Absorption, Distribution, Metabolism, Excretion, Toxicity Research Articles

Small molecule drug discovery for glioblastoma treatment based on bioinformatics and cheminformatics approaches.

Background: Glioblastoma (GBM) is a common and highly aggressive brain tumor with a poor prognosis for patients. It is urgently needed to identify potential small molecule drugs that specifically target key genes associated with GBM development and prognosis. Methods: Differentially expressed genes (DEGs) between GBM and normal tissues were obtained by data mining the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Gene function annotation was performed to investigate the potential functions of the DEGs. A protein-protein interaction (PPI) network was constructed to explore hub genes associated with GBM. Bioinformatics analysis was used to screen the potential therapeutic and prognostic genes. Finally, potential small molecule drugs were predicted using the DGIdb database and verified using chemical informatics methods including absorption, distribution, metabolism, excretion, toxicity (ADMET), and molecular docking studies. Results: A total of 429 DEGs were identified, of which 19 hub genes were obtained through PPI analysis. The hub genes were confirmed as potential therapeutic targets by functional enrichment and mRNA expression. Survival analysis and protein expression confirmed centromere protein A (CENPA) as a prognostic target in GBM. Four small molecule drugs were predicted for the treatment of GBM. Conclusion: Our study suggests some promising potential therapeutic targets and small molecule drugs for the treatment of GBM, providing new ideas for further research and targeted drug development.

An Evolved Transformer Model for ADME/Tox Prediction

Drug discovery aims to keep fueling new medicines to cure and palliate many ailments and some untreatable diseases that still afflict humanity. The ADME/Tox (absorption, distribution, metabolism, excretion/toxicity) properties of candidate drug molecules are key factors that determine the safety, uptake, elimination, metabolic behavior and effectiveness of drug research and development. The predictive technique of ADME/Tox drastically reduces the fraction of pharmaceutics-related failure in the early stages of drug development. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, artificial intelligence techniques such as Graphormer are showing increasing promise and usefulness to perform custom models for molecule modeling tasks. However, Graphormer and other transformer-based models do not consider the molecular fingerprint, as well as the physicochemicals that have been proved effective in traditional computational drug research. Here, we propose an enhanced model based on Graphormer which uses a tree model that fully integrates some known information and achieves better prediction and interpretability. More importantly, the model achieves new state-of-the-art results on ADME/Tox properties prediction benchmarks, surpassing several challenging models. Experimental results demonstrate an average SMAPE (Symmetric Mean Absolute Percentage Error) of 18.9 and a PCC (Pearson Correlation Coefficient) of 0.86 on ADME/Tox prediction test sets. These findings highlight the efficacy of our approach and its potential to enhance drug discovery processes. By leveraging the strengths of Graphormer and incorporating additional molecular descriptors, our model offers improved predictive capabilities, thus contributing to the advancement of ADME/Tox prediction in drug development. The integration of various information sources further enables better interpretability, aiding researchers in understanding the underlying factors influencing the predictions. Overall, our work demonstrates the potential of our enhanced model to expedite drug discovery, reduce costs, and enhance the success rate of our pharmaceutical development efforts.

In silico identification of quorum sensing inhibitors against LasR protein in a clinical isolate of multidrug resistant Pseudomonas aeruginosa DMC-27b

Background: Pseudomonas aeruginosa is an opportunistic pathogen that uses quorum-sensing (QS) and biofilm formation to subvert antibiotic therapy. Antibiotic resistance has led to a demand for alternative methods of treatment, and destabilizing the LasR-OdDHL binding with inhibitors offers a potential solution. Methods: This study aimed to construct a homology model of the LasR protein using the genetic sequence of the P. aeruginosa DMC-27b strain (GenBank: SMRY00000000.2). Molecular docking, molecular mechanics-based binding free energy calculation, and pharmacokinetic analysis were performed on 1900 3D structures collected from synthetic and natural compound databases to identify three potential lead molecules. These compounds were evaluated using ADMET (absorption, distribution, metabolism, excretion, toxicity) analysis, and molecular dynamics protocols were used to refine the results. Results: The three lead compounds showed higher binding capability with the LasR receptor than the native ligand and passed the ADMET evaluation stage. In total, 44 properties remained within the range of 95% of known drugs, indicating their potential efficacy as drugs against P. aeruginosa and other bacteria that use a similar QS system. Conclusions: This study provides insights into potential drug designing and development against clinical isolates of emerging P. aeruginosa strains and other bacteria that use a similar QS system.

Pharmaco-toxicological effects of the novel tryptamine hallucinogen 5-MeO-MiPT on motor, sensorimotor, physiological, and cardiorespiratory parameters in mice—from a human poisoning case to the preclinical evidence

RationaleThe 5-methoxy-N-methyl-N-isopropyltryptamine (5-MeO-MiPT, known online as “Moxy”) is a new psychedelic tryptamine first identified on Italian national territory in 2014. Its hallucinogen effects are broadly well-known; however, only few information is available regarding its pharmaco-toxicological effects.ObjectivesFollowing the seizure of this new psychoactive substances by the Arm of Carabinieri and the occurrence of a human intoxication case, in the current study we had the aim to characterize the in vivo acute effects of systemic administration of 5-MeO-MiPT (0.01–30 mg/kg i.p.) on sensorimotor (visual, acoustic, and overall tactile) responses, thermoregulation, and stimulated motor activity (drag and accelerod test) in CD-1 male mice. We also evaluated variation on sensory gating (PPI, prepulse inhibition; 0.01–10 mg/kg i.p.) and on cardiorespiratory parameters (MouseOx and BP-2000; 30 mg/kg i.p.). Lastly, we investigated the in silico ADMET (absorption, distribution, metabolism, excretion, toxicity) profile of 5-MeO-MiPT compared to 5-methoxy-N,N-diisopropyltryptamine (5-MeO-DIPT) and N,N-dimethyltryptamine (DMT).ResultsThis study demonstrates that 5-MeO-MiPT dose-dependently inhibits sensorimotor and PPI responses and, at high doses, induces impairment of the stimulated motor activity and cardiorespiratory changes in mice. In silico prediction shows that the 5-MeO-MiPT toxicokinetic profile shares similarities with 5-MeO-DIPT and DMT and highlights a cytochrome risk associated with this compound.ConclusionsConsumption of 5-MeO-MiPT can affect the ability to perform activities and pose a risk to human health status, as the correspondence between the effects induced in mice and the symptoms occurred in the intoxication case suggests. However, our findings suggest that 5-MeO-MiPT should not be excluded from research in the psychiatric therapy field.

6-Amino-4-aryl-7-phenyl-3-(phenylimino)-4,7-dihydro-3H-[1,2]dithiolo[3,4-b]pyridine-5-carboxamides: Synthesis, Biological Activity, Quantum Chemical Studies and In Silico Docking Studies.

New [1,2]dithiolo[3,4-b]pyridine-5-carboxamides were synthesized through the reaction of dithiomalondianilide (N,N'-diphenyldithiomalondiamide) with 3-aryl-2-cyanoacrylamides or via a three-component reaction involving aromatic aldehydes, cyanoacetamide and dithiomalondianilide in the presence of morpholine. The structure of 6-amino-4-(2,4-dichloro- phenyl)-7-phenyl-3-(phenylimino)-4,7-dihydro-3H-[1,2]dithiolo[3,4-b]pyridine-5-carboxamide was confirmed using X-ray crystallography. To understand the reaction mechanism in detail, density functional theory (DFT) calculations were performed with a Grimme B97-3c composite computational scheme. The results revealed that the rate-limiting step is a cyclization process leading to the closure of the 1,4-dihydropyridine ring, with an activation barrier of 28.8 kcal/mol. Some of the dithiolo[3,4-b]pyridines exhibited moderate herbicide safening effects against 2,4-D. Additionally, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) parameters were calculated and molecular docking studies were performed to identify potential protein targets.

Development of Novel Pyrrole Derivatives and Their Cinnamic Hybrids as Dual COX-2/LOX Inhibitors.

Molecular hybridization has emerged as a promising approach in the treatment of diseases exhibiting multifactorial etiology. With regard to this, dual cyclooxygenase-2/lipoxygenase (COX-2/LOX) inhibitors could be considered a safe alternative to traditional non-steroidal anti-inflammatory drugs (tNSAIDs) and selective COX-2 inhibitors (coxibs) for the treatment of inflammatory conditions. Taking this into account, six novel pyrrole derivatives and pyrrole-cinnamate hybrids were developed as potential COX-2 and soybean LOX (sLOX) inhibitors with antioxidant activity. In silico calculations were performed to predict their ADMET (absorption, distribution, metabolism, excretion, toxicity) properties and drug-likeness, while lipophilicity was experimentally determined as RM values. All synthesized compounds (1-4, 5-8) could be described as drug-like. The results from the docking studies on COX-2 were in accordance with the in vitro studies. According to molecular docking studies on soybean LOX, the compounds displayed allosteric interactions with the enzyme. Pyrrole 2 appeared to be the most potent s-LOX inhibitor (IC50 = 7.5 μM). Hybrids 5 and 6 presented a promising combination of in vitro LOX (IC50 for 5 = 30 μM, IC50 for 6 = 27.5 μM) and COX-2 (IC50 for 5 = 0.55 μM, IC50 for 6 = 7.0 μM) inhibitory activities, and therefore could be used as the lead compounds for the synthesis of more effective multi-target agents.

The discovery of subunit-selective GluN1/GluN2B NMDAR antagonist via pharmacophere-based virtual screening.

The incidence and mortality rates of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, are gradually increasing worldwide. Numerous studies have demonstrated that N-methyl-D-aspartic acid receptor (NMDAR)-mediated excitotoxicity contributes to neurodegenerative diseases. Ifenprodil, a subtype-selective NMDAR antagonist, showed strong therapeutic potential. However, it suffers from low oral bioavailability and off-target side effects. In this study, natural compounds were identified for selective inhibition of GluN1/GluN2B NMDAR of human. We obtained a set of natural compounds (n = 81,366) from COCONUT, TIPdb, PAMDB, CMNPD, YMDB, and NPAtlas databases, and then virtually screened by an ifenprodil-structure-based pharmacophore model and molecular docking. The top 100 compounds were selected for binding affinity prediction via batch drug-target affinity (BatchDTA). Then, the top 50 compounds were analyzed by absorption, distribution, metabolism, excretion, toxicity (ADMET). Molecular dynamics involving molecular mechanics/position-Boltzmann surface area (MM-PBSA) calculation were performed to further screening. The top 15 compounds with strong binding affinity and ifenprodil, a proven GluN2B-selective NMDAR antagonist, were subjected to molecular dynamic simulations (100 ns), root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), H-bonds, solvent accessible surface area (SASA), principal component analysis (PCA), and binding free energy calculations. Based on these analyses, one possible lead compound carrying positive charges (CNP0099440) was identified, with great binding affinity and less off-target activity by contrast to ifenprodil. CNP0099440 has great potential to be a GluN1/GluN2B NMDAR antagonist candidate and can be further detected via in vitro and in vivo experiments.

ADMET and Solubility Analysis of New 5-Nitroisatine-Based Inhibitors of CDK2 Enzymes.

The development of new substances with the ability to interact with a biological target is only the first stage in the process of the creation of new drugs. The 5-nitroisatin derivatives considered in this study are new inhibitors of cyclin-dependent kinase 2 (CDK2) intended for anticancer therapy. The research, carried out based on the ADMET (absorption, distribution, metabolism, excretion, toxicity) methods, allowed a basic assessment of the physicochemical parameters of the tested drugs to be made. The collected data clearly showed the good oral absorption, membrane permeability, and bioavailability of the tested substances. The analysis of the metabolite activity and toxicity of the tested drugs did not show any critical hazards in terms of the toxicity of the tested substances. The substances' low solubility in water meant that extended studies tested compounds were required, which helped to select solvents with a high dissolving capacity of the examined substances, such as DMSO or NMP. The use of aqueous binary mixtures based on these two solvents allowed a relatively high solubility with significantly reduced toxicity and environmental index compared to pure solvents to be maintained, which is important in the context of the search for green solvents for pharmaceutical use.

In silico approaches for the identification of novel ULK1 inhibitors: pharmacophore model, molecular docking and molecular dynamics simulations

The serine/threonine kinase unc-51-like autophagy activating kinase 1 (ULK1) has been regarded as an attractive target for tumor therapy. In this study, in silico approaches, such as the pharmacophore-based virtual screening strategy, molecular docking and molecular dynamics (MD) simulations, were applied to develop novel potential ULK1 inhibitors. The pharmacophore models based on known aminopyrimidine ULK1 inhibitors were constructed to screen the dataset of 1.68 million compounds, which were obtained via screening the 2.30 million compounds in ChEMBL database by Lipinski’s rule of five. Seven novel compounds and 1 known ULK1 inhibitor stand out for the strong virtual biological activity by molecular docking, cluster analysis, Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculation and Absorption Distribution Metabolism Excretion Toxicity (ADMET) prediction. Their results of MD included principal component analysis (PCA) and Free Energy Landscapes surface (FELs) indicated that the protein–ligand complex was stable in simulated trajectories of 100 ns. The binding free energy (BFE) calculations showed that a total of 6 novel compounds (CL130, CL834, CL961, CL966, CL163 and CL329) with the stable binding state and stronger BFE (−61.17 to −37.01 kcal/mol) than that of original ligand 3RF (−36.66 kcal/mol). With reference to the ULK1 inhibition of 3RF (IC50 = 160 nM), it can be inferred that these compounds could be used as a new type of potential ULK1 inhibitors and be worthy of further investigation for tumor treatments. Communicated by Ramaswamy H. Sarma

Structure-based discovery of pyrazole-benzothiadiazole hybrid as human HPPD inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) has attracted increasing attention as a target for treating type I tyrosinemia and other diseases with defects in tyrosine catabolism. Only one commercial drug, 2-(2-nitro-4-trifluoromethylbenzoyl)-1, 3-cyclohexanedione (NTBC), clinically treat type I tyrosinemia, but show some severe side effects in clinical application. Here, we determined the structure of human HPPD-NTBC complex, and developed new pyrazole-benzothiadiazole 2,2-dioxide hybrids from the binding of NTBC. These compounds showed improved inhibition against human HPPD, among which compound a10 was the most active candidate. The Absorption Distribution Metabolism Excretion Toxicity (ADMET) predicted properties suggested that a10 had good druggability, and was with lower toxicity than NTBC. The structure comparison between inhibitor-bound and ligand-free form human HPPD showed a large conformational change of the C-terminal helix. Furthermore, the loop 1 and α7 helix were found adopting different conformations to assist the gating of the cavity, which explains the gating mechanism of human HPPD.

Elucidating the Anti-Inflammatory Properties of Garcinia kola and Vernonia amygdalina through in silico Molecular Biology Techniques

Introduction: Inflammation is implicated in many disorders, including communicable and noncommunicable diseases. Cyclooxygenase-2 (Cox-2) is a key enzyme involved in the production of prostaglandins implicated in inflammatory disorders. Garcinia kola and Vernonia amygdalina are medicinal plants being used for treating various ailments in many parts of the world and extensive in vitro and in vivo studies have been conducted on them. Five phytochemicals were selected from the two plants; aspirin and celecoxib were used as reference drugs. This study investigated the interactions of the seven ligands with the Cox-2 enzyme, using in silico molecular biology techniques. Materials and Methods: The 3-D structures of the seven ligands were retrieved from the PubChem database in their Structure Data Format (SDF). Cox-2 was retrieved in its Protein Data Bank (PDB) format. The ligands and the protein were converted to their pdbqt formats through the open babel software. The Cox-2 was docked with the ligands using the Auto-Dock Vina software. The binding energies and the root mean square deviation values were noted. Pharmacophore modeling was visualized by using the Biovia Discovery Studio Visualizer. One of the ligands (luteolin) was further subjected to molecular dynamics simulation using the desmond maestro software. Results: While celecoxib had the best binding property with Cox-2 (-10.8 kcal/mol, 3 H bonds), the five ligands from the two plants had better binding properties than aspirin (which had -6.5kcal/mol, 1 H bond). Kolaviron, from G. kola (-9.1 kcal/mol, 3 H bonds) and luteolin, from V. amygdalina (-8.5kcal/mol, 2 H bonds) demonstrated the best binding properties among the five phytochemicals. Additional interactions of H bonds and hydrophobic bonds were noticed post molecular dynamics simulation of luteolin with Cox-2, indicating dynamic forces’ fluctuations. MD simulations showed that Ser530 and Tyr385 were the best amino acid side chains that interacted with luteolin for the stabilization of the protein-ligand complex. Conclusion: The energy values and protein-ligand interactions indicate affinity and stability of the complex. Luteolin can be taken as a promising drug target and subjected to ADMET (absorption, distribution, metabolism, excretion, toxicity) properties analysis and clinical trials. This is especially important in view of the various side effects associated with both nonselective and selective Cox-2 inhibitors, including aspirin and celecoxib.

Synthesis and Activity of Aurone and Indanone Derivatives.

Based on bioactive group splicing, classical bioisosterism, and the rule of alkene insertion, forty-eight aurone, and indanone derivatives were designed and synthesized. They were evaluated for inhibitory activity against C. albicans, E. coli, and S. aureus. Among them, thirty compounds exhibited moderate to excellent antibacterial activity. The maximum circle of inhibition was 18 mm (compounds B15, B16, and E7), and the minimum values of MIC and MBC were respectively 15.625 μM (compounds A5 and D2) and 62.5 μM (compounds A6, A8, and E7). The SAR showed that aurone and indanone derivatives could strongly inhibit the growth of Gram-positive bacteria. The introduction of electron-withdrawing groups or hydroxyl is beneficial to the activity. It was exciting that the 3-phenylallylbenzofuranone and 3-allylindanone skeletons with antimicrobial activity were first reported in this study. Compounds A5 and E7 were selected for molecular docking studies with targets MetRS (PBD: 7WPI) and PBP (PDB: 6C3K) to determine the binding interactions at the active site. On this basis, the physicochemical and pharmacological properties of the compounds were predicted and analyzed. The influence of these properties on antimicrobial activity and their implications for subsequent work were discussed. The ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) predictions showed that most of the compounds had good pharmacokinetic profiles and high safety profiles.

Machine Learning-Based Quantitative Structure-Activity Relationship and ADMET Prediction Models for ERα Activity of Anti-Breast Cancer Drug Candidates

Breast cancer is presently one of the most common malignancies worldwide, with a higher fatality rate. In this study, a quantitative structure-activity relationship (QSAR) model of compound biological activity and ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties prediction model were performed using estrogen receptor alpha (ERα) antagonist information collected from compound samples. We first utilized grey relation analysis (GRA) in conjunction with the random forest (RF) algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity, and then we used Spearman correlation analysis to identify 16 independent variables. Second, a QSAR model of the compound were developed based on BP neural network (BPNN), genetic algorithm optimized BP neural network (GA-BPNN), and support vector regression (SVR). The BPNN, the SVR, and the logistic regression (LR) models were then used to identify and predict the ADMET properties of substances, with the prediction impacts of each model compared and assessed. The results reveal that a SVR model was used in QSAR quantitative prediction, and in the classification prediction of ADMET properties: the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene) properties, the LR model predicts the cytochrome P450 enzyme 3A4 subtype (CYP3A4) and Micronucleus (MN) properties, and the BPNN model predicts the Human Oral Bioavailability (HOB) properties. Finally, information entropy theory is used to validate the rationality of variable screening, and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability, which can be used as a reference for screening probable active compounds and drug discovery.

Effects of anti-cyclooxygenases (COX-1 and COX-2), structure activity relationship, molecular docking and in silico ADMET of some synthesized chalcones

Purpose: To develop effective cancer chemopreventive and anti-inflammatory agents, a series of chalcones were prepared by reacting suitable aromatic aldehyde with appropriate acetophenones.
 Methods: Twenty-four synthesized chalcones (namely, 1 - 24) were assessed for their in vitro anti-cyclooxygenase-1 (COX-1) and anti-cyclooxygenase-2 (COX-2) activity in a COX catalyzed prostaglandin synthesis bioassay. Molecular docking was done to investigate the ligand-protein interactions, and selectivity on both enzymes. ADMET (absorption, distribution, metabolism, excretion, toxicity) modeling and software were also used.
 Results: The compounds inhibited both COX-1 and COX-2. Two compounds (3 and 19) demonstrated more marked COX-2 inhibition than compound 1. Indomethacin as a standard anti-cyclooxygenase shows unselective inhibition of 81.44 ± 6.5 and 91 ± 9.5, respectively. The in silico data revealed that a chalcone skeleton with C=O at 4-position, C2–C3 double bond and OH at 5-position are necessary properties for anti-cyclooxygenase effects. It was also revealed that the propenone moiety comprises of an appropriate scaffold which proposes a new acyclic 1,3-diphenylprop-2-en-1-ones with selective anti-COX effects. A molecular modeling investigations where these chalcones 1, 3 and 19 were docked in the active site of COX-2 depicted that the p-CH3 substituent on the C-4- phenyl ring A are oriented in the vicinity of the COX-2 secondary pocket Phe381, Gly526, Tyr385 and Val349.
 Conclusion: Based on the screening for oral bioavailability, in silico ADMET, and toxicity risk assessment, this study shows that these compounds could be a cornerstone for the development of new pharmaceuticals in the battle against COX-associated inflammatory disorders.

Molecular Docking and ADMET Study of Spice‐Derived Potential Phytochemicals against Human DNA Topoisomerase III Alpha

AbstractSmall phytochemicals play an important role in therapeutics hence potential molecules in different spices are screened using Lipinski's and Veber's rule. Pharmacodynamics studies are carried out against the cancer target, DNA topoisomerase III alpha. In silico ADMET (absorption, distribution, metabolism, excretion, toxicity) studies are done with these compounds. Lipinski's and Veber's rule indicates that all 10 small phytochemicals have the potential to behave as oral drug‐like molecules. A molecular docking study shows that shogoal, demethoxycurcumin, capsaicin, ellagic acid, 6‐paradol, 6‐gingerol, carnosic acid, and curcumin bind strongly with the cancer target: human DNA topoisomerase‐III alpha. In silico absorption, a study reveals that only cinnamic acid, allicin, alpha turmerone, cinnamyl acetate, and carnosic acid have more than 96% human intestinal absorption, but only alpha turmerone can cross both the blood–brain barrier and central nervous system. In silico phase‐I, the metabolism study shows 6‐paradol and carnosic acid as substrates for cytochrome 450 enzyme (CYP450 3A4) and curcumin and dillapiol as inhibitors for CYP 450 2D6. Overall the study concludes that small phytochemicals present in different spices have the potential to be proposed as oral anti‐cancer drugs against cancer targets DNA topoisomerase III alpha.

Chemical composition, antioxidant/antibacterial activities and ADMET study of the essential oil isolated from the aerial parts of Ziziphora hispanica grown in Morocco

ABSTRACT The present study aimed to characterize the chemical composition and evaluate the antioxidant and antibacterial activities along with the absorption, distribution, metabolism, excretion, toxicity (ADMET) study of the essential oil of Ziziphora hispanica (ZH-EO), collected from the Middle Atlas of Morocco (Boulmane). Results showed that a total of 119 volatile components, characterized by GC-FID and GC-MS analysis, which represents the 88.7% of the total of ZH-EO. ZH-EO displayed a bactericidal effect against Listeria monocytogenes, Pseudomonas aeruginosa and Enterococcus faecalis, and a bacteriostatic effect against Staphylococcus aureus. Furthermore, ZH-EO exhibited strong antioxidant activity against DPPH radical (IC50 = 1.3 mg/mL). The ADMET prediction showed good pharmacokinetic properties of the tested components. The findings obtained from this study suggest that Ziziphora hispanica essential oil could represent a source of bioactive molecules with antioxidant and antibacterial potential in the prevention against diseases related to oxidative stress and pathogenic bacteria.

Identification of potential inhibitors of ATM kinase: pharmacoinformatics and molecular dynamics simulation approach

ABSTRACT Ataxia-Telangiectasia Mutated kinase (ATM kinase) is a well-established protein, maintains genome integrity and modulates cellular responses to DNA breakage. It is acknowledged as a master regulator of Double-Strand Breaks (DSB) and redox sensor. In a variety of human malignancies, cells exhibit resistance to radiation and chemotherapeutic approach and thus, the suppression of ATM kinase is a promising strategy to promote cellular sensitivity. Several Miliusa plants are used for traditional medication and reveal appreciable cytotoxicity against tumours. The objective of this study is to achieve sensitization to resistant cells by identifying potent bioactive inhibitors which can block the activity of ATM kinase. Hence, we considered 14 phytoconstituents from genus Miliusa. In this study, we performed Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET), drug-likeliness prediction, molecular docking and Molecular Dynamics Simulation (MDS). The docking results were evaluated based on free energy binding (ΔG) and it revealed that conocarpan, epicatechin, rhamnazin and rhamnetin found to be potent inhibitors of ATM kinase. The MD simulation results showed the detailed structure and residue level changes upon binding with novel inhibitors. The present investigation proposes, rhamnazin as an efficient inhibitor owing to significant interaction with ATM kinase by exhibiting better binding energy of −29.72 kJ/mol than standard KU-55933 with −17.76 kJ/mol.

A machine learning-based approach to ERα bioactivity and drug ADMET prediction.

By predicting ERα bioactivity and mining the potential relationship between Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) attributes in drug research and development, the development efficiency of specific drugs for breast cancer will be effectively improved and the misjudgment rate of R&D personnel will be reduced. The quantitative prediction model of ERα bioactivity and classification prediction model of Absorption, Distribution, Metabolism, Excretion, Toxicity properties were constructed. The prediction results of ERα bioactivity were compared by XGBoot, Light GBM, Random Forest and MLP neural network. Two models with high prediction accuracy were selected and fused to obtain ERα bioactivity prediction model from Mean absolute error (MAE), mean squared error (MSE) and R2. The data were further subjected to model-based feature selection and FDR/FPR-based feature selection, respectively, and the results were placed in a voting machine to obtain Absorption, Distribution, Metabolism, Excretion, Toxicity classification prediction model. In this study, 430 molecular descriptors were removed, and finally 20 molecular descriptors with the most significant effect on biological activity obtained by the dual feature screening combined optimization method were used to establish a compound molecular descriptor prediction model for ERα biological activity, and further classification and prediction of the Absorption, Distribution, Metabolism, Excretion, Toxicity properties of the drugs were made. Eighty variables were selected by the model ExtraTreesClassifier Classifie, and 40 variables were selected by the model GradientBoostingClassifier to complete the model-based feature selection. At the same time, the feature selection method based on FDR/FPR is also selected, and the three classification models obtained by the two methods are placed into the voting machine to obtain the final model. The experimental results showed that the model's evaluation indexes and roc diagram were excellent and could accurately predict ERα bioactivity and Absorption, Distribution, Metabolism, Excretion, Toxicity properties. The model constructed in this study has high accuracy, fast convergence and robustness, has a very high accuracy for Absorption, Distribution, Metabolism, Excretion, Toxicity and ERα classification prediction, has bright prospects in the biopharmaceutical field, and is an important method for energy conservation and yield increase in the future.

Exposure to high-performance benzotriazole ultraviolet stabilizers: Advance in toxicological effects, environmental behaviors and remediation mechanism using in-silica methods

Benzotriazole ultraviolet stabilizers (BUVSs), as light stabilizers, have attracted widespread attention because of their easy migration in the environment and their acute toxicity and biological toxicity effects, such as immunotoxicity and hepatotoxicity. Accordingly, the treatment and remediation mechanisms of high-performance, environmentally friendly, and low human health risk BUVS substitutes were analyzed. Firstly, the weights and the comprehensive effect (CE) values of migration and toxicity of BUVSs were determined by Topsis assisted by the coefficient of variation (CV) method. From this, a three-dimensional quantitative structure activity relationship (3D-QSAR) model based on the CE values of the 13 BUVSs was constructed. Secondly, EPI software was used to predict the functionality and environmental friendliness of BUVS substitutes, and a partial least squares regression machine learning (ML-PLSR) model was used to analyze the mechanism. Then, ADMET (absorption, distribution, metabolism, excretion, toxicity), TOPKAT, and exposure dose models were used to evaluate the ecological and human health risks of BUVSs and their substitutes. Finally, the key charge information affecting the UV-326 substitutes was deduced by time dependent density functional theory (TDDFT). Using UV-326 as an example, 15 UV-326 substitutes with reduced CE values were designed (reductions of 2.61%–23.18%). Compared with ML-PLSR models of acute toxicity, immunotoxicity, and hepatotoxicity, it was found that the decrease of DM and Qyy values and the increase of Qzz value could further decrease the toxicity of the UV-326 substitutes. Ecological and human health risk assessment showed that the exposure risks of the six UV-326 substitutes were within acceptable limits. TDDFT showed that the change of electron distribution and electron excitation type were the key factors affecting the performance of the UV-326 substitutes, and a charge transfer excitation type was more conducive to obtaining high-performance, environmentally friendly UV-326 substitutes. This study aims to alleviate the toxic damage to the ecological environment and human health caused by BUVS exposure.

Insight into the Hantaan virus RNA-dependent RNA polymerase inhibition using in-silico approaches.

The Hantaan virus (HTN) is a member of the hantaviridae family. It is a segmented type, negative-strand virus (sNSVs). It causes hemorrhagic fever with renal syndrome, which includes fever, vascular hemorrhage, and renal failure. This illness is one of the most serious hemorrhagic diseases in the world, and it is a major public health concern due to its high mortality rate. The Hantaan virus RNA-dependent RNA polymerase complex (RdRp) is involved in viral RNA transcription and replication for the survival and transmission of this virus. Therefore, it is a primary target for antiviral drug development. Interference with the endonucleolytic "cap-snatching" reaction by the HTN virus RdRp endonuclease domain is a particularly appealing approach for drug discovery against this virus. This RdRp endonuclease domain of the HTN virus has a metal-dependent catalytic activity. We targeted this metal-dependent enzymatic activity to identify inhibitors that can bind and disrupt this endonuclease enzyme activity using in-silico approaches i.e., molecular docking, molecular dynamics simulation, predicted absorption, distribution, metabolism, excretion, toxicity (ADMET) and drug-likeness studies. The docking studies showed that peramivir, and ingavirin compounds can effectively bind with the manganese ions and engage with other active site residues of this protein. Molecular simulations also showed stable binding of these ligands with the active site of HTN RdRp. Simulation analysis showed that they were in constant contact with the active site manganese ions and amino acid residues of the HTN virus endonuclease domain. This study will help in better understanding the HTN and related viruses.