ADME evaluation in drug discovery. 9. Prediction of oral bioavailability in humans based on molecular properties and structural fingerprints

The potential of Chlorella spp. as antiviral source against African swine fever virus through a virtual screening pipeline

African swine fever (ASF) causes high mortality in pigs and threatens global swine production. There is still a lack of therapeutics available, with two vaccines under scrutiny and no approved small-molecule drugs. Eleven (11) viral proteins were used to identify potential antivirals in in silico screening of secondary metabolites (127) from Chlorella spp. The metabolites were screened for affinity and binding selectivity. High-scoring compounds were assessed through in silico ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) predictions, compared to structurally similar drugs, and checked for off-target docking with prepared swine receptors. Molecular dynamics (MD) simulations determined binding stability while binding energy was measured in Molecular Mechanics - Generalized Born Surface Area (MMGBSA) or Poisson-Boltzmann Surface Area (MMPBSA). Only six (6) compounds passed until MD analyses, of which five (5) were stable after 100 ns of MD runs. Of these five compounds, only three had binding affinities that were comparable to or stronger than controls. Specifically, phytosterols 24,25-dihydrolanosterol and CID 4206521 that interact with the RNA capping enzyme (pNP868R), and ergosterol which bound to the Erv-like thioreductase (pB119L). The compounds identified in this study can be used as a theoretical basis for in vitro screening to develop potent antiviral drugs against ASFV.

Exploring the mechanism of action of herbal compounding in the treatment of myasthenia gravis based on network pharmacology

Myasthenia gravis is a major disease in the context of an ageing society, and the discovery of effective herbal compound and herbal active ingredients is a highly promising direction for the treatment of myasthenia gravis. In this study, we selected shujiao, dried ginger and ginseng from the compound ingredients through a network pathology approach. The three ingredients were used to obtain drug targets in Traditional Chinese Medicine Systems Pharmacology (TCMSP), HERB and BATMAN-TCM data and intersected with the disease targets of myasthenia gravis. The resulting regulatory network maps were then used to identify core genes through the String database, and finally the core genes were molecularly aligned with the corresponding active ingredients using Autodock vina software. The 'herbal-component-target' regulatory network of the Chinese herbal formulae was constructed, which is important for finding the potential molecular mechanism for the treatment of myasthenia gravis. It will provide a theoretical basis for the therapeutic and clinical research of myasthenia gravis.

In silico evaluation of pharmacokinetic parameters, delivery, distribution and anticoagulative effects of new 4,7-dihydroxycoumarin derivative

In this study, pharmacological profiling and investigation of the anticoagulant activity of the newly synthesized coumarin derivative: (E)-3-(1-((4-hydroxy-3-methoxyphenyl)amino)ethylidene)-2,4-dioxochroman-7-yl acetate (L) were performed. The obtained results were compared with the parameters obtained for Warfarin (WF), which is a standard good oral anticoagulant. The estimated high binding affinity of L toward plasma proteins (PPS% value is > 90%) justifies the investigation of binding affinity and comparative analysis of L and WF to Human Serum Albumin (HSA) using the spectrofluorimetric method (296, 303 and 310 K) as well as molecular docking and molecular dynamics simulations. Compound L shows a very good binding affinity especially to the active site of WF (the active site I -subdomain IIA), quenching HSA fluorescence by a static process. Also, the finite element smeared model (Kojic Transport Model, KTM), which includes blood vessels and tissue, was implemented to compute the convective-diffusion transport of L and WF within the liver. Finally, compound L shows a high degree of inhibitory activity toward the VKOR receptor comparable to the inhibitory activity of WF. Stabilization and limited flexibility of amino acid residues in the active site of the VKOR after binding of L and WF indicates a very good inhibitory potential of compound L. The high affinity of the L for the VKOR enzyme (Vitamin K antagonist), as well as the structural similarity to commercial anticoagulants (WF), provide a basis for further studies and potential application in the treatment of venous thrombosis, pulmonary embolism and ischemic heart disease.Communicated by Ramaswamy H. Sarma.

Improving the Accuracy of Permeability Data to Gain Predictive Power: Assessing Sources of Variability in Assays Using Cell Monolayers

The ability to predict the rate of permeation of new compounds across biological membranes is of high importance for their success as drugs, as it determines their efficacy, pharmacokinetics, and safety profile. In vitro permeability assays using Caco-2 monolayers are commonly employed to assess permeability across the intestinal epithelium, with an extensive number of apparent permeability coefficient (Papp) values available in the literature and a significant fraction collected in databases. The compilation of these Papp values for large datasets allows for the application of artificial intelligence tools for establishing quantitative structure-permeability relationships (QSPRs) to predict the permeability of new compounds from their structural properties. One of the main challenges that hinders the development of accurate predictions is the existence of multiple Papp values for the same compound, mostly caused by differences in the experimental protocols employed. This review addresses the magnitude of the variability within and between laboratories to interpret its impact on QSPR modelling, systematically and quantitatively assessing the most common sources of variability. This review emphasizes the importance of compiling consistent Papp data and suggests strategies that may be used to obtain such data, contributing to the establishment of robust QSPRs with enhanced predictive power.

Evaluating physiochemical properties of FDA-approved orally administered drugs

INTRODUCTION

Analyses of orally administered FDA-approved drugs from 1990 to 1993 enabled the identification of a set of physiochemical properties known as Lipinski's Rule of Five (Ro5). The original Ro5 and extended versions still remain the reference criteria for drug development programs. Since many bioactive compounds do not conform to the Ro5, we validated the relevance of and adherence to these rulesets in a contemporary cohort of FDA-approved drugs.

AREAS COVERED

The authors noted that a significant proportion of FDA-approved orally administered parent compounds from 2011 to 2022 deviate from the original Ro5 criteria (~38%) or the Ro5 with extensions (~53%). They then evaluated if a contemporary Ro5 criteria (cRo5) could be devised to better predict oral bioavailability. Furthermore, they discuss many case studies showcasing the need for and benefit of increasing the size of certain compounds and cover several evolving strategies for improving oral bioavailability.

EXPERT OPINION

Despite many revisions to the Ro5, the authors find that no single proposed physiochemical rule has universal concordance with absolute oral bioavailability. Innovations in drug delivery and formulation have dramatically expanded the range of physicochemical properties and the chemical diversity for oral administration.

A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat

An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893-4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

An Effective, Green Synthesis Procedure for Obtaining Coumarin-Hydroxybenzohydrazide Derivatives and Assessment of Their Antioxidant Activity and Redox Status

In this study, green synthesis of two derivatives of coumarin-hydroxybenzohydrazide, (E)-2,4-dioxo-3-(1-(2-(2,3,4-trihydroxybenzoyl)hydrazyl)ethylidene)-chroman-7-yl acetate (C-HB1), and (E)-2,4-dioxo-3-(1-(2-(3,4,5-trihydroxybenzoyl)hydrazyl)ethylidene)chroman-7-yl acetate (C-HB2) is reported. Using vinegar and ethanol as a catalyst and solvent, the reactions were carried out between 3-acetyl-4-hydroxy-coumarin acetate and corresponding trihydroxybenzoyl hydrazide. The antioxidant potential of these compounds was investigated using the DPPH and ABTS assays, as well as the FRAP test. The obtained results reveal that even at very low concentrations, these compounds show excellent radical scavenging potential. The IC50 values for C-HB1 and C-HB2 in relation to the DPPH radical are 6.4 and 2.5 μM, respectively, while they are 4.5 and 2.0 μM in relation to the ABTS radical. These compounds have antioxidant activity that is comparable to well-known antioxidants such as gallic acid, NDGA, and trolox. These results are in good correlation with theoretical parameters describing these reactions. Moreover, it was found that inhibition of DPPH● follows HAT, while inactivation of ABTS+● follows SET-PT and HAT mechanisms. Additionally, coumarin-hydroxybenzohydrazide derivatives induced moderate cytotoxic activity and show significant potential to modulate redox status in HCT-116 colorectal cancer cells. The cytotoxicity was achieved via their prooxidative activity and ability to induce oxidative stress in cancer cells by increasing O2˙- concentrations, indicated by increased MDA and GSH levels. Thus, ROS manipulation can be a potential target for cancer therapies by coumarins, as cancer cells possess an altered redox balance in comparison to normal cells. According to the ADMET analysis, the compounds investigated show good pharmacokinetic and toxicological profiles similar to vitamin C and gallic acid, which makes them good candidates for application in various fields of industry and medicine.

Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants

Persistent organic pollutants (POPs) are ubiquitous and bioaccumulative, posing potential and long-term threats to human health and the ecological environment. Quantitative structure-activity relationship (QSAR) studies play a guiding role in analyzing the toxicity and environmental fate of different organic pollutants. In the current work, five molecular descriptors are utilized to construct QSAR models for predicting the mean and maximum air half-lives of POPs, including specifically the energy of the highest occupied molecular orbital (HOMO_Energy_DMol3), a component of the dipole moment along the z-axis (Dipole_Z), fragment contribution to SAscore (SAscore_Fragments), subgraph counts (SC_3_P), and structural information content (SIC). The QSAR models were achieved through the application of three machine learning methods: partial least squares (PLS), multiple linear regression (MLR), and genetic function approximation (GFA). The determination coefficients (R2) and relative errors (RE) for the mean air half-life of each model are 0.916 and 3.489% (PLS), 0.939 and 5.048% (MLR), 0.938 and 5.131% (GFA), respectively. Similarly, the determination coefficients (R2) and RE for the maximum air half-life of each model are 0.915 and 5.629% (PLS), 0.940 and 10.090% (MLR), 0.939 and 11.172% (GFA), respectively. Furthermore, the mechanisms that elucidate the significant factors impacting the air half-lives of POPs have been explored. The three regression models show good predictive and extrapolation abilities for POPs within the application domain.

Computer-aided analysis for identification of novel analogues of ketoprofen based on molecular docking, ADMET, drug-likeness and DFT studies for the treatment of inflammation

Computer-based drug design is increasingly used in strategies for discovering new molecules for therapeutic purposes. The targeted drug is ketoprofen (KTP), which belongs to the family of non-steroidal anti-inflammatory drugs, which are widely used for the treatment of pain, fever, inflammation and certain types of cancers. In an attempt to rationalize the search for 72 new potential anti-inflammatory compounds on the COX-2 enzyme, we carried out an in silico protocol that successfully combines molecular docking towards COX-2 receptor (5F1A), ADMET pharmacokinetic parameters, drug-likeness rules and molecular electrostatic potential (MEP). It was found that six of the compounds analyzed satisfy with the associated values to physico-chemical properties as key evaluation parameters for the drug-likeness and demonstrate a hydrophobic character which makes their solubility in aqueous media difficult and easy in lipids. All the compounds presented good ADMET profile and they showed an interaction with the amino acids responsible for anti-inflammatory activity of the COX-2 isoenzyme. The calculation of the MEP of the six analogues reveals new preferential sites involving the formation of new bonds. Consequently, this result allowed us to understand the origin of the potential increase in the anti-inflammatory activity of the candidates. Finally, it was obtained that six compounds have a binding mode, binding energy, and stability in the active site of COX-2 like the reference drug ketoprofen, suggesting that these compounds could become a powerful candidate in the inhibition of the COX-2 enzyme.Communicated by Ramaswamy H. Sarma.

Physiological Dynamics in the Upper Gastrointestinal Tract and the Development of Gastrointestinal Absorption Models for the Immediate-Release Oral Dosage Forms in Healthy Adult Human

This review is a revisit of various oral drug absorption models developed in the past decades, focusing on how to incorporate the physiological dynamics in the upper gastrointestinal (GI) tract. For immediate-release oral drugs, GI absorption is a critical input of drug exposure and subsequent human body response, yet difficult to model largely due to the complex GI environment. One of the biggest hurdles lies at capturing the high within-subject variability (WSV) of bioavailability measures, which can be mechanistically explained by the GI physiological dynamics. A thorough summary of how GI dynamics is handled in the absorption models would promote the development of mechanism-based oral drug absorption models, aid in the design of clinical studies regarding dosing regimens and bioequivalence studies based on WSV, and advance the decision-making on formulation selection.

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico, which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.

Integrated bioinformatics analysis and network pharmacology to explore the potential mechanism of Patrinia heterophylla Bunge against acute promyelocytic leukemia

INTRODUCTION

Current treatment with arsenic trioxide and all-trans retinoic acid has greatly improved the therapeutic efficacy and prognosis of acute promyelocytic leukemia (APL), but may cause numerous adverse effects. Patrinia heterophylla Bunge (PHEB), commonly known as "Mu-Tou-Hui" in China, is effective in treating leukemia. However, no studies have reported the use of PHEB for APL treatment. In this study, we aimed to investigate the potential anticancer mechanism of PHEB against APL.

METHODS

Public databases were used to search for bioactive compounds in PHEB, their potential targets, differentially expressed genes associated with APL, and therapeutic targets for APL. The core targets and signaling pathways of PHEB against APL were identified by the protein-protein interaction network, Kaplan-Meier curves, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and compound-target-pathway network analysis. Molecular docking was performed to predict the binding activity between the most active compounds and the key targets.

RESULTS

Quercetin and 2 other active components of PHEB may exert anti-APL effects through proteoglycans in cancer, estrogen signaling, and acute myeloid leukemia pathways. We also identified 6 core targets of the bioactive compounds of PHEB, including protein tyrosine phosphatase receptor type C, proto-oncogene tyrosine-protein kinase Src, mitogen-activated protein kinase phosphatase 3 (MAPK3), matrix metalloproteinase-9, vascular endothelial growth factor receptor-2, and myeloperoxidase, most of which were validated to improve the 5-year survival of patients. Molecular docking results showed that the active compound bound well to key targets.

CONCLUSION

The results not only predict the active ingredients and potential molecular mechanisms of PHEB against APL, but also help to guide further investigation into the anti-APL application of PHEB.

Virtual screening of flavonoids from Chamaecrista genus: ADME and pharmacokinetic properties, interactions of flavonoid-DNA complex by molecular docking and molecular dynamics

This research aimed to conduct an in silico study of compounds, mainly flavonoids, that are found in several plants, including the species of the Chamaecrista genus. The ADME properties, the drug-likeness score and properties of Lipinski and Veber rules of the molecules were determined using online databases. Based on the predicted properties, four flavonoids, apigenin, fisetin, luteolin and ononin were selected for molecular docking and dynamic simulations to study their interactions with DNA (PDB ID: 1BNA). The molecular docking showed that ononin has a high affinity for B-DNA, exhibiting a ΔG value of -9.3 kcal mol-1, compared with the other flavonoids. The molecular dynamic simulations of the flavonoid-DNA complexes showed that the flavonoids interacted with DNA by hydrogen bonding, hydrophobic interaction and π-stacking. The flavonoid ononin showed the best interaction energy value of -291.3490 kJ mol-1, compared with the other flavonoids.Communicated by Ramaswamy H. Sarma.

Repurposing FDA-approved drugs as FXR agonists: a structure based in silico pharmacological study

Farnesoid X receptor (FXR) modulates the expression of genes involved in lipid and carbohydrate homeostasis and inflammatory processes. This nuclear receptor is likely a tumor suppressor in several cancers, but its molecular mechanism of suppression is still under study. Several studies reported that FXR agonism increases the survival of colorectal, biliary tract, and liver cancer patients. In addition, FXR expression was shown to be down-regulated in many diseases such as obesity, irritable bowel syndrome, glomerular inflammation, diabetes, proteinuria, and ulcerative colitis. Therefore, development of novel FXR agonists may have significant potential in the prevention and treatment of these diseases. In this scenario, computer-aided drug design procedures can be resourcefully applied for the rapid identification of promising drug candidates. In the present study, we applied the molecular docking method in conjunction with molecular dynamics (MD) simulations to find out potential agonists for FXR based on structural similarity with the drug that is currently used as FXR agonist, obeticholic acid. Our results showed that alvimopan and montelukast could be used as potent FXR activators and outperform the binding affinity of obeticholic acid by forming stable conformation with the protein in silico. However, further investigational studies and validations of the selected drugs are essential to figure out their suitability for preclinical and clinical trials.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

In silico prediction of boiling point, octanol-water partition coefficient, and retention time index of polycyclic aromatic hydrocarbons through machine learning

Polycyclic aromatic hydrocarbons (PAHs), a special class of persistent organic pollutants (POPs) with two or more aromatic rings, have received extensive attention owing to their carcinogenic, mutagenic, and teratogenic effects. Quantitative structure-property relationship (QSPR) is powerful chemometric method to correlate structural descriptors of PAHs with their physicochemical properties. In this manuscript, a QSPR study of PAHs was performed to predict their boiling point (bp), octanol-water partition coefficient (LogK_ow ), and retention time index (RI). In addition to traditional molecular descriptors, structural fingerprints play an important role in the correlation of the above properties. Three regression methods, partial least squares (PLS), multiple linear regression (MLR), and genetic function approximation (GFA), were used to establish QSPR models for each property of PAHs. The correlation coefficient (R² _test ) and root mean square error (RMSE) of best model were 0.980 and 24.39% (PLS), 0.979 and 35.80% (GFA), 0.926 and 22.90% (MLR) for bp, LogK_ow, and RI, respectively. The model proposed here can be used to estimate physicochemical properties and inform toxicity prediction of environmental chemicals.

Identification of 1H-purine-2,6-dione derivative as a potential SARS-CoV-2 main protease inhibitor: molecular docking, dynamic simulations, and energy calculations

The rapid spread of the coronavirus since its first appearance in 2019 has taken the world by surprise, challenging the global economy, and putting pressure on healthcare systems across the world. The introduction of preventive vaccines only managed to slow the rising death rates worldwide, illuminating the pressing need for developing effective antiviral therapeutics. The traditional route of drug discovery has been known to require years which the world does not currently have. In silico approaches in drug design have shown promising results over the last decade, helping to decrease the required time for drug development. One of the vital non-structural proteins that are essential to viral replication and transcription is the SARS-CoV-2 main protease (Mpro). Herein, using a test set of recently identified COVID-19 inhibitors, a pharmacophore was developed to screen 20 million drug-like compounds obtained from a freely accessible Zinc database. The generated hits were ranked using a structure based virtual screening technique (SBVS), and the top hits were subjected to in-depth molecular docking studies and MM-GBSA calculations over SARS-COV-2 Mpro. Finally, the most promising hit, compound (1), and the potent standard (III) were subjected to 100 ns molecular dynamics (MD) simulations and in silico ADME study. The result of the MD analysis as well as the in silico pharmacokinetic study reveal compound 1 to be a promising SARS-Cov-2 MPro inhibitor suitable for further development.

An integrated approach for identifying the efficacy and potential mechanisms of TCM against atherosclerosis-Wu-Zhu-Yu decoction as a case study

ETHNOPHARMACOLOGICAL RELEVANCE

Atherosclerosis (AS) is a chronic disease that is associated with high morbidity. However, therapeutic approaches are limited. Wu-Zhu-Yu decoction (WZYD) is a well-known traditional Chinese medicine prescription that is traditionally used to treat headaches and vomiting. Modern studies have demonstrated the cardiotonic effects of WZYD. However, whether WZYD can alleviate AS and its underlying mechanisms remain unclear.

AIM OF THE STUDY

This study aims to investigate the antiatherosclerotic efficacy of WZYD and illustrate its potential mechanisms using an integrated approach combining in vivo and in vitro assessments, including metabolomics, network pharmacology, cell experiments, and molecular docking analyses.

MATERIALS AND METHODS

In this work, an atherosclerotic mouse model was established by administering a high-fat diet to apolipoprotein-E deficient (ApoE^-/-) mice for twelve weeks. Meanwhile, the mice were intragastrically administered WZYD at different dosages. Efficacy evaluation was performed through biochemical and histopathological assessments. The potential active constituents, metabolites, and targets of WZYD in atherosclerosis were predicted by metabolomics combined with network pharmacology analysis, the constituents and targets were further assessed through cell experiments and molecular docking analysis.

RESULTS

WZYD decreased the lipid levels in serum, reduced the areas of aortic lesions, and attenuated intimal thickening, which had antiatherosclerotic effects in ApoE^-/- mice. Metabolomics and network pharmacology approach revealed that the ten constituents (6-shogaol, evodiamine, isorhamnetin, quercetin, beta-carotene, 8-gingerol, kaempferol, 6-paradol, 10-gingerol, and 6-gingerol) of WZYD affected 24 metabolites by acting on the candidate targets, thus resulting in changes in five metabolic pathways (sphingolipid metabolism; glycine, serine and threonine metabolism; arachidonic acid metabolism; tryptophan metabolism; and fatty acid biosynthesis pathway). Cell experiments indicated that the ten key compounds showed antiproliferative effects on the vascular smooth muscle cell. Moreover, the key compounds exhibited direct interactions with the key targets, as assessed by molecular docking analysis.

CONCLUSION

This study revealed that WZYD exerted therapeutic effects on atherosclerosis, and the potential mechanisms were elucidated. Furthermore, it offered a powerful integrated strategy for studying the efficacy of traditional Chinese medicine and exploring its active components and possible mechanisms.

Mechanism of Yangxin Tongmai Decoction in the Treatment of Coronary Heart Disease with Blood Stasis Syndrome Based on Network Pharmacology and Molecular Docking

This study aimed to explore the mechanism of Yangxin Tongmai decoction (YXTMD) in the treatment of coronary heart disease (CHD) with blood stasis syndrome (BSS) using network pharmacology and molecular docking, and to verify these results through clinical trials. The active compounds of YXTMD were identified using the Traditional Chinese Medicine Systems Pharmacology database, and the targets of the active compounds were predicted using the SwissTarget Prediction database. The targets of CHD and BSS were predicted using the GeneCards, OMIM, PharmGKB, TTD, and DrugBank databases. The common targets of “herb-disease-phenotype” were obtained using a Venn diagram, then used Cytoscape software 3.8.2 and its plug-in CytoNCA and STRING database to construct the “herb active compounds-common target” and protein–protein interaction networks. R language software and bioconductor plug-in were used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. AutoDock was used for the molecular docking analysis. Finally, clinical trials were conducted to confirm the results of network pharmacology. Eighty-three active components were obtained, and the core active components were 5,7,4′-trimethoxyflavone, tetramethoxyluteolin, isosinensetin, sinensetin, and 5,7-dihydroxy-2-(3-hydroxy-4-methoxyphenyl)chroman-4-one. A total of 140 common targets were identified, and the core targets were EGFR, VEGFA, AKT1, STAT3, TP53, ERBB2, and PIK3CA. Biological processes identified by the GO analysis primarily involved wound healing, regulation of body fluid levels, and vascular process in circulatory system. The cellular components were primarily located in the membrane raft, membrane microdomain, and plasma membrane raft. The primary molecular functions were activity of transmembrane receptor protein kinase, transmembrane receptor protein tyrosine kinase, and protein tyrosine kinase. KEGG analysis showed that the PI3K-Akt signaling pathway was closely related to the treatment of CHD with BSS by YXTMD. Molecular docking results showed that the core active components had a good binding activity with the core targets. The clinical trial results showed that YXTMD improved the BSS scores and decreased the serum levels of total cholesterol and low-density lipoprotein cholesterol. Moreover, the levels of PI3k and AKt mRNA were upregulated and the levels of GSK-3β mRNA were downregulated. YXTMD has multicomponent, multitarget, and multipathway effects in the treatment of CHD with BSS, and its mechanism of action may involve activation of the PI3K-AKt signaling pathway, downregulation of GSK-3β, and mediation of in vivo lipid metabolism-based metabolic processes.

Network Pharmacology and Molecular Docking Analysis of the Mechanism Underlying Yikunyin's Therapeutic Effect on Menopausal Syndrome

Objective

Yikunyin is an empirical prescription that exhibits good efficacy in the clinical treatment of menopausal syndrome; however, its underlying mechanism remains unclear. This study investigates the mechanism implicated in the therapeutic effect of Yikunyin by identifying its hub genes, central pathways, and key active ingredients.

Method

The active ingredients and targets of Yikunyin were obtained from the Traditional Chinese Medicine Systems Pharmacology database, whereas the targets related to menopausal syndrome were obtained from GeneCards, PharmGKB, Therapeutic Target Database (TTD), and Comparative Toxicogenomics Database (CTD). To reveal the pharmacological mechanism, the component-target and the intersecting protein-protein interaction (PPI) networks were constructed, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. Finally, molecular docking was carried out to assess the strength of binding between the key active ingredients and key targets.

Results

A total of 418 targets and 121 active ingredients were identified in Yikunyin. The intersection of Yikunyin's 418 targets with the 2822 targets related to menopausal syndrome shows that there are 247 common targets that can be considered potential targets of Yikunyin in the treatment of menopausal syndrome. The topology analysis of the constructed PPI network conducted using the Cytoscape software shows that there are 15 hub genes implicated in the therapeutic effect of Yikunyin: AKT1, PRKCA, TLR9, CXCL10, PRKCD, PARP1, ABCB1, TP53, CAV1, MAPK8, PPARA, GRB2, EGFR, IL-6, and JAK2. Moreover, the key active components acting on these genes are paeoniflorin, luteolin, quercetin, beta-sitosterol, and kaempferol. GO and KEGG analyses indicate that Yikunyin can treat menopausal syndrome by regulating cellular response to chemical stress (GO:0062197), cellular response to oxidative stress (GO:0034599), phosphatase binding (GO:0019902), cytokine receptor binding (GO:0005126), PI3K-Akt signaling (hsa04151), lipid and atherosclerosis (hsa05417), and hepatitis B (hsa05161). Finally, the results of molecular docking suggest that the key active ingredients and key targets can bind well, with binding energies of less than −5 kJ/mol.

Conclusion

The research conducted herein reveals that Yikunyin treats menopausal syndrome by targeting AKT1 and IL-6 and by regulating the PI3K-Akt signaling pathway. Moreover, it provides a new idea for understanding the therapeutic effects of traditional Chinese medicines.

Utilising Network Pharmacology to Explore Underlying Mechanism of Astragalus membranaceus in Improving Sepsis-Induced Inflammatory Response by Regulating the Balance of IκBα and NF-κB in Rats

Objective

The purpose of the present study was to explore the mechanism of Astragalus membranaceus in the treatment of sepsis.

Methods

We searched the active components and targets of Astragalus membranaceus using the TCMSP and BATMAN databases. Then, the GeneCards, MalaCards, and OMIM databases were used to screen out relevant targets of sepsis. The common targets of the former two gene sets were uploaded to the STRING database to create an interaction network. DAVID was used to perform KEGG enrichment analysis of the core targets. Based on the results of KEGG and previous studies, key pathways for the development of sepsis were identified and experimentally validated.

Result

We obtained 3,370 sepsis-related targets in databases and 59 active components in Astragalus membranaceus through data mining, corresponding to 1,130 targets. The intersection of the two types of targets led to a total of 318 common targets and 84 core targets were obtained after screening again. The KEGG and previous studies showed that these 84 core targets were involved in sepsis by regulating TNF, MAPK, and PI3K pathways. TNF, MAPK8, NF-κB, and IκBα are crucial in sepsis. Experimental validation demonstrated that some markers in sepsis model rats were improved after the intervention with Astragalus granules and their chemical components. Among them, IL-1β, IL-6, and TNF-α in rat serum were reduced. The mRNA and protein expression of TNF-α, IL-6, MMP9, MAPK8, and NF-κB were reduced in rat blood. However, the mRNA and protein expression of IκBα and PI3K were increased in rat blood.

Conclusion

The AST could affect the TNF, PI3K, and MAPK pathway cascade responses centred on IκBα and NF-κB, attenuate the expression of IL-6 and MMP9, and interfere with the inflammatory response during sepsis.

Bioinformatics and Network Pharmacology-Based Approaches to Explore the Potential Mechanism of the Antidepressant Effect of Cyperi Rhizoma through Soothing the Liver

Major depressive disorder (MDD) has become the second most common disease worldwide, making it a threat to human health. Cyperi Rhizoma (CR) is a traditional herbal medicine with antidepressant properties. Traditional Chinese medicine theory states that CR relieves MDD by dispersing stagnated liver qi to soothe the liver, but the material basis and underlying mechanism have not been elucidated. In this study, we identified the active compounds and potential anti-MDD targets of CR by network pharmacology-based approaches. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, we hypothesized that the anti-MDD effect of CR may be mediated by an altered response of the liver to lipopolysaccharide (LPS) and glucose metabolism. Through bioinformatics analysis, comparing normal and MDD liver tissue in rats with spontaneous diabetes, we identified differentially expressed genes (DEGs) and selected PAI-1 (SERPINE1) as a target of CR in combating MDD. Molecular docking and molecular dynamics analysis also verified the binding of the active compound quercetin to PAI-1. It can be concluded that quercetin is the active compound of CR that acts against MDD by targeting PAI-1 to enhance the liver response to LPS and glucose metabolism. This study not only reveals the material basis and underlying mechanism of CR against MDD through soothing the liver but also provides evidence for PAI-1 as a potential target and quercetin as a potential agent for MDD treatment.

HobPre: accurate prediction of human oral bioavailability for small molecules

Human oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials. HOB is conventionally measured using expensive and time-consuming experimental tests. The use of computational models to evaluate HOB before the synthesis of new drugs will be beneficial to the drug development process. In this study, a total of 1588 drug molecules with HOB data were collected from the literature for the development of a classifying model that uses the consensus predictions of five random forest models. The consensus model shows excellent prediction accuracies on two independent test sets with two cutoffs of 20% and 50% for classification of molecules. The analysis of the importance of the input variables allowed the identification of the main molecular descriptors that affect the HOB class value. The model is available as a web server at www.icdrug.com/ICDrug/ADMET for quick assessment of oral bioavailability for small molecules. The results from this study provide an accurate and easy-to-use tool for screening of drug candidates based on HOB, which may be used to reduce the risk of failure in late stage of drug development.

Discovery and computational studies of 2-phenyl-benzoxazole acetamide derivatives as promising P2Y14R antagonists with anti-gout potential

The P2Y₁₄ nucleotide receptor, a subtype of P2Y receptors, is implicated in many human inflammatory diseases. Based on the identification of favorable residues of two screening hits in the almost symmetrical P2Y₁₄ binding domain, we describe the structural optimization of previously identified virtual screening hits 6 and 7 that result in the development of P2Y₁₄R antagonists with a novel 2-phenyl-benzoxazole acetamide chemical scaffold. Notably, compound 52 showed potent P2Y₁₄R antagonistic activity (IC₅₀ = 2 nM), and a stronger inhibitory effect on MSU-induced inflammatory in vitro, better than a previously described P2Y₁₄R antagonist PPTN. In vivo evaluation demonstrated that compound 52 also had satisfactory inhibitory activity on the inflammatory response of gout flares in mice. Moreover, P2Y₁₄R antagonist 52 decreased paw swelling and inflammatory cell infiltration through cAMP/NLRP3/GSDMD signaling pathways in MSU-induced acute gouty arthritis mice. The discussions on the binding mechanism that employ MM/GBSA free energy calculations/decompositions also provide some useful clues for further structural designing of compound 52. Taken together, 2-phenyl-benzoxazole acetamide derivative 52 with potent P2Y₁₄R antagonistic activity and in vivo potency could be a promising strategy for gout therapy and deserves further optimization.

Predicting the Molecular Mechanism of Sini Jia Renshen Decoction in Treating Severe COVID-19 Patients Based on Network Pharmacology and Molecular Docking

Objective:

To explore the potential mechanism of Sini jia Renshen Decoction (SJRD) in the treatment of COVID-19 based on network pharmacology and molecular docking.

Methods:

The active compounds and potential therapeutic targets of SJRD were collected through the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). Then a string database was used to build a protein–protein interactions (PPI) network between proteins, and use the David database to perform gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct an active ingredients-core target-signaling pathway network, and finally the active ingredients of SJRD were molecularly docked with the core targets to predict the mechanism of SJRD in the treatment of COVID-19.

Results:

A total of 136 active compounds, 51 core targets and 93 signaling pathways were selected. Molecular docking results revealed that quercetin, 3,22-dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid, 18α-hydroxyglycyrrhetic acid, gomisin B and ignavine had considerable binding ability with ADRB2, PRKACA, DPP4, PIK3CG and IL6.

Conclusions:

This study preliminarily explored the mechanism of multiple components，multiple targets，and multiple pathways of SJRD in the treatment of COVID-19 by network pharmacology．

Revealing the mechanism of "Huai Hua San" in the treatment of ulcerative colitis based on network pharmacology and experimental study

ETHNOPHARMACOLOGICAL RELEVANCE

"Huai Hua San" (HHS) is one of the first hundred ancient classic prescriptions drugs, which is commonly used to treat hemorrhoids, colitis, and other symptoms of wind heat in stool. However, the potential molecular mechanism of action of this substance remains unclear.

AIMS OF THE STUDY

In this study, we explored the active compounds of HHS for the treatment of ulcerative colitis (UC), predicted the potential targets of the drug, and studied its mechanism of action through network pharmacology via in vitro and in vivo experiments.

MATERIALS AND METHODS

First, we identified the active compounds and key targets of HHS for treating UC via network pharmacology. The key signaling pathways associated with the anti-inflammatory effect of HHS were analyzed. The anti-inflammatory effects of HHS and its active compounds were studied using the RAW264.7 inflammatory cell model in vitro. Furthermore, we used the dextran sulfate sodium (DSS) mouse model to explore the efficacy and mechanism of HHS in UC in vivo, and the expression level of key proteins were detected by Western blotting.

RESULTS

In all, 23 compounds and 97 targets were obtained from TCMSP database, PharmMapper database, and GeneCards database. After enrichment via Kyoto Encyclopedia of Genes and Genomes (KEGG), HIF-1 signaling pathway, PI3K/AKT signaling pathway, and VEGF signaling pathway were identified to be the top three signaling pathways associated with UC treatment. The results of molecular docking showed that the docking scores of the top 10 active compounds were higher than the threshold values. In vitro, different concentrations of HHS and the four main active compounds could effectively inhibit the release of inflammatory cytokines interleukin (IL)-6, tumor necrosis factor (TNF)-α, and IL-1 β. In vivo, HHS could alleviate UC symptoms.

CONCLUSION

Taken together, the treatment of UC with HHS may alleviate the inflammatory response of the colon, and HHS mainly inhibits the EGFR/PI3K/AKT/HIF-1/VEGF signaling pathways.

Artificial intelligence in drug discovery: applications and techniques

Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in many drug discovery applications, such as virtual screening and drug design. In this survey, we first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks, i.e. molecular property prediction and molecule generation. We then present common data resources, molecule representations and benchmark platforms. As a major part of the survey, AI techniques are dissected into model architectures and learning paradigms. To reflect the technical development of AI in drug discovery over the years, the surveyed works are organized chronologically. We expect that this survey provides a comprehensive review on AI in drug discovery. We also provide a GitHub repository with a collection of papers (and codes, if applicable) as a learning resource, which is regularly updated.

A drug-biomarker interaction model to predict the key targets of Scutellaria barbata D. Don in adverse-risk acute myeloid leukaemia

A poor prognosis, relapse and resistance are burning issues during adverse-risk acute myeloid leukaemia (AML) treatment. As a natural medicine, Scutellaria barbata D. Don (SBD) has shown impressive antitumour activity in various cancers. Thus, SBD may become a potential drug in adverse-risk AML treatment. This study aimed to screen the key targets of SBD in adverse-risk AML using the drug-biomarker interaction model through bioinformatics and network pharmacology methods. First, the adverse-risk AML-related critical biomarkers and targets of SBD active ingredient were obtained from The Cancer Genome Atlas database and several pharmacophore matching databases. Next, the protein-protein interaction network was constructed, and topological analysis and pathway enrichment were used to screen key targets and main pathways of intervention of SBD in adverse-risk AML. Finally, molecular docking was implemented for key target verification. The results suggest that luteolin and quercetin are the main active components of SBD against adverse-risk AML, and affected drug resistance, apoptosis, immune regulation and angiogenesis through the core targets AKT1, MAPK1, IL6, EGFR, SRC, VEGFA and TP53. We hope the proposed drug-biomarker interaction model provides an effective strategy for the research and development of antitumour drugs.

Predicting the Molecular Mechanism of Shenling Baizhu San in Treating Convalescent Patients With COVID-19 Based on Network Pharmacology and Molecular Docking

Objective: Shenling Baizhu San (SBS) was selected as the regimen for the treatment of COVID-19 in Guangdong Province. It is mainly used for the convalescent treatment of COVID-19 patients with deficiency of both lung and spleen. In this study, we aimed to explore the mechanism of SBS in the treatment of COVID-19 through network pharmacology combined with molecular docking. Methods: The targets of active components of SBS were collected through Traditional Chinese Medicine Systems Pharmacology (TCMSP) and ETCM databases. Using the Genecards, TTD, OMIM and other databases, the targets of COVID-19 were determined. The next step was to use a string database to build a protein–protein interactions (PPI) network between proteins, and use David database to perform gene ontology (GO) function enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct the active ingredients-core target-signaling pathway network, and finally the active ingredients of SBS were molecularly docked with the core targets to predict the mechanism of SBS in the treatment of COVID-19. Results: A total of 177 active compounds, 43 core targets and 58 signaling pathways were selected. Molecular docking results showed that the binding energies of the top six active components and the targets were all less than −5 kcal/MOL. Conclusion: The potential mechanism of action of SBS in the treatment of COVID-19 may be associated with the regulation of genes co-expressed with IL6, DPP4, PTGS2, PTGS1 and TNF.

Machine Learning in Drug Discovery: A Review

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

Predicting the Molecular Mechanism of “Angong Niuhuang Pills” in the Treatment of COVID-19 Based on Network Pharmacology

Introduction

Angong Niuhuang Pills (AGNH), a Chinese patent medicine recommended in the “Diagnosis and Treatment Plan for COVID-19 (8th Edition),” may be clinically effective in treating COVID-19. The active components and signal pathways of AGNH through network pharmacology have been examined, and its potential mechanisms determined.

Methods

We screened the components in the Traditional Chinese Medicine Systems Pharmacology (TCMSP) via Drug-like properties (DL) and Oral bioavailability (OB); PharmMapper and GeneCards databases were used to collect components and COVID-19 related targets; KEGG pathway annotation and GO bioinformatics analysis were based on KOBAS3.0 database; “herb-components-targets-pathways” (H-C-T-P) network and protein-protein interaction network (PPI) were constructed by Cytoscape 3.6.1 software and STRING 10.5 database; we utilized virtual molecular docking to predict the binding ability of the active components and key proteins.

Results

A total of 87 components and 40 targets were screened in AGNH. The molecular docking results showed that the docking scores of the top 3 active components and the targets were all greater than 90.

Conclusion

Through network pharmacology research, we found that moslosooflavone, oroxylin A, and salvigenin in AGNH can combine with ACE2 and 3CL, and then are involved in the MAPK and JAK-STAT signaling pathways. Finally, it is suggested that AGNH may have a role in the treatment of COVID-19.

Investigation of Anti-SARS, MERS, and COVID-19 Effect of Jinhua Qinggan Granules Based on a Network Pharmacology and Molecular Docking Approach

Objective

Jinhua Qinggan Granules (JQGs) have achieved certain results in the prevention and treatment of COVID-19 in China during this coronavirus storm. In this study, we aimed to analyze the common mechanisms of JQG in the treatment of coronavirus-induced diseases, such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19 via network pharmacology and molecular docking.

Methods

The active compounds of JQG were collected through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The common targets associated with these 3 diseases were screened from GeneCards database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of JQG’s core targets were analyzed using The Database for Annotation, Visualization, and Integrated Discovery and KOBAS 3.0 system. Further, the protein-protein interaction network was built using STRING database. The compound-target- signaling pathway network was constructed using Cytoscape 3.7.2. The core components of JQG were docked with core targets, COVID-19 coronavirus 3 Cl hydrolase, and angiotensin-converting enzyme 2 (ACE2) via Discovery Studio 2016 software.

Results

A total of 139 active compounds, 50 core targets, and 122 signaling pathways were screened out. The results of molecular docking showed that arctiin and linarin had a higher docking score with 3 Cl, ACE2, and core targets of JQH for antiviral effect.

Conclusion

The potential mechanism of action of JHQ in the treatment of MERS, SARS, and COVID-19 may be associated with the regulation of genes co-expressed with ACE2 and immune- related signaling pathways.

Virtual screening of curcumin analogues as DYRK2 inhibitor: Pharmacophore analysis, molecular docking and dynamics, and ADME prediction

Background: Curcumin reduces the proliferation of cancer cells through inhibition of the DYRK2 enzyme, which is a positive regulator of the 26S proteasome. Methods: In the present work, curcumin analogues have been screened from the MolPort database using a pharmacophore model that comprised a ligand-based approach. The result of the screening was then evaluated by molecular docking and molecular dynamics based on binding the free energy of the interaction between each compound with the binding pocket of DYRK2. The hit compounds were then confirmed by absorption, distribution, metabolism, and excretion (ADME) prediction. Results: Screening of 7.4 million molecules from the MolPort database afforded six selected hit compounds. By considering the ADME prediction, three prospective curcumin analogues have been selected. These are:  2‐[2‐(1‐methylpyrazol‐4‐yl)ethyl]‐1H,5H,6H,7H,8H‐imidazo[4,5‐c]azepin‐4‐one (Molport-035-369-361), methyl 4‐(3‐hydroxy‐1,2‐oxazol‐5‐yl)piperidine‐1‐carboxylate (Molport-000-004-273) and (1S)‐1‐[5‐(furan‐3‐carbonyl)‐4H,6H,7H‐pyrazolo[1,5‐a]pyrazin‐2‐yl]ethanol (MolPort-035-585-822). Conclusion: Pharmacophore modelling, combined with molecular docking and molecular dynamics simulation, as well as ADME prediction were successfully applied to screen curcumin analogues from the MolPort database as DYRK2 inhibitors. All selected compounds that have better predicted pharmacokinetic properties than that of curcumin are considered for further study.

QSAR analysis of 3-pyrimidin-4-yl-oxazolidin-2-one derivatives isocitrate dehydrogenase inhibitors using Topomer CoMFA and HQSAR methods

A series of mIDH1 inhibitors derived from 3-pyrimidine-4-oxazolidin-2-ketone derivatives were studied by QSAR model to explore the key factors that inhibit mIDH1 activity. The generated model was cross-verified and non-cross-verified by Topomer CoMFA and HQSAR methods; the independent test set was verified by PLS method; the Topomer search technology was used for virtual screening and molecular design; and the Surflex-Dock method and ADMET technology were used for molecular docking, pharmacology and toxicity prediction of the designed drug molecules. The Topomer CoMFA and HQSAR cross-validation coefficients q² are 0.783 and 0.784, respectively, and the non-cross-validation coefficients r² are 0.978 and 0.934, respectively. Ten new drug molecules have been designed using Topomer search technology. The results of molecular docking and ADMET show that the newly designed drug molecules are effective. The docking situation, pharmacology and toxicity prediction results are good. The model can be used to predict the bioactivity of the same type of new compounds and their derivatives. The prediction results of molecular design, molecular docking and ADMET can provide some ideas for the design and development of novel mIDH1 inhibitor anticancer drugs, and provide certain theoretical basis of the experimental verification of new compounds in the future. Newly designed molecules after docking with corresponding proteins in the PDB library, it can explore the targets of drug molecules acting with large proteins and the related force, which is very helpful for the design of new drugs and the mechanism of drug action.

Enteral Medication for the Tube-Fed Patient: Making This Route Safe and Effective

The administration of medication through an enteral access device requires important forethought. Meeting a patient's therapeutic needs requires achieving expected drug bioavailability without increasing the risk for toxicity, therapeutic failure, or feeding tube occlusion. Superimposing gut dysfunction, critical illness, or enteral nutrition-drug interaction further increases the need for a systematic approach to prescribing, evaluating, and preparing a drug for administration through an enteral access device. This review will explain the fundamental factors involved in drug bioavailability through the gut, address the influencing considerations for the enterally fed patient, and describe best practices for enteral drug preparation and administration.

Network Pharmacology Integrated Molecular Docking Analysis of Potential Common Mechanisms of Shu-Feng-Jie-Du Capsule in the Treatment of SARS, MERS, and COVID-19

Shu-Feng-Jie-Du Capsules (SFJDCs) have been clinically proven to have a good therapeutic effect on COVID-19 in China. This study aimed to analyze the common mechanisms of SFJDC in the treatment of severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19 via network pharmacology and molecular docking. We further explored the potential application value of SFJDC in the treatment of coronavirus infection. All components of SFJDC were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The viral associated targets of the active components were forecast using the Pharmmapper database and GeneCards. The Database for Annotation, Visualization, and Integrated Discovery and KOBAS 3.0 system were used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of SFJDC’s core targets. Further, the protein–protein interaction network was built using STRING database. The herb–component network and component–target–pathway network were constructed using Cytoscape 3.7.2. The core active components of SFJDC were docked with core targets and COVID-19 coronavirus 3 Cl hydrolase and angiotensin-converting enzyme 2 (ACE2) via Discovery Studio 2016 software. A total of 110 active components were filtered from SFJDC, with 47 core targets, including epidermal growth factor receptor, mitogen-activated protein kinase 1, mitogen-activated protein kinase 3, and interleukin 6. There were 416 GO items in the GO enrichment analysis ( P < .05) and 57 signaling pathways ( P < .05) in KEGG, mainly including pathways in cancer, pancreatic cancer, colorectal cancer, apoptosis, and neurotrophin signaling pathway, among others. The results of molecular docking showed that luteolin and rhein had a higher docking score with 3 Cl, ACE2, and core targets of SFJDC for antiviral effect. SFJDC is characterized by multicomponent, multitarget, and multisignaling pathways for the treatment of coronavirus infection. The mechanism of action of SFJDC in the treatment of MERS, SARS, and COVID-19 may be associated with the regulation of genes coexpressed with ACE2 and immune- related signaling pathways.

A network pharmacology study on analgesic mechanism of Yuanhu-Baizhi herb pair

BACKGROUND

Millions of people are suffering from chronic pain conditions, such as headache, arthritis, cancer. Apart from western medicines, traditional Chinese medicines are also well accepted for pain management, especially in Asian countries. Yuanhu-Baizhi herb pair (YB) is a typical herb pair applied to the treatment of stomach pain, hypochondriac pain, headache, and dysmenorrhea, due to its effects on analgesia and sedation. This study is to identify potentially active compounds and the underlying mechanisms of YB in the treatment of pain.

METHODS

Compounds in YB were collected from 3 online databases and then screened by bioavailability and drug likeness parameters. Swiss target prediction was applied to obtain targets information of the active compounds. Pain-related genes were conducted for Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Protein-protein interaction (PPI) networks of the genes were constructed using Cytoscape software. In addition, the hub genes were screened using maximal clique centrality (MCC) algorithm.

RESULTS

In total, 31 compounds from Yuanhu were screened out with 35 putative target genes, while 26 compounds in Baizhi with 43 target genes were discovered. Hence, 78 potential target genes of YB were selected for further study. After overlap analysis of the 78 genes of YB and 2408 pain-associated genes, we finally achieved 34 YB-pain target genes, as well as 10 hub genes and 23 core compounds. Go enrichment and KEGG pathway analysis indicated that YB had a strong integration with neuro system, which might significantly contribute to antinociceptive effect.

CONCLUSION

Our data provide deep understanding of the pharmacological mechanisms of YB in attenuating pain. The discovery shed new light on the development of active compounds of YB for the treatment of pain.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

The oxygenated products of cryptotanshinone by biotransformation with Cunninghamella elegans exerting anti-neuroinflammatory effects by inhibiting TLR 4-mediated MAPK signaling pathway

Cryptotanshinone (1), a major bioactive constituent in the traditional Chinese medicinal herb Dan-Shen Salvia miltiorrhiza Bunge, has been reported to possess remarkable pharmacological activities. To improve its bioactivities and physicochemical properties, in the present study, cryptotanshinone (1) was biotransformed with the fungus Cunninghamella elegans AS3.2028. Three oxygenated products (2-4) at C-3 of cryptotanshinone (1) were obtained, among them 2 was a new compound. Their structures were elucidated by comprehensive spectroscopic analysis including HRESIMS, NMR and ECD data. All of the biotransformation products (2-4) were found to inhibit significantly lipopolysaccharide-induced nitric oxide production in BV2 microglia cells with the IC₅₀ values of 0.16-1.16 μM, approximately 2-20 folds stronger than the substrate (1). These biotransformation products also displayed remarkably improved inhibitory effects on the production of inflammatory cytokines (IL-1β, IL-6, TNF-α, COX-2 and iNOS) in BV-2 cells via targeting TLR4 compared to substrate (1). The underlying mechanism of 2 was elucidated by comparative transcriptome analysis, which suggested that it reduced neuroinflammatory mainly through mitogen-activated protein kinase (MAPK) signaling pathway. Western blotting results revealed that 2 downregulated LPS-induced phosphorylation of JNK, ERK, and p38 in MAPK signaling pathway. These findings provide a basal material for the discovery of candidates in treating Alzheimer's disease.

Network Pharmacology to Identify the Pharmacological Mechanisms of a Traditional Chinese Medicine Derived from Trachelospermum jasminoides in Patients with Rheumatoid Arthritis

BACKGROUND This study used a network pharmacology approach to identify the pharmacological mechanisms of a traditional Chinese medicine derived from Trachelospermum jasminoides (Lindl.) Lem. in patients with rheumatoid arthritis (RA). MATERIAL AND METHODS Known compounds of T. jasminoides were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the Shanghai Institute of Organic Chemistry of Chinese Academy of Science, Chemistry (CASC) database, and a literature search. Putative targets of identified compounds were predicted by SwissTargetPrediction. RA-related targets were achieved from the Therapeutic Target database, Drugbank database, Pharmacogenomics Knowledgebase, and Online Mendelian Inheritance in Man database. The protein-protein interaction (PPI) network was built by STRING. CluGO was utilized for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis. RESULTS A total of 354 potential targets were predicted for the 17 bioactive compounds in T. jasminoides; 69 of these targets overlapped with RA-related targets. A PPI network was composed and 2 clusters of 59 and 42 nodes each were excavated. GO and KEGG enrichment analysis of the overlapping targets and the 2 clusters was mainly grouped into immunity, inflammation, estrogen, anxiety, and depression processes. CONCLUSIONS Our study illustrated that T. jasminoides alleviates RA through the interleukin-17 signaling pathway, the tumor necrosis factor signaling pathway, and other immune and inflammatory-related processes. It also may exert effects in regulating cell differentiation and potentially has anti-anxiety, anti-depression, and estrogen-like effects.

Network Pharmacology Integrated Molecular Docking Reveals the Anti-COVID-19 Mechanism of Qing-Fei-Da-Yuan Granules

Coronavirus disease (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a highly infectious viral disease. Clinical observations have shown that Qing-Fei-Da-Yuan (QFDY) granules have good anti-COVID-19 effects, but the underlying molecular mechanisms are unclear. In this study, we explored the potential mechanism of QFDY with regard to its anti-COVID-19 effect. We first screened the active chemical constituents of QFDY based on the pharmacodynamic activity parameters, followed by screening with the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The Uniprot database was used for querying the corresponding genes of the target, and Cyoscape 3.6.1 software was used to construct the network of herb-compound-target. Protein interaction analysis, target gene function enrichment analysis, and signal pathway analysis were performed via STRING database, Database for Annotation, Visualization, and Integrated Discovery, and KEGG Pathway database. Molecular docking was used to predict the binding capacity of the core compound with COVID-19 hydrolase 3CL and angiotensin converting enzyme 2 (ACE2). The results showed that a network of herb-compound-target was successfully constructed, with key targets involving PTGS2, HSP90AA1, CAMKK2, NCOA2, and ESR1. Major metabolic pathways affected were those in cancer, procancer, nonsmall cell lung cancer, and apoptosis. The core compounds, such as quercetin, luteolin, and naringenin, showed a strong binding ability with COVID-19 3CL hydrolase; compounds such as anemasaponin C and medicocarpin showed a strong binding ability with ACE2. Thus, it is predicted that QFDY has the characteristics for multicomponent, multitarget, and multichannel overall control. The mechanism of action of QFDY in the treatment of COVID-19 may be associated with the regulation of genes co-expressed with ACE2, the regulation of inflammation and immune-related signaling pathways, and the influence of COVID-19 3CL hydrolase and ACE2 binding ability.

ADME Prediction with KNIME: Development and Validation of a Publicly Available Workflow for the Prediction of Human Oral Bioavailability

In silico prediction of human oral bioavailability is a relevant tool for the selection of potential drug candidates and for the rejection of those molecules with less probability of success during the early stages of drug discovery and development. However, the high variability and complexity of oral bioavailability and the limited experimental data in the public domain have mainly restricted the development of reliable in silico models to predict this property from the chemical structure. In this study we present a KNIME automated workflow to predict human oral bioavailability of new drug and drug-like molecules based on five machine learning approaches combined into an ensemble model. The workflow is freely accessible and allows the quick and easy prediction of oral bioavailability for new molecules. Users do not require any knowledge or advanced experience in machine learning or statistical modeling to automatically obtain their predictions, increasing the potential use of the present proposal.

Network Pharmacology-based Research of Active Components of Albiziae Flos and Mechanisms of Its Antidepressant Effect

Albiziae Flos (AF) has been experimentally proven to have an antidepressant effect. However, due to the complexity of botanical ingredients, the exact pharmacological mechanism of action of AF in depression has not been completely deciphered. This study used the network pharmacology method to construct a component-target-pathway network to explore the active components and potential mechanisms of action of AF. The methods included collection and screening of chemical components, prediction of depression-associated targets of the active components, gene enrichment, and network construction and analysis. Quercetin and 4 other active components were found to exert antidepressant effects mainly via monoaminergic neurotransmitters and cAMP signaling and neuroactive ligand-receptor interaction pathways. DRD2, HTR1A, and SLC6A4 were identified as important targets of the studied bioactive components of AF. This network pharmacology analysis provides guidance for further study of the antidepressant mechanism of AF.

Discovery of novel and potent P2Y14R antagonists via structure-based virtual screening for the treatment of acute gouty arthritis

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A reliable Glide docking-based virtual screening (VS) pipeline for P2Y₁₄R was developed.

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Several potent P2Y₁₄R antagonists with novel scaffolds were identified utilizing the VS strategy.

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P2Y₁₄R inhibitory effect was evaluated by testing cAMP levels in HEK293 cells.

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Anti-gout activity of screened compound was detected in MSU-treated THP-1 cells.

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The mechanism of test compound in treating acute gouty arthritis was elucidated.

P2Y₁₄ nucleotide receptor is a Gi protein-coupled receptor, which is widely involved in physiological and pathologic events. Although several P2Y₁₄R antagonists have been developed thus far, few have successfully been developed into a therapeutic drug. In this study, on the basis of two P2Y₁₄R homology models, Glide docking-based virtual screening (VS) strategy was employed for finding potent P2Y₁₄R antagonists with novel chemical architectures. A total of 19 structurally diverse compounds identified by VS and drug-like properties testing were set to experimental testing. 10 of them showed good inhibitory effects against the P2Y₁₄R (IC₅₀ < 50 nM), including four compounds (compounds 8, 10, 18 and 19) with IC₅₀ value below 10 nM. The best VS hit, compound 8 exhibited the best antagonistic activity, with IC₅₀ value of 2.46 nM. More importantly, compound 8 restrained monosodium uric acid (MSU)-induced pyroptosis of THP-1 cells through blocking the activation of Nod-like receptor 3 (NLRP3) inflammasome, which was attributed to its inhibitory effects on P2Y₁₄R-cAMP pathways. The key favorable residues uncovered using MM/GBSA binding free energy calculations/decompositions were detected and discussed. These findings suggest that the compound 8 can be used as a good lead compound for further optimization to obtain more promising P2Y₁₄R antagonists for the treatment of acute gouty arthritis.

Recent Advances in Developing K-Ras Plasma Membrane Localization Inhibitors

The Ras proteins play an important role in cell growth, differentiation, proliferation and survival by regulating diverse signaling pathways. Oncogenic mutant K-Ras is the most frequently mutated class of Ras superfamily that is highly prevalent in many human cancers. Despite intensive efforts to combat various K-Ras-mutant-driven cancers, no effective K-Ras-specific inhibitors have yet been approved for clinical use to date. Since K-Ras proteins must be associated to the plasma membrane for their function, targeting K-Ras plasma membrane localization represents a logical and potentially tractable therapeutic approach. Here, we summarize the recent advances in the development of K-Ras plasma membrane localization inhibitors including natural product-based inhibitors achieved from high throughput screening, fragment-based drug design, virtual screening, and drug repurposing as well as hit-to-lead optimizations.

The fate of dietary advanced glycation end products in the body: from oral intake to excretion

Advanced glycation end products (AGEs), which are closely associated with various chronic diseases, are formed through the Maillard reaction when aldehydes react with amines in heated foods or in living organisms. The fate of dietary AGEs after oral intake plays a crucial role in regulating the association between dietary AGEs and their biological effects. However, the complexity and diversity of dietary AGEs make their fate ambiguous. Glycated modifications can impair the digestion, transport and uptake of dietary AGEs. High and low molecular weight AGEs may exhibit individual differences in their distribution, metabolism and excretion. Approximately 50-60% of free AGEs are excreted after dietary intake, whereas protein-bound AGEs exhibit a limited excretion rate. In this article, we summarize several AGE classification criteria and their abundance in foods, and in the body. A standardized static in vitro digestion method is strongly recommended to obtain comparable results of AGE digestibility. Sophisticated hypotheses regarding the intestinal transportation and absorption of drugs, as well as calculated physicochemical parameters, are expected to alleviate the difficulties determining the digestion, transport and uptake of dietary AGEs. Orally supplied AGEs with low or high molecular weights must be supported by well-defined amounts in investigations of excretion. Furthermore, unequivocal evidence should be obtained regarding the degradation and metabolism products of dietary AGEs.