Application of the SwissDrugDesign Online Resources in Virtual Screening

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.

ChemoDOTS: a web server to design chemistry-driven focused libraries

In drug discovery, the successful optimization of an initial hit compound into a lead molecule requires multiple cycles of chemical modification. Consequently, there is a need to efficiently generate synthesizable chemical libraries to navigate the chemical space surrounding the primary hit. To address this need, we introduce ChemoDOTS, an easy-to-use web server for hit-to-lead chemical optimization freely available at https://chemodots.marseille.inserm.fr/. With this tool, users enter an activated form of the initial hit molecule then choose from automatically detected reactive functions. The server proposes compatible chemical transformations via an ensemble of encoded chemical reactions widely used in the pharmaceutical industry during hit-to-lead optimization. After selection of the desired reactions, all compatible chemical building blocks are automatically coupled to the initial hit to generate a raw chemical library. Post-processing filters can be applied to extract a subset of compounds with specific physicochemical properties. Finally, explicit stereoisomers and tautomers are computed, and a 3D conformer is generated for each molecule. The resulting virtual library is compatible with most docking software for virtual screening campaigns. ChemoDOTS rapidly generates synthetically feasible, hit-focused, large, diverse chemical libraries with finely-tuned physicochemical properties via a user-friendly interface providing a powerful resource for researchers engaged in hit-to-lead optimization.

SwissDock 2024: major enhancements for small-molecule docking with Attracting Cavities and AutoDock Vina

Drug discovery aims to identify potential therapeutic compounds capable of modulating the activity of specific biological targets. Molecular docking can efficiently support this process by predicting binding interactions between small molecules and macromolecular targets and potentially accelerating screening campaigns. SwissDock is a computational tool released in 2011 as part of the SwissDrugDesign project, providing a free web-based service for small-molecule docking after automatized preparation of ligands and targets. Here, we present the latest version of SwissDock, in which EADock DSS has been replaced by two state-of-the-art docking programs, i.e. Attracting Cavities and AutoDock Vina. AutoDock Vina provides faster docking predictions, while Attracting Cavities offers more accurate results. Ligands can be imported in various ways, including as files, SMILES notation or molecular sketches. Targets can be imported as PDB files or identified by their PDB ID. In addition, advanced search options are available both for ligands and targets, giving users automatized access to widely-used databases. The web interface has been completely redesigned for interactive submission and analysis of docking results. Moreover, we developed a user-friendly command-line access which, in addition to all options of the web site, also enables covalent ligand docking with Attracting Cavities. The new version of SwissDock is freely available at https://www.swissdock.ch/.

R. PK, Joshi SD. Synthesis, molecular docking study and biological evaluation of new pyrrole scaffolds as potential antitubercular agents for dual targeting of enoyl ACP reductase and dihydrofolate reductase

In this study, new series of N'-(2-(substitutedphenoxy)acetyl)-4-(1H-pyrrol-1-yl)benzohydrazides (3a-j) 4-(2,5-dimethyl-1H-pyrrol-1-yl)-N'-(2-(substitutedphenoxy)acetyl)benzohydrazides (5a-j) were synthesized, characterized and assessed as inhibitors of enoyl ACP reductase and DHFR. Most of the compounds exhibited dual inhibition against the enzymes enoyl ACP reductase and DHFR. Several synthesized substances also demonstrated significant antibacterial and antitubercular properties. A molecular docking analysis was conducted in order to determine the potential mechanism of action of the synthesized compounds. The results indicated that there were binding interactions seen with the active sites of dihydrofolate reductase and enoyl ACP reductase. Additionally, important structural details were identified that play a critical role in sustaining the dual inhibitory activity. These findings were useful for the development of future dual inhibitors. Therefore, this study provided strong evidence that several synthesized molecules could exert their antitubercular properties at the cellular level through multi-target inhibition. By shedding light on the mechanisms through which these compounds exert their inhibitory effects, this research opens up promising avenues for the future development of dual inhibitors with enhanced antibacterial and antitubercular properties. The study's findings underscore the importance of multi-target approaches in drug design, providing a strong foundation for the design and optimization of novel compounds that can effectively target bacterial infections at the cellular level.

Structure-based virtual screening of mangiferin derivatives with antidiabetic action: a molecular docking and dynamics study and MPO-based drug-likeness approach

Extracts from Mangifera indica leaves and its main component, mangiferin, have proven antidiabetic activity. In this study, mangiferin and its natural derivatives Homomangiferin (HMF), Isomangiferin (IMF), Neomangiferin (NMF), Glucomangiferin (GMF), Mangiferin 6'-gallate (MFG), and Norathyriol (NRT) were compared regarding their action on Diabetes mellitus (DM), employing docking and molecular dynamics (MD) simulations to analyze interactions with the aldose reductase enzyme, the precursor to the conversion of glucose into sorbitol. Notably, HMF showed significant affinity to residues in the active site of the enzyme, including Trp 79, His 110, Trp 111, Phe 122, and Phe 300, with an energy of - 7.2 kcal/mol, observed in the molecular docking simulations. MD reinforced the formation of stable complexes for HMF and MFG with the aldose reductase, with interaction potential energies (IPE) in the order of - 300.812 ± 52 kJ/mol and - 304.812 ± 52 kJ/mol, respectively. The drug-likeness assessment, by multiparameter optimization (MPO), highlighted that HMF and IMF have similarities with polyphenols and glycosidic flavonoids recently patented as antidiabetics, revealing that high polarity (TPSA > 180 Å2) is a favorable property for subcutaneous administration, especially because of the gradual passive cell permeability values in biological tissues, with Papp values estimated at < 10 × 10-6 cm/s. These compounds are metabolically stable against metabolic enzymes, resulting in a low toxic incidence by metabolic activation, corroborating with a lethal dose (LD50) greater than 2000 mg/kg. In this way, HMF showed a systematic alignment between predicted pharmacokinetics and pharmacodynamics, characterizing it as the most favorable substance for inhibiting aldose reductase.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03978-9.

The anti-depression effect and potential mechanism of the petroleum ether fraction of CDB: Integrated network pharmacology and metabolomics

The combination of Chaidangbo (CDB) is an antidepressant traditional Chinese medicine (TCM) prescription simplified by Xiaoyaosan (a classic antidepressant TCM prescription) through dismantling research, which has the effect of dispersing stagnated liver qi and nourishing blood in TCM theory. Although the antidepressant effect of CBD has been confirmed in animal studies, the material basis and possible molecular mechanism for antidepressant activity in CBD have not been clearly elucidated. Herein, we investigated the effects and potential mechanisms of CDB antidepressant fraction (petroleum ether fraction of CDB, PEFC) on chronic unpredictable mild stress (CUMS)-induced depression-like behavior in mice using network pharmacology and metabolomics. First, a UPLC-QE/MS was employed to identify the components of PEFC. To extract active ingredients, SwissADME screening was used to the real PEFC components that were found. Potential PEFC antidepressant targets were predicted based on a network pharmacology approach, and a pathway enrichment analysis was performed for the predicted targets. Afterward, a CUMS mouse depression model was established and LC-MS-based untargeted hippocampal metabolomics was performed to identify differential metabolites, and related metabolic pathways. Finally, the protein expressions in mouse hippocampi were determined by Western blot to validate the network pharmacology and metabolomics deduction. A total of 16 active compounds were screened in SwissADME that acted on 73 core targets of depression, including STAT3, MAPKs, and NR3C1; KEGG enrichment analysis showed that PEFC modulated signaling pathways such as PI3K-Akt signaling pathway, endocrine resistance, and MAPK to exert antidepressant effects. PEFC significantly reversed abnormalities of hippocampus metabolites in CUMS mice, mainly affecting the synthesis and metabolism of glycine, serine, and threonine, impacting catecholamine transfer and cholinergic synapses and regulating the activity of the mTOR signaling pathway. Furthermore, Western blot analysis confirmed that PEFC significantly influenced the main protein levels of the PI3K/Akt/mTOR signaling pathways in the hippocampus of mice subjected to CUMS. This study integrated metabolomics, network pharmacology and biological verification to explore the potential mechanism of PEFC in treating depression, which is related to the regulation of amino acid metabolism dysfunction and the activation of PI3K/Akt/mTOR signaling pathways in the hippocampus. The comprehensive strategy also provided a reasonable way for unveiling the pharmacodynamic mechanisms of multi-components, multi-targets, and multi-pathways in TCM with antidepressant effect.

Neuroprotective effect of the traditional decoction Tian-Si-Yin against Alzheimer's disease via suppression of neuroinflammation

ETHNOPHARMACOLOGICAL RELEVANCE

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease among old adults. As a traditional Chinese medicine, the herbal decoction Tian-Si-Yin consists of Morinda officinalis How. and Cuscuta chinensis Lam., which has been widely used to nourish kidney. Interestingly, Tian-Si-Yin has also been used to treat dementia, depression and other neurological conditions. However, its therapeutic potential for neurodegenerative diseases such as AD and the underlying mechanisms remain unclear.

AIM OF THE STUDY

To evaluate the therapeutic effect of the herbal formula Tian-Si-Yin against AD and to explore the underlying mechanisms.

MATERIALS AND METHODS

The N2a cells treated with amyloid β (Aβ) peptide or overexpressing amyloid precursor protein (APP) were used to establish cellular models of AD. The in vivo anti-AD effects were evaluated by using Caenorhabditis elegans and 3 × Tg-AD mouse models. Tian-Si-Yin was orally administered to the mice for 8 weeks at a dose of 10, 15 or 20 mg/kg/day, respectively. Its protective role on memory deficits of mice was examined using the Morris water maze and fear conditioning tests. Network pharmacology, proteomic analysis and ultra-high performance liquid chromatography-mass spectrometry/mass spectrometry (UHPLC-MS/MS) were used to explore the underlying molecular mechanisms, which were further investigated by Western blotting and immunohistochemistry.

RESULTS

Tian-Si-Yin was shown to improve cell viability of Aβ-treated N2a cells and APP-expressing N2a-APP cells. Tian-Si-Yin was also found to reduce ROS level and extend lifespan of transgenic AD-like C. elegans model. Oral administration of Tian-Si-Yin at medium dose was able to effectively rescue memory impairment in 3 × Tg mice. Tian-Si-Yin was further shown to suppress neuroinflammation by inhibition of glia cell activation and downregulation of inflammatory cytokines, diminishing tau phosphoralytion and Aβ deposition in the mice. Using UHPLC-MS/MS and network pharmacology technologies, 17 phytochemicals from 68 components of Tian-Si-Yin were identified as potential anti-AD components. MAPK1, BRAF, TTR and Fyn were identified as anti-AD targets of Tian-Si-Yin from network pharmacology and mass spectrum.

CONCLUSIONS

This study has established the protective effect of Tian-Si-Yin against AD and demonstrates that Tian-Si-Yin is capable of improving Aβ level, tau pathology and synaptic disorder by regulating inflammatory response.

Inhibition of NS2B-NS3 protease from all four serotypes of dengue virus by punicalagin, punicalin and ellagic acid identified from Punica granatum

Despite a major threat to the public health in tropical and subtropical regions, dengue virus (DENV) infections are untreatable. Therefore, efforts are needed to investigate cost-effective therapeutic agents that could cure DENV infections in future. The NS2B-NS3 protease encoded by the genome of DENV is considered a critical target for the development of anti-dengue drugs. The objective of the current study was to find out a specific inhibitor of the NS2B-NS3 proteases from all four serotypes of DENV. To begin with, nine plant extracts with a medicinal history were evaluated for their role in inhibiting the NS2B-NS3 proteases by Fluorescence Resonance Energy Transfer (FRET) assay. Among the tested extracts, Punica granatum was found to be the most effective one. The metabolic profiling of this extract revealed the presence of several active compounds, including ellagic acid, punicalin and punicalagin, which are well-established antiviral agents. Further evaluation of IC50 values of these three antiviral molecules revealed punicalagin as the most potent anti-NS2B-NS3 protease drug with IC50 of 0.91 ± 0.10, 0.75 ± 0.05, 0.42 ± 0.03, 1.80 ± 0.16 µM against proteases from serotypes 1, 2, 3 and 4, respectively. The docking studies demonstrated that these compounds interacted at the active site of the enzyme, mainly with His and Ser residues. Molecular dynamics simulations analysis also showed the structural stability of the NS2B-NS3 proteases in the presence of punicalagin. In summary, this study concludes that the punicalagin can act as an effective inhibitor against NS2B-NS3 proteases from all four serotypes of DENV.Communicated by Ramaswamy H. Sarma.

Identification of anti-gastric cancer effects and molecular mechanisms of resveratrol: From network pharmacology and bioinformatics to experimental validation

BACKGROUND

Gastric cancer (GC) is one of the most aggressive malignancies with limited therapeutic options and a poor prognosis. Resveratrol, a non-flavonoid polyphenolic compound found in a variety of Chinese medicinal materials, has shown excellent anti-GC effect. However, its exact mechanisms of action in GC have not been clarified.

AIM

To identify the effects of resveratrol on GC progression and explore the related molecular mechanisms.

METHODS

Action targets of resveratrol and GC-related targets were screened from public databases. The overlapping targets between the two were confirmed using a Venn diagram, and a "Resveratrol-Target-GC" network was constructed using Cytoscape software version 3.9.1. The protein-protein interaction (PPI) network was constructed using STRING database and core targets were identified by PPI network analysis. The Database for Annotation, Visualization and Integrated Discovery database was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A "Target-Pathway" network was created by using Cytoscape 3.9.1. The RNA and protein expression levels of core target genes were observed using the Cancer Genome Atlas and the Human Protein Atlas databases. DriverDBv3 and Timer2.0 databases were used for survival and immune infiltration analysis. Subsequently, the findings were further verified by molecular docking technology and in vitro experiments.

RESULTS

A total of 378 resveratrol action targets and 2154 GC disease targets were obtained from public databases, and 181 intersection targets between the two were screened by Venn diagram. The top 20 core targets were identified by PPI network analysis of the overlapping targets. GO function analysis mainly involved protein binding, identical protein binding, cytoplasm, nucleus, negative regulation of apoptotic process and response to xenobiotic stimulus. KEGG enrichment analysis suggested that the involved signaling pathways mainly included PI3K-AKT signaling pathway, MAPK signaling pathway, IL-17 signaling pathway, TNF signaling pathway, ErbB signaling pathway, etc. FBJ murine osteosarcoma viral oncogene homolog (FOS) and matrix metallopeptidase 9 (MMP9) were selected by differential expression analysis, and they were closely associated with immune infiltration. Molecular docking results showed that resveratrol docked well with these two targets. Resveratrol treatment arrested the cell cycle at the S phase, induced apoptosis, and weakened viability, migration and invasion in a dose-dependent manner. Furthermore, resveratrol could exhibit anti-GC effect by regulating FOS and MMP9 expression.

CONCLUSION

The anti-GC effects of resveratrol are related to the inhibition of cell proliferation, migration, invasion and induction of cell cycle arrest and apoptosis by targeting FOS and MMP9.

Navigating the Frontiers of Machine Learning in Neurodegenerative Disease Therapeutics

Recent advances in machine learning hold tremendous potential for enhancing the way we develop new medicines. Over the years, machine learning has been adopted in nearly all facets of drug discovery, including patient stratification, lead discovery, biomarker development, and clinical trial design. In this review, we will discuss the latest developments linking machine learning and CNS drug discovery. While machine learning has aided our understanding of chronic diseases like Alzheimer's disease and Parkinson's disease, only modest effective therapies currently exist. We highlight promising new efforts led by academia and emerging biotech companies to leverage machine learning for exploring new therapies. These approaches aim to not only accelerate drug development but to improve the detection and treatment of neurodegenerative diseases.

SwissParam 2023: A Modern Web-Based Tool for Efficient Small Molecule Parametrization

Most steps of drug discovery are now routinely supported and accelerated by computer-aided drug design tools. Among them, structure-based approaches use the three-dimensional structure of the targeted biomacromolecule as a major source of information. When it comes to calculating the interactions of small molecules with proteins using the equations of molecular mechanics, topologies, atom typing, and force field parameters are required. However, generating parameters for small molecules remains challenging due to the large number of existing chemical groups. The SwissParam web tool was first released in 2011 with the aim of generating parameters and topologies for small molecules based on the Merck molecular force field (MMFF) while being compatible with the CHARMM22/27 force field. Here, we present an updated version of SwissParam, providing various new features, including the possibility to setup covalent ligands. Molecules can now be imported from different file formats or via a molecular sketcher. The MMFF-based approach has been updated to provide parameters and topologies compatible with the CHARMM36 force field. An option was added to generate small molecule parametrizations following the CHARMM General Force Field via the multipurpose atom-typer for CHARMM (MATCH) approach. Additionally, SwissParam now generates information on probable alternative tautomers and protonation states of the query molecule so that the user can consider all microspecies relevant to its compound. The new version of SwissParam is freely available at www.swissparam.ch and can also be accessed through a newly implemented command-line interface.

Anti-viral effect of usenamine a using SARS-CoV-2 pseudo-typed viruses

The escalating pandemic brought about by the novel SARS-CoV-2 virus is threatening global health, and thus, it is necessary to develop effective antiviral drugs. Usenamine A is a dibenzo-furan derivative separated from lichen Usnea diffracta showing broad-spectrum activity against different viruses. We evaluate that usenamine A has antiviral effects against novel SARS-CoV-2 Delta variant pseudotyped viruses (PVs) in A549 cells. In addition, usenamine A significantly suppresses SARS-CoV-2 PV-induced mitochondrial depolarization, elevated reactive oxygen species (ROS) levels, apoptosis, and inflammation. Usenamine A also causes the SARS-CoV-2 spike protein to become less stable. Thus, usenamine A shows potential as an antiviral drug that can provide protection against COVID-19.

Identification of Bulbocodin D and C as novel STAT3 inhibitors and their anticancer activities in lung cancer cells

Cancer stands as one of the predominant causes of mortality globally, necessitating ongoing efforts to develop innovative therapeutics. Historically, natural products have been foundational in the quest for anticancer agents. Bulbocodin D (BD) and Bulbocodin C (BC), two bibenzyls derived from Pleione bulbocodioides (Franch.) Rolfe, have demonstrated notable in vitro anticancer activity. In human lung cancer A549 cells, the IC50s for BD and BC were 11.63 and 11.71 μmol·L-1, respectively. BD triggered apoptosis, as evidenced by an upsurge in Annexin V-positive cells and elevated protein expression of cleaved-PARP in cancer cells. Furthermore, BD and BC markedly inhibited the migratory and invasive potentials of A549 cells. The altered genes identified through RNA-sequencing analysis were integrated into the CMap dataset, suggesting BD's role as a potential signal transducer and activator of transcription 3 (STAT3) inhibitor. SwissDock and MOE analyses further revealed that both BD and BC exhibited a commendable binding affinity with STAT3. Additionally, a surface plasmon resonance assay confirmed the direct binding affinity between these compounds and STAT3. Notably, treatment with either BD or BC led to a significant reduction in p-STAT3 (Tyr 705) protein levels, regardless of interleukin-6 stimulation in A549 cells. In addition, the extracellular signal-regulated kinase (ERK) was activated after BD or BC treatment. An enhancement in cancer cell mortality was observed upon combined treatment of BD and U0126, the MEK1/2 inhibitor. In conclusion, BD and BC emerge as promising novel STAT3 inhibitors with potential implications in cancer therapy.

In Silico and In Vitro Exploration of Poziotinib and Olmutinib Synergy in Lung Cancer: Role of hsa-miR-7-5p in Regulating Apoptotic Pathway Marker Genes

Background and objectives: Non-small cell lung cancer (NSCLC) is often caused by EGFR mutations, leading to overactive cell growth pathways. Drug resistance is a significant challenge in lung cancer treatment, affecting therapy effectiveness and patient survival. However, combining drugs in research shows promise in addressing or delaying resistance, offering a more effective approach to cancer treatment. In this study, we investigated the potential alterations in the apoptotic pathway in A549 cells induced by a combined targeted therapy using tyrosine kinase inhibitors (TKIs) olmutinib and poziotinib, focusing on cell proliferation, differential gene expression, and in silico analysis of apoptotic markers. Methods: A combined targeted therapy involving olmutinib and poziotinib was investigated for its impact on the apoptotic pathway in A549 cells. Cell proliferation, quantitative differential gene expression, and in silico analysis of apoptotic markers were examined. A549 cells were treated with varying concentrations (1, 2.5, and 5 μM) of poziotinib, olmutinib, and their combination. Results: Treatment with poziotinib, olmutinib, and their combination significantly reduced cell proliferation, with the most pronounced effect at 2.5 μM (p < 0.005). A synergistic antiproliferative effect was observed with the combination of poziotinib and olmutinib (p < 0.0005). Quantitative differential gene expression showed synergistic action of the drug combination, impacting key apoptotic genes including STK-11, Bcl-2, Bax, and the Bax/Bcl-2 ratio. In silico analysis revealed direct interactions between EGFR and ERBB2 genes, accounting for 77.64% of their interactions, and 8% co-expression with downstream apoptotic genes. Molecular docking indicated strong binding of poziotinib and olmutinib to extrinsic and intrinsic apoptotic pathway markers, with binding energies of -9.4 kcal/mol and -8.5 kcal/mol, respectively, on interacting with STK-11. Conclusions: Combining poziotinib and olmutinib therapies may significantly improve drug tolerance and conquer drug resistance more effectively than using them individually in lung cancer patients, as suggested by this study's mechanisms.

Evaluation of the Possible Pathways Involved in the Protective Effects of Quercetin, Naringenin, and Rutin at the Gene, Protein and miRNA Levels Using In-Silico Multidimensional Data Analysis

Flavonoids are secondary metabolites that are non-essential for plant growth or survival, and they also provide numerous health benefits to humans. They are antioxidants that shield plants from the ill effects of ultraviolet light, pests, and diseases. They are beneficial to health for several reasons, including lowering inflammation, boosting cardiovascular health, and lowering cancer risk. This study looked into the physicochemical features of these substances to determine the potential pharmacological pathways involved in their protective actions. Potential targets responsible for the protective effects of quercetin, naringenin, and rutin were identified with SwissADME. The associated biological processes and protein-protein networks were analyzed by using the GeneMANIA, Metascape, and STRING servers. All the flavonoids were predicted to be orally bioavailable, with more than 90% targets as enzymes, including kinases and lyases, and with common targets such as NOS2, CASP3, CASP9, CAT, BCL2, TNF, and HMOX1. TNF was shown to be a major target in over 250 interactions. To extract the "biological meanings" from the MCODE networks' constituent parts, a GO enrichment analysis was performed on each one. The most important transcription factors in gene regulation were RELA, NFKB1, PPARG, and SP1. Treatment with quercetin, naringenin, or rutin increased the expression and interaction of the microRNAs' hsa-miR-34a-5p, hsa-miR-30b-5p, hsa-let-7a-5p, and hsa-miR-26a-1-3p. The anticancer effects of hsa-miR-34a-5p have been experimentally confirmed. It also plays a critical role in controlling other cancer-related processes such as cell proliferation, apoptosis, EMT, and metastasis. This study's findings might lead to a deeper comprehension of the mechanisms responsible for flavonoids' protective effects and could present new avenues for exploration.

Food Toxicity of Mycotoxin Citrinin and Molecular Mechanisms of Its Potential Toxicity Effects through the Implicated Targets Predicted by Computer-Aided Multidimensional Data Analysis

The mycotoxin citrinin, which can contaminate food, is a major global concern. Citrinin is regarded as an inevitable pollutant in foods and feed since fungi are widely present in the environment. To identify contentious toxicity and lessen its severity by understanding the targets of citrinin in the human body and the impacted biosynthetic pathways, we analyzed the production of citrinin from Aspergillus flavus and Penicillium notatum and used a thorough bioinformatics analysis to characterize the toxicity and predict genes and protein targets for it. The predicted median fatal dosage (LD50) for citrinin was 105 mg/kg weight, and it belonged to toxicity class 3 (toxic if swallowed). Citrinin was found to be well absorbed by human intestinal epithelium and was a Pgp nonsubstrate (permeability glycoprotein), which means that once it is absorbed, it cannot be pumped out, hence leading to bioconcentration or biomagnification in the human body. The main targets of toxicity were casp3, TNF, IL10, IL1B, BAG3, CCNB1, CCNE1, and CDC25A, and the biological pathways implicated were signal transduction involved in DNA damage checkpoints, cellular and chemical responses to oxidative stress, DNA damage response signal transduction by P53, stress-activated protein kinase signaling cascade, netrin–UNC5B signaling, PTEN gene regulation, and immune response. Citrinin was linked to neutrophilia, squamous cell carcinoma, Fanconi anemia, leukemia, hepatoblastoma, and fatty liver diseases. The transcription factors E2F1, HSF1, SIRT1, RELA, NFKB, JUN, and MYC were found to be responsible. When data mining was performed on citrinin targets, the top five functional descriptions were a cell’s response to an organic cyclic compound, the netrin–UNC5B signaling pathway, lipids and atherosclerosis, thyroid cancer, and controlling the transcription of the PTEN gene.

Therapeutic Potency of Mono- and Diprenylated Acetophenones: A Case Study of In-Vivo Antimalarial Evaluation

Malaria remains a febrile infection of public health concern in many countries especially tropical countries in Africa and certain countries in Southern and North America such as Brazil, Costa Rica, Mexico, Dominican Republic, Colombia, and Ecuador. Hence this has made research into this area paramount. Acetophenones are active fragments in many compounds with promising antimalarial activity, such as chalcones. The aim of the present study was to investigate antimalarial activity of 3,5-diprenyl acetophenone (I) and 5-diprenyl acetophenone (II) in in vivo. In this study, compounds I and II were synthesized using an aromatic substitution reaction. The in-vivo antimalarial potential of compounds I and II was analyzed in Plasmodium berghei-infected mice. Our data showed that compound I (25, 50, and 100 mg/kg) had promising antimalarial activity, with parasitemia inhibited rate being 68.03, 65.16, and 69.75%, respectively. Compound II dose-dependently inhibited parasitemia levels, it demonstrated an infinitesimally higher activity (72.12%) when compared with compound I (69.75%) at 100 mg/kg dose. The two compounds passed the rule of three, Lipinski's rule of five, predicted plausible pharmacokinetic profile (ADME), and apparent safety profile, and demonstrated drug-like fragments. The study provided guidance in exploring novel antimalarial compounds based on the scaffolds of prenylated acetophenones.

An Insight into Wheat Germ Oil Nutrition, Identification of Its Bioactive Constituents and Computer-Aided Multidimensional Data Analysis of Its Potential Anti-Inflammatory Effect via Molecular Connections

Wheat germ oil (WGO) is the richest source of unexplored antioxidants and anti-inflammatory compounds. In this study, we identified the constituents of WGO by gas chromatography-mass spectrometry (GC-MS). The physicochemical and pharmacokinetic behaviors were evaluated for the top 12 constituents with the common target FABP4. Three fatty acids with significant anti-inflammatory activity were evaluated for their interaction with FABP4 by molecular docking. The molecular mechanisms involved in anti-inflammatory responses were analyzed by various in-silico analytical tools and multidimensional data analysis. WGO showed anti-inflammatory activities via FABP4 interacting physically with target genes (77.84%) and by co-expressing with 8.01% genes. Primary targets for inflammatory pathways were PPARα, PPARγ, LPL, LEP, and ADIPOQ, as depicted by gene network enrichment analysis. The key pathways implicated were the metabolism of lipids, PPAR signaling, cellular response to alcohol, oxygen and nitrogen pathway, inflammatory response pathway, and regulation of the inflammatory pathway. The common transcription factors implicated were HNF1, AP2α, CEBP, FOX, STATS, MYC, Zic, etc. In this study, we found that WGO possesses anti-inflammatory potential via FABP4 binding to PPARα, PPARγ, LPL, LEP, and ADIPOQ gene expression by regulatory transcription factors HNF, AP2α, and CEPB.

Deciphering SARS CoV-2-associated pathways from RNA sequencing data of COVID-19-infected A549 cells and potential therapeutics using in silico methods

BACKGROUND

Coronavirus (CoV) disease (COVID-19) identified in Wuhan, China, in 2019, is mainly characterized by atypical pneumonia and severe acute respiratory syndrome (SARS) and is caused by SARS CoV-2, which belongs to the Coronaviridae family. Determining the underlying disease mechanisms is central to the identification and development of COVID-19-specific drugs for effective treatment and prevention of human-to-human transmission, disease complications, and deaths.

METHODS

Here, next-generation RNA sequencing (RNA Seq) data were obtained using Illumina Next Seq 500 from SARS CoV-infected A549 cells and mock-treated A549 cells from the Gene Expression Omnibus (GEO) (GSE147507), and quality control (QC) was assessed before RNA Seq analysis using CLC Genomics Workbench 20.0. Differentially expressed genes (DEGs) were imported into BioJupies to decipher COVID-19 induced signaling pathways and small molecules derived from chemical synthesis or natural sources to mimic or reverse COVID -19 specific gene signatures. In addition, iPathwayGuide was used to identify COVID-19-specific signaling pathways, as well as drugs and natural products with anti-COVID-19 potential.

RESULTS

Here, we identified the potential activation of upstream regulators such as signal transducer and activator of transcription 2 (STAT2), interferon regulatory factor 9 (IRF9), and interferon beta (IFNβ), interleukin-1 beta (IL-1β), and interferon regulatory factor 3 (IRF3). COVID-19 infection activated key infectious disease-specific immune-related signaling pathways such as influenza A, viral protein interaction with cytokine and cytokine receptors, measles, Epstein-Barr virus infection, and IL-17 signaling pathway. Besides, we identified drugs such as prednisolone, methylprednisolone, diclofenac, compound JQ1, and natural products such as Withaferin-A and JinFuKang as candidates for further experimental validation of COVID-19 therapy.

CONCLUSIONS

In conclusion, we have used the in silico next-generation knowledge discovery (NGKD) methods to discover COVID-19-associated pathways and specific therapeutics that have the potential to ameliorate the disease pathologies associated with COVID-19.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Target-based virtual screening, computational multiscoring docking and molecular dynamics simulation of small molecules as promising drug candidate affecting kinesin-like protein KIFC1

The kinesin family member C1 (KIFC1) is an essential protein that facilitates the bipolar division of neoplastic cells. Inhibiting KIFC1 by small molecules is a lucrative strategy to impede bipolar mitosis leading to the apoptosis of cancerous cells. The research aims to envisage small-molecule inhibitors targeting KIFC1. The Mcule database, a comprehensive online digital platform containing more than five million chemical compounds, was used for structure-based virtual screening (SBVS). Druglikeness filtration sifted 2,293,282 chemical hits that further narrowed down to 49 molecules after toxicity profiling. Finally, 39 compounds that comply with the BOILED-Egg permeation predictive model of the ADME rules were carried forward for multiscoring docking using the AutoDock Vina inbuilt to Mcule drug discovery platform, DockThor and SwissDock tools. The mean of ΔG terms produced by docking tools was computed to find consensus top ligand hits. AZ82 exhibited stronger binding (Consensus ΔG: -7.99 kcal mol^-1 ) with KIFC1 among reference inhibitors, for example, CW069 (-7.57 kcal mol^-1 ) and SR31527 (-7.01 kcal mol^-1 ). Ten ligand hits namely, Mcule-4895338547 (Consensus ΔG: -8.69 kcal mol^-1 ), Mcule-7035674888 (-8.42 kcal mol^-1 ), Mcule-5531166845 (-8.53 kcal mol^-1 ), Mcule-3248415882 (-8.55 kcal mol^-1 ), Mcule-291881733 (-8.41 kcal mol^-1 ), Mcule-5918624394 (-8.44), Mcule-3470115427 (-8.47), Mcule-3686193135 (-8.18 kcal mol^-1 ), Mcule-3955355291 (8.09 kcal mol^-1 ) and Mcule-9534899193 (-8.01 kcal mol^-1 ) depicted strong binding interactions with KIFC1 in comparison to potential reference inhibitor AZ82. The top four ligands and AZ82 were considered for molecular dynamics simulation of 50 ns duration. Toxicity profiling, physicochemical properties, lipophilicity, solubility, pharmacokinetics, druglikeness, medicinal chemistry attributes, average potential energy, RMSD, RMSF, SASA, ΔGsolv and Rg analyses forecast the ligand mcule-4895338547 as a promising inhibitor of KIFC1.

Preparation of Novel Pyrazolo[4,3-e]tetrazolo[1,5-b][1,2,4]triazine Sulfonamides and Their Experimental and Computational Biological Studies

Pyrazolo[4,3-e]tetrazolo[1,5-b][1,2,4]triazine sulfonamides constitute a novel class of heterocyclic compounds with broad biological activity, including anticancer properties. Investigated in this study, MM-compounds (MM134, MM136, MM137, and MM139) exhibited cytotoxic and proapoptotic activity against cancer cell lines (BxPC-3, PC-3, and HCT-116) in nanomolar concentrations without causing cytotoxicity in normal cells (L929 and WI38). In silico predictions indicate that tested compounds exhibit favorable pharmacokinetic profiles and may exert anticancer activity through the inhibition of BTK kinase, the AKT-mTOR pathway and PD1-PD-L1 interaction. Our findings point out that these sulfonamide derivatives may constitute a source of new anticancer drugs after optimization.

Synthesis, Biological Activity, and Molecular Modelling Studies of Naphthoquinone Derivatives as Promising Anticancer Candidates Targeting COX-2

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-associated mortalities worldwide. Therefore, it is crucial to develop a novel therapeutic option targeting localized and metastatic NSCLC. In this paper, we describe the synthesis and biological activity characterization of naphthoquinone derivatives bearing selective anticancer activity to NSCLC via a COX-2 mediated pathway. The biological evaluation of compounds 9−16 showed promising structure-dependent anticancer activity on A549 cells in 2D and 3D models. Compounds were able to significantly (p < 0.05) reduce the A549 viability after 24 h of treatment in comparison to treated control. Compounds 9 and 16 bearing phenylamino and 4-hydroxyphenylamino substituents demonstrated the most promising anticancer activity and were able to induce mitochondrial damage and ROS formation. Furthermore, most promising compounds showed significantly lower cytotoxicity to non-cancerous Vero cells. The in silico ADMET properties revealed promising drug-like properties of compounds 9 and 16. Both compounds demonstrated favorable predicted GI absorption values, while only 16 was predicted to be permeable through the blood−brain barrier. Molecular modeling studies identified that compound 16 is able to interact with COX-2 in arachidonic acid site. Further studies are needed to better understand the safety and in vivo efficacy of compounds 9 and 16.

Thymoquinone (TQ) Inhibits Inflammation and Migration of THP-1 Macrophages: Mechanistic Insights into the Prevention of Atherosclerosis Using In-Vitro and In-Silico Analysis

Atherosclerosis is an inflammatory disease mediated by interferon (IFN-γ) in concert with cell adhesion molecules and chemokines. Thymoquinone (TQ), a flavonoid derived from Nigella sativa, is reported to have anti-inflammatory, antioxidant, and cardiovascular protective properties. We evaluated the effects of TQ on the key pathogenic stages of atherosclerosis, including cell viability, inflammatory gene expression, cell migration, and cholesterol efflux, on human THP-1 macrophages in-vitro. Moreover, in-silico analysis was performed to predict the molecular targets and signaling mechanisms. We demonstrated that TQ treatment had no effect on cell viability and decreased the expression of monocyte chemoattractant protein (MCP-1) and intercellular adhesion molecule (ICAM-1) in response to IFN-γ. In addition, we have also demonstrated that the THP-1 cell migration was inhibited by TQ in the absence or presence of MCP-1. Thymoquinone had no effect on cholesterol efflux from monocytes. In-silico analysis also identified several putative targets for TQ that are associated with inflammatory diseases and associated signaling pathways. Collectively, these results suggest that TQ has anti-inflammatory effects and may be a potential nutraceutical candidate for the prevention and treatment of atherosclerosis.

Buyang Huanwu Decoction promotes angiogenesis in myocardial infarction through suppression of PTEN and activation of the PI3K/Akt signalling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Myocardial infarction (MI) is the most severe subtype of coronary artery disease. Recent studies have demonstrated that the repair process and prognosis of MI are closely related to microcirculatory function in myocardial tissue. Buyang Huanwu Decoction (BYHWD) has shown great potential in the treatment of MI. However, the effects and mechanisms of BYHWD on angiogenesis post-MI remain unclear.

AIM OF THE STUDY

The study aimed to explore the promotion of angiogenesis by BYHWD post-MI and the potential mechanisms in vivo and in vitro.

MATERIALS AND METHODS

MI in mice was induced by permanent ligature of the coronary artery. The sample was divided into sham, model, and BYHWD treatment groups. After four weeks, the effects of BYHWD treatment on cardiac function were evaluated by echocardiography and HE and Masson staining. Angiogenesis was detected by CD 31 immunofluorescence staining in vivo. Then, various databases were searched to identify the corresponding targets of BYHWD in order to explore the molecular mechanisms underlying its effects in MI. Moreover, Western blot and immunohistochemistry were employed to measure the PTEN/PI3K/Akt/GSK3β signalling pathway and VEGFA expression in MI mice. Finally, the effects of BYHWD on cell angiogenesis and the activation of the PTEN/PI3K/Akt/GSK3β pathway in primary HUVECs were investigated. Overexpression of PTEN was achieved by an adenovirus vector encoding PTEN.

RESULTS

BYHWD significantly promoted angiogenesis and improved cardiac function in MI mice. Target prediction analysis suggested that BYHWD ameliorates MI via the PI3K/Akt pathway. BYHWD promoted angiogenesis post-MI by suppressing PTEN and activating the PI3K/Akt/GSK3β signalling pathway in vivo and in vitro. Moreover, the effects of BYHWD on HUVEC angiogenesis and the expression of PI3K/Akt/GSK3β signalling pathway-associated proteins were partially abrogated by the overexpression of PTEN.

CONCLUSION

Collectively, this study demonstrates that BYHWD exerts cardioprotective effects against MI by targeting angiogenesis. These effects are related to suppressing PTEN and activating the PI3K/Akt/GSK3β signalling pathway by BYHWD.

Design and Analysis of Pharmacokinetics, Pharmacodynamics and Toxicological analysis of Cannabidiol analogs using in silico Tools

Background:

Background: Cannabidiol (CBD), a non‐psychoactive phytocannabinoid from Cannabis Sativa, has become an interesting option for medicinal chemists in the development of new drug candidates

Objective:

Objective: This study aims to propose analogs with therapeutic potential from the CBD scaffold. Methods: The 16 analogs proposed were designed using the PubChem Sketcher V. 2.4® software. Already, CBD analogs were subjected to different in silico tools, such as Molinspiration®; SwissADME®; SwissTargetPrediction® and OSIRIS Property Explorer

Results and Discussio:

Results and Discussion: The screening of CBD analogs carried out in this study showed compounds 9 and 16 with good affinity for interactions with CB1 and CB2 receptors. Pharmacokinetic data showed that these two compounds have good oral absorption. Finally, in silico toxicity data showed that these compounds pose no risk of a toxic event in humans

Conclusion:

Conclusion: CBD analogs 9 and 16 would have a better profile of drug candidates to be further tested in vitro and in vivo models.: CBD analogs 9 and 16 would have a better profile of drug candidates to be further tested in vitro and in vivo models.

The SwissSimilarity 2021 Web Tool: Novel Chemical Libraries and Additional Methods for an Enhanced Ligand-Based Virtual Screening Experience

Hit finding, scaffold hopping, and structure–activity relationship studies are important tasks in rational drug discovery. Implementation of these tasks strongly depends on the availability of compounds similar to a known bioactive molecule. SwissSimilarity is a web tool for low-to-high-throughput virtual screening of multiple chemical libraries to find molecules similar to a compound of interest. According to the similarity principle, the output list of molecules generated by SwissSimilarity is expected to be enriched in compounds that are likely to share common protein targets with the query molecule and that can, therefore, be acquired and tested experimentally in priority. Compound libraries available for screening using SwissSimilarity include approved drugs, clinical candidates, known bioactive molecules, commercially available and synthetically accessible compounds. The first version of SwissSimilarity launched in 2015 made use of various 2D and 3D molecular descriptors, including path-based FP2 fingerprints and ElectroShape vectors. However, during the last few years, new fingerprinting methods for molecular description have been developed or have become popular. Here we would like to announce the launch of the new version of the SwissSimilarity web tool, which features additional 2D and 3D methods for estimation of molecular similarity: extended-connectivity, MinHash, 2D pharmacophore, extended reduced graph, and extended 3D fingerprints. Moreover, it is now possible to screen for molecular structures having the same scaffold as the query compound. Additionally, all compound libraries available for screening in SwissSimilarity have been updated, and several new ones have been added to the list. Finally, the interface of the website has been comprehensively rebuilt to provide a better user experience. The new version of SwissSimilarity is freely available starting from December 2021.

Synthesis of Novel Thiazole Derivatives Bearing β-Amino Acid and Aromatic Moieties as Promising Scaffolds for the Development of New Antibacterial and Antifungal Candidates Targeting Multidrug-Resistant Pathogens

Rapidly growing antimicrobial resistance among clinically important bacterial and fungal pathogens accounts for high morbidity and mortality worldwide. Therefore, it is critical to look for new small molecules targeting multidrug-resistant pathogens. Herein, in this paper we report a synthesis, ADME properties, and in vitro antimicrobial activity characterization of novel thiazole derivatives bearing β-amino acid, azole, and aromatic moieties. The in silico ADME characterization revealed that compounds 1-9 meet at least 2 Lipinski drug-like properties while cytotoxicity studies demonstrated low cytotoxicity to Vero cells. Further in vitro antimicrobial activity characterization showed the selective and potent bactericidal activity of 2a-c against Gram-positive pathogens (MIC 1-64 µg/mL) with profound activity against S. aureus (MIC 1-2 µg/mL) harboring genetically defined resistance mechanisms. Furthermore, the compounds 2a-c exhibited antifungal activity against azole resistant A. fumigatus, while only 2b and 5a showed antifungal activity against multidrug resistant yeasts including Candida auris. Collectively, these results demonstrate that thiazole derivatives 2a-c and 5a could be further explored as a promising scaffold for future development of antifungal and antibacterial agents targeting highly resistant pathogenic microorganisms.

Characterization of folic acid-functionalized PLA-PEG nanomicelle to deliver Letrozole: A nanoinformatics study

Effective cancer treatment is currently the number one challenge to human health. To date, several treatment methods have been introduced for cancer cell targeting. Among the proposed new methods for attacking cancer cells, nanotechnology has attracted much attention. Hence, various nanocarriers have been developed for targeted delivery of available drugs and improve their effectiveness against malignant cells. The PLA-PEG functionalised with folic acid (PLA-PEG-FA) is one of the nanocarriers with a limited range of applications for targeting cancer cells. In this investigation, different types of in-silico methods such as molecular docking approach, molecular dynamics simulation and free energy calculations are employed to characterise the carriers studied. The effectiveness of PLA-PEG-FA and PLA-PEG in delivering Letrozole as an aromatase inhibitor in cancer cells is examined. It is found that in the presence of folic acid, the stability and cell membrane permeability of nanomicelle are increased. Therefore, PLA-PEG-FA can be considered as a versatile carrier that can increase the effectiveness of aromatase inhibitors (such as Letrozole) and reduce their side effects.

SwissBioisostere 2021: updated structural, bioactivity and physicochemical data delivered by a reshaped web interface

At several stages of drug discovery, bioisosteric replacement is a common and efficient practice to find new bioactive chemotypes or to optimize series of molecules toward drug candidates. The critical steps consisting in selecting which molecular moiety should be replaced by which other chemical fragment is often relying on the expertise of specialists. Nowadays, valuable support can be obtained through the wealth of dedicated structural and knowledge data. The present article details the update of SwissBioisostere, a database of >25 millions of unique molecular replacements with data on bioactivity, physicochemistry, chemical and biological contexts extracted from the literature and related resources. The content of the database together with analysis and visualization capacities is freely available at www.swissbioisostere.ch.

Evidence-based complementary and alternative medicine bioinformatics approach through network pharmacology and molecular docking to determine the molecular mechanisms of Erjing pill in Alzheimer's disease

Erjing pill, a Traditional Chinese Medicine (TCM) formulation composed of Polygonatum sibiricum and Lycium chinense, has an important role in the treatment of Alzheimer's disease (AD). However, the underlying mechanisms of the action of Erjing pill in AD have remained elusive. In the present study, the key ingredients of Erjing pill were investigated and the active components and their mechanisms of action on AD were analyzed based on networks pharmacology. By using the TCM and TCM Systems Pharmacology and databases, the components of Erjing pill were screened and the data were captured using Discovery Studio. The SwissTarget webserver database was used to predict the potential protein targets of Erjing pill components for pathologies related to AD. The data were further analyzed with the disease targets of AD based on analysis of the Online Mendelian Inheritance in Man, DiGSeE and Therapeutic Target Database. Subsequent analysis of mechanistic pathways of the screened components and protein targets allowed us to construct a network by using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, which revealed potential molecular mechanisms of Erjing pill against AD. Finally, the protective effect of three active components on neurons was verified using an in vitro injury model of PC12 cells induced by Aβ25-35. The results indicated that 65 bioactive components of Erjing pill, including lauric acid and zederone, and 6 targets, including acetylcholinesterase, butylcholinesterase and amyloid protein precursor, were closely associated with the prevention and treatment of AD. The molecular components of Erjing pill were indicated to be involved in various biological signaling processes, mainly in synaptic signal transmission, transsynaptic signal transmission and chemical synaptic transmission. Furthermore, related pathways targeted by Erjing pill in AD included the regulation of neuroactive ligand-receptor interactions, the PI3K-Akt signaling pathway, serotoninergic synapses, calcium signaling pathways and dopaminergic synapses. A cell viability assay indicated that the compounds (polygonatine A, polygonatine C and 4',5-dihydroxyflavone) assessed were able to significantly improve the survival rate and increase the Ca2+ level in a PC12 cell model of AD induced by amyloid-β25-35. The present study revealed that the mechanisms of action of Erjing pill to prevent and treat AD included a multicompound, multitarget and multipathway regulatory network.

Blockchain and artificial intelligence technology in e-Health

Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.

iCDI-W2vCom: Identifying the Ion Channel-Drug Interaction in Cellular Networking Based on word2vec and node2vec

Ion channels are the second largest drug target family. Ion channel dysfunction may lead to a number of diseases such as Alzheimer’s disease, epilepsy, cephalagra, and type II diabetes. In the research work for predicting ion channel–drug, computational approaches are effective and efficient compared with the costly, labor-intensive, and time-consuming experimental methods. Most of the existing methods can only be used to deal with the ion channels of knowing 3D structures; however, the 3D structures of most ion channels are still unknown. Many predictors based on protein sequence were developed to address the challenge, while most of their results need to be improved, or predicting web servers are missing. In this paper, a sequence-based classifier, called “iCDI-W2vCom,” was developed to identify the interactions between ion channels and drugs. In the predictor, the drug compound was formulated by SMILES-word2vec, FP2-word2vec, SMILES-node2vec, and ECFPs via a 1184D vector, ion channel was represented by the word2vec via a 64D vector, and the prediction engine was operated by the LightGBM classifier. The accuracy and AUC achieved by iCDI-W2vCom via the fivefold cross validation were 91.95% and 0.9703, which outperformed other existing predictors in this area. A user-friendly web server for iCDI-W2vCom was established at http://www.jci-bioinfo.cn/icdiw2v. The proposed method may also be a potential method for predicting target–drug interaction.

Expasy, the Swiss Bioinformatics Resource Portal, as designed by its users

The SIB Swiss Institute of Bioinformatics (https://www.sib.swiss) creates, maintains and disseminates a portfolio of reliable and state-of-the-art bioinformatics services and resources for the storage, analysis and interpretation of biological data. Through Expasy (https://www.expasy.org), the Swiss Bioinformatics Resource Portal, the scientific community worldwide, freely accesses more than 160 SIB resources supporting a wide range of life science and biomedical research areas. In 2020, Expasy was redesigned through a user-centric approach, known as User-Centred Design (UCD), whose aim is to create user interfaces that are easy-to-use, efficient and targeting the intended community. This approach, widely used in other fields such as marketing, e-commerce, and design of mobile applications, is still scarcely explored in bioinformatics. In total, around 50 people were actively involved, including internal stakeholders and end-users. In addition to an optimised interface that meets users' needs and expectations, the new version of Expasy provides an up-to-date and accurate description of high-quality resources based on a standardised ontology, allowing to connect functionally-related resources.

Unraveling the Catha edulis Extract Effects on the Cellular and Molecular Signaling in SKOV3 Cells

Khat (Catha edulis (Vahl) Endl.) is an evergreen flowering shrub used as a stimulant in many regions worldwide including East Africa, the Arabian Peninsula, Europe, and the United States. Chewing leaves of khat induces excitement and euphoria, which are primarily attributed to two major constituents, cathinone and cathine. Khat also contains other important constituents such as cathedulins. A considerable number of studies reported side effects induced by the khat extracts to both embryos and adults. These include teratogenicity and developmental retardation, oral cancer and ulcers, high blood pressure, and myocardial infarction. So far, little attention has been paid to the effects of khat extracts on the molecular signaling interactions. We aimed in this study to investigate this through evaluating the effects of khat extracts on SKOV3, a human ovarian adenocarcinoma cell line. We show, by in vitro assays, that khat induces several cellular defects including reduced cell size, cell membrane damage, and apoptosis. At high khat extract concentrations, the cell metabolic activity, cell cycle, and cellular proliferation were affected. RT-qPCR analysis showed an increase in the gene expression of the apoptotic marker BAX, the tumor suppressor p53, and the inflammatory cytokine IL-6. Protein expression analysis by immunostaining showed downregulation of β-catenin, E-cadherin, and Ki-67 and upregulation of FZD8 and SPRY2, suggesting that Wnt and FGF signaling were implicated. SwissTargetPrediction in silico analysis showed that khat constituents cathine, cathinone, catheduline K2, and catheduline E5 bind to family A G-protein-coupled receptor, cause many neurological diseases and disorders such as Alzheimer's, schizophrenia, depression, and anxiety, and induce many ovarian cancer-related diseases. The analysis also showed that important signaling pathways such as CREB, Wnt, FGF, IL-6, and ERK/MAPK, and that of the endometrial cancer, and cell cycle were implicated. Upstream regulators of cathine and cathinone were found to potentially target several molecules including interleukin-8, MMP2, PLAU, and micro-RNAs. In conclusion, khat induces significant cellular and molecular changes that could potentially cause a wide range of serious diseases and syndromes. Such an impact could have a heavy burden on the health care system in the countries where khat is consumed.

An Integrative Pharmacology-Based Pattern to Uncover the Pharmacological Mechanism of Ginsenoside H Dripping Pills in the Treatment of Depression

Objectives: To evaluate the pharmacodynamical effects and pharmacological mechanism of Ginsenoside H dripping pills (GH) in chronic unpredictable mild stress (CUMS) model rats. Methods: First, the CUMS-induced rat model was established to assess the anti-depressant effects of GH (28, 56, and 112 mg/kg) by the changes of the behavioral indexes (sucrose preference, crossing score, rearing score) and biochemical indexes (serotonin, dopamine, norepinephrine) in Hippocampus. Then, the components of GH were identified by ultra-performance liquid chromatography-iron trap-time of flight-mass spectrometry (UPLC/IT-TOF MS). After network pharmacology analysis, the active ingredients of GH were further screened out based on OB and DL, and the PPI network of putative targets of active ingredients of GH and depression candidate targets was established based on STRING database. The PPI network was analyzed topologically to obtain key targets, so as to predict the potential pharmacological mechanism of GH acting on depression. Finally, some major target proteins involved in the predictive signaling pathway were validated experimentally. Results: The establishment of CUMS depression model was successful and GH has antidepressant effects, and the middle dose of GH (56 mg/kg) showed the best inhibitory effects on rats with depressant-like behavior induced by CUMS. Twenty-eight chemical components of GH were identified by UPLC/IT-TOF MS. Subsequently, 20(S)-ginsenoside Rh2 was selected as active ingredient and the PPI network of the 43 putative targets of 20(S)-ginsenoside Rh2 containing in GH and the 230 depression candidate targets, was established based on STRING database, and 47 major targets were extracted. Further network pharmacological analysis indicated that the cAMP signaling pathway may be potential pharmacological mechanism regulated by GH acting on depression. Among the cAMP signaling pathway, the major target proteins, namely, cAMP, PKA, CREB, p-CREB, BDNF, were used to verify in the CUMS model rats. The results showed that GH could activate the cAMP-PKA-CREB-BDNF signaling pathway to exert antidepressant effects. Conclusions: An integrative pharmacology-based pattern was used to uncover that GH could increase the contents of DA, NE and 5-HT, activate cAMP-PKA-CREB-BDNF signaling pathway exert antidepressant effects.

Selective Cytotoxicity of Complexes with N,N,N-Donor Dipodal Ligand in Tumor Cells

The present article demonstrates selective cytotoxicity against cancer cells of the complexes [Co(L^D)₂]I₂∙CH₃OH (1), [CoL^D(NCS)₂] (2) and [VOL^D(NCS)₂]∙C₆H₅CH₃ (3) containing the dipodal tridentate ligand LD = N,N-bis(3,5-dimethylpyrazol-1-ylmethyl)amine), formed in situ. All tested complexes expressed greater anticancer activities and were less toxic towards noncancerous cells than cisplatin. Cobalt complexes (1 and 2) combined high cytotoxicity with selectivity towards cancer cells and caused massive tumour cell death. The vanadium complex (3) induced apoptosis specifically in cancer cells and targeted proteins, controlling their invasive and metastatic properties. The presented experimental data and computational prediction of drug ability of coordination compounds may be helpful for designing novel and less toxic metal-based anticancer species with high specificities towards tumour cells.

2-Butyl-6-phenyl-4,5-dihydropyridazin-3(2H)-one: Synthesis, In Silico Studies and In Vitro Cyclooxygenase-2 Inhibitory Activity

Pyridazinone derivatives are a great template for developing cyclooxygenase-2 (COX-2) inhibitors. The 2-butyl-6-phenyl-4,5-dihydropyridazin-3(2H)-one was prepared by reacting 6-phenyl-4,5-dihydropyridazin-3(2H)-one with n-butyl bromide in the presence of potassium carbonate. The structure of the compound was confirmed based on its FTIR, 1H-NMR, 13C-NMR, and Mass data. The molecular docking studies assessed the COX-2 binding capability of the synthesized compound. The in silico physicochemical and pharmacokinetic parameters of this compound concerning selected drugs were also calculated. The COX-2/COX-1 analysis revealed the synthesized compound as a novel potent COX-2 inhibitor, in comparison to indomethacin, with a promising physicochemical and pharmacokinetic profile.

Decoding the Role of Sphingosine-1-Phosphate in Asthma and Other Respiratory System Diseases Using Next Generation Knowledge Discovery Platforms Coupled With Luminex Multiple Analyte Profiling Technology

Sphingosine-1-phosphate (S1P) is a pleiotropic sphingolipid derived by the phosphorylation of sphingosine either by sphingosine kinase 1 (SPHK1) or SPHK2. Importantly, S1P acts through five different types of G-protein coupled S1P receptors (S1PRs) in immune cells to elicit inflammation and other immunological processes by enhancing the production of various cytokines, chemokines, and growth factors. The airway inflammation in asthma and other respiratory diseases is augmented by the activation of immune cells and the induction of T-helper cell type 2 (Th2)-associated cytokines and chemokines. Therefore, studying the S1P mediated signaling in airway inflammation is crucial to formulate effective treatment and management strategies for asthma and other respiratory diseases. The central aim of this study is to characterize the molecular targets induced through the S1P/S1PR axis and dissect the therapeutic importance of this key axis in asthma, airway inflammation, and other related respiratory diseases. To achieve this, we have adopted both high throughput next-generation knowledge discovery platforms such as SwissTargetPrediction, WebGestalt, Open Targets Platform, and Ingenuity Pathway Analysis (Qiagen, United States) to delineate the molecular targets of S1P and further validated the upstream regulators of S1P signaling using cutting edge multiple analyte profiling (xMAP) technology (Luminex Corporation, United States) to define the importance of S1P signaling in asthma and other respiratory diseases in humans.