of cytochrome P450 enzymes by cyanide. Based on the demonstrated added value of this assay, it is to be initiated for use at GSK as part of the integrated hepatotoxicity strategy, with its performance being reviewed periodically as more data is generated.

Prediction of cytochrome P450-mediated bioactivation using machine learning models and in vitro validation

Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.e., Random Forest, Random Subspace, SVM and Naïve Bayes. The models were assessed by metrics like "Precision", "Recall", "F-Measure", "AUC" (Area Under the Curve), etc. Random Forest algorithms illustrated the best predictability, with nice AUC values of 0.949, 0.973 and 0.958 for the test sets of Benzene ring, Nitrogen heterocycle and Sulfur heterocycle models, respectively. 2D descriptors like topological indices, 2D autocorrelations and Burden eigenvalues, etc. contributed most to the models. Furthermore, the models were applied to predict the occurrence of bioactivation of an external verification set. Drugs like selpercatinib, glafenine, encorafenib, etc. were predicted to undergo bioactivation into toxic RMs. In vitro, IC50 shift experiment was performed to assess the potential of bioactivation to validate the prediction. Encorafenib and tirbanibulin were observed of bioactivation potential with shifts of 3-6 folds or so. Overall, this study provided a reliable and robust strategy to predict the P450-mediated bioactivation, which will be helpful to the assessment of adverse drug reactions (ADRs) in clinic and the design of new candidates with lower toxicities.