SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism

Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study

Optimization of the ADME properties and pharmacokinetic (PK) profile of compounds is one of the critical activities in any medicinal chemistry campaign to discover a future clinical candidate. Finding ways to expedite the process to address ADME/PK shortcomings and reduce the number of compounds to synthesize is highly valuable. This article provides practical guidelines and a case study on the use of ML ADME models to guide compound design in small molecule lead optimization. These guidelines highlight that ML models cannot have an impact in a vacuum: they help advance a program when they have the trust of users, are tuned to the needs of the program, and are integrated into decision-making processes in a way that complements and augments the expertise of chemists.

MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering

Metabolic processes can transform a drug into metabolites with different properties that may affect its efficacy and safety. Therefore, investigation of the metabolic fate of a drug candidate is of great significance for drug discovery. Computational methods have been developed to predict drug metabolites, but most of them suffer from two main obstacles: the lack of model generalization due to restrictions on metabolic transformation rules or specific enzyme families, and high rate of false-positive predictions. Here, we presented MetaPredictor, a rule-free, end-to-end and prompt-based method to predict possible human metabolites of small molecules including drugs as a sequence translation problem. We innovatively introduced prompt engineering into deep language models to enrich domain knowledge and guide decision-making. The results showed that using prompts that specify the sites of metabolism (SoMs) can steer the model to propose more accurate metabolite predictions, achieving a 30.4% increase in recall and a 16.8% reduction in false positives over the baseline model. The transfer learning strategy was also utilized to tackle the limited availability of metabolic data. For the adaptation to automatic or non-expert prediction, MetaPredictor was designed as a two-stage schema consisting of automatic identification of SoMs followed by metabolite prediction. Compared to four available drug metabolite prediction tools, our method showed comparable performance on the major enzyme families and better generalization that could additionally identify metabolites catalyzed by less common enzymes. The results indicated that MetaPredictor could provide a more comprehensive and accurate prediction of drug metabolism through the effective combination of transfer learning and prompt-based learning strategies.

In Silico Investigation against Inhibitors of Alpha-Amylase Using Structure-based Screening, Molecular Docking, and Molecular Simulations Studies

Type-II diabetes mellitus is a chronic disorder that results from fluctuations in the glucose level leading to hyperglycemia with severe adverse effects increasing worldwide. Alpha-Amylase is the key enzyme involved in the mechanism of glucose formation therefore Alpha-Amylase inhibitors have become a therapeutic target in the development of new leads as they have the potential to suppress glucose levels. Existing drugs targeting Alpha-Amylase highlight major drawbacks in terms of poor absorption rate that causes several gastrointestinal issues. So, this research is aimed to develop novel inhibitors interacting with Alpha-Amylase's active site using structural-based screening, binding pattern analysis, and molecular dynamic simulation. Hence, to search for a potential lead, we analyzed a total of 133 valiolamine derivatives and 535 desoxynojirimycin derivatives that exhibited drug-like properties screened through Lipinski filters. Virtual screening followed by binding interaction analysis we identified ten compounds that exhibited better binding energy scores compared to the standard drugs voglibose and miglitol, used in our study. The docking analysis, ADMET and metabolic site prediction estimated the best top two compounds with good drug profiles. Further, top compounds VG9 and VG15 were promoted to simulation study using the Biovia Discovery study to access the stability at a time interval of 100 ns. MD simulation results revealed that our compound VG9 possesses better conformational stability in the complex to the active site residues of Alpha-Amylase target protein than standard drug voglibose. Thus, our investigation revealed that compound VG9 also exhibits the best pharmacokinetic as well as binding affinity results and could act as a potential lead compound targeting Alpha-Amylase for Type II diabetes.

Exploring the selectivity of cytochrome P450 for enhanced novel anticancer agent synthesis

Cytochrome P450 monooxygenases are an extensive and unique class of enzymes, which can regio- and stereo-selectively functionalise hydrocarbons by way of oxidation reactions. These enzymes are naturally occurring but have also been extensively applied in a synthesis context, where they are used as efficient biocatalysts. Recently, a biosynthetic pathway where a cytochrome P450 monooxygenase catalyses a critical step of the pathway was uncovered, leading to the production of a number of products that display high antitumour potency. In this work, we use computational techniques to gain insight into the factors that determine the relative yields of the different products. We use conformational search algorithms to understand the substrate stereochemistry. On a machine-learned 3D protein structure, we use molecular docking to obtain a library of favourable poses for substrate-protein interaction. With molecular dynamics, we investigate the most favourable poses for reactivity on a molecular level, allowing us to investigate which protein-substrate interactions favour a given product and thus gain insight into the product selectivity.

Effect of sublethal concentrations of the bioinsecticide spinosyn treatment of Trichoplusia ni eggs on the caterpillar and its parasitoid, Trichogramma brassicae

BACKGROUND

Integrated Pest Management (IPM) seeks to combine multiple management strategies for optimal pest control. One method that is successfully employed in IPM is the use of beneficial organisms. However, in severe circumstances when pest insects exceed threshold limits, insecticides may still need to be implemented. Thus, understanding the effects of insecticides on biocontrol agents, such as parasitoid wasps, is paramount to ensure sustainable agroecosystems. Sublethal effects of the bioinsecticide spinosyn, a mixture of the bacterial Saccharopolyspora spinosa (Mertz and Yao) fermentation products spinosyn A and D, on eggs of Trichoplusia ni (Hübner), a cruciferous crop pest, and its egg parasitoid Trichogramma brassicae (Bezdenko) was investigated.

RESULTS

The LC50 for spinosyn A and D (dissolved in ethanol) on T. ni eggs is 54 ng mL-1. Transcriptomics on caterpillars (1st and 3rd instars) that hatched from eggs treated with sublethal concentrations of spinosyn identified the upregulation of several genes encoding proteins that may be involved in insecticide resistance including detoxification enzymes, such as cytochrome P450s, glutathione S-transferases and esterases. Sublethal T. ni egg treatments did not affect parasitoid emergence, however, there was a marked increase in the size of T. brassicae hind tibia and wings that emerged from spinosyn-treated eggs.

CONCLUSIONS

For the caterpillar, treatment of eggs with sublethal concentrations of spinosyn may induce insecticide resistance mechanisms. For the parasitoids, their increased size when reared in spinosyn-treated eggs suggests that the emerged wasps may have higher performance. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

DeepP450: Predicting Human P450 Activities of Small Molecules by Integrating Pretrained Protein Language Model and Molecular Representation

Cytochrome P450 enzymes (CYPs) play a crucial role in Phase I drug metabolism in the human body, and CYP activity toward compounds can significantly affect druggability, making early prediction of CYP activity and substrate identification essential for therapeutic development. Here, we established a deep learning model for assessing potential CYP substrates, DeepP450, by fine-tuning protein and molecule pretrained models through feature integration with cross-attention and self-attention layers. This model exhibited high prediction accuracy (0.92) on the test set, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.89 to 0.98 in substrate/nonsubstrate predictions across the nine major human CYPs, surpassing current benchmarks for CYP activity prediction. Notably, DeepP450 uses only one model to predict substrates/nonsubstrates for any of the nine CYPs and exhibits certain generalizability on novel compounds and different categories of human CYPs, which could greatly facilitate early stage drug design by avoiding CYP-reactive compounds.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Trichoplusia ni Transcriptomic Responses to the Phytosaponin Aglycone Hederagenin: Sex-Related Differences

Many plant species, particularly legumes, protect themselves with saponins. Previously, a correlation was observed between levels of oleanolic acid-derived saponins, such as hederagenin-derived compounds, in the legume Medicago truncatula and caterpillar deterrence. Using concentrations that reflect the foliar levels of hederagenin-type saponins, the sapogenin hederagenin was not toxic to 4th instar caterpillars of the cabbage looper Trichoplusia ni nor did it act as a feeding deterrent. Female caterpillars consumed more diet than males, presumably to obtain the additional nutrients required for oogenesis, and are, thus, exposed to higher hederagenin levels. When fed the hederagenin diet, male caterpillars expressed genes encoding trypsin-like proteins (LOC113500509, LOC113501951, LOC113501953, LOC113501966, LOC113501965, LOC113499659, LOC113501950, LOC113501948, LOC113501957, LOC113501962, LOC113497819, LOC113501946, LOC113503910) as well as stress-responsive (LOC113503484, LOC113505107) proteins and cytochrome P450 6B2-like (LOC113493761) at higher levels than females. In comparison, female caterpillars expressed higher levels of cytochrome P450 6B7-like (LOC113492289). Bioinformatic tools predict that cytochrome P450s could catalyze the oxygenation of hederagenin which would increase the hydrophilicity of the compound. Expression of a Major Facilitator Subfamily (MFS) transporter (LOC113492899) showed a hederagenin dose-dependent increase in gene expression suggesting that this transporter may be involved in sapogenin efflux. These sex-related differences in feeding and detoxification should be taken into consideration in insecticide evaluations to minimize pesticide resistance.

Insight into chemically reactive metabolites of aliphatic amine pollutants: A de novo prediction strategy and case study of sertraline

The uncommon metabolic pathways of organic pollutants are easily overlooked, potentially leading to idiosyncratic toxicity. Prediction of their biotransformation associated with the toxic effects is the very purpose that this work focuses, to develop a de novo method to mechanistically predict the reactive toxicity pathways of uncommon metabolites from start aliphatic amine molecules, which employed sertraline triggered by CYP450 enzymes as a model system, as there are growing concerns about the effects on human health posed by antidepressants in the aquatic environment. This de novo prediction strategy combines computational and experimental methods, involving DFT calculations upon sequential growth, in vitro and in vivo assays, dissecting chemically reactive mechanism relevant to toxicity, and rationalizing the fundamental factors. Significantly, desaturation and debenzylation-aromatization as the emerging metabolic pathways of sertraline have been elucidated, with the detection of DNA adducts of oxaziridine metabolite in mice, highlighting the potential reactive toxicity. Molecular orbital analysis supports the reactivity preference for toxicological-relevant C-N desaturation over N-hydroxylation of sertraline, possibly extended to several other aliphatic amines based on the Bell-Evans-Polanyi principle. It was further validated toward some other wide-concerned aliphatic amine pollutants involving atrazine, ε-caprolactam, 6PPD via in silico and in vitro assays, thereby constituting a complete path for de novo prediction from case study to general applications.

In vitro metabolism of the emerging contaminant 6PPD-quinone in human and rat liver microsomes: Kinetics, pathways, and mechanism

N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone (6PPD-Q) is an ozonation product of the rubber antioxidant N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD). 6PPD-Q has recently been detected in various environmental media, which may enter the human body via inhalation and skin contact pathways. However, the human metabolism of 6PPD-Q has remained unknown. This study investigated the in vitro Cytochrome P450-mediated metabolism of 6PPD-Q in human and rat liver microsomes (HLMs and RLMs). 6PPD-Q was significantly metabolized at lower concentrations but slowed at high concentrations. The intrinsic clearance (CLint) of 6PPD-Q was 21.10 and 18.58 μL min-1 mg-1 protein of HLMs and RLMs, respectively, suggesting low metabolic ability compared with other reported pollutants. Seven metabolites and one intermediate were identified, and metabolites were predicted immunotoxic or mutagenic toxicity. Mono- and di-oxygenation reactions were the main phase I in vitro metabolic pathways. Enzyme inhibition experiments and molecular docking techniques were further used to reveal the metabolic mechanism. CYP1A2, 3A4, and 2C19, especially CYP1A2, play critical roles in 6PPD-Q metabolism in HLMs, whereas 6PPD-Q is extensively metabolized in RLMs. Our study is the first to demonstrate the in vitro metabolic profile of 6PPD-Q in HLMs and RLMs. The results will significantly contribute to future human health management targeting the emerging pollutant 6PPD-Q.

Impact of plastic-related compounds on the gene expression signature of HepG2 cells transfected with CYP3A4

The presence of plastic and microplastic within the oceans as well as in marine flora and fauna have caused a multitude of problems that have been the topic of numerous investigations for many years. However, their impact on human health remains largely unknown. Such plastic and microplastic particles have been detected in blood and placenta, underlining their ability to enter the human body. Plastics also contain other compounds, such as plasticizers, antioxidants, or dyes, whose impact on human health is currently being studied. Critical enzymes within the metabolism of endogenous molecules, especially of xenobiotics, are the cytochrome P450 monooxygenases (CYPs). Although their importance in maintaining cellular balance has been confirmed, their interactions with plastics and related products are poorly understood. In this study, the possible relationship between different plastic-related compounds and CYP3A4 as one of the most important CYPs was analyzed using hepatic cells overexpressing this enzyme. Beginning with virtual compound screening and molecular docking of more than 1000 plastic-related compounds, several candidates were identified to interact with CYP3A4. In a second step, RNA-sequencing was used to study in detail the transcriptome-wide gene expression levels affected by the selected compounds. Three candidate molecules ((2,2'-methylenebis(6-tert-butyl-4-methylphenol), 1,1-bis(3,5-di-tert-butyl-2-hydroxyphenyl)ethane, and 2,2'-methylenebis(6-cyclohexyl-4-methylphenol)) had an excellent binding affinity to CYP3A4 in-silico as well as cytotoxic effects and interactions with several metabolic pathways in-vitro. We identified common pathways influenced by all three selected plastic-related compounds. In particular, the suppression of pathways related to mitosis and 'DNA-templated DNA replication' which were confirmed by cell cycle analysis and single-cell gel electrophoresis. Furthermore, several mis-regulated metabolic and inflammation-related pathways were identified, suggesting the induction of hepatotoxicity at different levels. These findings imply that these compounds may cause liver problems subsequently affecting the entire organism.

Removal of enrofloxacin using Eichhornia crassipes in microcosm wetlands

The global consumption of antibiotics leads to their possible occurrence in the environment. In this context, nature-based solutions (NBS) can be used to sustainably manage and restore natural and modified ecosystems. In this work, we studied the efficiency of the NBS free-water surface wetlands (FWSWs) using Eichhornia crassipes in microcosm for enrofloxacin removal. We also explored the behavior of enrofloxacin in the system, its accumulation and distribution in plant tissues, the detoxification mechanisms, and the possible effects on plant growth. Enrofloxacin was initially taken up by E. crassipes (first 100 h). Notably, it accumulated in the sediment at the end of the experimental time. Removal rates above 94% were obtained in systems with sediment and sediment + E. crassipes. In addition, enrofloxacin was found in leaves, petioles, and roots (8.8-23.6 µg, 11-78.3 µg, and 10.2-70.7 µg, respectively). Furthermore, enrofloxacin, the main degradation product (ciprofloxacin), and other degradation products were quantified in the tissues and chlorosis was observed on days 5 and 9. Finally, the degradation products of enrofloxacin were analyzed, and four possible metabolic pathways of enrofloxacin in E. crassipes were described.

Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation

The ability to determine and predict metabolically labile atom positions in a molecule (also called "sites of metabolism" or "SoMs") is of high interest to the design and optimization of bioactive compounds, such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have become available, many of which include a machine-learning component. The bottleneck in advancing these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data. Pharmaceutical companies typically have measured metabolism data available for several hundred to several thousand compounds. However, even for metabolism experts, interpreting these data and assigning SoMs are challenging and time-consuming. Therefore, a significant proportion of the potential of the existing metabolism data, particularly in machine learning, remains dormant. Here, we report on the development and validation of an active learning approach that identifies the most informative atoms across molecular data sets for SoM annotation. The active learning approach, built on a highly efficient reimplementation of SoM predictor FAME 3, enables experts to prioritize their SoM experimental measurements and annotation efforts on the most rewarding atom environments. We show that this active learning approach yields competitive SoM predictors while requiring the annotation of only 20% of the atom positions required by FAME 3. The source code of the approach presented in this work is publicly available.

Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules

We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications to bond dissociation energy (BDE) prediction, for example, in the context of cytochrome P450 (CYP) metabolism. The transferability of the MACE potential was validated on the COMP6 data set, containing only closed-shell molecules, where it reaches better accuracy than the readily available general ANI-2x potential. MACE achieves similar accuracy on two CYP metabolism-specific data sets, which include open- and closed-shell structures. This model enables us to calculate the aliphatic C-H BDE, which allows us to compare reaction energies of hydrogen abstraction, which is the rate-limiting step of the aliphatic hydroxylation reaction catalyzed by CYPs. On the "CYP 3A4" data set, MACE achieves a BDE RMSE of 1.37 kcal/mol and better prediction of BDE ranks than alternatives: the semiempirical AM1 and GFN2-xTB methods and the ALFABET model that directly predicts bond dissociation enthalpies. Finally, we highlight the smoothness of the MACE potential over paths of sp3C-H bond elongation and show that a minimal extension is enough for the MACE model to start finding reasonable minimum energy paths of methoxy radical-mediated hydrogen abstraction. Altogether, this work lays the ground for further extensions of scope in terms of chemical elements, (CYP-mediated) reaction classes and modeling the full reaction paths, not only BDEs.

Optimization of 3-Cyano-7-cyclopropylamino-pyrazolo[1,5-a]pyrimidines toward the Development of an In Vivo Chemical Probe for CSNK2A

3-Cyano-7-cyclopropylamino-pyrazolo[1,5-a]pyrimidines, including the chemical probe SGC-CK2-1, are potent and selective inhibitors of CSNK2A in cells but have limited utility in animal models due to their poor pharmacokinetic properties. While developing analogues with reduced intrinsic clearance and the potential for sustained exposure in mice, we discovered that phase II conjugation by GST enzymes was a major metabolic transformation in hepatocytes. A protocol for codosing with ethacrynic acid, a covalent reversible GST inhibitor, was developed to improve the exposure of analogue 2h in mice. A double codosing protocol, using a combination of ethacrynic acid and irreversible P450 inhibitor 1-aminobenzotriazole, increased the blood level of 2h by 40-fold at a 5 h time point.

Comparison and summary of in silico prediction tools for CYP450-mediated drug metabolism

The cytochrome P450 (CYP450) enzyme system is responsible for the metabolism of more than two-thirds of xenobiotics. This review summarizes reports of a series of in silico tools for CYP450 enzyme-drug interaction predictions, including the prediction of sites of metabolism (SOM) of a drug and the identification of inhibitor/substrates for CYP subtypes. We also evaluated four prediction tools to identify CYP inhibitors utilizing 52 of the most frequently prescribed drugs. ADMET Predictor and CYPlebrity demonstrated the best performance. We hope that this review provides guidance for choosing appropriate enzyme prediction tools from a variety of in silico platforms to meet individual needs. Such predictions are useful for medicinal chemists to prioritize their designed compounds for further drug discovery.

Targeting the Grb2 cSH3 domain: Design, synthesis and biological evaluation of the first series of modulators

Growth factor receptor bound protein 2 (Grb2) is an adaptor protein featured by a nSH3-SH2-cSH3 domains. Grb2 finely regulates important cellular pathways such as growth, proliferation and metabolism and a minor lapse of this tight control may totally change the entire pathway to the oncogenic. Indeed, Grb2 is found overexpressed in many tumours type. Consequently, Grb2 is an attractive therapeutic target for the development of new anticancer drug. Herein, we reported the synthesis and the biological evaluation of a series of Grb2 inhibitors, developed starting from a hit-compound already reported by this research unit. The newly synthesized compounds were evaluated by kinetic binding experiments, and the most promising derivatives were assayed in a short panel of cancer cells. Five of the newly synthesized derivatives proved to be able to bind the targeted protein with valuable inhibitory concentration in one-digit micromolar concentration. The most active compound of this series, derivative 12, showed an inhibitory concentration of about 6 μM for glioblastoma and ovarian cancer cells, and an IC50 of 1.67 for lung cancer cell. For derivative 12, the metabolic stability and the ROS production was also evaluated. The biological data together with the docking studies led to rationalize an early structure activity relationship.

Artificial Intelligence for Computer-Aided Drug Discovery

The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribute effectively in solving many challenges and constraints in the traditional development of the drug. Traditionally, large-scale chemical libraries are screened to find one promising medicine. In recent years, more reasonable structure-based drug design approaches have avoided the first screening phases while still requiring chemists to design, synthesize, and test a wide range of compounds to produce possible novel medications. The process of turning a promising chemical into a medicinal candidate can be expensive and time-consuming. Additionally, a new medication candidate may still fail in clinical trials even after demonstrating promise in laboratory research. In fact, less than 10% of medication candidates that undergo Phase I trials really reach the market. As a consequence, the unmatched data processing power of AI systems may expedite and enhance the drug development process in four different ways: by opening up links to novel biological systems, superior or distinctive chemistry, greater success rates, and faster and less expensive innovation trials. Since these technologies may be used to address a variety of discovery scenarios and biological targets, it is essential to comprehend and distinguish between use cases. As a result, we have emphasized how AI may be used in a variety of areas of the pharmaceutical sciences, including in-depth opportunities for drug research and development.

Evaluating chemotherapeutic potential of soya-isoflavonoids against high penetrance genes in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive molecular subtype of breast cancer (BC) associated with a poor prognosis. Owing to the structural similarity with 17-β-estradiol, consumption of soya-isoflavonoids are associated with a reduced rate of hormone-receptive BC incidence, but their role in TNBC is not deciphered in detail. This present study thus aims to investigate the therapeutic binding dynamics of dietary soya-flavonoids with the six high penetrance (HP) receptors in TNBC, viz. BRCA1, BRCA2, PALB2, PTEN, STK11 and TP53. Out of the 14 soya-flavonoids screened based on ADMET descriptors and several other physicochemical, bioavailability, drug and lead-likeness properties, four hits were shortlisted (Daidzein, Genistein, Glycitein and Biochanin A). Docking and molecular dynamics (MD) simulation revealed Genistein as the most potential multi-target inhibitor of the six TNBC HP genes. Additionally, Genistein exhibited excellent binding specificity with PTEN, a potent mediator of the PI3K signaling pathway in TNBC. The binding interaction of PTEN and Genistein was further compared against a standardized FDA-approved chemotherapeutic inhibitor, Olaparib, computed through various MD trajectory analysis, principal component analysis and computation of free energy landscape. This study reveals a comparatively better binding dynamics of PTEN-Genistein than PTEN-Olaparib. With a significant global surge in biomarker-based precision therapeutics in oncology, the results of this exhaustive in-silico study thus encourage the prospect of validating PTEN as a druggable target of Genistein, a unique drug-receptor combination in the future.Communicated by Ramaswamy H. Sarma.

The Effect of Plastic-Related Compounds on Transcriptome-Wide Gene Expression on CYP2C19-Overexpressing HepG2 Cells

In recent years, plastic and especially microplastic in the oceans have caused huge problems to marine flora and fauna. Recently, such particles have also been detected in blood, breast milk, and placenta, underlining their ability to enter the human body, presumably via the food chain and other yet-unknown mechanisms. In addition, plastic contains plasticizers, antioxidants, or lubricants, whose impact on human health is also under investigation. At the cellular level, the most important enzymes involved in the metabolism of xenobiotic compounds are the cytochrome P450 monooxygenases (CYPs). Despite their extensive characterization in the maintenance of cellular balance, their interactions with plastic and related products are unexplored. In this study, the possible interactions between several plastic-related compounds and one of the most important cytochromes, CYP2C19, were analyzed. By applying virtual compound screening and molecular docking to more than 1000 commercially available plastic-related compounds, we identified candidates that are likely to interact with this protein. A growth inhibition assay confirmed their cytotoxic activity on a CYP2C19-transfected hepatic cell line. Subsequently, we studied the effect of the selected compounds on the transcriptome-wide gene expression level by conducting RNA sequencing. Three candidate molecules were identified, i.e., 2,2'-methylene bis(6-tert-butyl-4-methylphenol), 1,1-bis(3,5-di-tert-butyl-2-hydroxyphenyl) ethane, and 2,2'-methylene bis(6-cyclohexyl-4-methylphenol)), which bound with a high affinity to CYP2C19 in silico. They exerted a profound cytotoxicity in vitro and interacted with several metabolic pathways, of which the 'cholesterol biosynthesis process' was the most affected. In addition, other affected pathways involved mitosis, DNA replication, and inflammation, suggesting an increase in hepatotoxicity. These results indicate that plastic-related compounds could damage the liver by affecting several molecular pathways.

Optimization of 3-Cyano-7-cyclopropylamino-pyrazolo[1,5-a]pyrimidines Toward the Development of an In Vivo Chemical Probe for CSNK2A

3-cyano-7-cyclopropylamino-pyrazolo[1,5-a]pyrimidines, including the chemical probe SGC-CK2-1, are potent and selective inhibitors of CSNK2A in cells but have limited utility in animal models due to their poor pharmacokinetic properties. While developing analogs with reduced intrinsic clearance and the potential for sustained exposure in mice, we discovered that Phase II conjugation by GST enzymes was a major metabolic transformation in hepatocytes. A protocol for co-dosing with ethacrynic acid, a covalent reversible GST inhibitor, was developed to improve the exposure of analog 2h in mice. A double co-dosing protocol, using a combination of ethacrynic acid and irreversible P450 inhibitor 1-aminobenzotriazole increased the blood level of 2h by 40-fold at a 5 h time point.

Prediction of Sites of Metabolism of CYP3A4 Substrates Utilizing Docking-Derived Geometric Features

Cytochrome P450 3A4 (CYP3A4) is one of the major drug-metabolizing enzymes in the human body and is responsible for the metabolism of ∼50% of clinically used drugs. Therefore, the identification of the compound's sites of metabolism (SOMs) mediated by CYP3A4 is of utmost importance in the early stage of drug discovery and development. Herein, docking-based approaches incorporating geometric features were used for SOMs prediction of CYP3A4 substrates. The cross-docking poses of a relatively large data set containing 474 substrates were analyzed in depth, and a widely observed geometric pattern called the close proximity of SOMs was derived from the poses. On the basis of the close proximity, several structure-based models have been constructed, which demonstrated better performance than those structure-based models using the criterion of Fe-SOM distance. For further improving the prediction performance, the structure-based models were also combined with the well-known ligand-based model SMARTCyp. One combined model exhibited good performance on the SOMs prediction of an external substrate set containing kinase inhibitors, PROTACs, approved drugs, and some lead compounds.

Green alga Chlamydomonas reinhardtii can effectively remove diclofenac from the water environment - A new perspective on biotransformation

The use of unicellular algae to remove xenobiotics (including drugs) from wastewaters is one of the rapidly developing areas of environmental protection. Numerous data indicate that for efficient phycoremediation three processes are important, i.e. biosorption, bioaccumulation, and biotransformation. Although biosorption and bioaccumulation do not raise any serious doubts, biotransformation is more problematic since its products can be potentially more toxic than the parent compounds posing a threat to organisms living in a given environment, including organisms that made this transformation. Thus, two questions need to be answered before the proper algae strain is chosen for phycoremediation, namely what metabolites are produced during biotransformation, and how resistant is the analyzed strain to a mixture of parent compound and metabolites that appear over the course of culture? In this work, we evaluated the remediation potential of the model green alga Chlamydomonas reinhardtii in relation to non-steroidal anti-inflammatory drugs (NSAIDs), as exemplified by diclofenac. To achieve this, we analysed the susceptibility of C. reinhardtii to diclofenac as well as its capability to biosorption, bioaccumulation, and biotransformation of the drug. We have found that even at a relatively high concentration of diclofenac the algae maintained their vitality and were able to remove (37.7%) DCF from the environment. A wide range of phase I and II metabolites of diclofenac (38 transformation products) was discovered, with many of them characteristic rather for animal and bacterial biochemical pathways than for plant metabolism. Due to such a large number of detected products, 18 of which were not previously reported, the proposed scheme of diclofenac transformation by C. reinhardtii not only significantly contributes to broadening the knowledge in this field, but also allows to suggest possible pathways of degradation of xenobiotics with a similar structure. It is worth pointing out that a decrease in the level of diclofenac in the media observed in this study cannot be fully explained by biotransformation (8.4%). The mass balance analysis indicates that other processes (total 22%), such as biosorption, a non-extractable residue formation, or complete decomposition in metabolic cycles can be involved in the diclofenac disappearance, and those findings open the prospects of further research.

Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, offering the potential to speed up drug development and improve clinical success rates. This review highlights recent advances in AI-based drug metabolism and excretion prediction, including deep learning and machine learning algorithms. We provide a list of public data sources and free prediction tools for the research community. We also discuss the challenges associated with the development of AI models for drug metabolism and excretion prediction and explore future perspectives in the field. We hope this will be a helpful resource for anyone who is researching in silico drug metabolism, excretion, and pharmacokinetic properties.

Metabolite Identification of Isopropoxy Benzene Guanidine in Rat Liver Microsomes by Using UHPLC-Q-TOF-MS/MS

Isopropoxy benzene guanidine (IBG) is a guanidine derivative with antibacterial activity against multidrug-resistant bacteria. A few studies have revealed the metabolism of IBG in animals. The aim of the current study was to identify potential metabolic pathways and metabolites of IBG. The detection and characterization of metabolites were performed with high-performance liquid chromatography tandem mass spectrometry (UHPLC-Q-TOF-MS/MS). Seven metabolites were identified from the microsomal incubated samples by using the UHPLC-Q-TOF-MS/MS system. The metabolic pathways of IBG in the rat liver microsomes involved O-dealkylation, oxygenation, cyclization, and hydrolysis. Hydroxylation was the main metabolic pathway of IBG in the liver microsomes. This research investigated the in vitro metabolism of IBG to provide a basis for the further pharmacology and toxicology of this compound.

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products

MOTIVATION

While traditionally utilized for identifying site-specific metabolic activity within a compound to alter its interaction with a metabolizing enzyme, predicting the site-of-metabolism (SOM) is essential in analyzing the promiscuity of enzymes on substrates. The successful prediction of SOMs and the relevant promiscuous products has a wide range of applications that include creating extended metabolic models (EMMs) that account for enzyme promiscuity and the construction of novel heterologous synthesis pathways. There is therefore a need to develop generalized methods that can predict molecular SOMs for a wide range of metabolizing enzymes.

RESULTS

This article develops a Graph Neural Network (GNN) model for the classification of an atom (or a bond) being an SOM. Our model, GNN-SOM, is trained on enzymatic interactions, available in the KEGG database, that span all enzyme commission numbers. We demonstrate that GNN-SOM consistently outperforms baseline machine learning models, when trained on all enzymes, on Cytochrome P450 (CYP) enzymes, or on non-CYP enzymes. We showcase the utility of GNN-SOM in prioritizing predicted enzymatic products due to enzyme promiscuity for two biological applications: the construction of EMMs and the construction of synthesis pathways.

AVAILABILITY AND IMPLEMENTATION

A python implementation of the trained SOM predictor model can be found at https://github.com/HassounLab/GNN-SOM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Kolocouris A. Synthesis and Testing of Analogs of the Tuberculosis Drug Candidate SQ109 against Bacteria and Protozoa: Identification of Lead Compounds against Mycobacterium abscessus and Malaria Parasites

SQ109 is a tuberculosis drug candidate that has high potency against Mycobacterium tuberculosis and is thought to function at least in part by blocking cell wall biosynthesis by inhibiting the MmpL3 transporter. It also has activity against bacteria and protozoan parasites that lack MmpL3, where it can act as an uncoupler, targeting lipid membranes and Ca2+ homeostasis. Here, we synthesized 18 analogs of SQ109 and tested them against M. smegmatis, M. tuberculosis, M. abscessus, Bacillus subtilis, and Escherichia coli, as well as against the protozoan parasites Trypanosoma brucei, T. cruzi, Leishmania donovani, L. mexicana, and Plasmodium falciparum. Activity against the mycobacteria was generally less than with SQ109 and was reduced by increasing the size of the alkyl adduct, but two analogs were ∼4-8-fold more active than SQ109 against M. abscessus, including a highly drug-resistant strain harboring an A309P mutation in MmpL3. There was also better activity than found with SQ109 with other bacteria and protozoa. Of particular interest, we found that the adamantyl C-2 ethyl, butyl, phenyl, and benzyl analogs had 4-10× increased activity against P. falciparum asexual blood stages, together with low toxicity to a human HepG2 cell line, making them of interest as new antimalarial drug leads. We also used surface plasmon resonance to investigate the binding of inhibitors to MmpL3 and differential scanning calorimetry to investigate binding to lipid membranes. There was no correlation between MmpL3 binding and M. tuberculosis or M. smegmatis cell activity, suggesting that MmpL3 is not a major target in mycobacteria. However, some of the more active species decreased lipid phase transition temperatures, indicating increased accumulation in membranes, which is expected to lead to enhanced uncoupler activity.

Galantamine Based Novel Acetylcholinesterase Enzyme Inhibitors: A Molecular Modeling Design Approach

Acetylcholinesterase (AChE) enzymes play an essential role in the development of Alzheimer's disease (AD). Its excessive activity causes several neuronal problems, particularly psychopathies and neuronal cell death. A bioactive pose on the hAChE B site of the human acetylcholinesterase (hAChE) enzyme employed in this investigation, which was obtained from the Protein Data Bank (PDB ID 4EY6), allowed for the prediction of the binding affinity and free binding energy between the protein and the ligand. Virtual screening was performed to obtain structures similar to Galantamine (GNT) with potential hAChE activity. The top 200 hit compounds were prioritized through the use of filters in ZincPharmer, with special features related to the pharmacophore. Critical analyses were carried out, such as hierarchical clustering analysis (HCA), ADME/Tox predictions, molecular docking, molecular simulation studies, synthetic accessibility (SA), lipophilicity, water solubility, and hot spots to confirm the stable binding of the two promising molecules (ZINC16951574-LMQC2, and ZINC08342556-LMQC5). The metabolism prediction, with metabolites M3-2, which is formed by Glutathionation reaction (Phase II), M1-2, and M2-2 formed from the reaction of S-oxidation and Aliphatic hydroxylation (Phase I), were both reactive but with no side effects. Theoretical synthetic routes and prediction of synthetic accessibility for the most promising compounds are also proposed. In conclusion, this study shows that in silico modeling can be used to create new drug candidate inhibitors for hAChE. The compounds ZINC16951574-LMQC2, and ZINC08342556-LMQC5 are particularly promising for oral administration because they have a favorable drug-likeness profile, excellent lipid solubility, high bioavailability, and adequate pharmacokinetics.

Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski's rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling. The commercial specialized software for performing such predictions, which is usually costly, is, in many cases, not among the possibilities for research laboratories in academia or at small biotech companies. Nevertheless, in recent years, many free online tools have become available, allowing, more or less accurately, for the prediction of the most relevant pharmacokinetic parameters. This paper studies 18 free web servers capable of predicting ADMET properties and analyzed their advantages and disadvantages, their model-based calculations, and their degree of accuracy by considering the experimental data reported for a set of 24 FDA-approved tyrosine kinase inhibitors (TKIs) as a model of a research project.

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.

Carbazole-based semicarbazones and hydrazones as multifunctional anti-Alzheimer agents

With the aim to combat a multi-faceted neurodegenerative Alzheimer's disease (AD), a series of carbazole-based semicarbazide and hydrazide derivatives were designed, synthesized and assessed for their cholinesterase (ChE) inhibitory, antioxidant and biometal chelating activity. Among them, (E)-2-((9-ethyl-9H-carbazol-3-yl)methylene)-N-(pyridin-2-yl)hydrazinecarbothioamide (62) and (E)-2-((9-ethyl-9H-carbazol-3-yl)methylene)-N-(5-chloropyridin-2-yl)hydrazinecarbothioamide (63) emerged as the premier candidates with good ChE inhibitory activities (IC₅₀ values of 1.37 µM and 1.18 µM for hAChE, IC₅₀ values of 2.69 µM and 3.31 µM for EqBuChE, respectively). All the test compounds displayed excellent antioxidant activity (reduction percentage of DPPH values for compounds (62) and (63) were 85.67% and 84.49%, respectively at 100 µM concentration). Compounds (62) and (63) conferred specific copper ion chelating property in metal chelation study. Molecular docking studies of compounds (62) and (63) indicate strong interactions within the active sites of both the ChE enzymes. Besides that, these compounds also exhibited significant in silico drug-like pharmacokinetic properties. Thus, taken together, they can serve as a starting point in the designing of multifunctional ligands in pursuit of potential anti-AD agents that might further prevent the progression of ADs.Communicated by Ramaswamy H. Sarma.

Pharmacokinetic Study and Metabolite Identification of 1-(3'-bromophenyl)-heliamine in Rats

Tetrahydroisoquinolines have been widely investigated for the treatment of arrhythmias. 1-(3'-bromophenyl)-heliamine (BH), an anti-arrhythmias agent, is a synthetic tetrahydroisoquinoline. This study focuses on the pharmacokinetic characterization of BH, as well as the identification of its metabolites, both in vitro and in vivo. A UHPLC-MS/MS method was developed and validated to quantify BH in rat plasma with a linear range of 1-1000 ng/mL. The validated method was applied to a pharmacokinetic study in rats. The maximum concentration C_max (568.65 ± 122.14 ng/mL) reached 1.00 ± 0.45 h after oral administration. The main metabolic pathways appeared to be phase-I of demethylation, dehydrogenation, and epoxidation, and phase II of glucuronide and sulfate metabolites. Finally, a total of 18 metabolites were characterized, including 10 phase I metabolites and 8 phase II metabolites. Through the above studies, we have gained a better understanding of the absorption and metabolism of BH in vitro and in vivo, which will provide us with guidance for future in-depth studies on this compound.

Discovery of 1,5-diaryl-1,2,4-triazole derivatives as myoferlin inhibitors and their antitumor effects in pancreatic cancer

Aim: The first inhibitor targeting myoferlin (MYOF), WJ460, bears poor metabolic stability and water solubility. Therefore, this study aimed to improve the druglike properties of WJ460. Materials & methods: The authors synthesized an array of 1,5-diaryl-1,2,4-triazole analogs and appraised the binding activities with MYOF and their antiproliferative and antimigratory activities against pancreatic cancer cells. Results: Molecular docking and surface plasmon resonance results showed that E4 was directly bound to the MYOF-C2D domain. E4 effectively inhibited the proliferation and migration of pancreatic cancer cells in vitro. In silico study suggested that the water solubility of E4 was improved by about 22-times than that of WJ460. Conclusion: The findings suggested that the druglike ability of E4 was significantly improved.

Impact of Established and Emerging Software Tools on the Metabolite Identification Landscape

Scientists’ ability to detect drug-related metabolites at trace concentrations has improved over recent decades. High-resolution instruments enable collection of large amounts of raw experimental data. In fact, the quantity of data produced has become a challenge due to effort required to convert raw data into useful insights. Various cheminformatics tools have been developed to address these metabolite identification challenges. This article describes the current state of these tools. They can be split into two categories: Pre-experimental metabolite generation and post-experimental data analysis. The former can be subdivided into rule-based, machine learning-based, and docking-based approaches. Post-experimental tools help scientists automatically perform chromatographic deconvolution of LC/MS data and identify metabolites. They can use pre-experimental predictions to improve metabolite identification, but they are not limited to these predictions: unexpected metabolites can also be discovered through fractional mass filtering. In addition to a review of available software tools, we present a description of pre-experimental and post-experimental metabolite structure generation using MetaSense. These software tools improve upon manual techniques, increasing scientist productivity and enabling efficient handling of large datasets. However, the trend of increasingly large datasets and highly data-driven workflows requires a more sophisticated informatics transition in metabolite identification labs. Experimental work has traditionally been separated from the information technology tools that handle our data. We argue that these IT tools can help scientists draw connections via data visualizations and preserve and share results via searchable centralized databases. In addition, data marshalling and homogenization techniques enable future data mining and machine learning.

Metabolism of the antipsychotic drug olanzapine by CYP3A43

1. Olanzapine is an atypical antipsychotic primarily used to treat schizophrenia and bipolar disorder. An intronic single nucleotide polymorphism (SNP) that highly significantly predicts increased olanzapine clearance (rs472660) was previously identified in the CYP3A43 gene, which encodes a cytochrome P450 enzyme. But until now there was no experimental evidence for the metabolism of olanzapine by the CYP3A43 enzyme.2. In the present study we provide this evidence, together with a thorough analysis of olanzapine metabolism by all human CYP3A enzymes. We also rationalize our findings by molecular docking experiments. Moreover, we describe the activities of several CYP3A43 mutants and present the first enzymatic activity data for the CYP3A43.3 variant; with respect to prostate cancer, this polymorphic variant is associated with both increased risk and increased mortality. The catalytic properties of the wild type enzyme and the tumor mutant were analyzed by molecular dynamics simulations, which fit very well with the observed experimental results.3. Our finding suggests that the SNP rs472660 likely causes an increased CYP3A43 expression level and demonstrate that, depending on the substrate under study, the tumor mutant CYP3A43.3 can have increased activity in comparison to the wild type enzyme CYP3A43.1.

Preliminary studies of an imidazole-based alcohol derivative for imaging of Heme oxygenase 1

Heme oxygenase-1 (HO-1) has been involved in the pathogenesis of Alzheimer's disease (AD), thus constituting a promising target for AD drug development. Positron emission tomography (PET) is a fully translational imaging technology, which will help us understand the role of HO-1 in the progression of AD, facilitating to validate promising HO-1 inhibitors in clinical trials. To our knowledge, there is no report on PET imaging probe targeting HO-1 in animals and humans. We report herein the synthesis and characterization of a ¹¹C-labeled imidazole-based alcohol derivative ([¹¹C]QC-33) for imaging of HO-1 in the brain. The desired product [¹¹C]QC-33 was afforded with a radiochemical yield of 16 ± 9% (n = 3, decay corrected). The radiochemical purity was greater than 99%, and the molar radioactivity was greater than 185 GBq/μmol. In vitro autoradiography studies indicated specific binding of [¹¹C]QC-33 in the HO-1 rich regions, showing 75%, 75%, and 69% radioactivity binding reductions in cerebellum, brain stem, and midbrain, respectively. PET/CT scanning in C57BL/6 mice showed low brain uptake and poor blood-brain barrier (BBB) penetration of [¹¹C]QC-33. These results suggested that [¹¹C]QC-33 can serve as a lead compound to advance the development of next generation PET tracer with the potential to monitor HO-1 in AD progression.

Novel deuterated Mnk1/2 protein degrader VNLG-152R analogs: Synthesis, In vitro Anti-TNBC activities and pharmacokinetics in mice

A new and improved synthesis of lead Mnk1/2 protein degrader, VNLG-152R, 4-(±)-(1H-imidazole-1-yl)-N-(4-fluorophenyl)-(E)-retinamide (1) has been developed from commercially available 4-oxo-ATRA (8). This procedure was also utilized to synthesize the seven possible deuterated analogs of compound 1 (11-17). The deuterated analogs were either better or equipotent to 1 in in vitro antiproliferative activities against MDA-MB-231 and MDA-MB-468 human TNBC cells. The Mnk1/2 degraders were equally effective as a standard TNBC therapy (paclitaxel). Importantly, the expression of Mnk1, peIF4E and their associated downstream targets, including cyclin D1 and Bcl2, were strongly decreased in compound 1/analogs (11-17)-treated TNBC cells signifying inhibition of Mnk1-eIF4E signaling. More importantly, we showed that deuterated analogs, 12, 16 and 17 possess improved pharmacokinetics parameters following oral administration to CD-1 female mice compared to the parent non-deuterated compound 1, thus addressing the rapid clearance (short half-life and short residence time) pharmacokinetic inadequacy of compound 1.

Optimizing cardio, hepato and phospholipidosis toxicity of the Bedaquiline by chemoinformatics and molecular modelling approach

The FDA granted expedited approval for Johnson and Johnson's Bedaquiline to treat pulmonary multidrug resistant tuberculosis on 28 December 2012 which is more common in China, Russian Federation and India. Bedaquiline is the first anti-tubercular drug approved by the FDA in the last 40 years, and it has become a cynosure in the circles of synthetic chemists researching new anti-tubercular drugs. Bedaquiline's highly lipophilic nature raises major concerns like suppression of the hERG gene, hepatotoxicity, and phospholipidosis despite its potential antitubercular profile. To address these toxicity concerns, in the present work, we have employed the structural optimization of Bedaquiline using the ADMETopt web server, which optimizes lead with scaffold hopping and ADMET screening. The ADMETopt web server yielded the 476 structures through optimization of three sites in Bedaquiline. Further, we have validated the optimized structures for their activity by performing molecular docking and molecular dynamics (MD) simulations against the mycobacterial ATP synthase enzyme and density functional theory (DFT) study further provides insight into the reactivity of the compounds. After screening and analysis, compound #449 was observed to be the most promising mycobacterial ATP synthase inhibitor with minimal cardiotoxicity, hepatotoxicity and phospholipidosis.

Synthesis and Characterization of 5-(2-Fluoro-4-[11C]methoxyphenyl)-2,2-dimethyl-3,4-dihydro-2H-pyrano[2,3-b]pyridine-7-carboxamide as a PET Imaging Ligand for Metabotropic Glutamate Receptor 2

Metabotropic glutamate receptor 2 (mGluR2) is a therapeutic target for several neuropsychiatric disorders. An mGluR2 function in etiology could be unveiled by positron emission tomography (PET). In this regard, 5-(2-fluoro-4-[11C]methoxyphenyl)-2,2-dimethyl-3,4-dihydro-2H-pyrano[2,3-b]pyridine-7-carboxamide ([11C]13, [11C]mG2N001), a potent negative allosteric modulator (NAM), was developed to support this endeavor. [11C]13 was synthesized via the O-[11C]methylation of phenol 24 with a high molar activity of 212 ± 76 GBq/μmol (n = 5) and excellent radiochemical purity (>99%). PET imaging of [11C]13 in rats demonstrated its superior brain heterogeneity and reduced accumulation with pretreatment of mGluR2 NAMs, VU6001966 (9) and MNI-137 (26), the extent of which revealed a time-dependent drug effect of the blocking agents. In a nonhuman primate, [11C]13 selectively accumulated in mGluR2-rich regions and resulted in high-contrast brain images. Therefore, [11C]13 is a potential candidate for translational PET imaging of the mGluR2 function.

Chemical reactivity prediction: current methods and different application areas

The ability to predict chemical reactivity of a molecule is highly desirable in drug discovery, both ex vivo (synthetic route planning, formulation, stability) and in vivo: metabolic reactions determine pharmacodynamics, pharmacokinetics and potential toxic effects, and early assessment of liabilities is vital to reduce attrition rates in later stages of development. Quantum mechanics offer a precise description of the interactions between electrons and orbitals in the breaking and forming of new bonds. Modern algorithms and faster computers have allowed the study of more complex systems in a punctual and accurate fashion, and answers for chemical questions around stability and reactivity can now be provided. Through machine learning, predictive models can be built out of descriptors derived from quantum mechanics and cheminformatics, even in the absence of experimental data to train on. In this article, current progress on computational reactivity prediction is reviewed: applications to problems in drug design, such as modelling of metabolism and covalent inhibition, are highlighted and unmet challenges are posed.

Novel Positron Emission Tomography Radiotracers for Imaging Mitochondrial Complex I

Mitochondrial dysfunction has been indicated in neurodegenerative and other disorders. The mitochondrial complex I (MC-I) of the electron transport chain (ETC) on the inner membrane is the electron entry point of the ETC and is essential for the production of reactive oxygen species. Based on a recently identified β-keto-amide type MC-I modulator from our laboratory, an ¹⁸F-labeled positron emission tomography (PET) tracer, ¹⁸F-2, was prepared. PET/CT imaging studies demonstrated that ¹⁸F-2 exhibited rapid brain uptake without significant wash out during the 60 min scanning time. In addition, the binding of ¹⁸F-2 was higher in the regions of the brain stem, cerebellum, and midbrain. The uptake of ¹⁸F-2 can be significantly blocked by its parent compound. Collectively, the results strongly suggest successful development of MC-I PET tracers from this chemical scaffold that can be used in future mitochondrial dysfunction studies of the central nervous system.

Metabolic study of cafestol using in silico approach, zebrafish water tank experiments and liquid chromatography high-resolution mass spectrometry analyses

Coffee is one of the most consumed beverages worldwide. Cafestol is an endogenous coffee diterpene present in raw coffee beans and also found in hot beverages, with several biological activities. However, there is still little information on this molecule after ingestion of coffee infusion. Zebrafish (Danio rerio) is a promising in vivo model for metabolic studies due to the annotation of mammalian orthologs to encode enzymes related to drug metabolism. Experiments using Zebrafish Water Tank (ZWT) model produce more significant number of metabolites for molecular investigation in a cleaner matrix than other classical models, such as purified hepatocytes. This work aimed to investigate the biotransformation of cafestol by the ZWT model using ultra-performance liquid chromatography coupled to hybrid quadrupole-orbitrap high-resolution mass spectrometry equipped with electrospray ionization (UPLC-HRMS) supported by in silico approach using SMARTCyp, Way2Drug and XenoSite Softwares. Twenty-five metabolites of cafestol were proposed by in silico analysis, in which 5 phase I metabolites were confirmed in the ZWT by UPLC and MS/HRMS investigation: 6-hydroxy-cafestol, 6,12-dihydroxy-cafestol, 2-oxo-cafestol, 6-oxo-cafestol and one isomer whose position in the carboxyl group was not determined. These metabolites were observed during 9 h of the experiment, whose contents were associated with the behavioral responses of the fish.

Structure-activity relationship and molecular modelling studies of quinazolinedione derivatives MMV665916 as potential antimalarial agent

A series of new quinazolinedione derivatives have been readily synthesized and evaluated for their in vitro antiplasmodial growth inhibition activity. Most of the compounds inhibited P. falciparum FcB1 strain in the low to medium micromolar concentration. The 2-ethoxy 8ag', 2-trifluoromethoxy 8ai' and 4-fluoro-2-methoxy 8ak' showed the best inhibitory activity with EC₅₀ values around 5 µM and were non-toxic to the primary human fibroblast cell line AB943. However, these compounds were less potent than the original hit MMV665916, which showed remarkable growth inhibition with EC₅₀ value of 0.4 µM and presented the highest selectivity index (SI > 250). In addition, a novel approach for determining the docking poses of these quinazolinedione derivatives with their potential protein target, the P. falciparum farnesyltransferase PfFT, was investigated.

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

Optimization of TopoIV Potency, ADMET Properties, and hERG Inhibition of 5-Amino-1,3-dioxane-Linked Novel Bacterial Topoisomerase Inhibitors: Identification of a Lead with In Vivo Efficacy against MRSA

Novel bacterial topoisomerase inhibitors (NBTIs) are among the most promising new antibiotics in preclinical/clinical development. We previously reported dioxane-linked NBTIs with potent antistaphylococcal activity and reduced hERG inhibition, a key safety liability. Herein, polarity-focused optimization enabled the delineation of clear structure-property relationships for both microsomal metabolic stability and hERG inhibition, resulting in the identification of lead compound 79. This molecule demonstrates potent antibacterial activity against diverse Gram-positive pathogens, inhibition of both DNA gyrase and topoisomerase IV, a low frequency of resistance, a favorable in vitro cardiovascular safety profile, and in vivo efficacy in a murine model of methicillin-resistant Staphylococcus aureus infection.

Computational identification of 2,4-disubstituted amino-pyrimidines as L858R/T790M-EGFR double mutant inhibitors using pharmacophore mapping, molecular docking, binding free energy calculation, DFT study and molecular dynamic simulation

Pharmacophore modelling studies have been performed for a series of 2,4-disubstituted-pyrimidines derivatives as EGFR L858R/T790M tyrosine kinase inhibitors. The high scoring AARR.15 hypothesis was selected as the best pharmacophore model with the highest survival score of 3.436 having two hydrogen bond acceptors and two aromatic ring features. Pharmacophore-based virtual screening followed by structure-based yielded the six molecules (ZINC17013227, ZINC17013215, ZINC9573324, ZINC9573445, ZINC24023331 and ZINC17013503) from the ZINC database with significant in silico predicted activity and strong binding affinity towords the EGFR L858R/T790M tyrosine kinase. In silico toxicity and cytochrome profiling indicates that all the 06 virtually screened compounds were substrate/inhibitors of the CYP-3A4 metabolizing enzyme and were non-carcinogenic and devoid of Ames mutagenesis. Density functional theory (DFT) and molecular dynamic (MD) simulation further validated the obtained hits.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-021-00113-x.

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.

NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism

The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

2,6-Di-tert-butylphenol and its quinone metabolite trigger aberrant transcriptional responses in C57BL/6 mice liver

2,6-Di-tert-butylphenol (2,6-DTBP) is used as an antioxidant with wide commercial applications and its residues have been detected in various environmental matrices. 2,6-DTBP may enter human body via ingestion, inhalation or other exposure pathways. However, its susceptibility to biotransformation and potential of the metabolic products to trigger aberrant transcriptional responses remain unclear. Here, we investigated in vitro and in vivo biotransformation of 2,6-DTBP and characterized the RNA-Seq based transcriptional profiling of C57BL/6 mice liver after the exposure to 2,6-DTBP and its metabolites. 2,6-DTBP was metabolized into hydroxylated (2,6-DTBH) and para-quinone (2,6-DTBQ) products with residues detected in serum and liver of C57BL/6 mice. 2,6-DTBP and 2,6-DTBQ induced the aberrant transcription in C57BL/6 mice liver featured with 373-2861 differentially expressed genes (DEGs). They also up-regulated 1.09-2.92 fold mRNA expression of carcinogenesis-related genes such as Ccnd1, TGFβ1 and FOS in C57BL/6 mice liver. Our study indicated potential carcinogenic risk of 2,6-DTBP and its metabolites, beneficial to further evaluation of health risk of TBPs-related contaminants.