Minimising the potential for metabolic activation in drug discovery

Use of Structural Alerts for Reactive Metabolites in the Application SpotRM

Reactive metabolite (RM) formation is widely accepted as playing a crucial role in causing idiosyncratic adverse drug reactions (IADRs), where the liver is most affected. An important goal of drug design is to avoid selection of drug candidates giving rise to RMs and therefore risk causing problems later on involving IADRs. The simplest, initial approach is to avoid test structures that have substructures known or strongly suspected to be associated with IADRs. However, as is evident from the many case reports of IADRs, in most cases a clear association with any (bio)chemical mechanism is lacking, which makes it hard to establish any structure-toxicity relationship. Separate studies of RM formation, in vitro and in vivo, have led to likely evidence and to establishing many structural alerts (SAs) that can be used for fast selection/deselection of planned test compounds. As a background to a discussion of the concept, 25 kinase inhibitor drugs with known problems of hepatotoxicity were probed against a set of SAs contained in the application SpotRM. A clear majority of the probed drugs show liabilities as evident by being flagged by more than one of the fairly established types of SAs. At the same time, no clear SAs were found in three drugs, which is discussed in the broader context of usefulness and selection tactics of SAs in drug design.

Screening and identification of reactive metabolic compounds of Cortex Periplocae based on glutathione capture-mass spectrometry

As a traditional Chinese medicine (TCM), Cortex Periplocae (CP) has a wide range of pharmacological effects, as well as toxic side effects. The main toxic components of it are cardiac glycosides, which tend to cause cardiotoxicity. Currently, it has also been reported in studies to cause hepatotoxicity, but it is not clear whether the hepatotoxicity is related to the toxicity caused by the reactive metabolites. This study aims to investigate the target components of CP that generate reactive metabolic toxicity. The fluorescent probe method was used to detect glutathione (GSH)-trapped reactive metabolites in a co-incubation system of CP extract with rat liver microsomes. Identification of GSH conjugates was performed by LC-MS/MS and that of the possible precursor components that produce reactive metabolites was conducted by UPLC-Q-TOF/MS. Cell viability assays were performed on HepG2 and L02 cells to determine the cytotoxicity of the target components. The findings of our study demonstrate that the extract derived from CP has the ability to generate metabolites that exhaust the intracellular GSH levels, resulting in the formation of GSH conjugates and subsequent cytotoxic effects. Through the utilization of the UPLC-Q-TOF/MS technique, we were able to accurately determine the molecular weight of the precursor compound in CP to be 355.1023. The primary evidence to determining the GSH conjugetes relies on the appearance of characteristic product ions resulting from central neutral loss (CNL) scanning of 129 Da and product scanning of m/z 660 in the positive MS/MS spectrum. Through analysis, it was ultimately ascertained that the presence of chlorogenic acid (CGA) and its isomers, namely neochlorogenic acid (NCGA) and cryptochlorogenic acid (CCGA), could lead to the production of GSH conjugates, resulting in cytotoxicity at elevated levels. Taking these findings into consideration, the underlying cause for the potential hepatotoxicity of CP was initially determined.

Development of a fluorous trapping reagent for rapid detection of electrophilic reactive metabolites

A cysteine-based fluorous trapping reagent, Rf8CYS, was developed. Rf8CYS formed adducts with soft and hard electrophilic reactive metabolites. These fluorous-tagged adducts were purified via both fluorous solid-phase extraction and the direct injection method. The highly sensitive mass spectrometric detection of an unprecedented adduct of the ticlopidine metabolite was realized.

WLB-87848, a Selective σ1 Receptor Agonist, with an Unusually Positioned NH Group as Positive Ionizable Moiety and Showing Neuroprotective Activity

The synthesis and pharmacological activity of a new series of thieno[2,3-d]pyrimidin-4(3H)-one derivatives as sigma-1 receptor (σ1R) ligands are reported. A hit from a high-throughput screening program was evolved into a highly potent and selective σ1R agonist (14qR) that contains a free NH group as positive ionizable moiety, not fulfilling the usual pharmacophoric features of the σ1R. The compound shows good physicochemical and ADMET characteristics, displays an agonist profile in the binding immunoglobulin protein/σ1R association assay, induces neuron viability in an in vitro model of β-amyloid peptide intoxication, and presents positive results against recognition memory impairment induced by hippocampal injection of Aβ peptide in rats after oral treatment, altogether making 14qR (WLB-87848) an interesting candidate for neuroprotection.

Patients with naproxen-induced liver injury display T-cell memory responses toward an oxidative (S)-O-desmethyl naproxen metabolite but not the acyl glucuronide

BACKGROUND

Exposure to nonsteroidal anti-inflammatory drugs (NSAIDs) such as ibuprofen (IBU) and naproxen (NAP) is associated with idiosyncratic drug-induced liver injury (DILI). Carboxylate bioactivation into reactive metabolites (e.g., acyl glucuronides, AG) and resulting T-cell activation is hypothesized as causal for this adverse event. However, conclusive evidence supporting this is lacking.

METHODS

In this work, we identify CD4+ and CD8+ T-cell hepatic infiltration in a biopsy from an IBU DILI patient. Lymphocyte transformation test and IFN-γ ELIspot, conducted on peripheral blood mononuclear cells (PBMCs) of patients with NAP-DILI, were used to explore drug-specific T-cell activation. T-cell clones (TCC) were generated and tested for drug specificity, phenotype/function, and pathways of T-cell activation. Cells were exposed to NAP, its oxidative metabolite 6-O-desmethyl NAP (DM-NAP), its AG or synthesized NAP-AG human-serum albumin adducts (NAP-AG adduct).

RESULTS

CD4+ and CD8+ T-cells from patients expressing a range of different Vβ receptors were stimulated to proliferate and secrete IFN-γ and IL-22 when exposed to DM-NAP, but not NAP, NAP-AG or the NAP-AG adduct. Activation of the CD4+ TCC was HLA-DQ-restricted and dependent on antigen presenting cells (APC); most TCC were activated with DM-NAP-pulsed APC, while fixation of APC blocked the T-cell response. Cross-reactivity was not observed with structurally-related drugs.

CONCLUSION

Our results confirm hepatic T-cell infiltrations in NSAID-induced DILI, and show a T-cell memory response toward DM-NAP indicating an immune-mediated basis for the adverse event. Whilst bioactivation at the carboxylate group is widely hypothesized to be pathogenic for NSAID associated DILI, we found no evidence of this with NAP.

Drug-target affinity prediction method based on multi-scale information interaction and graph optimization

Drug-target affinity (DTA) prediction as an emerging and effective method is widely applied to explore the strength of drug-target interactions in drug development research. By predicting these interactions, researchers can assess the potential efficacy and safety of candidate drugs at an early stage, narrowing down the search space for therapeutic targets and accelerating the discovery and development of new drugs. However, existing DTA prediction models mainly use graphical representations of drug molecules, which lack information on interactions between individual substructures, thus affecting prediction accuracy and model interpretability. Therefore, transformer and diffusion on drug graphs in DTA prediction (TDGraphDTA) are introduced to predict drug-target interactions using multi-scale information interaction and graph optimization. An interactive module is integrated into feature extraction of drug and target features at different granularity levels. A diffusion model-based graph optimization module is proposed to improve the representation of molecular graph structures and enhance the interpretability of graph representations while obtaining optimal feature representations. In addition, TDGraphDTA improves the accuracy and reliability of predictions by capturing relationships and contextual information between molecular substructures. The performance of the proposed TDGraphDTA in DTA prediction was verified on three publicly available benchmark datasets (Davis, Metz, and KIBA). Compared with state-of-the-art baseline models, it achieved better results in terms of consistency index, R-squared, etc. Furthermore, compared with some existing methods, the proposed TDGraphDTA is demonstrated to have better structure capturing capabilities by visualizing the feature capturing capabilities of the model using Grad-AAM toxicity labels in the ToxCast dataset. The corresponding source codes are available at https://github.com/Lamouryz/TDGraph.

Mechanisms and Clinical Significance of Pharmacokinetic Drug Interactions Mediated by FDA and EMA-approved Hepatitis C Direct-Acting Antiviral Agents

The treatment of patients infected with the hepatitis C virus (HCV) has been revolutionised by the development of direct-acting antiviral agents (DAAs) that target specific HCV proteins involved in viral replication. The first DAAs were associated with clinical problems such as adverse drug reactions and pharmacokinetic drug-drug interactions (DDIs). Current FDA/EMA-approved treatments are combinations of DAAs that simultaneously target the HCV N5A-protein, the HCV N5B-polymerase and the HCV NS3/4A-protease. Adverse events and DDIs are less likely with these DAA combinations but several DDIs of potential clinical significance remain. Much of the available information on the interaction of DAAs with CYP drug-metabolising enzymes and influx and efflux transporters is contained in regulatory summaries and is focused on DDIs of likely clinical importance. Important DDIs perpetrated by current DAAs include increases in the pharmacokinetic exposure to statins and dabigatran. Some mechanistic information can be deduced. Although the free concentrations of DAAs in serum are very low, a number of these DDIs are likely mediated by the inhibition of systemic influx transporters, especially OATP1B1/1B3. Other DDIs may arise by DAA-mediated inhibition of intestinal efflux transporters, which increases the systemic concentrations of some coadministered drugs. Conversely, DAAs are victims of DDIs mediated by cyclosporin, ketoconazole, omeprazole and HIV antiretroviral drug combinations, especially when boosted by ritonavir and, to a lesser extent, cobicistat. In addition, concurrent administration of inducers, such as rifampicin, carbamazepine and efavirenz, decreases exposure to some DAAs. Drug-drug interactions that increase the accumulation of HCV N3/4A-protease inhibitors like grazoprevir may exacerbate hepatic injury in HCV patients.

De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep-Learning Framework

Likely effective pharmacological interventions for the treatment of opioid addiction include attempts to attenuate brain reward deficits during periods of abstinence. Pharmacological blockade of the κ-opioid receptor (KOR) has been shown to abolish brain reward deficits in rodents during withdrawal, as well as to reduce the escalation of opioid use in rats with extended access to opioids. Although KOR antagonists represent promising candidates for the treatment of opioid addiction, very few potent selective KOR antagonists are known to date and most of them exhibit significant safety concerns. Here, we used a generative deep-learning framework for the de novo design of chemotypes with putative KOR antagonistic activity. Molecules generated by models trained with this framework were prioritized for chemical synthesis based on their predicted optimal interactions with the receptor. Our models and proposed training protocol were experimentally validated by binding and functional assays.

De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep Learning Framework

Likely effective pharmacological interventions for the treatment of opioid addiction include attempts to attenuate brain reward deficits during periods of abstinence. Pharmacological blockade of the κ-opioid receptor (KOR) has been shown to abolish brain reward deficits in rodents during withdrawal, as well as to reduce the escalation of opioid use in rats with extended access to opioids. Although KOR antagonists represent promising candidates for the treatment of opioid addiction, very few potent selective KOR antagonists are known to date and most of them exhibit significant safety concerns. Here, we used a generative deep learning framework for the de novo design of chemotypes with putative KOR antagonistic activity. Molecules generated by models trained with this framework were prioritized for chemical synthesis based on their predicted optimal interactions with the receptor. Our models and proposed training protocol were experimentally validated by binding and functional assays.

Optimization of Physicochemical Properties of Pyrrolidine GPR40 AgoPAMs Results in a Differentiated Profile with Improved Pharmacokinetics and Reduced Off-Target Activities

GPR40 AgoPAMs are highly effective antidiabetic agents that have a dual mechanism of action, stimulating both glucose-dependent insulin and GLP-1 secretion. The early lipophilic, aromatic pyrrolidine and dihydropyrazole GPR40 AgoPAMs from our laboratory were highly efficacious in lowering plasma glucose levels in rodents but possessed off-target activities and triggered rebound hyperglycemia in rats at high doses. A focus on increasing molecular complexity through saturation and chirality in combination with reducing polarity for the pyrrolidine AgoPAM chemotype resulted in the discovery of compound 46, which shows significantly reduced off-target activities as well as improved aqueous solubility, rapid absorption, and linear PK. In vivo, compound 46 significantly lowers plasma glucose levels in rats during an oral glucose challenge yet does not demonstrate the reactive hyperglycemia effect at high doses that was observed with earlier GPR40 AgoPAMs.

Molecular mechanisms of hepatotoxicity induced by compounds occurring in Evodiae Fructus

Evodiae Fructus (EF) is a common herbal medicine with thousands of years of medicinal history in China, which has been demonstrated with many promising pharmacological effects on cancer, cardiovascular diseases and Alzheimer's disease. However, there have been increasing reports of hepatotoxicity associated with EF consumption. Unfortunately, in a long term, many implicit constituents of EF as well as their toxic mechanisms remain poorly understood. Recently, metabolic activation of hepatotoxic compounds of EF to generate reactive metabolites (RMs) has been implicated. Herein, we capture metabolic reactions relevant to hepatotoxicity of these compounds. Initially, catalyzed by the hepatic cytochrome P450 enzymes (CYP450s), the hepatotoxic compounds of EF are oxidized to generate RMs. Subsequently, the highly electrophilic RMs could react with nucleophilic groups contained in biomolecules, such as hepatic proteins, enzymes, and nucleic acids to form conjugates and/or adducts, leading to a sequence of toxicological consequences. In addition, currently proposed biological pathogenesis, including oxidative stress, mitochondrial damage and dysfunction, endoplasmic reticulum (ER) stress, hepatic metabolism disorder, and cell apoptosis are represented. In short, this review updates the knowledge on the pathways of metabolic activation of seven hepatotoxic compounds of EF and provides considerable insights into the relevance of proposed molecular hepatotoxicity mechanisms from a biochemical standpoint, for the purpose of providing a theoretical guideline for the rational application of EF in clinics.

GSAML-DTA: An interpretable drug-target binding affinity prediction model based on graph neural networks with self-attention mechanism and mutual information

Identifying drug-target affinity (DTA) has great practical importance in the process of designing efficacious drugs for known diseases. Recently, numerous deep learning-based computational methods have been developed to predict drug-target affinity and achieved impressive performance. However, most of them construct the molecule (drug or target) encoder without considering the weights of features of each node (atom or residue). Besides, they generally combine drug and target representations directly, which may contain irrelevant-task information. In this study, we develop GSAML-DTA, an interpretable deep learning framework for DTA prediction. GSAML-DTA integrates a self-attention mechanism and graph neural networks (GNNs) to build representations of drugs and target proteins from the structural information. In addition, mutual information is introduced to filter out redundant information and retain relevant information in the combined representations of drugs and targets. Extensive experimental results demonstrate that GSAML-DTA outperforms state-of-the-art methods for DTA prediction on two benchmark datasets. Furthermore, GSAML-DTA has the interpretation ability to analyze binding atoms and residues, which may be conducive to chemical biology studies from data. Overall, GSAML-DTA can serve as a powerful and interpretable tool suitable for DTA modelling.

Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation

A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is using reinforcement learning to update a recurrent neural network or language-based de novo molecule generator. However, reinforcement learning can be inefficient, sometimes requiring up to 10⁵ molecules to be sampled to optimize more complex objectives, which poses a limitation when using computationally expensive scoring functions like docking or computer-aided synthesis planning models. In this work, we propose a reinforcement learning strategy called Augmented Hill-Climb based on a simple, hypothesis-driven hybrid between REINVENT and Hill-Climb that improves sample-efficiency by addressing the limitations of both currently used strategies. We compare its ability to optimize several docking tasks with REINVENT and benchmark this strategy against other commonly used reinforcement learning strategies including REINFORCE, REINVENT (version 1 and 2), Hill-Climb and best agent reminder. We find that optimization ability is improved ~ 1.5-fold and sample-efficiency is improved ~ 45-fold compared to REINVENT while still delivering appealing chemistry as output. Diversity filters were used, and their parameters were tuned to overcome observed failure modes that take advantage of certain diversity filter configurations. We find that Augmented Hill-Climb outperforms the other reinforcement learning strategies used on six tasks, especially in the early stages of training or for more difficult objectives. Lastly, we show improved performance not only on recurrent neural networks but also on a reinforcement learning stabilized transformer architecture. Overall, we show that Augmented Hill-Climb improves sample-efficiency for language-based de novo molecule generation conditioning via reinforcement learning, compared to the current state-of-the-art. This makes more computationally expensive scoring functions, such as docking, more accessible on a relevant timescale.

Structure-Activity Relationship (SAR) Model for Predicting Teratogenic Risk of Antiseizure Medications in Pregnancy by Using Support Vector Machine

Teratogenicity is one of the main concerns in clinical medications of pregnant women. Prescription of antiseizure medications (ASMs) in women with epilepsy during pregnancy may cause teratogenic effects on the fetus. Although large scale epilepsy pregnancy registries played an important role in evaluating the teratogenic risk of ASMs, for most ASMs, especially the newly approved ones, the potential teratogenic risk cannot be effectively assessed due to the lack of evidence. In this study, the analyses are performed on any medication, with a focus on ASMs. We curated a list containing the drugs with potential teratogenicity based on the US Food and Drug Administration (FDA)-approved drug labeling, and established a support vector machine (SVM) model for detecting drugs with high teratogenic risk. The model was validated by using the post-marketing surveillance data from US FDA Spontaneous Adverse Events Reporting System (FAERS) and applied to the prediction of potential teratogenic risk of ASMs. Our results showed that our proposed model outperformed the state-of-art approaches, including logistic regression (LR), random forest (RF) and extreme gradient boosting (XGBoost), when detecting the high teratogenic risk of drugs (MCC and recall rate were 0.312 and 0.851, respectively). Among 196 drugs with teratogenic potential reported by FAERS, 136 (69.4%) drugs were correctly predicted. For the eight commonly used ASMs, 4 of them were predicted as high teratogenic risk drugs, including topiramate, phenobarbital, valproate and phenytoin (predicted probabilities of teratogenic risk were 0.69, 0.60 0.59, and 0.56, respectively), which were consistent with the statement in FDA-approved drug labeling and the high reported prevalence of teratogenicity in epilepsy pregnancy registries. In addition, the structural alerts in ASMs that related to the genotoxic carcinogenicity and mutagenicity, idiosyncratic adverse reaction, potential electrophilic agents and endocrine disruption were identified and discussed. Our findings can be a good complementary for the teratogenic risk assessment in drug development and facilitate the determination of pharmacological therapies during pregnancy.

Metabolic Activation of Perampanel Mediated by CYP1A2

Perampanel (PRP), a noncompetitive α-amino-3-hydroxy-5-methyl-4-isoxazolepropanoic acid (AMPA) receptor antagonist with high selectivity, has been used as a new adjuvant for the treatment of fractional seizures with or without primary generalized tonic-clonic seizures and secondary generalized seizures in epilepsy patients over the age of 12. Adverse events such as liver injury have been reported during the clinical application of PRP. The purpose of the study is to explore the in vitro and in vivo metabolic activation of PRP. Two GSH conjugates were detected in rat liver microsomal incubations containing PRP, GSH, and NADPH. The two GSH conjugates were both obtained from the bile of rats and rat primary hepatocytes after exposure to PRP. Similar microsomal incubations complemented with N-acetylcysteine (NAC) in place of GSH offered two NAC conjugates. As expected, the NAC conjugates were detected in the urine of PRP-treated rats. One of the two NAC conjugates was identified as NAC conjugate 12 verified by chemical synthesis. The individual human recombinant P450 enzyme incubation assay demonstrated that CYP1A2 dominated the catalysis for the metabolic activation of PRP. Pretreatment with α-naphthoflavone (NTF) decreased the formation of PRP-derived GSH conjugates in both livers of rats and cultured primary hepatocytes after being treated with PRP. Additionally, NTF was found to decrease the susceptibility of primary hepatocytes to the cytotoxicity of PRP. The findings indicate that PRP was metabolized to the corresponding epoxide, which could participate in PRP-induced cytotoxicity.

MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction

Predicting drug-target affinity (DTA) is beneficial for accelerating drug discovery. Graph neural networks (GNNs) have been widely used in DTA prediction. However, existing shallow GNNs are insufficient to capture the global structure of compounds. Besides, the interpretability of the graph-based DTA models highly relies on the graph attention mechanism, which can not reveal the global relationship between each atom of a molecule. In this study, we proposed a deep multiscale graph neural network based on chemical intuition for DTA prediction (MGraphDTA). We introduced a dense connection into the GNN and built a super-deep GNN with 27 graph convolutional layers to capture the local and global structure of the compound simultaneously. We also developed a novel visual explanation method, gradient-weighted affinity activation mapping (Grad-AAM), to analyze a deep learning model from the chemical perspective. We evaluated our approach using seven benchmark datasets and compared the proposed method to the state-of-the-art deep learning (DL) models. MGraphDTA outperforms other DL-based approaches significantly on various datasets. Moreover, we show that Grad-AAM creates explanations that are consistent with pharmacologists, which may help us gain chemical insights directly from data beyond human perception. These advantages demonstrate that the proposed method improves the generalization and interpretation capability of DTA prediction modeling.

Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it.

Put a ring on it: application of small aliphatic rings in medicinal chemistry

Aliphatic three- and four-membered rings including cyclopropanes, cyclobutanes, oxetanes, azetidines and bicyclo[1.1.1]pentanes have been increasingly exploited in medicinal chemistry for their beneficial physicochemical properties and applications as functional group bioisosteres. This review provides a historical perspective and comparative up to date overview of commonly applied small rings, exemplifying key principles with recent literature examples. In addition to describing the merits and advantages of each ring system, potential hazards and liabilities are also illustrated and explained, including any significant chemical or metabolic stability and toxicity risks.

Ataluren metabolism: Ataluren-O-1β-acyl glucuronide is a stable circulating metabolite in mouse, rat, dog and human

Ataluren is an aromatic acid derivative with a 1,2,4-oxodiazole moiety. Ataluren-O-1β-acyl glucuronide is a prominent circulatory metabolite in mice, rats, dogs, and humans following oral administration of ataluren. The objective of this paper was to evaluate the stability in vitro and in vivo of ataluren-O-1β-acyl glucuronide metabolite. Ultrahigh performance liquid chromatography-mass spectrometry methods were developed to separate and monitor ataluren-O-1β-acyl glucuronide and its possible migration isomers. In vitro stability was assessed in phosphate buffered saline as well as in control rat and human plasma. The disappearance of ataluren-O-1β-acyl glucuronide and the formation of migration isomers were monitored by the ultrahigh performance liquid chromatography-mass spectrometry methods. In vitro, ataluren-O-1β-acyl glucuronide underwent isomerization with an estimated half-life of approximately 1 h. However, ataluren-O-1β-acyl glucuronide was stable and was the only detectable acyl glucuronide following oral administration of ataluren in mice, rats, dogs, and humans using the same analytical methods. Ataluren acyl glucuronide in mouse, rat, dog, and human plasma could be hydrolyzed by β-glucuronidase, further confirming the structure of O-1β-acyl glucuronide. These results demonstrated that ataluren-O-1β-acyl glucuronide did not undergo migration in vivo. No clinical safety concern related to ataluren-O-1β-acyl glucuronide migration has been detected.

Modeling the Bioactivation and Subsequent Reactivity of Drugs

Electrophilically reactive drug metabolites are implicated in many adverse drug reactions. In this mechanism-termed bioactivation-metabolic enzymes convert drugs into reactive metabolites that often conjugate to nucleophilic sites within biological macromolecules like proteins. Toxic metabolite-product adducts induce severe immune responses that can cause sometimes fatal disorders, most commonly in the form of liver injury, blood dyscrasia, or the dermatologic conditions toxic epidermal necrolysis and Stevens-Johnson syndrome. This study models four of the most common metabolic transformations that result in bioactivation: quinone formation, epoxidation, thiophene sulfur-oxidation, and nitroaromatic reduction, by synthesizing models of metabolism and reactivity. First, the metabolism models predict the formation probabilities of all possible metabolites among the pathways studied. Second, the exact structures of these metabolites are enumerated. Third, using these structures, the reactivity model predicts the reactivity of each metabolite. Finally, a feedfoward neural network converts the metabolism and reactivity predictions to a bioactivation prediction for each possible metabolite. These bioactivation predictions represent the joint probability that a metabolite forms and that this metabolite subsequently conjugates to protein or glutathione. Among molecules bioactivated by these pathways, we predicted the correct pathway with an AUC accuracy of 89.98%. Furthermore, the model predicts whether molecules will be bioactivated, distinguishing bioactivated and nonbioactivated molecules with 81.06% AUC. We applied this algorithm to withdrawn drugs. The known bioactivation pathways of alclofenac and benzbromarone were identified by the algorithm, and high probability bioactivation pathways not yet confirmed were identified for safrazine, zimelidine, and astemizole. This bioactivation model-the first of its kind that jointly considers both metabolism and reactivity-enables drug candidates to be quickly evaluated for a toxicity risk that often evades detection during preclinical trials. The XenoSite bioactivation model is available at http://swami.wustl.edu/xenosite/p/bioactivation.

A history of the roles of cytochrome P450 enzymes in the toxicity of drugs

The history of drug metabolism began in the 19th Century and developed slowly. In the mid-20th Century the relationship between drug metabolism and toxicity became appreciated, and the roles of cytochrome P450 (P450) enzymes began to be defined in the 1960s. Today we understand much about the metabolism of drugs and many aspects of safety assessment in the context of a relatively small number of human P450s. P450s affect drug toxicity mainly by either reducing exposure to the parent molecule or, in some cases, by converting the drug into a toxic entity. Some of the factors involved are enzyme induction, enzyme inhibition (both reversible and irreversible), and pharmacogenetics. Issues related to drug toxicity include drug-drug interactions, drug-food interactions, and the roles of chemical moieties of drug candidates in drug discovery and development. The maturation of the field of P450 and drug toxicity has been facilitated by advances in analytical chemistry, computational capability, biochemistry and enzymology, and molecular and cell biology. Problems still arise with P450s and drug toxicity in drug discovery and development, and in the pharmaceutical industry the interaction of scientists in medicinal chemistry, drug metabolism, and safety assessment is critical for success.

De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization

Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated de novo design of compounds as a result of a humanlike creative process. In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated de novo design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the de novo design of chemical libraries targeting neuraminidase, acetylcholinesterase, and the main protease of severe acute respiratory syndrome coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content, and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the de novo generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multiobjective optimization allow us to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at https://github.com/alberdom88/moo-denovo and all of the data are available as Supporting Information.

4-Aryl-1-oxa-4,9-diazaspiro[5.5]undecane Derivatives as Dual μ-Opioid Receptor Agonists and σ1 Receptor Antagonists for the Treatment of Pain

The synthesis and pharmacological activity of a new series of 1-oxa-4,9-diazaspiro[5.5]undecane derivatives as potent dual ligands for the sigma-1 receptor (σ1R) and the μ-opioid receptor (MOR) are reported. The different positions of the central scaffold, designed using a merging strategy of both target pharmacophores, were explored using a versatile synthetic approach. Phenethyl derivatives in position 9, substituted pyridyl moieties in position 4 and small alkyl groups in position 2 provided the best profiles. One of the best compounds, 15au, showed a balanced dual profile (i.e., MOR agonism and sigma antagonism) and a potent analgesic activity, comparable to the MOR agonist oxycodone in the paw pressure test in mice. Contrary to oxycodone, as expected from the addition of σ1R antagonism, 15au showed local, peripheral activity in this test, which was reversed by the σ1R agonist PRE-084. At equianalgesic doses, 15au showed less constipation than oxycodone, providing evidence that dual MOR agonism and σ1R antagonism may be a useful strategy for obtaining potent and safer analgesics.

Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions

Cytochrome P450 (CYP) enzyme kinetics often do not conform to Michaelis-Menten assumptions, and time-dependent inactivation (TDI) of CYPs displays complexities such as multiple substrate binding, partial inactivation, quasi-irreversible inactivation, and sequential metabolism. Additionally, in vitro experimental issues such as lipid partitioning, enzyme concentrations, and inactivator depletion can further complicate the parameterization of in vitro TDI. The traditional replot method used to analyze in vitro TDI datasets is unable to handle complexities in CYP kinetics, and numerical approaches using ordinary differential equations of the kinetic schemes offer several advantages. Improvement in the parameterization of CYP in vitro kinetics has the potential to improve prediction of clinical drug-drug interactions (DDIs). This manuscript discusses various complexities in TDI kinetics of CYPs, and numerical approaches to model these complexities. The extrapolation of CYP in vitro TDI parameters to predict in vivo DDIs with static and dynamic modeling is discussed, along with a discussion on current gaps in knowledge and future directions to improve the prediction of DDI with in vitro data for CYP catalyzed drug metabolism.

In vitro and in vivo studies of the metabolic activation of chelidonine

Chelidonium majus L. is a herbal medicine widely employed in Europe and Western Asia. Chelidonine (CHE) is a major constituent of the herb and has been reported to be an inhibitor of the cytochrome P450 enzymes (CYP). The major objective of the present study was to study the metabolic pathways of CHE in order to identify potential reactive metabolites responsible for the enzyme inhibition. Three oxidative metabolites (M1-M3) were detected in human liver microsomal incubations after exposure to CHE. M1 and M2 were two isomers of catechol derivatives, and M3 was a dicatechol compound. The M1-M3 metabolites were also observed in bile of rats given CHE. A total of five glutathione (GSH) conjugates (M4-M8) were detected in microsomes containing CHE, GSH, and NADPH. Moreover, M4 and M6 originated from M1, M5 and M7 resulted from M2, and M8 was a M3-derived GSH conjugate. Three biliary CHE-derived GSH conjugates (M4, M5 and M8) were found in CHE-treated rats. This indicates that CHE was bioactivated to ortho-quinone derivatives both in vitro and in vivo. Recombinant P450 enzyme incubations demonstrated that the CYPs3A4, 1A2, 2C19 and 2D6 were mainly involved in metabolic activation of CHE. This study generated data that may be useful in understanding possible mechanisms of CHE-induced P450 inhibition.

Phase I and phase II metabolism simulation of antitumor-active 2-hydroxyacridinone with electrochemistry coupled on-line with mass spectrometry

Here, we report the metabolic profile and the results of associated metabolic studies of 2-hydroxy-acridinone (2-OH-AC), the reference compound for antitumor-active imidazo- and triazoloacridinones. Electrochemistry coupled with mass spectrometry was applied to simulate the general oxidative metabolism of 2-OH-AC for the first time. The reactivity of 2-OH-AC products to biomolecules was also examined. The usefulness of the electrochemistry for studying the reactive drug metabolite trapping (conjugation reactions) was evaluated by the comparison with conventional electrochemical (controlled-potential electrolysis) and enzymatic (microsomal incubation) approaches. 2-OH-AC oxidation products were generated in an electrochemical thin-layer cell. Their tentative structures were assigned based on tandem mass spectrometry in combination with accurate mass measurements. Moreover, the electrochemical conversion of 2-OH-AC in the presence of reduced glutathione and/or N-acetylcysteine unveiled the formation of reactive metabolite-nucleophilic trapping agent conjugates (m/z 517 and m/z 373, respectively) through the thiol group. This glutathione S-conjugate was also identified after electrolysis experiment as well as was detected in liver microsomes. Summing up, the present work illustrates that the electrochemical simulation of metabolic reactions successfully supports the results of classical electrochemical and enzymatic studies. Therefore, it can be a useful tool for synthesis of drug metabolites, including reactive metabolites.

Metabolism and Bioactivation of Corynoline With Characterization of the Glutathione/Cysteine Conjugate and Evaluation of Its Hepatotoxicity in Mice

Corynoline (CRL), an isoquinoline alkaloid, is the major constituent derived from Corydalis bungeana Herba, which is a well-known Chinese herbal medicine widely used in many prescriptions. The purpose of this study was to comprehensively investigate the metabolism and bioactivation of CRL, and identify the CYP450 isoforms involved in reactive ortho-benzoquinone metabolites formation and evaluate its hepatotoxicity in mice. Here, high resolution and triple quadrupole mass spectrometry were used for studying the metabolism of CRL. Three metabolites (M1-M3) and four glutathione conjugates (M4-M7) of CRL ortho-benzoquinone reactive metabolite were found in vitro using rat and human liver microsomes supplemented with NADPH and glutathione. Four cysteine conjugates (M8-M11) were trapped in mice besides M1-M7. Using human recombinant CYP450 enzymes and chemical inhibitor method, we found that CYP3A4, CYP2C19, CYP2C9, and CYP2D6 were mainly involved in the bioactivation of CRL. Furthermore, CRL had no obvious hepatotoxicity and did not induce acute liver injuries in the experimental dosage (125-500 mg/kg) used in this study. However, phenomena of abnormal behaviors and low body temperature appeared in mice after drug administration, and three of them were dead. Tissue distribution study of CRL in mice showed that the main target organ of CRL was liver, then kidney, heart, and brain. CRL could traverse the blood-brain barrier, and have relative high concentration in brain. So, we surmise that toxicity effect of CRL on other organs may have occurred, and more attention should be paid on the traditional Chinese medicine contained CRL in clinic.

The use of structural alerts to avoid the toxicity of pharmaceuticals

In order to identify compounds with potential toxicity problems, particular attention is paid to structural alerts, which are high chemical reactivity molecular fragments or fragments that can be transformed via bioactivation by human enzymes into fragments with high chemical reactivity. The concept has been introduced in order to reduce the likelihood that future candidate substances as pharmaceuticals will have undesirable toxic effects. A significant proportion (∼78–86%) of drugs characterized by residual toxicity contain structural alerts; there is also evidence indicating the formation of active metabolites as a causal factor for the toxicity of 62–69% of these molecules. On the other hand, the pharmacological action of certain drugs depends on the formation of reactive metabolites. Detailed assessment of the potential for the formation of active metabolites is recommended to characterize a biologically active compound. Although many prescribed drugs frequently contain structural alerts and form reactive metabolites, the vast majority of these drugs are administered in low daily doses. Avoiding structural alerts has become almost a norm in new drug design. An in-depth review of the biochemical reactivity of these structural alerts for new drug candidates is critical from a safety point of view and is currently being monitored in the discovery of drugs. The chemical strategies applied to structural alerts in molecules to limit the toxicity are:

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partial replacement or full replacement of the structural alert;

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reduction of electronic density;

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introduction of a structural element of metabolic interest (metabolic switching);

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multiple approaches.

Therefore, chemical intervention strategies to eliminate bioactivation are often interactive processes; their success depends largely on a close working relationship between drug chemists, pharmacologists and researchers in metabolic science.

A new quinoline-based chemical probe inhibits the autophagy-related cysteine protease ATG4B

The cysteine protease ATG4B is a key component of the autophagy machinery, acting to proteolytically prime and recycle its substrate MAP1LC3B. The roles of ATG4B in cancer and other diseases appear to be context dependent but are still not well understood. To help further explore ATG4B functions and potential therapeutic applications, we employed a chemical biology approach to identify ATG4B inhibitors. Here, we describe the discovery of 4-28, a styrylquinoline identified by a combined computational modeling, in silico screening, high content cell-based screening and biochemical assay approach. A structure-activity relationship study led to the development of a more stable and potent compound LV-320. We demonstrated that LV-320 inhibits ATG4B enzymatic activity, blocks autophagic flux in cells, and is stable, non-toxic and active in vivo. These findings suggest that LV-320 will serve as a relevant chemical tool to study the various roles of ATG4B in cancer and other contexts.

Benzenesulphonamide inhibitors of the cytolytic protein perforin

The pore-forming protein perforin is a key component of mammalian cell-mediated immunity and essential to the pathway that allows elimination of virus-infected and transformed cells. Perforin activity has also been implicated in certain auto-immune conditions and therapy-induced conditions such as allograft rejection and graft versus host disease. An inhibitor of perforin activity could be used as a highly specific immunosuppressive treatment for these conditions, with reduced side-effects compared to currently accepted therapies. Previously identified first-in-class inhibitors based on a 2-thioxoimidazolidin-4-one core show suboptimal physicochemical properties and toxicity toward the natural killer (NK) cells that secrete perforin in vivo. The current benzenesulphonamide-based series delivers a non-toxic bioisosteric replacement possessing improved solubility.

Generic method for the absolute quantification of glutathione S-conjugates: Application to the conjugates of acetaminophen, clozapine and diclofenac

Modification of cellular macromolecules by reactive drug metabolites is considered to play an important role in the initiation of tissue injury by many drugs. Detection and identification of reactive intermediates is often performed by analyzing the conjugates formed after trapping by glutathione (GSH). Although sensitivity of modern mass spectrometrical methods is extremely high, absolute quantification of GSH-conjugates is critically dependent on the availability of authentic references. Although ¹H NMR is currently the method of choice for quantification of metabolites formed biosynthetically, its intrinsically low sensitivity can be a limiting factor in quantification of GSH-conjugates which generally are formed at low levels. In the present study, a simple but sensitive and generic method for absolute quantification of GSH-conjugates is presented. The method is based on quantitative alkaline hydrolysis of GSH-conjugates and subsequent quantification of glutamic acid and glycine by HPLC after precolumn derivatization with o-phthaldialdehyde/N-acetylcysteine (OPA/NAC). Because of the lower stability of the glycine OPA/NAC-derivate, quantification of the glutamic acid OPA/NAC-derivate appeared most suitable for quantification of GSH-conjugates. The novel method was used to quantify the concentrations of GSH-conjugates of diclofenac, clozapine and acetaminophen and quantification was consistent with ¹H NMR, but with a more than 100-fold lower detection limit for absolute quantification.

Deleterious effects of reactive metabolites

A number of drugs have been withdrawn from the market or severely restricted in their use because of unexpected toxicities that become apparent only after the launch of new drug entities. Circumstantial evidence suggests that, in most cases, reactive metabolites are responsible for these unexpected toxicities. In this review, a general overview of the types of reactive metabolites and the consequences of their formation are presented. The current approaches to evaluate bioactivation potential of new compounds with particular emphasis on the advantages and limitation of these procedures will be discussed. Reasonable reasons for the excellent safety record of certain drugs susceptible to bioactivation will also be explored and should provide valuable guidance in the use of reactive-metabolite assessments when nominating drug candidates for development. This will, in turn, help us to design and bring safer drugs to the market.

The pathogenesis of drug-induced liver injury

Drugs can induce liver injury when taken as an over-dose, or even at therapeutic doses in susceptible individuals. Although severe drug-induced liver injury (DILI) is a relatively uncommon clinical event, it is a potentially life threatening adverse drug reaction and is the most common indication for the drug withdrawal. Areas covered: However, the diagnosis of DILI remains a significant challenge, because the establishment of causality is very difficult, and the histopathologic findings of DILI may be indistinguishable from those of other hepatic disorders, such as viral and alcoholic hepatitis. In this review, we provide an overview of recent advances in identification of serologic markers of diagnosis and prognosis, etiologic factors for susceptibility and diagnostic evaluation of DILI, with a focus on its pathogenic mechanisms and the role of liver biopsy. Expert commentary: Further studies of divergent research platforms, using a systems biology approach such as genomics and transcriptomics, may provide a deeper understanding of human drug metabolism and the causes, risk factors, and pathogenesis of DILI.

Metabolic activation and drug-induced liver injury: in vitro approaches for the safety risk assessment of new drugs

Drug-induced liver injury (DILI) is a significant leading cause of hepatic dysfunction, drug failure during clinical trials and post-market withdrawal of approved drugs. Many cases of DILI are unexpected reactions of an idiosyncratic nature that occur in a small group of susceptible individuals. Intensive research efforts have been made to understand better the idiosyncratic DILI and to identify potential risk factors. Metabolic bioactivation of drugs to form reactive metabolites is considered an initiation mechanism for idiosyncratic DILI. Reactive species may interact irreversibly with cell macromolecules (covalent binding, oxidative damage), and alter their structure and activity. This review focuses on proposed in vitro screening strategies to predict and reduce idiosyncratic hepatotoxicity associated with drug bioactivation. Compound incubation with metabolically competent biological systems (liver-derived cells, subcellular fractions), in combination with methods to reveal the formation of reactive intermediates (e.g., formation of adducts with liver proteins, metabolite trapping or enzyme inhibition assays), are approaches commonly used to screen the reactivity of new molecules in early drug development. Several cell-based assays have also been proposed for the safety risk assessment of bioactivable compounds. Copyright © 2015 John Wiley & Sons, Ltd.

Characterization of glutathione conjugates derived from reactive metabolites of bakuchiol

Bakuchiol belongs to a family of monoterpene phenols occurring in plant Psoralea corylifolia L., a traditional herbal medicine. Bakuchiol has also demonstrated multiple pharmacologic activities. However, metabolism of bakuchiol had never been investigated. The major objective of the present study was to study the metabolic pathways of bakuchiol in order to identify potential reactive metabolites. A total of five glutathione (GSH) conjugates (M1-M5) were detected in rat/human liver microsomes containing NADPH, GSH, and bakuchiol. M1 and M2 resulted from GSH conjugated on the phenol ring. M3, M4, and M5 were derived from GSH adducted on the side chain. The results displayed that bakuchiol can be bioactivated by oxidation of the phenol moiety to the corresponding ortho-quinone and by epoxidation of the aliphatic side chain to epoxide metabolites. No bakuchiol-derived GSH conjugates were detected in urine of rats given bakuchiol, but six corresponding cysteinylglycine (Cys-Gly) conjugates and mercapturic acids were observed instead. A 2'-iodoxybenzoic acid-mediated oxidation reaction of bakuchiol in the presence of GSH produced M1 and M2, and m-chloroperoxybenzoicacid-mediated oxidation of bakuchiol trapped with GSH generated M3 and M4. The four synthetic metabolites were detected in microsomal incubations. In addition, recombinant P450 enzyme incubations showed that CYP 1A2 was the predominant P450 responsible for the metabolism of bakuchiol. In summary, our results demonstrated that bakuchiol can be bioactivated to quinone and epoxide metabolites. These findings facilitate the understanding of the mechanisms of bakuchiol-induced cytotoxicity.