Site of reactivity models predict molecular reactivity of diverse chemicals with glutathione

Mentha arvensis oil exhibits repellent acute toxic and antioxidant activities in Nauphoeta cinerea

Mentha arvensis is an herbaceous plant commonly known as peppermint or Japanese mint. This study investigated the toxic potential and repellent efficacy of M. arvensis essential oil (MaEO) at varying concentrations (15.625-250 mg/mL) in Nauphoeta cinerea, along with its impact on biochemical parameters in N. cinerea. The potential of the major compounds as a new analgesic target was investigated using molecular docking. The essential oil was analyzed by gas Chromatography-mass spectrometry (GC-MS) and the toxic potential, repellent property, and changes in lipid peroxidation (LPO) levels were evaluated as markers of oxidative stress. GC-MS results revealed that the main components were oxygenated monoterpenes such as menthol (71.31%), mentone (13.34%) and isomentone (5.35%). MaEO significantly reduced lipid peroxidation (LPO), the levels of non-protein thiols and iron(II) at the concentration of 125 mg/mL in N. cinerea. Furthermore, the major components, L-(-)-Menthol and menthone demonstrated high gastrointestinal absorption and high affinity with the target protein, suggesting possible links that contribute to the analgesic effect of MaEO.

Biomimetic cytotoxicity control of select nitrogenous disinfection byproducts in water

Nitrogenous disinfection byproducts (N-DBPs) in water are carcinogenic, teratogenic, and mutagenic. In this work, we developed a biomimetic reduction approach based on the cysteine thiol that destructed the highly toxic, select nitrogenous haloacetamides (HAMs) and haloacetonitriles (HANs) while effectively controlling the cytotoxicity of the degradation products to serve as a basis for further technological applications (e.g. immobilized contact bed for terminal users). Mechanisms on toxicity control were elucidated. Results showed the degradation and cytotoxicity control of HAMs as more efficient than that of the HANs. The cytotoxicity of the chlorinated, brominated, and iodinated HAMs and HANs was reduced to 25 %- 0.25 % of the original after biomimetic reduction using a reasonable concentration ratio. Through a combination of thiol-specific reactivity, dehalogenation, and quantitative structure-activity relationship analyses, the major toxicity control mechanisms were found to be the reductive dehalogenation of the N-DBPs. The halogenated functional groups on the N-DBPs had a more pronounced effect than the amide and nitrile groups on the cytotoxicity and detoxification effect. Patterns of toxicity interaction variations with DBPs concentrations were identified to detect possible synergistic cytotoxicity interactions under various combinations of HAMs and HANs in the presence of the cysteine thiol. Results could benefit future N-DBPs control efforts.

Development of a Novel In Silico Classification Model to Assess Reactive Metabolite Formation in the Cysteine Trapping Assay and Investigation of Important Substructures

Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of drug mechanism. The cysteine trapping assay is a method for detecting reactive metabolites that bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silico classification model for predicting a positive/negative outcome in the cysteine trapping assay. We collected 475 compounds (436 in-house compounds and 39 publicly available drugs) based on experimental data performed in this study, and the composition of the results showed 248 positives and 227 negatives. Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. In the time-split dataset, AUC-ROC of MPNN and RF were 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that the in silico MPNN classification model for the cysteine trapping assay has a better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous results.

The role of glutathione in stabilizing aromatic volatile organic compounds in Rougui Oolong tea: A comprehensive study from content to mechanisms

Chinese Oolong tea is widely known for its intricate aroma. However, the degradation of volatile organic compounds (VOCs) poses significant challenges for the tea products. In this study, glutathione (GSH) has an excellent preservation effect on VOCs in both the VOCs extract and the tea infusion during storage, specifically slowing the degradation of hexanal (by 66.39% and 35.09%) and heptanal (by 67.46% and 63.50%). Additionally, the addition of GSH maintained higher levels of active ingredients in tea infusion, including epigallocatechin, procyanidin B1, glutamic acid, and L-(+)-arginine, with respective increases of 184.09, 2.92, 4.10, and 6.35 times. The sulfhydryl group of GSH formed a covalent bond with hexanal and 2-methylbutanal, therefore improving the stability of VOCs. These findings provided a valuable insight for developing effective VOC preservation techniques for water-based tea products.

AOP Report: Glutathione Conjugation Leading to Reproductive Dysfunction via Oxidative Stress

We propose an adverse outcome pathway (AOP) for reproductive dysfunction via oxidative stress (OS). The AOP was developed based on Organisation for Economic Co-operation and Development (OECD) Guidance Document 184 and on the specific considerations of the OECD users' handbook supplement to the guidance document for developing and assessing AOPs (no. 233). According to the qualitative and quantitative experimental data evaluation, glutathione (GSH) conjugation is the first upstream key event (KE) of this AOP to reproductive dysfunction triggering OS. This event causes depletion of GSH basal levels (KE2 ). Consequently, this drop of free GSH induces an increase of reactive oxygen species (KE3 ) generated by the natural cellular metabolic processes (cellular respiration) of the organism. Increased levels of these reactive species, in turn, induce an increase of lipid peroxidation (KE4 ). This KE consequently leads to a rise in the amount of toxic substances, such as malondialdehyde and hydroxynonenal, which are associated with decreased quality and competence of gamete cell division, consequently impairing fertility (KE5 and adverse outcome). The overall assessment of the general biological plausibility, the empirical support, and the essentiality of KE relationships was considered as high for this AOP. We conclude that GSH conjugation is able to lead to reproductive disorder in fishes and mammals, via OS, but that the amount of stressor needed to trigger the AOP differs between stressors. Environ Toxicol Chem 2023;42:2519-2528. © 2023 SETAC.

Quantum Descriptors for Predicting and Understanding the Structure-Activity Relationships of Michael Acceptor Warheads

Predictive modeling and understanding of chemical warhead reactivities have the potential to accelerate targeted covalent drug discovery. Recently, the carbanion formation free energies as well as other ground-state electronic properties from density functional theory (DFT) calculations have been proposed as predictors of glutathione reactivities of Michael acceptors; however, no clear consensus exists. By profiling the thiol-Michael reactions of a diverse set of singly- and doubly-activated olefins, including several model warheads related to afatinib, here we reexamined the question of whether low-cost electronic properties can be used as predictors of reaction barriers. The electronic properties related to the carbanion intermediate were found to be strong predictors, e.g., the change in the Cβ charge accompanying carbanion formation. The least expensive reactant-only properties, the electrophilicity index, and the Cβ charge also show strong rank correlations, suggesting their utility as quantum descriptors. A second objective of the work is to clarify the effect of the β-dimethylaminomethyl (DMAM) substitution, which is incorporated in the warheads of several FDA-approved covalent drugs. Our data suggest that the β-DMAM substitution is cationic at neutral pH in solution and promotes acrylamide's intrinsic reactivity by enhancing the charge accumulation at Cα upon carbanion formation. In contrast, the inductive effect of the β-trimethylaminomethyl substitution is diminished due to steric hindrance. Together, these results reconcile the current views of the intrinsic reactivities of acrylamides and contribute to large-scale predictive modeling and an understanding of the structure-activity relationships of Michael acceptors for rational TCI design.

MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database

The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions. The models were based on a set of structural, physicochemical, and stereo-electronic descriptors and were generated by the random forest algorithm. For each considered biotransformation, two types of models were developed: the first type involved all non-reactive atoms and included atom types among the descriptors, while the second type involved only non-reactive centers having the same atom type(s) of the reactive atoms. All the models of the first type revealed very high performances; the models of the second type show on average worst performances while being almost always able to recognize the reactive centers; only conjugations with glucuronic acid are unsatisfactorily predicted by the models of the second type. Feature evaluation confirms the major role of lipophilicity, self-polarizability, and H-bonding for almost all considered reactions. The obtained results emphasize the possibility of recognizing the sites of metabolism by classification models trained on MetaQSAR database. The two types of models can be synergistically combined since the first models identify which atoms can undergo a given metabolic reactions, while the second models detect the truly reactive centers. The generated models are available as scripts for the VEGA program.

Reactive intermediates formation and bioactivation pathways of spebrutinib revealed by LC-MS/MS: In vitro and in silico metabolic study

Spebrutinib is a new Bruton tyrosine kinase inhibitor developed by Avila Therapeutics and Celgene. Spebrutinib (SPB) is currently in phase Ib clinical trials for the treatment of lymphoma in the United States. Preliminary in-silico studies were first performed to predict susceptible sites of metabolism, reactivity pathways and structural alerts for toxicities by StarDrop WhichP450™ module, Xenosite web predictor tool and DEREK software; respectively. SPB metabolites and adducts were characterized in vitro from rat liver microsomes (RLM) using LC-MS/MS. Formation of reactive intermediates was investigated using potassium cyanide (KCN), glutathione (GSH) and methoxylamine as trapping nucleophiles for the unstable and reactive iminium, iminoquinone and aldehyde intermediates, respectively, with the aim to produce stable adducts that can be detected and characterized using mass spectrometry. Fourteen phase I metabolites, four cyanide adducts, six GSH adducts and three methoxylamine adducts of SPB were identified and characterized. The proposed metabolic pathways involved in generation of phase I metabolites of SPB are oxidation, hydroxylation, o-dealkylation, epoxidation, defluorination and reduction. Several in vitro reactive intermediates were identified and characterized, the formation of which can aid in explaining the adverse drug reactions of SPB. Several iminium, 2-iminopyrimidin-5(2H)-one and aldehyde intermediates of SPB were revealed. Acrylamide is identified as a structural alert for toxicity by DEREK report and was found to be involved in the formation of several glycidamide and aldehyde reactive intermediates.

Structure-based pharmacophore modeling, virtual screening and simulation studies for the identification of potent anticancerous phytochemical lead targeting cyclin-dependent kinase 2

Cyclin-dependent kinases are of critical importance in directing various cell cycle phases making them as potential tumor targets. Cyclin-dependent kinase 2 (CDK2) in particular plays a significant part during cell cycle events and its imbalance roots out tumorogenic environment. Herein, we built a structure-based pharmacophore model complementing the ATP pocket site of CDK2 with four pharmacophoric features, using a series of structures obtained from cluster analysis during MD simulation assessment. This was followed by its validation and further database screening against Taiwan indigenous plants database (5284 compounds). The screened compounds were subjected toward Lipinski's rule (RO5) and ADMET filter followed by docking analysis and simulation study. In filtering hits (10 compounds) via molecular docking against CDK2, Schinilenol with -8.1 kcal/mol fetched out as a best lead phytoinhibitor in the presence of standard drug (Dinaciclib). Additionally, pharmacophore mapping analysis also indicated relative fit values of dinaciclib and schinilenol as 2.37 and 2.31, respectively. Optimization, flexibility prediction and the stability of CDK2 in complex with the ligands were also ascertained by means of molecular dynamics for 50 ns, which further proposed schinilenol having better binding stability than dinaciclib with RMSD values ranging from 0.31 to 0.34 nm. Reactivity site, biological activity detection and cardiotoxicity assessment also proposed schinilenol as a better phytolead inhibitor than the existing dinaciclib. Abbreviations: CDK2: Cyclin dependent kinase2; ATP: Adenosine triphosphate; MD: Molecular dynamics, RO5: Rule of five; ADMET: Absorption, distribution, metabolism, and excretion; RMSD: Root mean square deviation; DS: Discovery Studio; SOM: Site of metabolism; RBPM: receptor based pharmacophore model; TIP: Schinilenol; hERG: human Ether-à-go-go - Related GeneCommunicated by Ramaswamy H. Sarma.

In Silico Tools and Software to Predict ADMET of New Drug Candidates

Implication of computational techniques and in silico tools promote not only reduction of animal experimentations but also save time and money followed by rational designing of drugs as well as controlled synthesis of those "Hits" which show drug-likeness and possess suitable absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile. With globalization of diseases, resistance of drugs over the time and modification of viruses and microorganisms, computational tools, and artificial intelligence are the future of drug design and one of the important areas where the principles of sustainability and green chemistry (GC) perfectly fit. Most of the new drug entities fail in the clinical trials over the issue of drug-associated human toxicity. Although ecotoxicity related to new drugs is rarely considered, but this is the high time when ecotoxicity prediction should get equal importance along with human-associated drug toxicity. Thus, the present book chapter discusses the available in silico tools and software for the fast and preliminary prediction of a series of human-associated toxicity and ecotoxicity of new drug entities to screen possibly safer drugs before going into preclinical and clinical trials.

Novel insights into conjugation of antitumor-active unsymmetrical bisacridine C-2028 with glutathione: Characteristics of non-enzymatic and glutathione S-transferase-mediated reactions

Unsymmetrical bisacridines (UAs) are a novel potent class of antitumor-active therapeutics. A significant route of phase II drug metabolism is conjugation with glutathione (GSH), which can be non-enzymatic and/or catalyzed by GSH-dependent enzymes. The aim of this work was to investigate the GSH-mediated metabolic pathway of a representative UA, C-2028. GSH-supplemented incubations of C-2028 with rat, but not with human, liver cytosol led to the formation of a single GSH-related metabolite. Interestingly, it was also revealed with rat liver microsomes. Its formation was NADPH-independent and was not inhibited by co-incubation with the cytochrome P450 (CYP450) inhibitor 1-aminobenzotriazole. Therefore, the direct conjugation pathway occurred without the prior CYP450-catalyzed bioactivation of the substrate. In turn, incubations of C-2028 and GSH with human recombinant glutathione S-transferase (GST) P1-1 or with heat-/ethacrynic acid-inactivated liver cytosolic enzymes resulted in the presence or lack of GSH conjugated form, respectively. These findings proved the necessary participation of GST in the initial activation of the GSH thiol group to enable a nucleophilic attack on the substrate molecule. Another C-2028-GSH S-conjugate was also formed during non-enzymatic reaction. Both GSH S-conjugates were characterized by combined liquid chromatography/tandem mass spectrometry. Mechanisms for their formation were proposed. The ability of C-2028 to GST-mediated and/or direct GSH conjugation is suspected to be clinically important. This may affect the patient's drug clearance due to GST activity, loss of GSH, or the interactions with GSH-conjugated drugs. Moreover, GST-mediated depletion of cellular GSH may increase tumor cell exposure to reactive products of UA metabolic transformations.

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We investigated the GSH-mediated metabolic pathway of antitumor bisacridine C-2028.

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Non-enzymatic and GST-catalyzed GSH conjugation of C-2028 was observed.

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The action of human recombinant GSTP1-1 in C-2028 metabolism was proved.

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GSH conjugation occurred without the prior CYP450-mediated activation of C-2028.

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GSH conjugation of C-2028 molecule took place on the system containing nitro group.

Profiling of in vivo, in vitro and reactive zorifertinib metabolites using liquid chromatography ion trap mass spectrometry

Zorifertinib (AZD-3759; ZFB) is a potent, novel, oral, small molecule used for the treatment of non-small cell lung cancer (NSCLC). ZFB is Epidermal Growth Factor Receptor (EGFR) inhibitor that is characterized by good permeability of the blood–brain barrier for (NSCLC) patients with EGFR mutations. The present research reports the profiling of in vitro, in vivo and reactive metabolites of ZFB. Prediction of vulnerable metabolic sites and reactivity pathways (cyanide and GSH) of ZFB were performed by WhichP450™ module (StarDrop software package) and XenoSite reactivity model (XenoSite Web Predictor-Home), respectively. ZFB in vitro metabolites were done by incubation with isolated perfused rat liver hepatocytes and rat liver microsomes (RLMs). Extraction of ZFB and its related metabolites from the incubation matrix was done by protein precipitation. In vivo metabolism was performed by giving ZFB (10 mg kg⁻¹) through oral gavage to Sprague Dawley rats that were housed in metabolic cages. Urine was collected at specific time intervals (0, 6, 12, 18, 24, 48, 72, 96 and 120 h) from ZFB dosing. The collected urine samples were filtered then stored at −70 °C. N-Methyl piperazine ring of ZFB undergoes phase I metabolism forming iminium intermediates that were stabilized using potassium cyanide as a trapping agent. Incubation of ZFB with RLMs were performed in the presence of 1.0 mM KCN and 1.0 mM glutathione to check reactive intermediates as it is may be responsible for toxicities associated with ZFB usage. For in vitro metabolites there were six in vitro phase I metabolites, three in vitro phase II metabolites, seven reactive intermediates (four GSH conjugates and three cyano adducts) of ZFB were detected by LC-IT-MS. For in vivo metabolites there were six in vivo phase I and three in vivo phase II metabolites of ZFB were detected by LC-IT-MS. In vitro and in vivo phase I metabolic pathways were N-demethylation, O-demethylation, hydroxylation, reduction, defluorination and dechlorination. In vivo phase II metabolic reaction was direct sulphate and glucuronic acid conjugation with ZFB.

Six in vitro phase I metabolites, three in vitro phase II metabolites, seven reactive intermediates (four GSH conjugates and three cyano adducts), six in vivo phase I and three in vivo phase II metabolites of ZFB were detected by LC-IT-MS.

Fatty acid nitroalkene reversal of established lung fibrosis

Tissue fibrosis occurs in response to dysregulated metabolism, pro-inflammatory signaling and tissue repair reactions. For example, lungs exposed to environmental toxins, cancer therapies, chronic inflammation and other stimuli manifest a phenotypic shift to activated myofibroblasts and progressive and often irreversible lung tissue scarring. There are no therapies that stop or reverse fibrosis. The 2 FDA-approved anti-fibrotic drugs at best only slow the progression of fibrosis in humans. The present study was designed to test whether a small molecule electrophilic nitroalkene, nitro-oleic acid (NO₂-OA), could reverse established pulmonary fibrosis induced by the intratracheal administration of bleomycin in C57BL/6 mice. After 14 d of bleomycin-induced fibrosis development in vivo, lungs were removed, sectioned and precision-cut lung slices (PCLS) from control and bleomycin-treated mice were cultured ex vivo for 4 d with either vehicle or NO₂-OA (5 μM). Biochemical and morphological analyses showed that over a 4 d time frame, NO₂-OA significantly inhibited pro-inflammatory mediator and growth factor expression and reversed key indices of fibrosis (hydroxyproline, collagen 1A1 and 3A1, fibronectin-1). Quantitative image analysis of PCLS immunohistology reinforced these observations, revealing that NO₂-OA suppressed additional hallmarks of the fibrotic response, including alveolar epithelial cell loss, myofibroblast differentiation and proliferation, collagen and α-smooth muscle actin expression. NO₂-OA also accelerated collagen degradation by resident macrophages. These effects occurred in the absence of the recognized NO₂-OA modulation of circulating and migrating immune cell activation. Thus, small molecule nitroalkenes may be useful agents for reversing pathogenic fibrosis of lung and other organs.

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Small molecule electrophiles, pleiotropic anti-inflammatory and anti-fibrotic drugs.

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NO₂-OA inhibits activated myofibroblasts, induces dedifferentiation to fibroblasts.

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NO₂-OA activates extracellular matrix degradation by macrophages.

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NO₂-OA promotes proliferation of alveolar type 1 and 2 epithelial cells.

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NO₂-OA reverses established lung fibrosis in murine lung slices.

Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA

Background

The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them (MeIQx, PhIP, A\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}αC).

Results

We have developed a modeling approach in order to predict all the possible metabolites of a xenobiotic and enzymatic profiles that are linked to the production of metabolites able to bind DNA. Our prediction of metabolites approach relies on the construction of an enriched and annotated map of metabolites from an input metabolite.The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures.

Conclusions

Overall, a predictive toxicological model based on an in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04363-6.

Fluorescent kinase inhibitors as probes in cancer

Fluorescent dyes attached to kinase inhibitors (KIs) can be used to probe kinases in vitro, in cells, and in vivo. Ideal characteristics of the dyes vary with their intended applications. Fluorophores used in vitro may inform on kinase active site environments, hence the dyes used should be small and have minimal impact on modes of binding. These probes may have short wavelength emissions since blue fluorophores are perfectly adequate in this context. Thus, for instance, KI fragments that mimic nucleobases may be modified to be fluorescent with minimal perturbation to the kinase inhibitor structure. However, progressively larger dyes, that emit at longer wavelengths, are required for cellular and in vivo work. In cells, it is necessary to have emissions above autofluorescence of biomolecules, and near infrared dyes are needed to enable excitation and observation through tissue in vivo. This review is organized to describe probes intended for applications in vitro, in cells, then in vivo. The readers will observe that the probes featured tend to become larger and responsive to the near infared end of the spectrum as the review progresses. Readers may also be surprised to realize that relatively few dyes have been used for fluorophore-kinase inhibitor conjugates, and the area is open for innovations in the types of fluorophores used.

Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose

An effective approach for assessing a drug’s potential to induce autoimmune diseases (ADs) is needed in drug development. Here, we aim to develop a workflow to examine the association between structural alerts and drugs-induced ADs to improve toxicological prescreening tools. Considering reactive metabolite (RM) formation as a well-documented mechanism for drug-induced ADs, we investigated whether the presence of certain RM-related structural alerts was predictive for the risk of drug-induced AD. We constructed a database containing 171 RM-related structural alerts, generated a dataset of 407 AD- and non-AD-associated drugs, and performed statistical analysis. The nitrogen-containing benzene substituent alerts were found to be significantly associated with the risk of drug-induced ADs (odds ratio = 2.95, p = 0.0036). Furthermore, we developed a machine-learning-based predictive model by using daily dose and nitrogen-containing benzene substituent alerts as the top inputs and achieved the predictive performance of area under curve (AUC) of 70%. Additionally, we confirmed the reactivity of the nitrogen-containing benzene substituent aniline and related metabolites using quantum chemistry analysis and explored the underlying mechanisms. These identified structural alerts could be helpful in identifying drug candidates that carry a potential risk of drug-induced ADs to improve their safety profiles.

Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors

The 3,5-dimethylisoxazole motif has become a useful and popular acetyl-lysine mimic employed in isoxazole-containing bromodomain and extra-terminal (BET) inhibitors but may introduce the potential for bioactivations into toxic reactive metabolites. As a test, we coupled deep neural models for quinone formation, metabolite structures, and biomolecule reactivity to predict bioactivation pathways for 32 BET inhibitors and validate the bioactivation of select inhibitors experimentally. Based on model predictions, inhibitors were more likely to undergo bioactivation than reported non-bioactivated molecules containing isoxazoles. The model outputs varied with substituents indicating the ability to scale their impact on bioactivation. We selected OXFBD02, OXFBD04, and I-BET151 for more in-depth analysis. OXFBD’s bioactivations were evenly split between traditional quinones and novel extended quinone-methides involving the isoxazole yet strongly favored the latter quinones. Subsequent experimental studies confirmed the formation of both types of quinones for OXFBD molecules, yet traditional quinones were the dominant reactive metabolites. Modeled I-BET151 bioactivations led to extended quinone-methides, which were not verified experimentally. The differences in observed and predicted bioactivations reflected the need to improve overall bioactivation scaling. Nevertheless, our coupled modeling approach predicted BET inhibitor bioactivations including novel extended quinone methides, and we experimentally verified those pathways highlighting potential concerns for toxicity in the development of these new drug leads.

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.

Recent advances in computational metabolite structure predictions and altered metabolic pathways assessment to inform drug development processes

Many drug candidates fail during preclinical and clinical trials due to variable or unexpected metabolism which may lead to variability in drug efficacy or adverse drug reactions. The drug metabolism field aims to address this important issue from many angles which range from the study of drug-drug interactions, pharmacogenomics, computational metabolic modeling, and others. This manuscript aims to provide brief but comprehensive manuscript summaries highlighting the conclusions and scientific importance of seven exceptional manuscripts published in recent years within the field of drug metabolism. Two main topics within the field are reviewed: novel computational metabolic modeling approaches which provide complex outputs beyond site of metabolism predictions, and experimental approaches designed to discern the impacts of interindividual variability and species differences on drug metabolism. The computational approaches discussed provide novel outputs in metabolite structure and formation likelihood and/or extend beyond the saturated field of drug phase I metabolism, while the experimental metabolic pathways assessments aim to highlight the impacts of genetic polymorphisms and clinical animal model metabolic differences on human metabolism and subsequent health outcomes.

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.

Significance of Multiple Bioactivation Pathways for Meclofenamate as Revealed through Modeling and Reaction Kinetics

Meclofenamate is a nonsteroidal anti-inflammatory drug used in the treatment of mild-to-moderate pain yet poses a rare risk of hepatotoxicity through an unknown mechanism. Nonsteroidal anti-inflammatory drug (NSAID) bioactivation is a common molecular initiating event for hepatotoxicity. Thus, we hypothesized a similar mechanism for meclofenamate and leveraged computational and experimental approaches to identify and characterize its bioactivation. Analyses employing our XenoNet model indicated possible pathways to meclofenamate bioactivation into 19 reactive metabolites subsequently trapped into glutathione adducts. We describe the first reported bioactivation kinetics for meclofenamate and relative importance of those pathways using human liver microsomes. The findings validated only four of the many bioactivation pathways predicted by modeling. For experimental studies, dansyl glutathione was a critical trap for reactive quinone metabolites and provided a way to characterize adduct structures by mass spectrometry and quantitate yields during reactions. Of the four quinone adducts, we were able to characterize structures for three of them. Based on kinetics, the most efficient bioactivation pathway led to the monohydroxy para-quinone-imine followed by the dechloro-ortho-quinone-imine. Two very inefficient pathways led to the dihydroxy ortho-quinone and a likely multiply adducted quinone. When taken together, bioactivation pathways for meclofenamate accounted for approximately 13% of total metabolism. In sum, XenoNet facilitated prediction of reactive metabolite structures, whereas quantitative experimental studies provided a tractable approach to validate actual bioactivation pathways for meclofenamate. Our results provide a foundation for assessing reactive metabolite load more accurately for future comparative studies with other NSAIDs and drugs in general.

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.

The integrated use of in silico methods for the hepatotoxicity potential of Piper methysticum

Herbal products as supplements and therapeutic intervention have been used for centuries. However, their toxicities are not completely evaluated and the mechanisms are not clearly understood. Dried rhizome of the plant kava (Piper methysticum) is used for its anxiolytic, and sedative effects. The drug is also known for its hepatotoxicity potential. Major constituents of the plant were identified as kavalactones, alkaloids and chalcones in previous studies. Kava hepatotoxicity mechanism and the constituent that causes the toxicity have been debated for decades. In this paper, we illustrated the use of computational tools for the hepatotoxicity of kava constituents. The proposed mechanisms and major constituents that are most probably responsible for the toxicity have been scrutinized. According to the experimental and prediction results, the kava constituents play a substantial role in hepatotoxicity by some means or other via glutathione depletion, CYP inhibition, reactive metabolite formation, mitochondrial toxicity and cyclooxygenase activity. Some of the constituents, which have not been tested yet, were predicted to involve mitochondrial membrane potential, caspase-3 stimulation, and AhR activity. Since Nrf2 activation could be favorable for prevention of hepatotoxicity, we also suggest that these compounds should undergo testing given that they were predicted not to be activating Nrf2. Among the major constituents, alkaloids appear to be the least studied and the least toxic group in general. The outcomes of the study could help to appreciate the mechanisms and to prioritize the kava constituents for further testing.

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.

Quantitative structure activity relationship and molecular simulations for the exploration of natural potent VEGFR-2 inhibitors: an in silico anti-angiogenic study

VEGFR-2 has recently become an eye-catching molecular target for the novel therapeutic designs against cancer for its well known role in persuading angiogenesis in tumor cells. The current study set sights on the exploration of novel potent natural compound targeting VEGFR-2 via computational ligand-based modeling and database screening followed by binding pattern analysis, reactivity site prediction and MD simulation studies. The known 53 VEGFR-2 inhibitors (with IC50 ranging from 0.7 nM to 9700 nM) were headed for development of Ligand based pharmacophore model using 3 D QSAR pharmacophore generation module of DS Client. Training set inhibitors (23 compounds) were exploited to create pharmacophore model based on their chemical features. The model was validated through 30 test set inhibitors and exploited further for screening of 62,082 natural compounds from InterBioscreen natural compound database. Screened compounds further went through Drug-Likeliness study, ADMET prediction, Binding pattern analysis, In silico prediction of reactivity sites, Biological activity spectra prediction, pan assay interference compound identification and MD simulation analysis. Out of 5 screened compounds, Compound A and Compound B exhibited highest binding energy judged against the standard drug "Sorafenib". On further conducting reactivity site prediction, BAS prediction, and pan assay interference compound identification, Compound B exhibited better result which was carried forward for MD simulation study for 50 ns. MD simulation results suggested that Compound B exhibited more stable binding to the active site of VEGFR-2 without causing any conformational changes in protein-ligand complex. Thereby, the investigation proposes Compound B to hold potent antiangiogenic potential targeting VEGFR-2. [Formula: see text] Communicated by Ramaswamy H. Sarma.

Spectral deep learning for prediction and prospective validation of functional groups

State-of-the-art identification of the functional groups present in an unknown chemical entity requires the expertise of a skilled spectroscopist to analyse and interpret Fourier transform infra-red (FTIR), mass spectroscopy (MS) and/or nuclear magnetic resonance (NMR) data. This process can be time-consuming and error-prone, especially for complex chemical entities that are poorly characterised in the literature, or inefficient to use with synthetic robots producing molecules at an accelerated rate. Herein, we introduce a fast, multi-label deep neural network for accurately identifying all the functional groups of unknown compounds using a combination of FTIR and MS spectra. We do not use any database, pre-established rules, procedures, or peak-matching methods. Our trained neural network reveals patterns typically used by human chemists to identify standard groups. Finally, we experimentally validated our neural network, trained on single compounds, to predict functional groups in compound mixtures. Our methodology showcases practical utility for future use in autonomous analytical detection.

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.

Pesticide application inhibit the microbial carbonic anhydrase-mediated carbon sequestration in a soil microcosm

Heterotrophic system for carbon sequestration is gaining importance in the recent decades. Carbonic anhydrase (CA) is a major enzyme involved in carbon sequestration and biomineralization process. In this paper, we evaluate the effect of pesticide on CA activity using inhibitory assay. 2,4-D, being one of the most extensively used pesticide, being deleterious to soil health, its usage should be minimized to regain the soil health. Maximum inhibitory constant (K_i) was observed for 5% 2,4-D (49.53 mM) followed by 5% glyphosate (43.92 mM). The maximum Km increase with increase in pesticide concentration by 3.05-fold was in case of glyphosate which was higher than that of 2,4-D (2.08-fold) and dichlorvos (2.38-fold). Moreover, we evaluated the carbon sequestration using CA enzyme in the soil microcosm. In the present study, we identified the negative impact of 2,4-D on carbonic anhydrase produced by Bacillus halodurans PO15. The inhibition was a mixed type and had significantly lowered the carbon reduction to about 2.38 ± 0.17% in a soil microcosm study. Based on the molecular docking, the inhibition was contributed due to weak H-bonding interaction with amino acid residues (Gly65, Gly95, Val147, Ser150 and Gly65, Ser146, and Ser150).

A Time-Embedding Network Models the Ontogeny of 23 Hepatic Drug Metabolizing Enzymes

Pediatric patients are at elevated risk of adverse drug reactions, and there is insufficient information on drug safety in children. Complicating risk assessment in children, there are numerous age-dependent changes in the absorption, distribution, metabolism, and elimination of drugs. A key contributor to age-dependent drug toxicity risk is the ontogeny of drug metabolism enzymes, the changes in both abundance and type throughout development from the fetal period through adulthood. Critically, these changes affect not only the overall clearance of drugs but also exposure to individual metabolites. In this study, we introduce time-embedding neural networks in order to model population-level variation in metabolism enzyme expression as a function of age. We use a time-embedding network to model the ontogeny of 23 drug metabolism enzymes. The time-embedding network recapitulates known demographic factors impacting 3A5 expression. The time-embedding network also effectively models the nonlinear dynamics of 2D6 expression, enabling a better fit to clinical data than prior work. In contrast, a standard neural network fails to model these features of 3A5 and 2D6 expression. Finally, we combine the time-embedding model of ontogeny with additional information to estimate age-dependent changes in reactive metabolite exposure. This simple approach identifies age-dependent changes in exposure to valproic acid and dextromethorphan metabolites and suggests potential mechanisms of valproic acid toxicity. This approach may help researchers evaluate the risk of drug toxicity in pediatric populations.

A Fast Ab Initio Predictor Tool for Covalent Reactivity Estimation of Acrylamides

Thanks to their unique mode of action, covalent drugs represent an exceptional opportunity for drug design. After binding to a biologically relevant target system, covalent compounds form a reversible or irreversible covalent bond with a nucleophilic amino acid. Due to the inherently large binding energy of a covalent bond, covalent binders exhibit higher potencies and thus allow potentially lower drug dosages. However, a proper balancing of compound reactivity is key for the design of covalent binders, to achieve high levels of target inhibition while minimizing promiscuous covalent binding to nontarget proteins. In this work, we demonstrated the possibility to apply the electrophilicity index concept to estimate covalent compound reactivity. We tested this approach on acrylamides, one of the most prominent classes of covalent warheads. Our study clearly demonstrated that, for compounds with molecular weight (MW) below 250 Da, the electrophilicity index can be directly used to estimate compound reactivity. On the other hand, for leadlike molecules (MW > 250 Da) we implemented a new truncation algorithm that has to be applied before reactivity calculations. This algorithm can ensure the localization of HOMO/LUMO orbitals on the compound warhead and thus a correct estimation of its reactivity. Our results also indicate that caution should be used when employing the electrophilicity index to estimate the reactivity of nonterminal acrylamides. The nonparametric nature of this method and its reasonable computational cost make it a suitable tool to support covalent drug design.

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.

Deep learning for predicting toxicity of chemicals: a mini review

Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-throughput screening assays provide a higher-speed alternative for conventional toxicity testing. Advancement of high-throughput bioassay technology has greatly increased chemical toxicity data volumes in the past decade, pushing toxicology research into a "big data" era. However, traditional data analysis methods fail to effectively process large data volumes, presenting both a challenge and an opportunity for toxicologists. Deep learning, a machine learning method leveraging deep neural networks (DNNs), is a proven useful tool for building quantitative structure-activity relationship (QSAR) models for toxicity prediction utilizing these new large datasets. In this mini review, a brief technical background on DNNs is provided, and the current state of chemical toxicity prediction models built with DNNs is reviewed. In addition, relevant toxicity data sources are summarized, possible limitations are discussed, and perspectives on DNN utilization in chemical toxicity prediction are given.

MetScore: Site of Metabolism Prediction Beyond Cytochrome P450 Enzymes

The metabolism of xenobiotics by humans and other organisms is a complex process involving numerous enzymes that catalyze phase I (functionalization) and phase II (conjugation) reactions. Herein we introduce MetScore, a machine learning model that can predict both phase I and phase II reaction sites of drugs in a single prediction run. We developed cheminformatics workflows to filter and process reactions to obtain suitable phase I and phase II data sets for model training. Employing a recently developed molecular representation based on quantum chemical partial charges, we constructed random forest machine learning models for phase I and phase II reactions. After combining these models with our previous cytochrome P450 model and calibrating the combination against Bayer in-house data, we obtained the MetScore model that shows good performance, with Matthews correlation coefficients of 0.61 and 0.76 for diverse phase I and phase II reaction types, respectively. We validated its potential applicability to lead optimization campaigns for a new and independent data set compiled from recent publications. The results of this study demonstrate the usefulness of quantum-chemistry-derived molecular representations for reactivity prediction.

Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds

Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies

Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of time and money in developing cellular assays, animal models, and computational models to predict its occurrence in humans. Underperformance in predicting hepatotoxicity associated with drugs and drug candidates has been attributed to existing gaps in our understanding of the mechanisms involved in driving hepatic injury after these compounds perfuse and are metabolized by the liver. Herein we assess in vitro, in vivo (animal), and in silico strategies used to develop predictive DILI models. We address the effectiveness of several two- and three-dimensional in vitro cellular methods that are frequently employed in hepatotoxicity screens and how they can be used to predict DILI in humans. We also explore how humanized animal models can recapitulate human drug metabolic profiles and associated liver injury. Finally, we highlight the maturation of computational methods for predicting hepatotoxicity, the untapped potential of artificial intelligence for improving in silico DILI screens, and how knowledge acquired from these predictions can shape the refinement of experimental methods.

Computationally Assessing the Bioactivation of Drugs by N-Dealkylation

Cytochromes P450 (CYPs) oxidize alkylated amines commonly found in drugs and other biologically active molecules, cleaving them into an amine and an aldehyde. Metabolic studies usually neglect to report or investigate aldehydes, even though they can be toxic. It is assumed that they are efficiently detoxified into carboxylic acids and alcohols. Nevertheless, some aldehydes are reactive and escape detoxification pathways to cause adverse events by forming DNA and protein adducts. Herein, we modeled N-dealkylations that produce both amine and aldehyde metabolites and then predicted the reactivity of the aldehyde. This model used a deep learning approach previously developed by our group to predict other types of drug metabolism. In this study, we trained the model to predict N-dealkylation by human liver microsomes (HLM), finding that including isozyme-specific metabolism data alongside HLM data significantly improved results. The final HLM model accurately predicted the site of N-dealkylation within metabolized substrates (97% top-two and 94% area under the ROC curve). Next, we combined the metabolism, metabolite structure prediction, and previously published reactivity models into a bioactivation model. This combined model predicted the structure of the most likely reactive metabolite of a small validation set of drug-like molecules known to be bioactivated by N-dealkylation. Applying this model to approved and withdrawn medicines, we found that aldehyde metabolites produced from N-dealkylation may explain the hepatotoxicity of several drugs: indinavir, piperacillin, verapamil, and ziprasidone. Our results suggest that N-dealkylation may be an under-appreciated bioactivation pathway, especially in clinical contexts where aldehyde detoxification pathways are inhibited. Moreover, this is the first report of a bioactivation model constructed by combining a metabolism and reactivity model. These results raise hope that more comprehensive models of bioactivation are possible. The model developed in this study is available at http://swami.wustl.edu/xenosite/ .

Learning a Local-Variable Model of Aromatic and Conjugated Systems

A collection of new approaches to building and training neural networks, collectively referred to as deep learning, are attracting attention in theoretical chemistry. Several groups aim to replace computationally expensive ab initio quantum mechanics calculations with learned estimators. This raises questions about the representability of complex quantum chemical systems with neural networks. Can local-variable models efficiently approximate nonlocal quantum chemical features? Here, we find that convolutional architectures, those that only aggregate information locally, cannot efficiently represent aromaticity and conjugation in large systems. They cannot represent long-range nonlocality known to be important in quantum chemistry. This study uses aromatic and conjugated systems computed from molecule graphs, though reproducing quantum simulations is the ultimate goal. This task, by definition, is both computable and known to be important to chemistry. The failure of convolutional architectures on this focused task calls into question their use in modeling quantum mechanics. To remedy this heretofore unrecognized deficiency, we introduce a new architecture that propagates information back and forth in waves of nonlinear computation. This architecture is still a local-variable model, and it is both computationally and representationally efficient, processing molecules in sublinear time with far fewer parameters than convolutional networks. Wave-like propagation models aromatic and conjugated systems with high accuracy, and even models the impact of small structural changes on large molecules. This new architecture demonstrates that some nonlocal features of quantum chemistry can be efficiently represented in local variable models.

In vitro metabolism study of a novel P38 kinase inhibitor: in silico predictions, structure elucidation using MS/MS-I

Aim: Metabolism study of PH-797804, a promising newly developed drug for treatment of chronic inflammation which inhibits P38 mitogen-activated protein kinase. Materials & methods: Susceptibility of PH-797804 to metabolism was first investigated using SMARTCyp and Xenosite web servers. Molecular docking of the drug into CYP3A4 crystal structures evaluated binding interactions with active site. The predicted results were confirmed by in vitro incubation with rat S9 fraction. Metabolites of PH-797804 were identified by MS/MS. Results: A hydroxy metabolite and a cysteine/glutathione conjugate were detected. Computational prediction of reactive site of PH-797804 was conducted. Conclusion: The probable cysteine/glutathione adduct is indicative of potential drug chemical reactivity with potential to damage DNA and may provide guidance to the design of analogs with minimum toxicity.

Monohalogenated acetamide-induced cellular stress and genotoxicity are related to electrophilic softness and thiol/thiolate reactivity

Haloacetamides (HAMs) are cytotoxic, genotoxic, and mutagenic byproducts of drinking water disinfection. They are soft electrophilic compounds that form covalent bonds with the free thiol/thiolate in cysteine residues through an S_N2 reaction mechanism. Toxicity of the monohalogenated HAMs (iodoacetamide, IAM; bromoacetamide, BAM; or chloroacetamide, CAM) varied depending on the halogen substituent. The aim of this research was to investigate how the halogen atom affects the reactivity and toxicological properties of HAMs, measured as induction of oxidative/electrophilic stress response and genotoxicity. Additionally, we wanted to determine how well in silico estimates of electrophilic softness matched thiol/thiolate reactivity and in vitro toxicological endpoints. Each of the HAMs significantly induced nuclear Rad51 accumulation and ARE signaling activity compared to a negative control. The rank order of effect was IAM>BAM>CAM for Rad51, and BAM≈IAM>CAM for ARE. In general, electrophilic softness and in chemico thiol/thiolate reactivity provided a qualitative indicator of toxicity, as the softer electrophiles IAM and BAM were more thiol/thiolate reactive and were more toxic than CAM.