XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks

Deep Learning Coordinate-Free Quantum Chemistry

Computing quantum chemical properties of small molecules and polymers can provide insights valuable into physicists, chemists, and biologists when designing new materials, catalysts, biological probes, and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of time required by commonly used methods such as density functional theory. Most current approaches to deep learning in quantum chemistry begin with geometric information from experimentally derived molecular structures or pre-calculated atom coordinates. These approaches have many useful applications, but they can be costly in time and computational resources. In this study, we demonstrate that accurate quantum chemical computations can be performed without geometric information by operating in the coordinate-free domain using deep learning on graph encodings. Coordinate-free methods rely only on molecular graphs, the connectivity of atoms and bonds, without atom coordinates or bond distances. We also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The structures of these graph-encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: what types of quantum chemical properties can be represented by local variable models? We find that Wave, a local variable model, accurately calculates the quantum chemical properties, while graph convolutional architectures require global variables. Furthermore, local variable Wave models outperform global variable graph convolution models on complex molecules with large, correlated systems.

Machine Learning in Drug Discovery: A Review

This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

HIFs: New arginine mimic inhibitors of the Hv1 channel with improved VSD-ligand interactions

The human voltage-gated proton channel Hv1 is a drug target for cancer, ischemic stroke, and neuroinflammation. It resides on the plasma membrane and endocytic compartments of a variety of cell types, where it mediates outward proton movement and regulates the activity of NOX enzymes. Its voltage-sensing domain (VSD) contains a gated and proton-selective conduction pathway, which can be blocked by aromatic guanidine derivatives such as 2-guanidinobenzimidazole (2GBI). Mutation of Hv1 residue F150 to alanine (F150A) was previously found to increase 2GBI apparent binding affinity more than two orders of magnitude. Here, we explore the contribution of aromatic interactions between the inhibitor and the channel in the presence and absence of the F150A mutation, using a combination of electrophysiological recordings, classic mutagenesis, and site-specific incorporation of fluorinated phenylalanines via nonsense suppression methodology. Our data suggest that the increase in apparent binding affinity is due to a rearrangement of the binding site allowed by the smaller residue at position 150. We used this information to design new arginine mimics with improved affinity for the nonrearranged binding site of the wild-type channel. The new compounds, named “Hv1 Inhibitor Flexibles” (HIFs), consist of two “prongs,” an aminoimidazole ring, and an aromatic group connected by extended flexible linkers. Some HIF compounds display inhibitory properties that are superior to those of 2GBI, thus providing a promising scaffold for further development of high-affinity Hv1 inhibitors.

The Influence of Long-Term Treatment with Asenapine on Liver Cytochrome P450 Expression and Activity in the Rat. The Involvement of Different Mechanisms

Therapy of schizophrenia requires long-term treatment with a relevant antipsychotic drug to achieve a therapeutic effect. The aim of the present study was to investigate the influence of prolonged treatment with the atypical neuroleptic asenapine on the expression and activity of rat cytochrome P450 (CYP) in the liver. The experiment was carried out on male Wistar rats. Asenapine (0.3 mg/kg s.c.) was administered for two weeks. The levels of CYP mRNA protein and activity were determined in the liver and hormone concentrations were measured in the pituitary gland and blood serum. Asenapine significantly decreased the activity of CYP1A (caffeine 8-hydroxylation and 3-N-demethylation), CYP2B, CYP2C11 and CYP3A (testosterone hydroxylation at positions 16β; 2α and 16α; 2β and 6β, respectively). The neuroleptic did not affect the activity of CYP2A (testosterone 7α-hydroxylation), CYP2C6 (warfarin 7-hydroxylation) and CYP2E1 (chlorzoxazone 6-hydroxylation). The mRNA and protein levels of CYP1A2, CYP2B1, CYP2C11 and CYP3A1 were decreased, while those of CYP2B2 and CYP3A2 were not changed. Simultaneously, pituitary level of growth hormone-releasing hormone and serum concentrations of growth hormone and corticosterone were reduced, while that of triiodothyronine was enhanced. In conclusion, chronic treatment with asenapine down-regulates liver cytochrome P450 enzymes, which involves neuroendocrine mechanisms. Thus, chronic asenapine treatment may slow the metabolism of CYP1A, CYP2B, CYP2C11 and CYP3A substrates (steroids and drugs). Since asenapine is metabolized by CYP1A and CYP3A, the neuroleptic may inhibit its own metabolism, therefore, the plasma concentration of asenapine in patients after prolonged treatment may be higher than expected based on a single dose.

Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments

The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.

Natural dye from Caesalpinia sappan L. heartwood for eco-friendly coloring of recycled paper based packing material and its in silico toxicity analysis

The uses of natural dyes are getting popularized due to the increased awareness regarding the toxicity of many chemical colorants. The chemical colorants are being replaced by the natural colorants for the various industrial applications. The plant-based natural colorants are considered eco-friendly and toxic free. In the present study, we report a natural dye from the heartwood of Caesalpinia sappan suitable for paper based packing materials. This forms the first report on the study of natural dye obtained from the heartwood of C. sappan on paper material. The extracted dye had a good photostability and able to make imprints on recycled paper bags. Moreover, a significant inhibition of bacterial growth was observed at a higher dye concentration of 100 μg mL^-1 against P. aeruginosa which was higher than the standard antibiotics. Growth inhibition was also observed in case of B. subtilis (22 ± 0.17 mm) and K. pneumonia (21 ± 0.53 mm) at 100 μg mL^-1. The dye could be used in making medicated packing materials and have many other bio-potential which was validated through in silico toxicity analysis. The application of such natural dyes in paper material value addition will help in a cleaner and sustainable process during paper recycling.

CyProduct: A Software Tool for Accurately Predicting the Byproducts of Human Cytochrome P450 Metabolism

In silico metabolism prediction is a cheminformatic task of autonomously predicting the set of metabolic byproducts produced from a specified molecule and a set of enzymes or reactions. Here, we describe a novel machine learned in silico cytochrome P450 (CYP450) metabolism prediction suite, called CyProduct, that accurately predicts metabolic byproducts for a specified molecule and a human CYP450 isoform. It includes three modules: (1) CypReact, a tool that predicts if the query compound reacts with a given CYP450 enzyme, (2) CypBoM, a tool that accurately predicts the "bond site" of the reaction (i.e., which specific bonds within the query molecule react with the CYP isoform), and (3) MetaboGen, a tool that generates the metabolic byproducts based on CypBoM's bond-site prediction. CyProduct predicts metabolic biotransformation products for each of the nine most important human CYP450 enzymes. CypBoM uses an important new concept called "bond of metabolism" (BoM), which extends the traditional "site of metabolism" (SoM) by specifying the information about the set of chemical bonds that is modified or formed in a metabolic reaction (rather than the specific atom). We created a BoM database for 1845 CYP450-mediated Phase I reactions, then used this to train the CypBoM Predictor to predict the reactive bond locations on substrate molecules. CypBoM Predictor's cross-validated Jaccard score for reactive bond prediction ranged from 0.380 to 0.452 over the nine CYP450 enzymes. Over variants of a test set of 68 known CYP450 substrates and 30 nonreactants, CyProduct outperformed the other packages, including ADMET Predictor, BioTransformer, and GLORY, by an average of 200% (with respect to Jaccard score) in terms of predicting metabolites. The CyProduct suite and the data sets are freely available at https://bitbucket.org/wishartlab/cyproduct/src/master/.

Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug-drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

Metabolite profiling of IMID-2, a novel anticancer molecule of piperazine derivative: In silico prediction, in vitro and in vivo metabolite characterization using UPLC-QTOF-MS/MS

IMID-2, a newly identified piperazine-based anticancer molecule, has been shown to be cytotoxic against various cancer cell lines. The primary aim of this research was to identify and characterize possible metabolites of the molecule formed during biotransformation. A metabolite identification study was first executed using an in silico tool to predict the possible metabolism sites of IMID-2. Thereafter, metabolites generated in vitro (rat liver microsomes, rat S9 fractions and human liver microsomes) and in vivo (rat plasma, urine and feces) were identified and characterized employing UPLC-QTOF-MS/MS. A total of eight metabolites, among which were six in phase I and two in phase II reactions, were recognized. The plausible structure of the metabolites and probable metabolic pathway have been established based on the mass fragmentation pattern, mass ppm error, ring double bond calculation and nitrogen rule. The majority of phase I metabolites were generated by N-oxidation, hydroxylation, oxidative deamination followed by reduction, oxidative dechlorination, N-dearylation, and N-dealkylation. Glucuronidation played a significant role in the formation of phase II metabolites of the molecule.

Molecular modeling approaches to address drug-metabolizing enzymes (DMEs) mediated chemoresistance: a review

Resistance against clinically approved anticancer drugs is the main roadblock in cancer treatment. Drug metabolizing enzymes (DMEs) that are capable of metabolizing a variety of xenobiotic get overexpressed in malignant cells, therefore, catalyzing drug inactivation. As evident from the literature reports, the levels of DMEs increase in cancer cells that ultimately lead to drug inactivation followed by drug resistance. To puzzle out this issue, several strategies inclusive of analog designing, prodrug designing, and inhibitor designing have been forged. On that front, the implementation of computational tools can be considered a fascinating approach to address the problem of chemoresistance. Various research groups have adopted different molecular modeling tools for the investigation of DMEs mediated toxicity problems. However, the utilization of these in-silico tools in maneuvering the DME mediated chemoresistance is least considered and yet to be explored. These tools can be employed in the designing of such chemotherapeutic agents that are devoid of the resistance problem. The current review canvasses various molecular modeling approaches that can be implemented to address this issue. Special focus was laid on the development of specific inhibitors of DMEs. Additionally, the strategies to bypass the DMEs mediated drug metabolism were also contemplated in this report that includes analogs and pro-drugs designing. Different strategies discussed in the review will be beneficial in designing novel chemotherapeutic agents that depreciate the resistance problem.

Comprehensive insights into the formation of metabolites of the ghrelin mimetics capromorelin, macimorelin and tabimorelin as potential markers for doping control purposes

Analytical methods to determine the potential misuse of the ghrelin mimetics capromorelin (CP-424,391), macimorelin (macrilen, EP-01572) and tabimorelin (NN703) in sports were developed. Therefore, different extraction strategies, i.e. solid-phase extraction, protein precipitation, as well as a "dilute-and-inject" approach, from urine and EDTA-plasma were assessed and comprehensive in vitro/in vivo experiments were conducted, enabling the identification of reliable target analytes by means of high resolution mass spectrometry. The drugs' biotransformation led to the preliminary identification of 51 metabolites of capromorelin, 12 metabolites of macimorelin and 13 metabolites of tabimorelin. Seven major metabolites detected in rat urine samples collected post-administration of 0.5-1.0 mg of a single oral dose underwent in-depth characterization, facilitating their implementation into future confirmatory test methods. In particular, two macimorelin metabolites exhibiting considerable abundances in post-administration rat urine samples were detected, which might contribute to an improved sensitivity, specificity, and detection window in case of human sports drug testing programs. Further, the intact drugs were implemented into World Anti-Doping Agency-compliant initial testing (limits of detection 0.02-0.60 ng/ml) and confirmation procedures (limits of identification 0.18-0.89 ng/ml) for human urine and blood matrices. The obtained results allow extension of the test spectrum of doping agents in multitarget screening assays for growth hormone-releasing factors from human urine.

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.

Characterization of Stable and Reactive Metabolites of the Anticancer Drug, Ensartinib, in Human Liver Microsomes Using LC-MS/MS: An in silico and Practical Bioactivation Approach

BACKGROUND

Ensartinib (ESB) is a novel anaplastic lymphoma kinase inhibitor (ALK) with additional activity against Abelson murine leukemia (ABL), met proto-oncogene (MET), receptor tyrosine kinase (AXL), and v-ros UR2 sarcoma virus oncogene homolog 1 (ROS1) and is considered a safer alternative for other ALK inhibitors. ESB chemical structure contains a dichloro-fluorophenyl ring and cyclic tertiary amine rings (piperazine) that can be bioactivated generating reactive intermediates.

METHODS

In vitro metabolic study of ESB with human liver microsomes (HLMs) was performed and the hypothesis of generating reactive intermediates during metabolism was tested utilizing trapping agents to capture and stabilize reactive intermediates to facilitate their LC-MS/MS detection. Reduced glutathione (GSH) and potassium cyanide (KCN) were utilized as trapping agents for quinone methide and iminium intermediates, respectively.

RESULTS

Four in vitro ESB phase I metabolites were characterized. Three reactive intermediates including one epoxide and one iminium intermediates were characterized. ESB bioactivation is proposed to occur through unexpected metabolic pathways. The piperazine ring was bioactivated through iminium ions intermediates generation, while the dichloro-phenyl group was bioactivated through a special mechanism that was revealed by LC-MS/MS.

CONCLUSION

These findings lay the foundations for additional work on ESB toxicity. Substituents to the bioactive centers (piperazine ring), either for blocking or isosteric replacement, would likely block or interrupt hydroxylation reaction that will end the bioactivation sequence.

Filling data gap for nicotinic acid, nicotinate esters and nicotinamide for the determination of permitted daily exposure by a category approach

This study aimed to obtain necessary toxicological data using experimental and computational methods for the calculation of a common permitted daily exposure (PDE) which can be relevant for nicotinic acid and its esters and nicotinamide according to European Medicines Agency Guideline on setting health-based exposure limits. PDE calculation is mainly based on critical toxicological endpoints. During this procedure, critical toxicological endpoints data of an active pharmaceutical ingredient (API) may not be able to find satisfactorily. Hence, using toxicological data for another API that has a similar chemical structure can be a useful way. In this study, toxicological endpoints of nicotinic acid and its esters and nicotinamide were evaluated. Then, the data gaps in the toxicological endpoints were filledusing the read-across approach. Based on the current existing data, nicotinic acid and its esters and also nicotinamide are not genotoxic and do not have skin sensitization potential. These compounds do not present a concern for carcinogenicity and developmental/reproductive toxicity. Based on these critical endpoints and available experimental data, the final PDE of 10 mg/day was calculated for all category members. Our study showed the utility of the read-across for PDE calculation of APIs with experimental toxicological data gap.

Further Studies on Triazinoindoles as Potential Novel Multitarget-Directed Anti-Alzheimer's Agents

The inadequate clinical efficacy of the present anti-Alzheimer's disease (AD) drugs and their low impact on the progression of Alzheimer's disease in patients have revised the research focus from single targets to multitarget-directed ligands. A novel series of substituted triazinoindole derivatives were obtained by introducing various substituents on the indole ring for the development of multitarget-directed ligands as anti-AD agents. The experimental data indicated that some of these compounds exhibited significant anti-AD properties. Among them, 8-(piperidin-1-yl)-N-(6-(pyrrolidin-1-yl)hexyl)-5H-[1,2,4]triazino[5,6-b]indol-3-amine (60), the most potent cholinesterase inhibitor (AChE, IC₅₀ value of 0.32 μM; BuChE, IC₅₀ value of 0.21 μM), was also found to possess significant self-mediated Aβ_1-42 aggregation inhibitory activity (54% at 25 μM concentration). Additionally, compound 60 showed strong antioxidant activity. In the PAMPA assay, compound 60 exhibited blood-brain barrier penetrating ability. An acute toxicity study in rats demonstrated no sign of toxicity at doses up to 2000 mg/kg. Furthermore, compound 60 significantly restored the cognitive deficits in the scopolamine-induced mice model and Aβ_1-42-induced rat model. In the in silico ADMET prediction studies, the compound satisfied all the parameters of CNS acting drugs. These results highlighted the potential of compound 60 to be a promising multitarget-directed ligand for the development of potential anti-AD drugs.

Human Biotransformation Pathway of Temephos Using an In Silico Approach

Temephos is an organophosphorothioate (OPT) larvicide used for controlling vectors of diseases such as dengue, chikungunya, and Zika. OPTs require a metabolic activation mediated by cytochrome P540 (CYP) to cause toxic effects, such as acetylcholinesterase (AChE) activity inhibition. There is no information about temephos biotransformation in humans, and it is considered to have low toxicity in mammals. Recent studies have reported that temephos-oxidized derivatives cause AChE inhibition. The aim of this study was to propose the human biotransformation pathway of temephos using in silico tools. The metabolic pathway was proposed using the MetaUltra program of MultiCase software as well as the Way2Drug and Xenosite web servers. The results show the following three essential reactions of phase I metabolism: (1) S-oxidation, (2) oxidative desulfurization, and (3) dephosphorylation, as well as the formation of 19 possible intermediary metabolites. Temephos dephosphorylation is the most likely reaction, and it enables phase II metabolism for glucuronidation to be excreted. However, the CYP-dependent metabolism showed that temephos oxon can be formed, which could lead to toxic effects in mammals. CYP2B6, 2C9, and 2C19 are the main isoforms involved in temephos metabolism, and CYP3A4 and 2D6 have minor contributions. According to computational predictions, the highest probability of temephos metabolism is dephosphorylation and phase II reactions that do not produce cholinergic toxic effects; nonetheless, the participation of CYPs is highly possible if the primary reaction is depleted.

Identification of Iminium Intermediates Generation in the Metabolism of Tepotinib Using LC-MS/MS: In Silico and Practical Approaches to Bioactivation Pathway Elucidation

Tepotinib (Tepmetko™, Merck) is a potent inhibitor of c-Met (mesenchymal-epithelial transition factor). In March 2020, tepotinib (TEP) was approved for use in Japan for the treatment of patients who suffered from non-small cell lung cancers (NSCLC) harboring an MET exon 14 skipping alteration and have progressed after platinum-based therapy. Practical and in silico experiments were used to screen for the metabolic profile and reactive intermediates of TEP. Knowing the bioactive center and structural alerts in the TEP structure helped in making targeted modifications to improve its safety. First, the prediction of metabolism vulnerable sites and reactivity metabolic pathways was performed using the StarDrop WhichP450™ module and the online Xenosite reactivity predictor tool, respectively. Subsequently, in silico data were used as a guide for the in vitro practical work. Second, in vitro phase I metabolites of TEP were generated from human liver microsome (HLM) incubations. Testing for the generation of unstable reactive intermediates was performed using potassium cyanide as a capturing agent forming stable cyano adduct that can be characterized and identified using liquid chromatography tandem mass spectrometry (LC-MS/MS). Third, in silico toxicity assessment of TEP metabolites was performed, and structural modification was proposed to decrease their side effects and to validate the proposed bioactivation pathway using the DEREK software. Four TEP phase I metabolites and four cyano adducts were characterized. The reactive intermediate generation mechanism of TEP may provide an explanation of its adverse reactions. The piperidine ring is considered a structural alert for toxicity as proposed by the DEREK software and a Xenosite reactivity model, which was confirmed by practical experiments. Steric hindrance or isosteric replacement at α-carbon of the piperidine ring stop the bioactivation sequence that was confirmed using the DEREK software. More drug discovery studies can be performed using this perception permitting the design of new drugs with an increased safety profile. To our knowledge, this is the first study for the identification of in vitro phase I metabolites and reactive intermediates in addition to toxicological properties of the metabolites for TEP that will be helpful for the evaluation of TEP side effects and drug-drug interactions in TEP-treated patients.

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule's properties. For example, the metabolite's reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined system-termed the metabolic forest-for generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.

Site-Level Bioactivity of Small-Molecules from Deep-Learned Representations of Quantum Chemistry

Atom- or bond-level chemical properties of interest in medicinal chemistry, such as drug metabolism and electrophilic reactivity, are important to understand and predict across arbitrary new molecules. Deep learning can be used to map molecular structures to their chemical properties, but the data sets for these tasks are relatively small, which can limit accuracy and generalizability. To overcome this limitation, it would be preferable to model these properties on the basis of the underlying quantum chemical characteristics of small molecules. However, it is difficult to learn higher level chemical properties from lower level quantum calculations. To overcome this challenge, we pretrained deep learning models to compute quantum chemical properties and then reused the intermediate representations constructed by the pretrained network. Transfer learning, in this way, substantially outperformed models based on chemical graphs alone or quantum chemical properties alone. This result was robust, observable in five prediction tasks: identifying sites of epoxidation by metabolic enzymes and identifying sites of covalent reactivity with cyanide, glutathione, DNA and protein. We see that this approach may substantially improve the accuracy of deep learning models for specific chemical structures, such as aromatic systems.

Metabolic stability studies of lead compounds supported by separation techniques and chemometrics analysis

With metabolism being one of the main routes of drug elimination from the body (accounting for removal of around 75% of known drugs), it is crucial to understand and study metabolic stability of drug candidates. Metabolically unstable compounds are uncomfortable to administer (requiring repetitive dosage during therapy), while overly stable drugs increase risk of adverse drug reactions. Additionally, biotransformation reactions can lead to formation of toxic or pharmacologically active metabolites (either less-active than parent drug, or even with different action). There were numerous approaches in estimating metabolic stability, including in vitro, in vivo, in silico, and high-throughput screening to name a few. This review aims at describing separation techniques used in in vitro metabolic stability estimation, as well as chemometric techniques allowing for creation of predictive models which enable high-throughput screening approach for estimation of metabolic stability. With a very low rate of drug approval, it is important to understand in silico methods that aim at supporting classical in vitro approach. Predictive models that allow assessment of certain biological properties of drug candidates allow for cutting not only cost, but also time required to synthesize compounds predicted to be unstable or inactive by in silico models.

Open access in silico tools to predict the ADMET profiling of drug candidates

INTRODUCTION

We are in an era of bioinformatics and cheminformatics where we can predict data in the fields of medicine, the environment, engineering and public health. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs.

AREAS COVERED

This review meticulously encompasses the fundamental functions of open access in silico prediction tools (webservers and standalone software) and advocates their use in drug discovery research for the safety and reliability of any candidate-drug. This review also aims to help support new researchers in the field of drug design.

EXPERT OPINION

The choice of in silico tools is critically important for drug discovery and the accuracy of ADMET prediction. The accuracy largely depends on the types of dataset, the algorithm used, the quality of the model, the available endpoints for prediction, and user requirement. The key is to use multiple in silico tools for predictions and comparing the results, followed by the identification of the most probable prediction.

The art of atom descriptor design

This review provides an overview of descriptions of atoms applied to the understanding of phenomena like chemical reactivity and selectivity, pK_a values, Site of Metabolism prediction, or hydrogen bond strengths, but also the substitution of quantum mechanical calculations by machine learning models for energies, forces or even spectrosocopic properties and finally the fast calculation of atomic charges for force field parametrization. The descriptor space ranges from derivatives of the wavefunctions or electron density via quantum mechanics derived descriptors to classical descriptions of atoms and their embedding in a molecule. The common denominator for all approaches is the thorough understanding of the physics of the chemical problem that guided the design of the atom descriptor. Quantum mechanics (QM) and machine learning (ML) finally are converging to a new discipline, namely QM/ML.

Discovery of an Orally Active and Long-Acting DPP-IV Inhibitor through Property-Based Optimization with an in Silico Biotransformation Prediction Tool

Long-acting dipeptidyl peptidase IV inhibitors have emerged as promising molecules for interventions for type 2 diabetes. Once weekly dosing brings greater patient compliance and more stable glycemic control. Starting from our previous highly potent compound with a thienoprimidine scaffold, which is unfortunately severely hit by hepatic biotransformation, a lead compound was rapidly generated by drawing on the experience of our previously discovered long-acting compounds with pyrrolopyrimidine scaffold. With the aid of an in silico biotransformation prediction tool, (R)-2-((2-(3-aminopiperidin-1-yl)-4-oxo-6-(pyridin-3-yl)thieno[3,2-d]pyrimidin-3(4H)-yl)methyl)-4-fluorobenzonitrile was eventually generated and determined to have high potency, a fine pharmacokinetic profile, and a long-acting in vivo efficacy.

Predicting reactivity to drug metabolism: beyond P450s-modelling FMOs and UGTs

We present a study based on density functional theory calculations to explore the rate limiting steps of product formation for oxidation by Flavin-containing Monooxygenase (FMO) and glucuronidation by the UDP-glucuronosyltransferase (UGT) family of enzymes. FMOs are responsible for the modification phase of metabolism of a wide diversity of drugs, working in conjunction with Cytochrome P450 (CYP) family of enzymes, and UGTs are the most important class of drug conjugation enzymes. Reactivity calculations are important for prediction of metabolism by CYPs and reactivity alone explains around 70-85% of the experimentally observed sites of metabolism within CYP substrates. In the current work we extend this approach to propose model systems which can be used to calculate the activation energies, i.e. reactivity, for the rate-limiting steps for both FMO oxidation and glucuronidation of potential sites of metabolism. These results are validated by comparison with the experimentally observed reaction rates and sites of metabolism, indicating that the presented models are suitable to provide the basis of a reactivity component within generalizable models to predict either FMO or UGT metabolism.

Pre-Clinical Pharmacokinetics, Tissue Distribution and Physicochemical Studies of CLBQ14, a Novel Methionine Aminopeptidase Inhibitor for the Treatment of Infectious Diseases

INTRODUCTION

CLBQ14, a derivative of 8-hydroxyquinoline, exerts its chemotherapeutic effect by inhibiting methionine aminopeptidase (MetAP), the enzyme responsible for the post-translational modification of several proteins and polypeptides. MetAP is a novel target for infectious diseases. CLBQ14 is selective and highly potent against replicating and latent Mycobacterium tuberculosis making it an appealing lead for further development.

METHODS

The physicochemical properties (solubility, pH stability and lipophilicity), in vitro plasma stability and metabolism, pre-clinical pharmacokinetics, plasma protein binding and tissue distribution of CLBQ14 in adult male Sprague-Dawley rats were characterized.

RESULTS

At room temperature, CLBQ14 is practically insoluble in water (<0.07 mg/mL) but freely soluble in dimethyl acetamide (>80 mg/mL); it has a log P value of 3.03 ± 0.04. CLBQ14 exhibits an inverse Z-shaped pH decomposition profile; it is stable at acidic pH but is degraded at a faster rate at basic pH. It is highly bound to plasma proteins (>91%), does not partition to red blood cells (B/P ratio: 0.83 ± 0.03), and is stable in mouse, rat, monkey and human plasma. CLBQ14 exhibited a bi-exponential pharmacokinetics after intravenous administration in rats, bioavailability of 39.4 and 90.0%, respectively from oral and subcutaneous route. We observed a good correlation between predicted and observed rat clearance, 1.90 ± 0.17 L/kg/h and 1.67 ± 0.08 L/kg/h, respectively. Human hepatic clearance predicted from microsomal stability data and from the single species scaling were 0.80 L/hr/kg and 0.69 L/h/kg, respectively. CLBQ14 is extensively distributed in rats; following a 5 mg/kg intravenous administration, lowest and highest concentrations of 15.6 ± 4.20 ng/g of heart and 405.9 ± 77.11 ng/g of kidneys, respectively, were observed. In vitro CYP reaction phenotyping demonstrates that CLBQ14 is metabolized primarily by CYP 1A2.

CONCLUSION

CLBQ14 possess appealing qualities of a drug candidate. The studies reported herein are imperative to the development of CLBQ14 as a new chemical entity for infectious diseases.

Disentanglement of the chemical, physical, and biological processes aids the development of quantitative structure-biodegradation relationships for aerobic wastewater treatment

Attenuation of organic compounds in sewage treatment plants (STPs) is affected by a complex interplay between chemical (e.g. ionization, hydrolysis), physical (e.g. sorption, volatilization), and biological (e.g. biodegradation, microbial acclimation) processes. These effects should be accounted for individually, in order to develop predictive cheminformatics tools for STPs. Using measured data from 70 STPs in the Netherlands for 69 chemicals (pharmaceuticals, herbicides, etc.), we highlighted the influences of 1) chemical ionization, 2) sorption to sludge, and 3) acclimation of the microbial consortia on the primary removal of chemicals. We used semi-empirical corrections for each of these influences to deduce biodegradation rate constants upon which quantitative structure-biodegradation relationships (QSBRs) were developed. As shown by a global QSBR, biodegradation in STPs generally relates to structural complexity, size, energetics, and charge distribution. Statistics of the global QSBR were reasonable, being R²_training=0.69 (training set of 51 compounds) and R²_validation=0.50 (validation set of 18 compounds). Class-specific QSBRs utilized electronic properties potentially relating to rate-limiting enzymatic steps. For class-specific QSBRs, values of R² of in between 0.7 and 0.8 were obtained. With caution, environmental risk assessment methodologies may apply these models to estimate biodegradation rates for 'data-poor' compounds. The approach also highlights 'meta data' on STP operational parameters needed to develop QSBRs of better predictability in the future.

Synthetic Derivatives of (+)-epi-α-Bisabolol Are Formed by Mammalian Cytochromes P450 Expressed in a Yeast Reconstituted Pathway

Identification of the enzyme(s) involved in complex biosynthetic pathways can be challenging. An alternative approach might be to deliberately diverge from the original natural enzyme source and use promiscuous enzymes from other organisms. In this paper, we have tested the ability of a series of human and animal cytochromes P450 involved in xenobiotic detoxification to generate derivatives of (+)-epi-α-bisabolol and attempt to produce the direct precursor of hernandulcin, a sweetener from Lippia dulcis for which the last enzymatic steps are unknown. Screening steps were implemented in vivo in S. cerevisiae optimized for the biosynthesis of oxidized derivatives of (+)-epi-α-bisabolol by coexpressing two key enzymes: the (+)-epi-α-bisabolol synthase and the NADPH cytochrome P450 reductase. Five out of 25 cytochromes P450 were capable of producing new hydroxylated regioisomers of (+)-epi-α-bisabolol. Of the new oxidized bisabolol products, the structure of one compound, 14-hydroxy-(+)-epi-α-bisabolol, was fully elucidated by NMR while the probable structure of the second product was determined. In parallel, the production of (+)-epi-α-bisabolol derivatives was enhanced through the addition of a supplementary genomic copy of (+)-epi-α-bisabolol synthase that augmented the final titer of hydroxylated product to 64 mg/L. We thus demonstrate that promiscuous drug metabolism cytochromes P450 can be used to produce novel compounds from a terpene scaffold.

QM Calculations in ADMET Prediction

In recent years, there has been an increase in the application of quantum mechanics (QM) methods to describe properties related to the ADMET profile of small molecules. The application of these methods allows calculating useful descriptors and physiochemical properties contributing to ADMET prediction. Considering that QM methods are the only one that describe the electronic state of a molecules, such methods are particularly useful for studying the metabolism of drugs; furthermore, the introduction of mixed QM and molecular mechanics (QM/MM) is also increasing the understanding of drug interaction with cytochromes from a mechanistic point of view. Finally, combining the increase number of experimental data with machine learning algorithms and QM-derived descriptors allowed the creation of an end-user software capable of affecting the drug discovery process.

1-(Benzo[b]thiophen-4-yl)piperazine Ring Induced Bioactivation of Brexpiprazole in Liver Microsomes: Identification and Characterization of Reactive Conjugates Using Ultra-High-Performance Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry

BACKGROUND AND OBJECTIVES

Brexpiprazole is an atypical antipsychotic approved for the treatment of schizophrenia and major depressive disorders in adults. The structure of brexpiprazole contains well-known structural alerts like a thiophene ring, piperazine ring and quinolinone motifs. Additionally, the literature reveals that its structural analog, aripiprazole, could generate reactive intermediates. However, the bioactivation potential of brexpiprazole is yet unknown. Therefore, this study was planned to identify and characterize reactive adducts of brexpiprazole and its metabolites.

METHODS

Based on the reactivity, the potential atomic sites for a reactive intermediate generation were predicted by a xenosite web predictor tool for glutathione, cyanide, protein and DNA. To study the metabolic activation of brexpiprazole, the drug was individually incubated for 2 h at 37 °C with pooled male rat liver microsomes and human liver microsomes in microcentrifuge tubes fortified with glutathione/N-acetyl cysteine. Nicotinamide adenine dinucleotide phosphate reduced tetrasodium salt was used as a co-factor.

RESULTS

A total of six glutathione and N-acetyl cysteine conjugates of brexpiprazole metabolites were identified and characterized using ultra-high-performance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry. Reactive metabolite 1 (RM1), RM3, RM4 and RM6 reactive conjugates were formed due to reactive quinone-imine or quinone intermediates, while RM2 and RM5 reactive adducts were generated because of a thiophene-S-oxide intermediate.

CONCLUSION

Brexpirazole is bioactivated due to the presence of a 1-(benzo[b]thiophen-4-yl)piperazine ring in its structure. In contrast to aripiprazole, the quinolinone motif was found latent towards bioactivation in brexpiprazole.

Update on metabolism of abemaciclib: In silico, in vitro, and in vivo metabolite identification and characterization using high resolution mass spectrometry

Abemaciclib was approved by the US Food and Drug Administration in 2015 as an advanced treatment for metastatic breast cancer. Identification and characterization of limited numbers of abemaciclib metabolites have been reported in the literature. Therefore, the current study focused on the investigation of the in vitro and in vivo metabolic fate of abemaciclib using high resolution mass spectrometry. Initially, a vulnerable site of metabolism was predicted by the Xenosite web predictor tool. Later, in vitro metabolites were identified from pooled rat liver microsomes, rat S9 fractions, and human liver microsomes. Finally, in vivo metabolites have been detected in plasma, urine, and feces matrix of male Sprague-Dawley rats. A total of 12 putative metabolites (11 phase I and 1 phase II) of abemaciclib and their metabolic pathways were proposed by considering accurate mass, mass fragmentation pattern, nitrogen rule, and ring double bonds of the detected metabolites. Abemaciclib was metabolized via hydroxylation, N-oxidation, N-dealkylation, oxidative deamination followed by reduction and sulfate conjugation. In the human liver microsomes, maximum numbers of metabolites (11 metabolites) were observed, from which M7, M8, M9, and M11 were human specific.

Overcoming Fungal Echinocandin Resistance through Inhibition of the Non-essential Stress Kinase Yck2

New strategies are urgently needed to counter the threat to human health posed by drug-resistant fungi. To explore an as-yet unexploited target space for antifungals, we screened a library of protein kinase inhibitors for the ability to reverse resistance of the most common human fungal pathogen, Candida albicans, to caspofungin, a widely used antifungal. This screen identified multiple 2,3-aryl-pyrazolopyridine scaffold compounds capable of restoring caspofungin sensitivity. Using chemical genomic, biochemical, and structural approaches, we established the target for our most potent compound as Yck2, a casein kinase 1 family member. Combination of this compound with caspofungin eradicated drug-resistant C. albicans infection while sparing co-cultured human cells. In mice, genetic depletion of YCK2 caused an ∼3-log₁₀ decline in fungal burden in a model of systemic caspofungin-resistant C. albicans infection. Structural insights and our tool compound's profile in culture support targeting the Yck2 kinase function as a broadly active antifungal strategy.

In silico and in vitro metabolism of ribociclib: a mass spectrometric approach to bioactivation pathway elucidation and metabolite profiling

Ribociclib (RBC, Kisqali®) is a highly selective CDK4/6 inhibitor that has been approved for breast cancer therapy. Initially, prediction of susceptible sites of metabolism and reactivity pathways were performed by the StarDrop WhichP450™ module and the Xenosite web predictor tool, respectively. Later, in vitro metabolites and adducts of RBC were characterized from rat liver microsomes using LC-MS/MS. Subsequently, in silico data was used as a guide for the in vitro work. Finally, in silico toxicity assessment of RBC metabolites was carried out using DEREK software and structural modification was proposed to reduce their side effects and to validate the bioactivation pathway theory using the StarDrop DEREK module. In vitro phase I metabolic profiling of RBC was performed utilizing rat liver microsomes (RLMs). Generation of reactive metabolites was investigated using potassium cyanide (KCN) as a trapping nucleophile for the transient and reactive iminium intermediates to form a stable cyano adduct that can be identified and characterized using mass spectrometry. Nine phase I metabolites and one cyano adduct of RBC were characterized. The proposed metabolic pathways involved in generation of these metabolites are hydroxylation, oxidation and reduction. The reactive intermediate generation mechanism of RBC may provide an explanation of its adverse reactions. Aryl piperazine is considered a structural alert for toxicity as proposed by the DEREK report. We propose that the generation of only one reactive metabolite of RBC in a very small concentration is due to the decreased reactivity of the piperazine ring compared to previous reports of similar drugs. Docking analysis was performed for RBC and its proposed derivatives at the active site of the human CDK6 enzyme. Methyl-RBC exhibited the best ADMET and docking analysis and fewer side effects compared to RBC and fluoro-RBC. Further drug discovery studies can be conducted taking into account this concept allowing the development of new drugs with enhanced safety profiles that were confirmed by using StarDrop software. To the best of our knowledge, this is the first literature report of RBCin vitro metabolic profiling and structural characterization and toxicological properties of the generated metabolites.

Nine phase I metabolites and one product of KCN trapping of RBC were characterized. Aryl piperazine is considered a structural alert for toxicity as proposed by the DEREK report. Methyl-RBC exhibited less toxicity and more binding affinity to CDK6.

Identification and characterization of in silico, in vivo, in vitro, and reactive metabolites of infigratinib using LC-ITMS: bioactivation pathway elucidation and in silico toxicity studies of its metabolites

Infigratinib (INF) is a novel, small molecule that is orally administered to inhibit human fibroblast growth factor receptors (FGFRs), which are a family of receptor tyrosine kinases that may be upregulated in different tumor cell types. On 6 January 2020, the FDA granted fast track designation to INF for first-line treatment of cholangiocarcinoma. Prediction of susceptible sites of metabolism and reactivity pathways (cyanide and GSH) for INF was performed by the Xenosite web predictor tool. Then, we report the characterization and identification of in vitro, in vivo, and reactive intermediates of INF using liquid chromatography ion trap mass spectrometry (LC-ITMS). Finally, an in silico toxicity assessment of INF metabolites was carried out using the StarDrop DEREK module showing structural alerts. Rat liver microsomes (RLMs) and isolated perfused rat liver hepatocytes were incubated with INF in vitro and the generated metabolites were collected by protein precipitation. In vivo metabolism was evaluated by time-course urine sampling from Sprague-Dawley rats administered a single INF oral dose. A similar volume of acetonitrile was added to each collected urine sample and both organic and aqueous layers were analyzed by LC-ITMS to detect in vivo INF metabolites. N-Ethyl piperazine rings and benzene at part A of the INF structure are metabolized to form iminium and 1,4-benzoquinone, respectively, which are very reactive toward nucleophilic macromolecules. Incubation of INF with RLMs in the presence of 1.0 mM KCN and 1.0 mM glutathione was used to evaluate reactive metabolites potentially responsible for toxicities associated with INF. There were seven in vitro phase I metabolites, three in vitro phase II metabolites, three cyano adducts, and three GSH conjugate metabolites of INF detected by LC-ITMS. In vivo INF metabolites identified included four in vivo phase I and three in vivo phase II metabolites. In vitro and in vivo phase I metabolic pathways included N-dealkylation, N-demethylation, O-demethylation, hydroxylation, and dechlorination, while the in vivo phase II metabolic reaction was a direct conjugation of INF with glucuronic acid and sulphate.

An in silico web designer tool was utilized to guide laboratory work for infigratinib metabolism. Sixteen metabolites of infigratinib and seven reactive intermediates (three iminium ions and four 1,4 benzoquinones) were characterized using LC-ITMS.

Synthesis, bioconversion, pharmacokinetic and pharmacodynamic evaluation of N-isopropyl-oxy-carbonyloxymethyl prodrugs of CZh-226, a potent and selective PAK4 inhibitor

We have previously disclosed compound 3 (CZh-226), a potent and selective PAK4 inhibitor, but its development was delayed due to poor oral pharmacokinetics. In an attempt to improve this issue, we synthesised a series of prodrugs by masking its terminal nitrogen of the piperazine moiety. Most synthesised prodrugs of 3 have low or no inhibition of PAK4 activity. The stability of synthetic prodrugs was evaluated in PBS, SGF, SIF, rat plasma and liver S9 fraction. Of these, prodrug 19 was not only stable under both acidic and neutral conditions but also could be quickly converted to parent drug 3 in rat plasma and liver S9 fraction. Such effective conversion into parent drug 3 was observed in rats, providing higher exposure of 3 compared to its direct administration. When given via oral route at daily doses of 25 and 50 mg/kg, the prodrug 19 was effective and well tolerated in mouse model of HCT-116 and B16F10.

Towards the Development of an In vivo Chemical Probe for Cyclin G Associated Kinase (GAK)

SGC-GAK-1 (1) is a potent, selective, cell-active chemical probe for cyclin G-associated kinase (GAK). However, 1 was rapidly metabolized in mouse liver microsomes by cytochrome P450-mediated oxidation, displaying rapid clearance in liver microsomes and in mice, which limited its utility in in vivo studies. Chemical modifications of 1 that improved metabolic stability, generally resulted in decreased GAK potency. The best analog in terms of GAK activity in cells was 6-bromo-N-(1H-indazol-6-yl)quinolin-4-amine (35) (IC₅₀ = 1.4 μM), showing improved stability in liver microsomes while still maintaining a narrow spectrum activity across the kinome. As an alternative to scaffold modifications we also explored the use of the broad-spectrum cytochrome P450 inhibitor 1-aminobenzotriazole (ABT) to decrease intrinsic clearance of aminoquinoline GAK inhibitors. Taken together, these approaches point towards the development of an in vivo chemical probe for the dark kinase GAK.

Molecular Docking Supplements an In vitro Determination of the Leading CYP Isoform for Arylpiperazine Derivatives

Background:

Molecular docking has often been used before to calculate in silico affinity of drugs towards their molecular target, but not to estimate leading CYP isoform responsible for metabolism of studied compounds.

Objective:

The aim of this study is to present molecular docking as a valid alternative for costly in vitro studies resulting in estimation of leading CYP isoform.

Method:

In vitro part was based on incubations of studied compounds with isolated CYP3A4 isoform followed by LC-MS analysis. The in silico stage consisted of docking three-dimensional models of the studied compounds with a three-dimensional model of the leading metabolizing isoform (CYP3A4), which was designated during the in vitro part of the study. XenoSite P450 metabolism prediction was also used to predict sites of metabolism and calculate probability values.

Results:

The calculated affinities showed a clear similarity when the in vitro results were compared with the calculated in silico affinity values. XenoSite CYP3A4 metabolism probability values also confirm significant participation of CYP3A4 in metabolism of studied compounds.

Conclusion:

Both molecular docking and XenoSite P450 metabolism prediction provide data that stands in agreement with in vitro studies, granting a more detailed spectrum on predicting CYP3A4 metabolism, and presenting molecular docking as a promising tool to cut costs and increase effectiveness in early drug development stages.

FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes

In this work we present the third generation of FAst MEtabolizer (FAME 3), a collection of extra trees classifiers for the prediction of sites of metabolism (SoMs) in small molecules such as drugs, druglike compounds, natural products, agrochemicals, and cosmetics. FAME 3 was derived from the MetaQSAR database ( Pedretti et al. J. Med. Chem. 2018 , 61 , 1019 ), a recently published data resource on xenobiotic metabolism that contains more than 2100 substrates annotated with more than 6300 experimentally confirmed SoMs related to redox reactions, hydrolysis and other nonredox reactions, and conjugation reactions. In tests with holdout data, FAME 3 models reached competitive performance, with Matthews correlation coefficients (MCCs) ranging from 0.50 for a global model covering phase 1 and phase 2 metabolism, to 0.75 for a focused model for phase 2 metabolism. A model focused on cytochrome P450 metabolism yielded an MCC of 0.57. Results from case studies with several synthetic compounds, natural products, and natural product derivatives demonstrate the agreement between model predictions and literature data even for molecules with structural patterns clearly distinct from those present in the training data. The applicability domains of the individual models were estimated by a new, atom-based distance measure (FAMEscore) that is based on a nearest-neighbor search in the space of atom environments. FAME 3 is available via a public web service at https://nerdd.zbh.uni-hamburg.de/ and as a self-contained Java software package, free for academic and noncommercial research.

Synthesis of new 5,6,7,8-tetrahydropyrido[1,2-c]pyrimidine derivatives with rigidized tryptamine moiety as potential SSRI and 5-HT1A receptor ligands

Extended studies in the 4-aryl-pyrido[1,2-c]pyrimidine group resulted in 27 new compounds (10.1-10.27), 5,6,7,8-tetrahydropyrido[1,2-c]pyrimidine derivatives. In vitro tests (RBA) were carried out for 10.1-10.27 compounds in order to determine their affinity to 5-HT_1A receptor and SERT protein. 10.1-10.3, 10.6, 10.7, 10.16 and 10.27 compounds had high binding ability to both molecular targets (5-HT_1A K_i = 8-87 nM; SERT K_i = 8-52 nM). For these compounds (10.1-10.3, 10.6, 10.7, 10.16, 10.27) further in vitro, in vivo and metabolic stability tests were performed. In vitro studies in the extended receptor profile (D₂, 5-HT_2A, 5-HT₆ and 5-HT₇) showed their selectivity towards 5-HT_1A receptor and SERT protein. In vivo tests revealed that compounds 10.7 and 10.16 had the properties of presynaptic antagonists of the 5-HT_1A receptor. The redesign of the 2H-pyrido[1,2-c]pyrimidine residue present in the terminal part towards 5,6,7,8-tetrahydropyrido[1,2-c]pyrimidine resulted in the improved metabolic stability and enhanced affinity to both molecular targets (5-HT_1A-R and SERT) compared to the precursors.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Novel Electrotopological Atomic Descriptors for the Prediction of Xenobiotic Cytochrome P450 Reactions

Cytochrome P450 (CYP) is an enzyme family that plays a crucial role in metabolism, mainly metabolizing xenobiotics to produce non-toxic structures, however, some metabolized products can cause hepatotoxicity. Hence, predicting the structures of CYP products is an important task in designing non-hepatotoxic drugs. Here, we have developed novel atomic descriptors to predict the sites of metabolism (SoM) in CYP substrates. We proposed descriptors that describe topological and electrostatic characteristics of CYP substrates using Gasteiger charge. The proposed descriptors were applied to CYP3A4 data analysis as a case study. As a result of the descriptor selection, we obtained a gradient boosting decision tree-based SoM classification model that used 139 existing descriptors and the proposed 45 descriptors, and the model performed well in terms of the Matthews correlation coefficient. We also developed a structure converter to predict CYP products. This converter correctly generated 51 structural formulas of experimentally observed CYP3A4 products according to a manual evaluation.

GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism

Computational prediction of xenobiotic metabolism can provide valuable information to guide the development of drugs, cosmetics, agrochemicals, and other chemical entities. We have previously developed FAME 2, an effective tool for predicting sites of metabolism (SoMs). In this work, we focus on the prediction of the chemical structures of metabolites, in particular metabolites of xenobiotics. To this end, we have developed a new tool, GLORY, which combines SoM prediction with FAME 2 and a new collection of rules for metabolic reactions mediated by the cytochrome P450 enzyme family. GLORY has two modes: MaxEfficiency and MaxCoverage. For MaxEfficiency mode, the use of predicted SoMs to restrict the locations in the molecule at which the reaction rules could be applied was explored. For MaxCoverage mode, the predicted SoM probabilities were instead used to develop a new scoring approach for the predicted metabolites. With this scoring approach, GLORY achieves a recall of 0.83 and can predict at least one known metabolite within the top three ranked positions for 76% of the molecules of a new, manually curated test set. GLORY is freely available as a web server at https://acm.zbh.uni-hamburg.de/glory/, and the datasets and reaction rules are provided in the Supplementary Material.

In silico, in vitro and in vivo metabolite identification of brexpiprazole using ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry

RATIONALE

Brexpiprazole is a novel serotonin-dopamine activity modulator approved by the USFDA in July 2015 for the treatment of schizophrenia and as an adjunctive therapy with other antidepressants for major depressive disorder in adults. However, limited numbers of metabolites are reported in the literature for brexpiprazole. Our prime intent behind this study is to revisit metabolite profiling of brexpiprazole and to identify and characterize all possible in vitro and in vivo metabolites.

METHODS

Firstly, the site of metabolism for brexpiprazole was predicted by a Xenosite web predictor model. Secondly, in vitro metabolite profiling was performed by incubating the drug individually with rat liver microsomes, human liver microsomes and rat S9 fraction at 37°C for 1 h in incubator shaker. Finally, for in vivo metabolite identification, a 50 mg kg^-1 dose of brexpiprazole was administered to male Sprague-Dawley rats and the presence of various metabolites was confirmed in rat plasma, urine and feces.

RESULTS

The predicted atomic site of metabolism was obtained as a color gradient by the Xenosite web predictor tool and, from this study, probable metabolites were listed. In total, 14 phase I and 2 phase II metabolites were identified and characterized in the in vitro and in vivo matrices using ultra-high-performance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry (UHPLC/QTOF-MS/MS). The majority of metabolites were found in the sample incubated with human liver microsomes and in rat urine, while in the other matrices only a few metabolites were detected.

CONCLUSIONS

All the 16 metabolites were identified and characterized using UHPLC/QTOF-MS/MS. The study revealed that brexpiprazole is metabolized via hydroxylation, glucuronidation, S-oxidation, N-oxidation, dioxidation, oxidative deamination, N-dealkylation, etc.

Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database

Even though glucuronidations are the most frequent metabolic reactions of conjugation, both in quantitative and qualitative terms, they have rather seldom been investigated using computational approaches. To fill this gap, we have used the manually collected MetaQSAR metabolic reaction database to generate two models for the prediction of UGT-mediated metabolism, both based on molecular descriptors and implementing the Random Forest algorithm. The first model predicts the occurrence of the reaction and was internally validated with a Matthew correlation coefficient (MCC) of 0.76 and an area under the ROC curve (AUC) of 0.94, and further externally validated using a test set composed of 120 additional xenobiotics (MCC of 0.70 and AUC of 0.90). The second model distinguishes between O- and N-glucuronidations and was optimized by the random undersampling procedure to improve the predictive accuracy during the internal validation, with the recall measure of the minority class increasing from 0.55 to 0.78.

CYP2C19 and 3A4 Dominate Metabolic Clearance and Bioactivation of Terbinafine Based on Computational and Experimental Approaches

Lamisil (terbinafine) is an effective, widely prescribed antifungal drug that causes rare idiosyncratic hepatotoxicity. The proposed toxic mechanism involves a reactive metabolite, 6,6-dimethyl-2-hepten-4-ynal (TBF-A), formed through three N-dealkylation pathways. We were the first to characterize them using in vitro studies with human liver microsomes and modeling approaches, yet knowledge of the individual enzymes catalyzing reactions remained unknown. Herein, we employed experimental and computational tools to assess terbinafine metabolism by specific cytochrome P450 isozymes. In vitro inhibitor phenotyping studies revealed six isozymes were involved in one or more N-dealkylation pathways. CYP2C19 and 3A4 contributed to all pathways, and so, we targeted them for steady-state analyses with recombinant isozymes. N-Dealkylation yielding TBF-A directly was catalyzed by CYP2C19 and 3A4 similarly. Nevertheless, CYP2C19 was more efficient than CYP3A4 at N-demethylation and other steps leading to TBF-A. Unlike microsomal reactions, N-denaphthylation was surprisingly efficient for CYP2C19 and 3A4, which was validated by controls. CYP2C19 was the most efficient among all reactions. Nonetheless, CYP3A4 was more selective at steps leading to TBF-A, making it more effective in terbinafine bioactivation based on metabolic split ratios for competing pathways. Model predictions did not extrapolate to quantitative kinetic constants, yet some results for CYP3A4 and CYP2C19 agreed qualitatively with preferred reaction steps and pathways. Clinical data on drug interactions support the CYP3A4 role in terbinafine metabolism, while CYP2C19 remains understudied. Taken together, knowledge of P450s responsible for terbinafine metabolism and TBF-A formation provides a foundation for investigating and mitigating the impact of P450 variations in toxic risks posed to patients.