Predicting drug metabolism: experiment and/or computation?

Screening the effective components of Lysionotus pauciflorus Maxim. on the treatment of LPS induced acute lung injury mice by integrated UHPLC-Q-TOF-MS/MS and network pharmacology

ETHNOPHARMACOLOGICAL RELEVANCE

Acute lung injury (ALI) is an inflammatory reaction produced through various injury-causing factors acting on the lungs in a direct or indirect way, with a high morbidity and mortality rate. A review of clinical experience has revealed that Lysionotus pauciflorus Maxim (LP) has a significant therapeutic effect on ALI. However, the comprehensive effective components of LP are uncertain, and the mechanisms, especially the potential therapeutic target for anti-ALI, are still unknown.

AIMS OF THE STUDY

In vitro and in vivo validation of the pharmacodynamics of LP in the treatment of ALI and exploration of its potential mechanism of action based on network pharmacology, molecular docking and experimental validation.

MATERIALS AND METHODS

Ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) was employed to identify the ingredients of LP extracts. The potential bioactive ingredients, key targets and signalling pathways were identified by network pharmacology, based on the results of the mass spectrometry analysis. Subsequently, molecular docking was performed on the screened core components and key targets to calculate their molecular binding energies and binding potentials, and to explore the mutual binding modes of small-molecule ligands and large-molecule proteins. Finally, lipopolysaccharide (LPS)-induced RAW264.7 cell model and ALI mice model were used to validate the therapeutic effects and potential mechanism of LP extract towards ALI.

RESULTS

From the mass spectrometry results of LP extracts, a total of 89 chemical components were identified, including 46 phenylethanol glycosides, 26 flavonoids, 9 organic acids and their derivatives and 8 other compounds. And furthermore 39 core active components were screened by network pharmacology. The top 10 core components (4 phenylethanol glycosides, 6 flavonoids) have been screened in the composition -target-disease network, and 37 core targets related to LP efficacy were obtained by fitting PPI network analysis. 10 signalling pathways and their targets associated with LP treatment of ALI were obtained by GO/KEGG analysis, indicating that LP could regulate TLR4 and NF-κB signalling pathways through 4 key targets, namely NFKB1, RELA, TLR4 and TNF. The results of the molecular docking procedure indicated a strong affinity, with the binding energies between each component and the target site being less than -6 kcal/mol. The binding modes included Hydrogen Bonds, Pi-Pi interaction, Hydrophobic Interactions, Salt Bridges, Pi-cation interactions. These observations were subsequently validated in vitro and in vivo experiments. The outcomes of in vitro and in vivo experiments demonstrated that LP was effective in reducing the infiltration of inflammatory bacteria in lung tissues and attenuated the expression of pro-inflammatory cytokines in LPS-stimulated mice bronchoalveolar lavage fluid (BALF) and RAW264.7 cells. Furthermore, LP inhibited the expression and phosphorylation of TLR4 protein and NF-κB protein, thus playing a role in the prevention of ALI.

CONCLUSIONS

In this study, mass spectrometry analysis was combined with biomolecular networks to initially elucidate the potential of LP to treat ALI by modulating the TLR4/NF-κB pathway. This offers a definitive experimental basis for the development of new LP drugs.

Examination of common culture medium for human hepatocytes and engineered heart tissue: Towards an evaluation of cardiotoxicity associated with hepatic drug metabolism in vitro

Cardiotoxicity associated with hepatic metabolism and drug-drug interactions is a serious concern. Predicting drug toxicity using animals remains challenging due to species and ethical concerns, necessitating the need to develop alternative approaches. Drug cardiotoxicity associated with hepatic metabolism cannot be detected using a cardiomyocyte-only evaluation system. Therefore, we aimed to establish a system for evaluating cardiotoxicity via hepatic metabolism by co-culturing cryopreserved human hepatocytes (cryoheps) and human iPS cell-derived engineered heart tissues (hiPSC-EHTs) using a stirrer-based microphysiological system. We investigated candidate media to identify a medium that can be used commonly for hepatocytes and cardiomyocytes. We found that the contraction length was significantly greater in the HM Dex (-) medium, the medium used for cryohep culture without dexamethasone, than that in the EHT medium used for hiPSC-EHT culture. Additionally, the beating rate, contraction length, contraction speed, and relaxation speed of hiPSC-EHT cultured in the HM Dex (-) medium were stable throughout the culture period. Among the major CYPs, the expression of CYP3A4 alone was low in cryoheps cultured in the HM Dex (-) medium. However, improved oxygenation using the InnoCell plate increased CYP3A4 expression to levels comparable to those found in the human liver. In addition, CYP3A4 activity was also increased by the improved oxygenation. Furthermore, expression levels of hepatic function-related gene and nuclear receptors in cryoheps cultured in HM Dex (-) medium were comparable to those in the human liver. These results suggest that the HM Dex (-) medium can be applied to co-culture and may allow the evaluation of cardiotoxicity via hepatic metabolism. Moreover, CYP induction by typical inducers was confirmed in cryoheps cultured in the HM Dex (-) medium, suggesting that drug-drug interactions could also be evaluated using this medium. Our findings may facilitate the evaluation of cardiotoxicity via hepatic metabolism, potentially reducing animal testing, lowering costs, and expediting drug development.

Evidence for the formation of 6PPD-quinone from antioxidant 6PPD by cytochrome P450

N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) as a rubber antioxidant has attracted global concern, since its ozone-oxidation product 6PPD-quinone (6PPDQ) was found to be the primary toxicant responsible for urban runoff mortality syndrome in coho salmon. However, the biotransformation fate and associated toxicological mechanism of 6PPD have not received much study yet. In this work, the in vitro assays showed 6PPD can be transformed into 6PPDQ by cytochromes P450 (CYP450) in human liver microsomes (HLMs) with 0.98 % production rate, and the adducts of 6PPDQ with calf thymus DNA and the N-N coupling product between 6PPD and 6PPDQ were further identified after 6PPD incubation in HLMs. Further evidence for the 6PPDQ formation can be obtained from the in vivo assays that the 6PPDQ-DNA adducts and 6PPD-N-N-6PPDQ dimer were detected in mice by oral gavage with 6PPD, and the latter dimer species was detected as well in 6PPD exposure to zebrafish larvae. Especially, the bioaccumulation property and high reactivity of 6PPDQ result in the continuous formation of the significant DNA adducts and 6PPD-N-N-6PPDQ dimer even in case of low production rate of biotransformation of 6PPD to 6PPDQ, which may provide potentially effective biomarkers for such process. DFT computations revealed the formation mechanism of 6PPDQ is the (N)H-abstraction of 6PPD by CYP450, followed by amino radical rebound at the nearby ortho-carbon, yielding a quinol intermediate due to spin delocalization, that might readily undergo further oxidation by CYP450 into 6PPDQ.

Neuroprotective Properties of Coriander-Derived Compounds on Neuronal Cell Damage under Oxidative Stress-Induced SH-SY5Y Neuroblastoma and in Silico ADMET Analysis

An imbalance between reactive oxygen species (ROS) production and antioxidant defense driven by oxidative stress and inflammation is a critical factor in the progression of neurodegenerative diseases such as Alzheimer's and Parkinson's. Coriander (Coriandrum sativum L.), a culinary plant in the Apiaceae family, displays various biological activities, including anticancer, antimicrobial, and antioxidant effects. Herein, neuroprotective properties of three major bioactive compounds derived from coriander (i.e., linalool, linalyl acetate, and geranyl acetate) were investigated on hydrogen peroxide-induced SH-SY5Y neuroblastoma cell death by examining cell viability, ROS production, mitochondrial membrane potential, and apoptotic profiles. Moreover, underlying mechanisms of the compounds were determined by measuring intracellular sirtuin 1 (SIRT1) enzyme activity incorporated with molecular docking. The results showed that linalool, linalyl acetate, and geranyl acetate elicited their neuroprotection against oxidative stress via protecting cell death, reducing ROS production, preventing cell apoptosis, and modulating SIRT1 longevity. Additionally, in silico pharmacokinetic predictions indicated that these three compounds are drug-like agents with a high probability of absorption and distribution, as well as minimal potential toxicities. These findings highlighted the potential neuroprotective linalool, linalyl acetate, and geranyl acetate for developing alternative natural compound-based neurodegenerative therapeutics and prevention.

HiMolformer: Integrating graph and sequence representations for predicting liver microsome stability with SMILES

In the initial stages of drug discovery or pre-clinical studies, understanding the metabolic stability of new molecules is crucial. Recently, research on pre-trained deep learning for molecular property prediction has been actively progressing, with various models being made open-source. However, most of these models rely on either 2D graph or 1D sequence for training, and the representation varies depending on the data format used. Consequently, combining multiple representations can broaden the scope of learning and may potentially be a manageable and most effective method to enhance performance. Therefore, we propose a novel hybrid model for predicting metabolic stability, which integrates representations from both graph-based and sequence-based models pre-trained for molecular features. This approach utilizes the combined strengths of 2D topological and 1D sequential information of molecules. HiMol, a graph-based graph neural network (GNN) model, and Molformer, a sequence-based Transformer model, were selected for integration, thus we named it HiMolformer. HiMolformer demonstrated superior performance compared to other models. We also focus on regression task for prediction with a empirical dataset from Korea Chemical Bank (KCB), comprising 3,498 molecules with mouse liver microsome (MLM) and human liver microsome (HLM) data obtained from actual metabolic reaction experiments. To the best of our knowledge, it is the first attempt to develop MLM and HLM prediction models using regression with a single SMILES input. The source code of this model is available at https://github.com/YUNSEOKWOO/HiMolformer.

Alkaloids Isolated from Vepris glandulosa with Antidiabetic Properties: An In Vitro and In Silico Analysis

Diabetes is a major global health issue and as current treatments fail, the search for new antidiabetic drugs is crucial. This investigation, focusing on identifying potential antidiabetic compounds from the endangered plant species Vepris glandulosa, led to the isolation of two known alkaloids, choisyine acetate (1) and choisyine (2). The study established the in vitro inhibitory activities and in silico molecular interaction of the two alkaloids with α-amylase based on IC50 values, Linewaever-Burk/Dixon plot kinetic analyses and Molecular docking, respectively. The α-amylase inhibition assay revealed noncompetitive inhibition for both compounds with IC50 and Ki values of 4.74±0.17 and 4.75 mM for compound 1, and 11.29±0.44 and 12.37 mM for compound 2, respectively. In comparison, the standard drug acarbose displayed a competitive mode of inhibition, with IC50 and Ki values of 11.99±0.02 and 12.68 mM, respectively. The binding affinities with α-amylase were -6.42 and -6.07 kcal/mol for compounds 1 and 2, respectively relative to acarbose -8.03 Kcal/mol. Moreover, these two compounds' predicted physicochemical and ADMET properties justified their potential as lead compounds for drug discovery. These compounds demonstrated remarkable inhibition potential comparable to the standard drug, highlighting their potential as viable alternatives in managing diabetes.

Widely-targeted in silico and in vitro evaluation of veratrum alkaloid analogs as FAK inhibitors and dual targeting of FAK and Hh/SMO pathways for cancer therapy: A critical analysis

Focal Adhesive Kinase (FAK), a key player in aggressive cancers, mediates signals crucial for progression, invasion, and metastasis. Despite advances in targeted therapies, drug resistance is still a challenge, and survival rates remain low, particularly for late-stage patients, emphasizing the need for innovative cancer therapeutics. Cyclopamine, a veratrum alkaloid, has shown promising anti-tumor properties, but the search for more potent analogs with enhanced affinity for the biological target continues. This study employs a hybrid virtual screening approach combining pharmacophore model-based virtual screening (PB-VS) and docking-based virtual screening (DB-VS) to identify potential inhibitors of the FAK catalytic domain. PB-VS on the PubChem database yielded a set of hits, which were then docked with the FAK catalytic domain in two stages (1st and 2nd DB-VS). Hits were ranked based on docking scores and interactions with the active site. The top three compounds underwent molecular dynamics simulations, alongside two control compounds (SMO inhibitor(s) and FAK inhibitor(s)), to assess stability through RMSD, RMSF, Rg, and SASA analyses. ADMET properties were evaluated, and compounds were filtered based on drug-likeness criteria. Molecular dynamics simulations demonstrated the stability of compounds when complexed with the FAK catalytic domain. Compounds 16 (-25 kcal/mol), 87 (-27.47 kcal/mol), and 88 (-18.94 kcal/mol) exhibited comparable docking scores, interaction profiles, stability, and binding energies, indicating their potential as lead candidates. However, further validation and optimization through quantitative structure-activity relationship (QSAR) studies are essential to refine their efficacy and therapeutic potential. The in vitro cell-based assay demonstrated that compound 101PF, a FAK inhibitor, significantly inhibited the proliferation and migration of A549 cells. However, the results regarding the combined effects of FAK and SMO inhibitors were inconclusive, highlighting the need for further investigation. This study contributes to developing more effective anti-cancer drugs by improving the understanding of potential cyclopamine-based veratrum alkaloid analogs with enhanced interactions with the FAK catalytic domain.

Roles of Individual Human Cytochrome P450 Enzymes in Drug Metabolism

Our knowledge of the roles of individual cytochrome P450 (P450) enzymes in drug metabolism has developed considerably in the past 30 years, and this base has been of considerable use in avoiding serious issues with drug interactions and issues due to variations. Some newer approaches are being considered for "phenotyping" metabolism reactions with new drug candidates. Endogenous biomarkers are being used for noninvasive estimation of levels of individual P450 enzymes. There is also the matter of some remaining "orphan" P450s, which have yet to be assigned reactions. Practical problems that continue in drug development include predicting drug-drug interactions, predicting the effects of polymorphic and other P450 variations, and evaluating interspecies differences in drug metabolism, particularly in the context of "metabolism in safety testing" regulatory issues ["disproportionate (human) metabolites"]. SIGNIFICANCE STATEMENT: Cytochrome P450 enzymes are the major catalysts involved in drug metabolism. The characterization of their individual roles has major implications in drug development and clinical practice.

Comprehensive Analysis of Angiogenesis and Ferroptosis Genes for Predicting the Survival Outcome and Immunotherapy Response of Hepatocellular Carcinoma

Background

Angiogenesis and ferroptosis are both linked to hepatocellular carcinoma (HCC) development, recurrence, and medication resistance. As a result, a thorough examination of the link between genes associated with angiogenesis and ferroptosis and immunotherapy efficacy is required to improve the dismal prognosis of HCC patients.

Methods

The molecular subtypes were found using a non-negative matrix factorization technique (NMF) based on the genes associated with angiogenesis and ferroptosis. Based on the differentially expressed genes (DEGs) screed between different molecular subtypes, an angiogenesis and ferroptosis-related prognostic stratification model was built using LASSO-COX regression, random forest technique, and extreme gradient boosting (XGBoost), which was further validated in the ICGC and GSE14520 databases. The impact of this model on tumor microenvironment (TME) and immunotherapy sensitivity was also investigated. The expression levels of candidate genes were detected and validated by Real-Time PCR and immunohistochemistry between liver cancer tissues and adjacent non-tumor liver tissues.

Results

Both angiogenesis and ferroptosis-related genes can significantly divide HCC patients into two subgroups with different survival outcomes, mutation profiles, and immune microenvironments. We screened six core genes (SLC10A1, PAEP, DPYSL4, MSC, NQO1, and CD24) for the construction of prognostic models by three machine learning methods after intersecting DEGs between angiogenesis and ferroptosis-related subgroups. In both the TCGA, ICGC, and GSE14520 datasets, the model exhibits high prediction efficiency based on the analysis of KM survival curves and ROC curves. Immunomodulatory genes analysis suggested that the model could be used to predict which patients are most likely to benefit from immunotherapy. Furthermore, the transcriptional expression levels of SLC10A1 in the validation experiment matched the outcomes derived from public datasets.

Conclusions

We identified a new angiogenesis and ferroptosis-related signature that might offer the molecular characteristic information needed for an efficient prognostic assessment and perhaps tailored treatment for HCC patients.

Protective Effects of Anethole in Foeniculum vulgare Mill. Seed Ethanol Extract on Hypoxia/Reoxygenation Injury in H9C2 Heart Myoblast Cells

BACKGROUND

Active compounds from plants and herbs are increasingly incorporated into modern medical systems to address cardiovascular diseases (CVDs). Foeniculum vulgare Mill., commonly known as fennel, is an aromatic medicinal plant and culinary herb that is popular worldwide.

METHODS

Protective effects against cellular damage were assessed in the H9C2 cardiomyocyte hypoxia/reoxygenation (H/R) experimental model. The identities of phytochemicals in FVSE were determined by GC-MS analysis. The phytochemical's potential for nutrients and pharmacokinetic properties was assessed by ADMET analysis.

RESULTS

GC-MS analysis of the ethanol extracts of F. vulgare identified 41 bioactive compounds, with four prominent ones: anethole, 1-(4-methoxyphenyl)-2-propanone, ethoxydimethylphenylsilane, and para-anisaldehyde diethyl acetal. Among these, anethole stands out due to its potential for nutrients and pharmacokinetic properties assessed by ADMET analysis, such as bioavailability, lipophilicity, flexibility, and compliance with Lipinski's Rule of Five. In the H/R injury model of H9C2 heart myoblast cells, FVSE and anethole suppressed H/R-induced reactive oxygen species (ROS) generation, DNA double-strand break damage, nuclear condensation, and the dissipation of mitochondrial membrane potential (ΔΨm).

CONCLUSIONS

These findings highlight the therapeutic potential of FVSE and its prominent component, anethole, in the treatment of CVDs, particularly those associated with hypoxia-induced damage.

In vitro antibacterial and antioxidant activity of flavonoids from the roots of Tephrosia vogelii: a combined experimental and computational study

Tephrosia vogelii is a traditional medicinal plant used to treat hypertension, diarrhea and urinary disorders. Silica gel chromatographic separation of CH2Cl2/MeOH (1:1) roots extract of T. vogelii afforded seven compounds namely; β-sitosterol (1a), stigmasterol (1b), 6a, 12a-dehydro-deguelin (2), tephrosin (3), maackiain (4), obovatin (5) and 6-oxo, 6a, 12a-dehydro-deguelin (6). GC-MS analysis of essential oils from the root of T. vogelii displayed a total of 17 compounds of which cis-nerolidol (41.7 %) and cadinol (19.7 %) were the major constituents. CH2Cl2/MeOH (1:1) extract, MeOH extract, maackiain (4) and obovatin (5) showed moderate inhibitory activity against Pseudomonas aeruginosa with MIC value of 0.5, 0.66, 0.83 and 0.83 mg/mL, respectively, compared to ciprofloxacin (MIC of 0.078 μg/mL). 6a, 12a-dihydro-deguelin (2), and 6-oxo, 6a, 12a-dehydro-deguelin (6) displayed significant activity against S. epidermis with MIC values of 0.66 mg/mL. Tephrosin (3) and maackiain (4) also showed moderate antibacterial activity against Staphylococcus aureus and Proteus mirabilis with MIC values of 0.83 and 0.5 mg/mL, respectively, compared to ciprofloxacin (0.312 μg/mL). The radical scavenging activity results indicated that tephrosin (3), obovatin (5) and 6-oxo, 6a, 12a-dehydro-deguelin (6) showed potent DPPH scavenging activity with IC50 values of 10.97, 10.43 and 10.73 μg/mL, respectively, compared to ascorbic acid (IC50 of 5.83 μg/mL). The docking prediction results revealed that 6a, 12a-dehydro-deguelin (2) displayed the best binding energy of -8.1 kcal/mol towards pyruvate kinase of S. aureus (PDB ID: 3T07) and -7.9 kcal/mol towards P. mirabilis urease (PDB ID: 1E9Y) and DNA gyrase B of Escherichia coli (PDB: 4F86) receptors compared to ciprofloxacin (-7.2 to -8.0 kcal/mol). Maackiain (4) and obovatin (5) displayed the minimum binding energy of -7.9 and -8.2 kcal/mol towards the LasR protein of P. aeruginosa (PDB: ID 2UV) and S. epidermidis FtsZ (PDB: ID 4M8I), respectively. The SwissADME drug-likeness and Pro Tox II toxicity prediction results indicated that compounds (2-6) obeyed Lipinski's rule of five with 0 violations and none of them were found to be hepatotoxic, mutagenic, and cytotoxic, respectively. The in vitro assessment results supported by the in silico analysis revealed that crude extracts and isolated compounds showed promising antibacterial and antioxidant activity, which proves the therapeutic potential of the roots of T. vogelii.

The potential of Mitragyna speciosa leaves as a natural source of antioxidants for disease prevention

Mitragyna speciosa is famous for its addictive effect. On the other hand, this plant has good potential as an antioxidant agent, and so far, it was not explicitly explained what the most contributing compound in the leaves to that activity is. This study has been conducted using several computational methods to determine which compounds are the most active in interacting with cytochrome P450, myeloperoxidase, and NADPH oxidase proteins. First, virtual screening was carried out based on molecular docking, followed by profiling the properties of adsorption, distribution, metabolism, excretion, and toxicity (ADMET); the second one is the molecular dynamics (MD) simulations for 100 ns. The virtual screening results showed that three compounds acted as inhibitors for each protein: (-)-epicatechin, sitogluside, and corynoxeine. The ADMET profiles of the three compounds exhibit good drug ability and toxicity. The trajectories study from MD simulations predicts that the complexes of these three compounds with their respective target proteins are stable. Furthermore, these compounds identified in this computational study can be a potential guide for future experiments aimed at assessing the antioxidant properties through in vitro testing.

D-Band-Center-Engineered Platinum-Based Nanozyme for Personalized Pharmacovigilance

A high-efficiency PtZnCd nanozyme was screened with density functional theory (DFT) and unique d-orbital coupling features for sensitive enrichment and real-time analysis of CO-releasing molecule-3 (CORM-3). Multicatalytic sites in the nanozyme showed a high reactivity of up to 72.89 min-1 for peroxidase (POD)-like reaction, which was 2.2, 4.07, and 14.67 times higher than that of PtZn (32.67 min-1), PtCd (17.89 min-1), and Pt (4.97 min-1), respectively. Normalization of the catalytic sites showed that the catalytic capacity of the active site in PtZnCd was 2.962 U μmol-1, which was four times higher than that of a pure Pt site (0.733 U μmol-1). DFT calculations showed that improved d-orbital coupling between different metals reduces the position of the center of the shifted whole d-band relative to the Fermi energy level, thereby increasing the contribution of the sites to the electron transfer from the active center, accompanied by enhanced substrate adsorption and intermediate conversion in the catalytic process. The potential adsorption principle and color development mechanism of CORM-3 on PtZnCd were determined, and its practical application in drug metabolism was validated in vitro and in zebrafish and mice models, demonstrating that transition-metal doping effectively engineers high-performance nanozymes and optimizes artificial enzymes.

An improved dataset of force fields, electronic and physicochemical descriptors of metabolic substrates

In silico prediction of xenobiotic metabolism is an important strategy to accelerate the drug discovery process, as candidate compounds often fail in clinical phases due to their poor pharmacokinetic profiles. Here we present MetaQM, a dataset of quantum-mechanical (QM) optimized metabolic substrates, including force field parameters, electronic and physicochemical properties. MetaQM comprises 2054 metabolic substrates extracted from the MetaQSAR database. We provide QM-optimized geometries, General Amber Force Field (FF) parameters for all studied molecules, and an extended set of structural and physicochemical descriptors as calculated by DFT and PM7 methods. The generated data can be used in different types of analysis. FF parameters can be applied to perform classical molecular mechanics calculations as exemplified by the validating molecular dynamics simulations reported here. The calculated descriptors can represent input features for developing improved predictive models for metabolism and drug design, as exemplified in this work. Finally, the QM-optimized molecular structures are valuable starting points for both ligand- and structure-based analyses such as pharmacophore mapping and docking simulations.

Controlling Pericellular Oxygen Tension in Cell Culture Reveals Distinct Breast Cancer Responses to Low Oxygen Tensions

In oxygen (O2)-controlled cell culture, an indispensable tool in biological research, it is presumed that the incubator setpoint equals the O2 tension experienced by cells (i.e., pericellular O2). However, it is discovered that physioxic (5% O2) and hypoxic (1% O2) setpoints regularly induce anoxic (0% O2) pericellular tensions in both adherent and suspension cell cultures. Electron transport chain inhibition ablates this effect, indicating that cellular O2 consumption is the driving factor. RNA-seq analysis revealed that primary human hepatocytes cultured in physioxia experience ischemia-reperfusion injury due to cellular O2 consumption. A reaction-diffusion model is developed to predict pericellular O2 tension a priori, demonstrating that the effect of cellular O2 consumption has the greatest impact in smaller volume culture vessels. By controlling pericellular O2 tension in cell culture, it is found that hypoxia vs. anoxia induce distinct breast cancer transcriptomic and translational responses, including modulation of the hypoxia-inducible factor (HIF) pathway and metabolic reprogramming. Collectively, these findings indicate that breast cancer cells respond non-monotonically to low O2, suggesting that anoxic cell culture is not suitable for modeling hypoxia. Furthermore, it is shown that controlling atmospheric O2 tension in cell culture incubators is insufficient to regulate O2 in cell culture, thus introducing the concept of pericellular O2-controlled cell culture.

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.

Predicting phase-I metabolism of piceatannol: an in silico study

Piceatannol is a natural compound found in plants and can be derived from resveratrol. While resveratrol has been extensively researched for its effects and how the body processes it, there are concerns about its use. These concerns include its limited absorption in the body, the need for specific dosages, potential interactions with other drugs, lack of standardization, and limited clinical evidence to support its benefits. Interestingly, Piceatannol, another compound derived from resveratrol, has received less attention from researchers but appears to offer advantages. It has better bioavailability and seems to have a more favorable therapeutic profile compared to resveratrol. Surprisingly, no previous attempts have been made to explore or predict the metabolites of piceatannol when it interacts with the enzyme cytochrome P450. This study aims to fill that gap by predicting how piceatannol is metabolized by cytochrome P450 and assessing any potential toxicity associated with its metabolites. This research is interesting because it's the first of its kind to investigate the metabolic fate of piceatannol, especially in the context of cytochrome P450. The findings have the potential to significantly contribute to the field of piceatannol research, particularly in the food industry where this compound has applications and implications.

Graphical abstract

In vitro and in silico study on the seeds of Veitchia merrillii on trematode worms

Background and Aim

The potential of plants as anthelmintics is very large, but there is still very little research conducted in the search for effective, safe, easily obtained, and affordable anthelmintic candidates. Palem putri (Veitchia merrillii) is an ornamental plant that is interesting to study because it is included in the areca nut group which is reported to have strong abilities as anthelmintics. The study aims to evaluate the anthelmintic efficacy of Veitchia merrillii against trematode worms such as Paramphistomum spp. and Fasciola hepatica.

Materials and Methods

This research employs both in vitro and computational techniques. An anthelmintic in vitro test was carried out on Paramphistomum spp. worms at concentrations of 10%, 25%, and 40% (gr/v), assessing mortality index as the observable outcome, followed by a histopathological investigation of the deceased worms for tissue and cellular damage evaluation. Seventeen compounds from V. merrillii seeds were studied in silico for their anthelmintic activity against F. hepatica worms using the quantitative structure-activity relationship technique, molecular docking, and Lipinski's rule analysis for orally administered medication.

Results

About 25% and 40% extracts of V. merrillii damaged the tegument organs in the worms. Seventeen compounds in V. merrillii seed extract, on average, yielded a higher anthelmintic index on F. hepatica than praziquantel. Eleven of the 17 compounds exhibit stronger affinity than praziquantel, with routine and gallic acid being the top two ligands (∆Gbinding values: -11.65 kcal/mol and -11.07 kcal/mol, respectively). According to Lipinski's rule analysis, only routine compounds cannot be orally administered.

Conclusion

The seeds of V. merrilli have potential as an anthelmintic agent for Paramphistomum spp. at concentrations of 25%-40% (gr/v).

DrugMetric: quantitative drug-likeness scoring based on chemical space distance

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

In vivo and in vitro metabolite profiling of nirmatrelvir using LC-Q-ToF-MS/MS along with the in silico approaches for prediction of metabolites and their toxicity

Nirmatrelvir (NRV), a 3C-like protease or Mpro inhibitor of SARS-CoV-2, is used for the treatment of COVID-19 in adult and paediatric patients. The present study was accomplished to investigate the comprehensive metabolic fate of NRV using in vitro and in vivo models. The in vitro models used for the study were microsomes (human liver microsomes, rat liver microsomes, mouse liver microsomes) and S9 fractions (human liver S9 fractions and rat liver S9 fractions) with the appropriate cofactors, whereas Sprague-Dawley rats were used as the in vivo models. Nirmatrelvir was administered orally to Sprague-Dawley rats, which was followed by the collection of urine, faeces and blood at pre-determined time intervals. Protein precipitation was used as the sample preparation method for all the samples. The samples were then analysed by liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-Q-ToF-MS/MS) using an Acquity BEH C18 column with 0.1% formic acid and acetonitrile as the mobile phase. Four metabolites were found to be novel, which were formed via amide hydrolysis, oxidation and hydroxylation. Furthermore, an in silico analysis was performed using Meteor Nexus software to predict the probable metabolic changes of NRV. The toxicity and mutagenicity of NRV and its metabolites were also determined using DEREK Nexus and SARAH Nexus.

Unveiling the Potential of Ent-Kaurane Diterpenoids: Multifaceted Natural Products for Drug Discovery

Natural products hold immense potential for drug discovery, yet many remain unexplored in vast libraries and databases. In an attempt to fill this gap and meet the growing demand for effective drugs, this study delves into the promising world of ent-kaurane diterpenoids, a class of natural products with huge therapeutic potential. With a dataset of 570 ent-kaurane diterpenoids obtained from the literature, we conducted an in silico analysis, evaluating their physicochemical, pharmacokinetic, and toxicological properties with a focus on their therapeutic implications. Notably, these natural compounds exhibit drug-like properties, aligning closely with those of FDA-approved drugs, indicating a high potential for drug development. The ranges of the physicochemical parameters were as follows: molecular weights-288.47 to 626.82 g/mol; number of heavy atoms-21 to 44; the number of hydrogen bond donors and acceptors-0 to 8 and 1 to 11, respectively; the number of rotatable bonds-0 to 11; fraction Csp3-0.65 to 1; and TPSA-20.23 to 189.53 Ų. Additionally, the majority of these molecules display favorable safety profiles, with only 0.70%, 1.40%, 0.70%, and 46.49% exhibiting mutagenic, tumorigenic, reproduction-enhancing, and irritant properties, respectively. Importantly, ent-kaurane diterpenoids exhibit promising biopharmaceutical properties. Their average lipophilicity is optimal for drug absorption, while over 99% are water-soluble, facilitating delivery. Further, 96.5% and 28.20% of these molecules exhibited intestinal and brain bioavailability, expanding their therapeutic reach. The predicted pharmacological activities of these compounds encompass a diverse range, including anticancer, immunosuppressant, chemoprotective, anti-hepatic, hepatoprotectant, anti-inflammation, antihyperthyroidism, and anti-hepatitis activities. This multi-targeted profile highlights ent-kaurane diterpenoids as highly promising candidates for further drug discovery endeavors.

2-(3-Indolyl)acetamides and their oxazoline analogues: Anticancer SAR study

We previously studied 2-aryl-2-(3-indolyl)acetohydroxamates as potential agents against melanoma. These compounds were ineffective in a mouse melanoma xenograft model, most likely due to unfavorable metabolic properties, specifically due to glucuronidation of the N-hydroxyl of the hydoxamic moiety. In the present work, we prepared a series of analogues, 2-aryl-2-(3-indolyl)acetamides and their oxazoline derivatives, which do not contain the N-hydroxyl group. We investigated the structure-activity relationship in both series of compounds and found that the 2-naphthyl is a preferred group at C-2 of the indole in the amide series, whereas the tetralin moiety is favorable in the same location in the oxazoline series. Overall, three compounds in the amide series have GI50 values as low as 0.2-0.3 µM and the results clearly indicate that the N-hydroxyl group is not necessary for high potency in vitro.

Design, synthesis and bioactivity study on oxygen-heterocyclic-based pyran analogues as effective P-glycoprotein-mediated multidrug resistance in MCF-7/ADR cell

P-glycoprotein (P-gp) imparts multi-drug resistance (MDR) on the cancers cell and malignant tumor clinical therapeutics. We report a class of newly designed and synthesized oxygen-heterocyclic-based pyran analogues (4a-l) bearing different aryl/hetaryl-substituted at the 1-postion were synthesized, aiming to impede the P-gp function. These compounds (4a-l) have been tested against cancerous PC-3, SKOV-3, HeLa, and MCF-7/ADR cell lines as well as non-cancerous HFL-1 and WI-38 cell lines to determine their anti-proliferative potency.The findings demonstrated the superior potency of 4a-c with 4-F, 2-Cl, and 3-Cl derivatives and 4h,g with 4-NO2, 4-MeO derivatives against PC-3, SKOV-3, HeLa, and MCF-7/ADR cell lines.Compounds 4a-c were tested for P-gp inhibition and demonstrated significant vigour against MCF-7/ADR cells with IC50 = 5.0-10.7 μM. The Rho123 accumulation assay showed that compounds 4a-c adequately inhibited P-gp function, as predicted. Furthermore, 4a or 4b administration resulted in MCF-7/ADR cell accumulation in the S phase, while compound 4c induced apoptosis by causing cell cycle arrest at G2/M. The molecular docking was applied to understand the likely modes of action and guide us in the rational design of more potent analogs. The investigate derivatives showed their good binding potential for p-gp active site with excellent docking scores and interactions. Finally, the majority of investigated derivatives 4a-c derivatives showed high oral bioavailability, but they did not cross the blood-brain barrier. These results suggest that they have favorable pharmacokinetic properties. Therefore, these compounds could serve as leads for designing more potent and stable drugs in the future.

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.

Synthesis, Anticancer Activity, and In Silico Modeling of Alkylsulfonyl Benzimidazole Derivatives: Unveiling Potent Bcl-2 Inhibitors for Breast Cancer

A series of alkylsulfonyl 1H-benzo[d]imidazole derivatives were synthesized and evaluated for anticancer activity against human breast cancer cells, MCF-7 in vitro. The cytotoxic potential was determined using the xCELLigence real-time cell analysis, and expression levels of genes related to microtubule organization, tumor suppression, apoptosis, cell cycle, and proliferation were examined by quantitative real-time polymerase chain reaction. Molecular docking against Bcl-2 was carried out using AutoDock Vina, while ADME studies were performed to predict the physicochemical and drug-likeness properties of the synthesized compounds. The results revealed that compounds 23 and 27 were the most potent cytotoxic derivatives against MCF-7 cells. Gene expression analysis showed that BCL-2 was the most prominent gene studied. Treatment of MCF-7 cells with compounds 23 and 27 resulted in significant downregulation of the BCL-2 gene, with fold changes of 128 and 256, respectively. Docking analysis predicted a strong interaction between the compounds and the target protein. Interestingly, all of the compounds exhibit a higher binding affinity toward Bcl-2 than the standard drug (compound 27 vina score = -9.6 kcal/mol, vincristine = -6.7 kcal/mol). Molecular dynamics simulations of compounds 23 and 27 showed a permanent stabilization in the binding site of Bcl-2 for 200 ns. Based on Lipinski and Veber's filters, all synthesized compounds displayed drug-like characteristics. These findings suggest that compounds 23 and 27 were the most promising cytotoxic compounds and downregulated the expression of the BCL-2 gene. These derivatives could be further explored as potential candidates for the treatment of breast cancer.

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.

Structure-based design of potent FABP4 inhibitors with high selectivity against FABP3

Fatty-acid binding protein 4 (FABP4) presents an attractive target for therapeutic intervention in metabolic and inflammatory diseases in recent years. However, highly similar three-dimensional structures and fatty acid binding ability of multiple FABP family members pose a significant challenge in design of FABP4-selective inhibitors. Particularly, inhibition of FABP3 raises safety concerns such as cardiac dysfunction and exercise intolerance. Here, we reported the discovery of new FABP4 inhibitors with high selectivity over FABP3 by exploiting the little structural difference in the ligand binding pockets of FABP4 and FABP3. On the basis of our previously reported FABP4 inhibitors with nanomolar potency, different substituents were further introduced to perfectly occupy two sub-pockets of FABP4 that are distinct from those of FABP3. Remarkably, a single methyl group introduction leads to the discovery of compound C3 that impressively exhibits a 601-fold selectivity over FABP3 when maintained nanomolar binding affinity for FABP4. Moreover, C3 also shows good metabolic stability and potent cellular anti-inflammatory activity, making it a promising inhibitor for further development. Therefore, the present study highlights the utility of the structure-based rational design strategy for seeking highly selective and potent inhibitors of FABP4 and the importance of identifying the appropriate subsite as well as substituent for gaining the desired selectivity.

Chemical Catalysis Guides Structural Identification for the Major In Vivo Metabolite of the BET Inhibitor JQ1

The bromodomain inhibitor (+)-JQ1 is a highly validated chemical probe; however, it exhibits poor in vivo pharmacokinetics. To guide efforts toward improving its pharmacological properties, we identified the (+)-JQ1 primary metabolite using chemical catalysis methods. Treatment of (+)-JQ1 with tetrabutylammonium decatungstate under photochemical conditions resulted in selective formation of an aldehyde at the 2-position of the thiophene ring [(+)-JQ1-CHO], which was further reduced to the 2-hydroxymethyl analog [(+)-JQ1-OH]. Comparative LC/MS analysis of (+)-JQ1-OH to the product obtained from liver microsomes suggested (+)-JQ1-OH as the major metabolite of (+)-JQ1. The 2-thienyl position was then substituted to generate a trideuterated (-CD3, (+)-JQ1-D) analog having half-lives that were 1.8- and 2.8-fold longer in mouse and human liver microsomes, respectively. This result unambiguously confirmed (+)-JQ1-OH as the major metabolite of (+)-JQ1. These studies demonstrate an efficient process for studying drug metabolism and identifying the metabolic soft spots of bioactive compounds.

INTEDE 2.0: the metabolic roadmap of drugs

The metabolic roadmap of drugs (MRD) is a comprehensive atlas for understanding the stepwise and sequential metabolism of certain drug in living organisms. It plays a vital role in lead optimization, personalized medication, and ADMET research. The MRD consists of three main components: (i) the sequential catalyses of drug and its metabolites by different drug-metabolizing enzymes (DMEs), (ii) a comprehensive collection of metabolic reactions along the entire MRD and (iii) a systematic description on efficacy & toxicity for all metabolites of a studied drug. However, there is no database available for describing the comprehensive metabolic roadmaps of drugs. Therefore, in this study, a major update of INTEDE was conducted, which provided the stepwise & sequential metabolic roadmaps for a total of 4701 drugs, and a total of 22 165 metabolic reactions containing 1088 DMEs and 18 882 drug metabolites. Additionally, the INTEDE 2.0 labeled the pharmacological properties (pharmacological activity or toxicity) of metabolites and provided their structural information. Furthermore, 3717 drug metabolism relationships were supplemented (from 7338 to 11 055). All in all, INTEDE 2.0 is highly expected to attract broad interests from related research community and serve as an essential supplement to existing pharmaceutical/biological/chemical databases. INTEDE 2.0 can now be accessible freely without any login requirement at: http://idrblab.org/intede/.

In silico and in vitro metabolism studies of the new synthetic opiate AP-237 (bucinnazine) using bioinformatics tools

The recent emergence of new synthetic opioids (NSOs) compounds in the illicit market is increasingly related to fatal cases. Identification and medical care of NSO intoxication cases are challenging, particularly due to high frequency of new products and extensive metabolism. As the study of NSO metabolism is crucial for the identification of these drugs in cases of intoxication, we aimed to investigate the metabolism of the piperazine NSO AP-237 (= bucinnazine). Two complementary approaches (in silico and in vitro) were used to identify putative AP-237 metabolites which could be used as consumption markers. In silico metabolism studies were realized by combining four open access softwares (MetaTrans, SyGMa, Glory X, Biotransformer 3.0). In vitro experiments were performed by incubating AP-237 (20 µM) in differentiated HepaRG cells during 0 h, 8 h, 24 h or 48 h. Cell supernatant were extracted and analyzed by liquid chromatography coupled to high-resolution mass spectrometry and data were reprocessed using three strategies (MetGem, GNPS or Compound Discoverer®). A total of 28 phase I and six phase II metabolites was predicted in silico. Molecular networking identified seven putative phase I metabolites (m/z 203.154, m/z 247.180, m/z 271.180, two m/z 289.191 isomers, m/z 305.186, m/z 329.222), including four previously unknown metabolites. Overall, this cross-disciplinary approach with molecular networking on data acquired in vitro and in silico prediction enabled to propose relevant candidate as AP-237 consumption markers that could be added to mass spectrometry libraries to help diagnose intoxication.

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Metabolites in the regulatory risk assessment of pesticides in the EU

A large majority of chemicals is converted into metabolites through xenobiotic-metabolising enzymes. Metabolites may present a spectrum of characteristics varying from similar to vastly different compared with the parent compound in terms of both toxicokinetics and toxicodynamics. In the pesticide arena, the role of metabolism and metabolites is increasingly recognised as a significant factor particularly for the design and interpretation of mammalian toxicological studies and in the toxicity assessment of pesticide/metabolite-associated issues for hazard characterization and risk assessment purposes, including the role of metabolites as parts in various residues in ecotoxicological adversities. This is of particular relevance to pesticide metabolites that are unique to humans in comparison with metabolites found in in vitro or in vivo animal studies, but also to disproportionate metabolites (quantitative differences) between humans and mammalian species. Presence of unique or disproportionate metabolites may underlie potential toxicological concerns. This review aims to present the current state-of-the-art of comparative metabolism and metabolites in pesticide research for hazard and risk assessment, including One Health perspectives, and future research needs based on the experiences gained at the European Food Safety Authority.

Exploring Metabolic Pathways of Anamorelin, a Selective Agonist of the Growth Hormone Secretagogue Receptor, via Molecular Networking

In this study, we delineated the poorly characterized metabolism of anamorelin, a growth hormone secretagogue receptor agonist, in vitro using human liver microsomes (HLM), based on classical molecular networking (MN) and feature-based molecular networking (FBMN) from the Global Natural Products Social Molecular Networking platform. Following the in vitro HLM reaction, the MN analysis showed 11 neighboring nodes whose information propagated from the node corresponding to anamorelin. The FBMN analysis described the separation of six nodes that the MN analysis could not achieve. In addition, the similarity among neighboring nodes could be discerned via their respective metabolic pathways. Collectively, 18 metabolites (M1-M12) were successfully identified, suggesting that the metabolic pathways involved were demethylation, hydroxylation, dealkylation, desaturation, and N-oxidation, whereas 6 metabolites (M13a*-b*, M14a*-b*, and M15a*-b*) remained unidentified. Furthermore, the major metabolites detected in HLM, M1 and M7, were dissimilar from those observed in the CYP3A4 isozyme assay, which is recognized to be markedly inhibited by anamorelin. Specifically, M7, M8, and M9 were identified as the major metabolites in the CYP3A4 isozyme assay. Therefore, a thorough investigation of metabolism is imperative for future in vivo studies. These findings may offer prospective therapeutic opportunities for anamorelin.

Computational prediction of the metabolites of agrochemicals formed in rats

Today, computational tools for the prediction of the metabolite structures of xenobiotics are widely available and employed in small-molecule research. Reflecting the availability of measured data, these in silico tools are trained and validated primarily on drug metabolism data. In this work, we assessed the capacity of five leading metabolite structure predictors to represent the metabolism of agrochemicals observed in rats. More specifically, we tested the ability of SyGMa, GLORY, GLORYx, BioTransformer 3.0, and MetaTrans to correctly predict and rank the experimentally observed metabolites of a set of 85 parent compounds. We found that the models were able to recover about one to two-thirds of the experimentally observed first-generation, second-generation and third-generation metabolites, confirming their value in applications such as metabolite identification. However, precision was low for all investigated tools and did not exceed approximately 18 % for the pool of first-generation metabolites and 2 % for the pool of compounds representing the first three generations of metabolites. The variance in prediction success rates was high across the individual metabolic maps, meaning that outcomes depend strongly on the specific compound under investigation. We also found that the predictions for individual parent compounds differed strongly between the tools, particularly between those built on orthogonal technologies (e.g., rule-based and end-to-end machine learning approaches). This renders ensemble model strategies promising for improving success rates. Overall, the results of this benchmark study show that there is still considerable room for the improvement of metabolite structure predictors left. Our discussion points out several avenues to progress. The bottleneck in method development certainly has been, and will remain, for the foreseeable future, the limited quantity and quality of available measured data on small-molecule metabolism.

Controlling pericellular oxygen tension in cell culture reveals distinct breast cancer responses to low oxygen tensions

Oxygen (O2) tension plays a key role in tissue function and pathophysiology. O2-controlled cell culture, in which the O2 concentration in an incubator's gas phase is controlled, is an indispensable tool to study the role of O2 in vivo. For this technique, it is presumed that the incubator setpoint is equal to the O2 tension that cells experience (i.e., pericellular O2). We discovered that physioxic (5% O2) and hypoxic (1% O2) setpoints regularly induce anoxic (0.0% O2) pericellular tensions in both adherent and suspension cell cultures. Electron transport chain inhibition ablates this effect, indicating that cellular O2 consumption is the driving factor. RNA-seq revealed that primary human hepatocytes cultured in physioxia experience ischemia-reperfusion injury due to anoxic exposure followed by rapid reoxygenation. To better understand the relationship between incubator gas phase and pericellular O2 tensions, we developed a reaction-diffusion model that predicts pericellular O2 tension a priori. This model revealed that the effect of cellular O2 consumption is greatest in smaller volume culture vessels (e.g., 96-well plate). By controlling pericellular O2 tension in cell culture, we discovered that MCF7 cells have stronger glycolytic and glutamine metabolism responses in anoxia vs. hypoxia. MCF7 also expressed higher levels of HIF2A, CD73, NDUFA4L2, etc. and lower levels of HIF1A, CA9, VEGFA, etc. in response to hypoxia vs. anoxia. Proteomics revealed that 4T1 cells had an upregulated epithelial-to-mesenchymal transition (EMT) response and downregulated reactive oxygen species (ROS) management, glycolysis, and fatty acid metabolism pathways in hypoxia vs. anoxia. Collectively, these results reveal that breast cancer cells respond non-monotonically to low O2, suggesting that anoxic cell culture is not suitable to model hypoxia. We demonstrate that controlling atmospheric O2 tension in cell culture incubators is insufficient to control O2 in cell culture and introduce the concept of pericellular O2-controlled cell culture.

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.

Identification of Xenobiotic Biotransformation Products Using Mass Spectrometry-Based Metabolomics Integrated with a Structural Elucidation Strategy by Assembling Fragment Signatures

The identification of xenobiotic biotransformation products is crucial for delineating toxicity and carcinogenicity that might be caused by xenobiotic exposures and for establishing monitoring systems for public health. However, the lack of available reference standards and spectral data leads to the generation of multiple candidate structures during identification and reduces the confidence in identification. Here, a UHPLC-HRMS-based metabolomics strategy integrated with a metabolite structure elucidation approach, namely, FragAssembler, was proposed to reduce the number of false-positive structure candidates. biotransformation product candidates were filtered by mass defect filtering (MDF) and multiple-group comparison. FragAssembler assembled fragment signatures from the MS/MS spectra and generated the modified moieties corresponding to the identified biotransformation products. The feasibility of this approach was demonstrated by the three biotransformation products of di(2-ethylhexyl)phthalate (DEHP). Comprehensive identification was carried out, and 24 and 13 biotransformation products of two xenobiotics, DEHP and 4'-Methoxy-α-pyrrolidinopentiophenone (4-MeO-α-PVP), were annotated, respectively. The number of 4-MeO-α-PVP biotransformation product candidates in the FragAssembler calculation results was approximately 2.1 times lower than that generated by BioTransformer 3.0. Our study indicates that the proposed approach has great potential for efficiently and reliably identifying xenobiotic biotransformation products, which is attributed to the fact that FragAssembler eliminates false-positive reactions and chemical structures and distinguishes modified moieties on isomeric biotransformation products. The FragAssembler software and associated tutorial are freely available at https://cosbi.ee.ncku.edu.tw/FragAssembler/ and the source code can be found at https://github.com/YuanChihChen/FragAssembler.

PubChemQC B3LYP/6-31G*//PM6 Data Set: The Electronic Structures of 86 Million Molecules Using B3LYP/6-31G* Calculations

The presented "PubChemQC B3LYP/6-31G*//PM6" data set is composed of the electronic properties of 85,938,443 molecules, encompassing a broad spectrum of molecules from essential compounds to biomolecules with a molecular weight up to 1000. These molecules account for 94.0% of the original PubChem Compound catalog as of August 29, 2016. The electronic properties, including orbitals, orbital energies, total energies, dipole moments, and other pertinent properties, were computed by using the B3LYP/6-31G* and PM6 methods. The data set, available in three formats, namely, GAMESS quantum chemistry program files, selected JSON output files, and a PostgreSQL database, provides researchers with the ability to query molecular properties. It is further subdivided into five subdata sets for more specific data. The first two subsets encompass molecules with carbon, hydrogen, oxygen, and nitrogen with molecular weights under 300 and 500, respectively. The third and fourth subsets incorporate molecules with carbon, hydrogen, nitrogen, oxygen, phosphorus, sulfur, fluorine, and chlorine, with molecular weights under 300 and 500, respectively. The fifth subset comprises molecules with carbon, hydrogen, nitrogen, oxygen, phosphorus, sulfur, fluorine, chlorine, sodium, potassium, magnesium, and calcium, with a molecular weight of under 500. The coefficients of determination for the highest occupied molecular orbital-lowest unoccupied molecular orbital energy gap range from 0.892 (for CHON500) to 0.803 (for the whole data set). These comprehensive results pave the way for applications in drug discovery and materials science, among others. The data sets can be accessed under the Creative Commons Attribution 4.0 International license at the following web address: https://nakatamaho.riken.jp/pubchemqc.riken.jp/b3lyp_pm6_datasets.html.

Identification of microbial markers associated with lung cancer based on multi-cohort 16 s rRNA analyses: A systematic review and meta-analysis

BACKGROUND

The relationship between commensal microbiota and lung cancer (LC) has been studied extensively. However, developing replicable microbiological markers for early LC diagnosis across multiple populations has remained challenging. Current studies are limited to a single region, single LC subtype, and small sample size. Therefore, we aimed to perform the first large-scale meta-analysis for identifying micro biomarkers for LC screening by integrating gut and respiratory samples from multiple studies and building a machine-learning classifier.

METHODS

In total, 712 gut and 393 respiratory samples were assessed via 16 s rRNA amplicon sequencing. After identifying the taxa of differential biomarkers, we established random forest models to distinguish between LC populations and normal controls. We validated the robustness and specificity of the model using external cohorts. Moreover, we also used the KEGG database for the predictive analysis of colony-related functions.

RESULTS

The α and β diversity indices indicated that LC patients' gut microbiota (GM) and lung microbiota (LM) differed significantly from those of the healthy population. Linear discriminant analysis (LDA) of effect size (LEfSe) helped us identify the top-ranked biomarkers, Enterococcus, Lactobacillus, and Escherichia, in two microbial niches. The area under the curve values of the diagnostic model for the two sites were 0.81 and 0.90, respectively. KEGG enrichment analysis also revealed significant differences in microbiota-associated functions between cancer-affected and healthy individuals that were primarily associated with metabolic disturbances.

CONCLUSIONS

GM and LM profiles were significantly altered in LC patients, compared to healthy individuals. We identified the taxa of biomarkers at the two loci and constructed accurate diagnostic models. This study demonstrates the effectiveness of LC-specific microbiological markers in multiple populations and contributes to the early diagnosis and screening of LC.

Targeting Staphylococcal Cell-Wall Biosynthesis Protein FemX Through Steered Molecular Dynamics and Drug-Repurposing Approach

Staphylococcus aureus-mediated infection is a serious threat in this antimicrobial-resistant world. S. aureus has become a "superbug" by challenging conventional as well as modern treatment strategies. Nowadays, drug repurposing has become a new trend for the discovery of new drug molecules. This study focuses on evaluating FDA-approved drugs that can be repurposed against S. aureus infection. Steered molecular dynamics (SMD) has been performed for Lumacaftor and Olaparib against staphylococcal FemX to understand their binding to the active site. A time-dependent external force or rupture force has been applied to the ligands to calculate the force required to dislocate the ligand from the binding pocket. SMD analysis indicates that Lumacaftor has a high affinity for the substrate binding pocket in comparison to Olaparib. Umbrella sampling exhibits that Lumacaftor possesses a higher free energy barrier to displace it from the ligand-binding site. The bactericidal activity of Lumacaftor and Olaparib has been tested, and it shows that Lumacaftor has moderate activity along with biofilm inhibition potential (MIC value with conc. 128 μg/mL). Pharmacokinetic and toxicology evaluations indicate that Lumacaftor has higher pharmacokinetic potential with lower toxicity. This is the first experimental report where staphylococcal FemX has been targeted for the discovery of new drugs. It is suggested that Lumacaftor may be a potential lead molecule against S. aureus.

CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning

MOTIVATION

Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as the optimization of lead compounds. Considering wet-lab experiment is time-consuming, laborious, and expensive, in silico prediction of metabolic stability is an alternative choice. However, few computational methods have been developed to address this task. In addition, it remains a significant challenge to explain key functional groups determining metabolic stability.

RESULTS

To address these issues, we develop a novel cross-modality graph contrastive learning model named CMMS-GCL for predicting the metabolic stability of drug candidates. In our framework, we design deep learning methods to extract features for molecules from two modality data, i.e. SMILES sequence and molecule graph. In particular, for the sequence data, we design a multihead attention BiGRU-based encoder to preserve the context of symbols to learn sequence representations of molecules. For the graph data, we propose a graph contrastive learning-based encoder to learn structure representations by effectively capturing the consistencies between local and global structures. We further exploit fully connected neural networks to combine the sequence and structure representations for model training. Extensive experimental results on two datasets demonstrate that our CMMS-GCL consistently outperforms seven state-of-the-art methods. Furthermore, a collection of case studies on sequence data and statistical analyses of the graph structure module strengthens the validation of the interpretability of crucial functional groups recognized by CMMS-GCL. Overall, CMMS-GCL can serve as an effective and interpretable tool for predicting metabolic stability, identifying critical functional groups, and thus facilitating the drug discovery process and lead compound optimization.

AVAILABILITY AND IMPLEMENTATION

The code and data underlying this article are freely available at https://github.com/dubingxue/CMMS-GCL.

Recent Advances in Organ-on-Chips Integrated with Bioprinting Technologies for Drug Screening

Currently, the demand for more reliable drug screening devices has made scientists and researchers develop novel potential approaches to offer an alternative to animal studies. Organ-on-chips are newly emerged platforms for drug screening and disease metabolism investigation. These microfluidic devices attempt to recapitulate the physiological and biological properties of different organs and tissues using human-derived cells. Recently, the synergistic combination of additive manufacturing and microfluidics has shown a promising impact on improving a wide array of biological models. In this review, different methods are classified using bioprinting to achieve the relevant biomimetic models in organ-on-chips, boosting the efficiency of these devices to produce more reliable data for drug investigations. In addition to the tissue models, the influence of additive manufacturing on microfluidic chip fabrication is discussed, and their biomedical applications are reviewed.

Computational modeling and synthesis of pyridine variants of benzoyl-phenoxy-acetamide with high glioblastoma cytotoxicity and brain tumor penetration

Glioblastomas are highly aggressive brain tumors for which therapeutic options are very limited. In a quest for new anti-glioblastoma drugs, we focused on specific structural modifications to the benzoyl-phenoxy-acetamide (BPA) structure present in a common lipid-lowering drug, fenofibrate, and in our first prototype glioblastoma drug, PP1. Here, we propose extensive computational analyses to improve the selection of the most effective glioblastoma drug candidates. Initially, over 100 structural BPA variations were analyzed and their physicochemical properties, such as water solubility (- logS), calculated partition coefficient (ClogP), probability for BBB crossing (BBB_SCORE), probability for CNS penetration (CNS-MPO) and calculated cardiotoxicity (hERG), were evaluated. This integrated approach allowed us to select pyridine variants of BPA that show improved BBB penetration, water solubility, and low cardiotoxicity. Herein the top 24 compounds were synthesized and analyzed in cell culture. Six of them demonstrated glioblastoma toxicity with IC50 ranging from 0.59 to 3.24 µM. Importantly, one of the compounds, HR68, accumulated in the brain tumor tissue at 3.7 ± 0.5 µM, which exceeds its glioblastoma IC50 (1.17 µM) by over threefold.

Synthesis, Characterization, and DFT Calculations of a New Sulfamethoxazole Schiff Base and Its Metal Complexes

A new Schiff base, 4-((1E,2E)-3-(furan-2-yl)allylidene)amino)-N-(5-methylisoxazol-3-yl) benzene-sulfonamide (L), was synthesized by thermal condensation of 3-(2-furyl)acrolein and sulfamethoxazole (SMX), and the furan Schiff base (L) was converted to a phenol Schiff base (L') according to the Diels-Alder [4 + 2] cycloaddition reaction and studied experimentally. The structural and spectroscopic properties of the Schiff base were also corroborated by utilizing density functional theory (DFT) calculations. Furthermore, a series of lanthanide and transition metal complexes of the Schiff base were synthesized from the nitrate salts of Gd, Sm, Nd, and Zn (L1, L2, L3, and L4), respectively. Various spectroscopic studies confirmed the chemical structures of the Schiff-base ligand and its complexes. Based on the spectral studies, a nine-coordinated geometry was assigned to the lanthanide complexes and a six-coordinated geometry to the zinc complex. The elemental analysis data confirmed the suggested structure of the metal complexes, and the TGA studies confirmed the presence of one coordinated water molecule in the lanthanide complexes and one crystalline water molecule in the zinc complex; in addition, the conductivity showed the neutral nature of the complexes. Therefore, it is suggested that the ligand acts as a bidentate through coordinates to each metal atom by the isoxazole nitrogen and oxygen atoms of the sulfur dioxide moiety of the SMX based on FTIR studies. The ligand and its complexes were tested for their anti-inflammatory, anti-hemolytic, and antioxidant activities by various colorimetric methods. These complexes were found to exhibit potential effects of the selected biological activities.

Biological activity and structure-activity relationship of dehydrodieugenol B analogues against visceral leishmaniasis

Visceral leishmaniasis is a neglected protozoan disease with high mortality. Existing treatments exhibit a number of limitations, resulting in a significant challenge for public health, especially in developing countries in which the disease is endemic. With a limited pipeline of potential drugs in clinical trials, natural products could offer an attractive source of new pharmaceutical prototypes, not least due to their high chemodiversity. In the present work, a study of anti-L. (L.) infantum potential was carried out for a series of 39 synthetic compounds based on the core scaffold of the neolignan dehydrodieugenol B. Of these, 14 compounds exhibited activity against intracellular amastigotes, with 50% inhibitory concentration (IC₅₀) values between 3.0 and 32.7 μM. A structure-activity relationship (SAR) analysis demonstrated a requirement for polar functionalities to improve activity. Lacking mammalian cytotoxicity and presenting the highest potency against the clinically relevant form of the parasite, compound 24 emerged as the most promising, fulfilling the hit criteria for visceral leishmaniasis defined by the Drugs for Neglected Diseases initiative (DNDi). This study emphasizes the potential of dehydrodieugenol B analogues as new candidates for the treatment of visceral leishmaniasis and suggests 24 to be a suitable compound for future optimization, including mechanism of action and pharmacokinetic studies.

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.

Stepwise Targeted Matching Strategy for Comprehensive Profiling of Xanthohumol Metabolites In Vivo and In Vitro Using UHPLC-Q-Exactive Orbitrap Mass Spectrometer

Xanthohumol (XN), a natural prenylated flavonoid extracted and isolated from the hop plant (Humulus lupulus), possesses diverse pharmacological activities. Although the metabolites of XN have been investigated in the previous study, a comprehensive metabolic profile has been insufficient in vivo or in vitro until now. The current study was aimed at systematically elucidating the metabolic pathways of XN after oral administration to rats. Herein, a UHPLC-Q-Exactive Orbitrap MS was adopted for the potential metabolites detection. A stepwise targeted matching strategy for the overall identification of XN metabolites was proposed. A metabolic net (53 metabolites included) on XN in vivo and in vitro, as well as the metabolic profile investigation, were designed, preferably characterizing XN metabolites in rat plasma, urine, liver, liver microsomes, and feces. On the basis of a stepwise targeted matching strategy, the net showed that major in vivo metabolic pathways of XN in rats include glucuronidation, sulfation, methylation, demethylation, hydrogenation, dehydrogenation, hydroxylation, and so on. The proposed metabolic pathways in this research will provide essential data for further pharmaceutical studies of prenylated flavonoids and lay the foundation for further toxicity and safety studies.

Identification of Some Glutamic Acid Derivatives with Biological Potential by Computational Methods

Glutamic acid is a non-essential amino acid involved in multiple metabolic pathways. Of high importance is its relationship with glutamine, an essential fuel for cancer cell development. Compounds that can modify glutamine or glutamic acid behaviour in cancer cells have resulted in attractive anticancer therapeutic alternatives. Based on this idea, we theoretically formulated 123 glutamic acid derivatives using Biovia Draw. Suitable candidates for our research were selected among them. For this, online platforms and programs were used to describe specific properties and their behaviour in the human organism. Nine compounds proved to have suitable or easy to optimise properties. The selected compounds showed cytotoxicity against breast adenocarcinoma, lung cancer cell lines, colon carcinoma, and T cells from acute leukaemia. Compound 2Ba5 exhibited the lowest toxicity, and derivative 4Db6 exhibited the most intense bioactivity. Molecular docking studies were also performed. The binding site of the 4Db6 compound in the glutamine synthetase structure was determined, with the D subunit and cluster 1 being the most promising. In conclusion, glutamic acid is an amino acid that can be manipulated very easily. Therefore, molecules derived from its structure have great potential to become innovative drugs, and further research on these will be conducted.

Pharmacological Characteristics of the Hydroethanolic Extract of Acmella oleracea (L) R. K. Jansen Flowers: ADME/Tox In Silico and In Vivo Antihypertensive and Chronic Toxicity Evaluation

Acmella oleracea (L.) R. K. Jansen, popularly known as jambu in Northern Brazil, is widely used in folk medicine and local cuisine. Its consumption in different ways reinforces the need for safety assessments. In this study, the major compounds found in the hydroethanolic extract of A. oleracea flowers (EHFAO) were characterized by ultra-performance liquid mass spectrometry (UHPLC-ESI-QTOF-MS/MS). The effects of oral administration of 100/mg/kg of EHFAO extract over 60 days in male spontaneously hypertensive (SHR) and Wistar (WR) rats and the in silico ADME/Tox predictions, lipophilicity, and water solubility were accomplished for the compounds identified. Spilanthol was detected as the foremost major compound at a concentration of 97.7%, followed by 1.53% scopoletin and 0.77% d-limonene. The treatment with EHFAO did not alter the animals´ weight over the studied period. Moderate alterations were observed solely in the hepatic enzymes AST (WR = 97 UI/L and SHR = 150 UI/L ∗ p < 0.05) and ALT (WR = 55 UI/L and SHR = 95 UI/L ∗ p < 0.05), while no relevant histopathological alterations were found. The in-silico study confirmed the in vivo findings, as the identified compounds were considered highly bioactive orally, due to their drug similarity profiles, adequate lipid solubility, bioavailability, and pharmacokinetics. Therefore, the chronic treatment with EHFAO was found safe at the concentration of 100/mg/kg, with no interference in the blood pressure levels neither appreciable toxic effects.

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.