Current Computer - Aided Drug Design - Volume 21, Issue 4, 2025

Astragaloside IV (AS-IV) has been shown to have a curative effect on non-small cell lung cancer (NSCLC). This study aimed to elucidate the role of AS-IV in NSCLC cell anlotinib resistance (AR).

The NSCLC/AR cells, resistant to anlotinib, have been produced. The role of AS-IV in the AR of NSCLC cells about the miR-181a-3p/unfolded protein response (UPR)-endoplasmic reticulum associated degradation (ERAD) pathway was then discussed by treating the cells with anlotinib or AS-IV, or by manipulating them with inhibitors or mimics of miR-181a-3p, HRD1 or Derlin-1 overexpression plasmids.

We found that AS-IV could suppress the AR of NSCLC cells. In addition, miR-181a-3p was elevated in NSCLC/AR cells. Functionally, AS-IV limited the AR of NSCLC cells by reducing miR-181a-3p. Further, activation of the UPR-ERAD pathway was correlated with AR in NSCLC cells. Increased sensitivity of NSCLC cells to anlotinib caused by miR-181a-3p inhibitor could be reversed by overexpression of HRD1 or Derlin-1.

This research revealed a promising NSCLC/AR treatment approach by showing that AS-IV exposed NSCLC cells to anlotinib by inhibiting the miR-181a-3p/UPR-ERAD axis.

Dapagliflozin is commonly used to treat type 2 diabetes mellitus (T2DM). However, research into the specific anti-T2DM mechanisms of dapagliflozin remains scarce.

This study aimed to explore the underlying mechanisms of dapagliflozin against T2DM.

Dapagliflozin-associated targets were acquired from CTD, SwissTargetPrediction, and SuperPred. T2DM-associated targets were obtained from GeneCards and DigSee. VennDiagram was used to obtain the overlapping targets of dapagliflozin and T2DM. GO and KEGG analyses were performed using clusterProfiler. A PPI network was built by STRING database and Cytoscape, and the top 30 targets were screened using the degree, maximal clique centrality (MCC), and edge percolated component (EPC) algorithms of CytoHubba. The top 30 targets screened by the three algorithms were intersected with the core pathway-related targets to obtain the key targets. DeepPurpose was used to evaluate the binding affinity of dapagliflozin with the key targets.

In total, 155 overlapping targets of dapagliflozin and T2DM were obtained. GO and KEGG analyses revealed that the targets were primarily enriched in response to peptide, membrane microdomain, protein serine/threonine/tyrosine kinase activity, PI3K-Akt signaling pathway, MAPK signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. AKT1, PIK3CA, NOS3, EGFR, MAPK1, MAPK3, HSP90AA1, MTOR, RELA, NFKB1, IKBKB, ITGB1, and TP53 were the key targets, mainly related to oxidative stress, endothelial function, and autophagy. Through the DeepPurpose algorithm, AKT1, HSP90AA1, RELA, ITGB1, and TP53 were identified as the top 5 anti-targets of dapagliflozin.

Dapagliflozin might treat T2DM mainly by targeting AKT1, HSP90AA1, RELA, ITGB1, and TP53 through PI3K-Akt signaling.

SARS-CoV-2's remarkable capacity for genetic mutation enables it to swiftly adapt to environmental changes, influencing critical attributes, such as antigenicity and transmissibility. Thus, multi-target inhibitors capable of effectively combating various viral mutants concurrently are of great interest.

This study aimed to investigate natural compounds that could unitedly inhibit spike glycoproteins of various Omicron mutants. Implementation of various in silico approaches allows us to scan a library of compounds against a variety of mutants in order to find the ones that would inhibit the viral entry disregard of occurred mutations.

An extensive analysis of relevant literature was conducted to compile a library of chemical compounds sourced from citrus essential oils. Ten homology models representing mutants of the Omicron variant were generated, including the latest 23F clade (EG.5.1), and the compound library was screened against them. Subsequently, employing comprehensive molecular docking and molecular dynamics simulations, we successfully identified promising compounds that exhibited sufficient binding efficacy towards the receptor binding domains (RBDs) of the mutant viral strains. The scoring of ligands was based on their average potency against all models generated herein, in addition to a reference Omicron RBD structure. Furthermore, the toxicity profile of the highest-scoring compounds was predicted.

Out of ten built homology models, seven were successfully validated and showed to be reliable for in silico studies. Three models of clades 22C, 22D, and 22E had major deviations in their secondary structure and needed further refinement. Notably, through a 100 nanosecond molecular dynamics simulation, terpinen-4-ol emerged as a potent inhibitor of the Omicron SARS-CoV-2 RBD from the 21K clade (BA.1); however, it did not show high stability in complexes with other mutants. This suggests the need for the utilization of a larger library of chemical compounds as potential inhibitors.

The outcomes of this investigation hold significant potential for the utilization of a homology modeling approach for the prediction of RBD’s secondary structure based on its sequence when the 3D structure of a mutated protein is not available. This opens the opportunities for further advancing the drug discovery process, offering novel avenues for the development of multifunctional, non-toxic natural medications.

Virtual screening (VS) is essential for analyzing potential drug candidates in drug discovery. Often, this involves the conversion of large volumes of compound data into specific formats suitable for computational analysis. Managing and processing this wealth of information, especially when dealing with vast numbers of compounds in various forms, such as names, identifiers, or SMILES strings, can present significant logistical and technical challenges.

To streamline this process, we developed PyComp, a software tool using Python's PyQt5 library, and compiled it into an executable with Pyinstaller. PyComp provides a systematic way for users to retrieve and convert a list of compound names, IDs (even in a range), or SMILES strings into the desired 3D format.

PyComp greatly enhances the efficiency of data extraction, conversion, and storage processes involved in VS. It searches for similar compounds coupled with its ability to handle misidentified compounds and offers users an easy-to-use, customizable tool for managing large-scale compound data. By streamlining these operations, PyComp allows researchers to save significant time and effort, thus accelerating the pace of drug discovery research.

PyComp effectively addresses some of the most pressing challenges in high-throughput VS: efficient management and conversion of large volumes of compound data. As a user-friendly, customizable software tool, PyComp is pivotal in improving the efficiency and success of large-scale drug screening efforts, paving the way for faster discovery of potential therapeutic compounds.

Alzheimer's disease is a type of dementia that affects neuronal function, leading to a decline in cognitive functions. Serotonin-6 (5HT6) receptors are implicated in the etiology of neurological diseases. 5HT6 receptor antagonists act as anti-dementia agents. PDB ID: 7YS6 represents a membrane protein, and amplification and overexpression of this protein are associated with Alzheimer's disease. Coumarin-fused phenothiazines are significant anti-Alzheimer's agents due to their inhibitory activity on the Serotonin-6 receptor.

Numerous previously unreported Coumarin-substituted Phenothiazines [A2 to A50] were designed using in-silico methods to evaluate their 5HT6 receptor antagonistic activity. Molecular modeling techniques were employed to study the ligands [A2 to A50] in interaction with the Serotonin-6 receptor (PDB ID: 7YS6) using Schrödinger Suite 2019-4.

Molecular modeling studies of the designed ligands [A2 to A50] were conducted using the Glide module. In-silico ADMET screening was performed using the QikProp module, and binding free energy calculations were carried out using the Prime MM-GBSA module within the Schrödinger Suite. The binding affinity of the designed ligands [A2 to A50] towards 5HT6 receptors was determined based on Glide scores. Subsequently, ligand A31 underwent a 100 ns molecular dynamics simulation using the Desmond module of Schrödinger Suite 2020-1, which is based in New York, NY.

The majority of the designed ligands exhibited strong hydrogen bonding interactions and hydrophobic associations with the serotonin-6 receptor, which hinder its activity. These ligands achieved remarkable Glide scores within the range of -4.2859 to -7.7128, in comparison to reference standards such as Idalopirdine (-7.78149), Intepirdine (-5.20103), Latrepirdine (-5.54853), and the co-crystallized ligand (-7.02889). In-silico ADMET properties for these ligands fell within the recommended values for drug-likeness. It is worth noting that the MM-GBSA binding free energy of the most potent inhibitor was positive, indicating a strong binding interaction. Additionally, the dynamic behavior of the protein (7YS6)-ligand (A31) complex was studied by subjecting ligand A31 to a 100 ns molecular dynamics simulation.

The results of this study reveal strong evidence supporting the potential of coumarin-substituted phenothiazine derivatives as effective Serotonin-6 receptor antagonists. Ligands [A2 to A50], which exhibited noteworthy Glide scores, hold promise for significant anti-Alzheimer activity. Further in-vitro and in-vivo investigations are warranted to explore and confirm their therapeutic potential.

This study utilized transcriptomic sequencing combined with cellular and animal models to explore the potential mechanisms of Xuebijing in treating sepsis-induced myocardial dysfunction, also known as sepsis-induced myocardial injury.

We investigated potential targets and regulatory mechanisms of XBJ injection using network pharmacology and RNA sequencing. The effects of XBJ on oxidative stress and apoptosis levels in human cardiac myocytes (AC16) and C57BL/6 mice exposed to lipopolysaccharide (LPS) were evaluated by Enzyme-Linked Immunosorbent Assay (ELISA), fluorescent probe, Fluorescent Quantitative Polymerase Chain Reaction (qPCR), Western Blot, Transmission Electron Microscopy, oxidative stress-related indicators detection kit, flow cytometry, and Immunohistochemistry (IHC).

First, it was verified that XBJ can reduce the deformation of AC16 cardiomyocytes induced by LPS and the production and secretion of ROS (P <0.01). The transcriptome sequencing results showed that the TRIM16 gene was significantly increased after XBJ treatment, and the data of KEGG and GO analyses demonstrated that XBJ could inhibit the pathway expression of oxidative stress damage in AC16 cells, and PCR verified that XBJ could indeed increase the expression level of TRIM16 gene in AC16 cells (P <0.01). Basic animal and cell experiments showed that LPS could inhibit the expression of TRIM16 and NRF2 in cardiomyocytes (P <0.05) and promote the expression of Keap1 (P <0.01), while XBJ could significantly up-regulate the expression levels of TRIM16 and NRF2 (P <0.01) and inhibit the expression of Keap1 (P <0.01), thereby affecting the expression levels of downstream proinflammatory cytokines and alleviating LPS-induced oxidative stress damage. In addition, XBJ also inhibited the expression of the pro-apoptotic proteins Bax and c-caspase3 (P <0.01), promoted the expression of the anti-apoptotic protein Bcl2 (P <0.01), and reduced LPS-induced apoptosis by upregulating TRIM16.

Our comprehensive data demonstrated that TRIM16 is a key gene in the therapeutic action of Xuebijing in sepsis-induced myocardial dysfunction, protecting myocardial cells from injury through antioxidative stress and anti-apoptotic mechanisms.

With a projected mortality toll of 1.4 million in 2019, tuberculosis (TB) continues to be a significant public health concern around the world. Studies of novel treatments are required due to decreased bioavailability, increased toxicity, increased side effects, and resistance of several first- and second-line TB therapies, including isoniazid and ethionamide.

This study reports the synthesis of oxindole-based hybrids as potent InhA inhibitors targeting Mycobacterium tuberculosis. The synthesized compounds (5a-5e and 8a-8c) were evaluated for their anti-mycobacterial activity against Mycobacterium tuberculosis and nontuberculous mycobacteria (NTMs), viz. M. abscessus (ATCC 19977), M. fortuitum (ATCC 6841), and M. chelonae (ATCC 35752) using the Microplate Alamar Blue Assay (MABA). Molecular docking studies were performed using AutoDock Vina to explore the binding interactions of these compounds with the InhA enzyme (PDB: 2NSD). Additionally, biochemical and histopathological studies were conducted to assess the hepatotoxicity of the lead compounds. In-silico molecular properties and ADMET properties of the synthesized compounds were predicted using SwissADME and Deep-PK online tools to assess their drug-likeness.

Among the tested compounds, 8b exhibited significant anti-mycobacterial activity with a minimum inhibitory concentration (MIC = 1 μg/mL) comparable to the reference drug ethambutol. Further, the compound demonstrated a binding affinity and orientation similar to the reference inhibitor 4PI, indicating its potential as a potent InhA inhibitor, and was found to be stabilized within the binding pocket of InhA through H-bonding, hydrophobic and van der Waal’s interactions. Besides, the compounds hepatotoxicity assessment studies depicted that 8b showed no significant liver dysfunction or damage to liver tissues. Additionally, 8b adhered to Lipinski’s rule of five and Veber’s rule, displaying favourable pharmacokinetic and drug-like properties, including high human intestinal absorption, distribution, and acceptable metabolic stability and excretion.

Compound 8b emerged as a promising candidate for further optimization and development as a therapeutic agent for tuberculosis, offering a new avenue for tackling tuberculosis.

The Qing’e Pill (QEP) is widely used to alleviate low back pain and sciatica caused by Intervertebral Disc Degeneration (IDD). However, its active components, key targets, and molecular mechanisms are not fully understood. The aim of this study is to elucidate the molecular mechanisms through which the QEP improves IDD using database mining techniques.

Active components and candidate targets of the QEP were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and the Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine. IDD-related targets were obtained from the GeneCards database, and liver- and kidney-specific genes were retrieved from the BioGPS database. The intersection of these candidate targets was analyzed to identify potential targets for the QEP in IDD. A protein-protein interaction network analysis was performed using STRING and Cytoscape 3.7.2 software. Core targets were further analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking was used to assess the binding affinity of active components to candidate targets, and animal experiments were conducted for validation.

We identified 65 potentially active components of the QEP that corresponded to 1,093 candidate targets, 2,108 IDD-related targets, and 1,113 liver- and kidney-specific genes. Key components included quercetin, berberine, isorhamnetin, and emodin. The primary candidate targets were Wnt5A, CTNNB1, IL-1β, MAPK14, MMP9, and MMP3. The GO and KEGG analyses revealed the involvement of these targets in Wnt signaling, TNF signaling, Wnt receptor activation, Frizzled binding, and Wnt-protein interactions. Molecular docking showed strong binding between these components and their targets. Animal experiments demonstrated that the QEP treatment significantly reduced the expression of Wnt5A, CTNNB1, IL-1β, MAPK14, MMP9, and MMP3 at high, medium, and low doses compared with the model group.

The QEP alleviated IDD by modulating the Wnt/MAPK/MMP signaling pathways and reducing the release and activation of key factors.

The Computer-Aided Drug Discovery (CADD) approach was used to develop a few Epidermal Growth Factor Receptor (EGFR) kinase inhibitors. EGFR kinase expression is highly associated with genomic instability, higher proliferation, lower hormone receptor levels, and HER2 over-expression. It is more common in breast cancer. Thus, EGFR Kinase is one of the main targets in discovering new cancer medicine.

To computationally validate some amides substituted β-amino enones as EGFR inhibitors and to carry out associated in vitro anticancer agents.

We used tools such as molecular docking, MD simulations, DFT calculations, and ADMET predictions in silico to establish a preliminary SAR. In vitro, we used BT474 (ER+HER2+) and MCF-7 (ER-HER2) cell lines along with normal breast cell epithelial cells (MFC-10a) for anticancer studies and EGFR kinase inhibition assay studies. As the Reactive Oxygen Species (ROS) plays the main role in cancer development, we also analyzed the antioxidant potentials of these compounds.

Among the family of eleven amides substituted (Z)-β-amino enones (5a-k), compounds 5b, 5c, 5g, and 5h showed valuable in silico and in vitro bio-activity. Remarkably, the in-silico results almost coincided with in vitro study results.

We recommend compounds 5b, 5c, 5g, and 5h for pre-clinical and clinical evaluation to establish them as future cancer therapeutics.

To a certain extent, traditional Chinese medicine (TCM)-based anesthesia has replaced opiate administration in recent years. Preliminary drug screening has revealed that scopolamine may affect breast cancer (BC) metastasis by an unknown mechanism.

Network pharmacology, bioinformatics, and protein-protein interaction (PPI) topological analysis were implemented to identify the core genes linking scopolamine and BC. The core genes were then subjected to gene expression profiling interactive analysis (GEPIA). The top ten pathways were detected by gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The impact of immune infiltration on the core gene difference and survival analyses was then determined. Molecular docking was then performed on the core genes and the main active components.

Protein kinase 1 (AKT1), epidermal growth factor receptor (EGFR), heat shock protein 90 alpha class A (HSP90AA1), caspase 3 (CASP3), and estrogen receptor 1 (ESR1) were the key genes in the interaction between scopolamine and BC cells. The KEGG enrichment analysis disclosed that the top ten pathways significantly associated with the scopolamine response in BC included “protein glycosylation,” “phosphoinositide 3-kinase (PI3K)-Akt signaling,” “mitogen-activated protein kinase (MAPK) signaling” and others. The AKT1, EGFR, and especially the HSP90AA1 expression levels were correlated with survival in patients with BC. Immune infiltration also influenced the survival outcome. Molecular docking demonstrated that scopolamine bound and formed stable complexes with the protein products of all five aforementioned genes.

Scopolamine has multiple targets regulating BC cell function and may increase the risk of metastasis during treatment. Therefore, it should be preoperatively administered with caution to patients with BC.