Discovery of novel IDO1/TDO2 dual inhibitors: a consensus Virtual screening approach with molecular dynamics simulations, and binding free energy analysis.

The pursuit of effective cancer immunotherapy drugs remains challenging, with overexpression of indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase 2 (TDO2) allowing cancer cells to evade immune attacks. While several IDO1 inhibitors have undergone clinical testing, only three dual IDO1/TDO2 inhibitors have reached human trials. Hence, this study focuses on identifying novel IDO1/TDO2 dual inhibitors through consensus structure-based virtual screening (SBVS). ZINC15 natural products library was refined based on molecular descriptors, and the selected compounds were docked to the holo form IDO1 and TDO2 using two different software programs and ranked according to their consensus docking scores. The top-scoring compounds underwent in silico evaluations for pharmacokinetics, toxicity, CYP3A4 affinity, molecular dynamics (MD) simulations, and MM-GBSA binding free energy calculations. Five compounds (ZINC00000079405/10, ZINC00004028612/11, ZINC00013380497/12, ZINC00014613023/13, and ZINC00103579819/14) were identified as potential IDO1/TDO2 dual inhibitors due to their high consensus docking scores, key residue interactions with the enzymes, favorable pharmacokinetics, and avoidance of CYP3A4 binding. MD simulations of the top three hits with IDO1 indicated conformational changes and compactness, while MM-GBSA analysis revealed strong binding free energy for compounds 10 (ΔG: -20.13 kcal/mol) and 11 (ΔG: -16.22 kcal/mol). These virtual hits signify a promising initial step in identifying candidates as supplementary therapeutics to immune checkpoint inhibitors in cancer treatment. Their potential to deliver potent dual inhibition of IDO1/TDO2, along with safety and favorable pharmacokinetics, makes them compelling. Validation through in vitro and in vivo assays should be conducted to confirm their activity, selectivity, and preclinical potential as holo IDO1/TDO2 dual inhibitors.Communicated by Ramaswamy H. Sarma.

Computational Screening Using a Combination of Ligand-Based Machine Learning and Molecular Docking Methods for the Repurposing of Antivirals Targeting the SARS-CoV-2 Main Protease.

BACKGROUND: COVID-19 is an infectious disease caused by SARS-CoV-2, a close relative of SARS-CoV. Several studies have searched for COVID-19 therapies. The topics of these works ranged from vaccine discovery to natural products targeting the SARS-CoV-2 main protease (Mpro), a potential therapeutic target due to its essential role in replication and conserved sequences. However, published research on this target is limited, presenting an opportunity for drug discovery and development. METHOD: This study aims to repurpose 10692 drugs in DrugBank by using ligand-based virtual screening (LBVS) machine learning (ML) with Konstanz Information Miner (KNIME) to seek potential therapeutics based on Mpro inhibitors. The top candidate compounds, the native ligand (GC-376) of the Mpro inhibitor, and the positive control boceprevir were then subjected to absorption, distribution, metabolism, excretion, and toxicity (ADMET) characterization, drug-likeness prediction, and molecular docking (MD). Protein-protein interaction (PPI) network analysis was added to provide accurate information about the Mpro regulatory network. RESULTS: This study identified 3,166 compound candidates inhibiting Mpro. The random forest (RF) molecular access system ML model provided the highest confidence score of 0.95 (bromo-7-nitroindazole) and identified the top 22 candidate compounds. Subjecting the 22 candidate compounds, the native ligand GC-376, and boceprevir to further ADMET property characterization and drug-likeness predictions revealed that one compound had two violations of Lipinski's rule. Additional MD results showed that only five compounds had more negative binding energies than the native ligand (- 12.25 kcal/mol). Among these compounds, CCX-140 exhibited the lowest score of - 13.64 kcal/mol. Through literature analysis, six compound classes with potential activity for Mpro were discovered. They included benzopyrazole, azole, pyrazolopyrimidine, carboxylic acids and derivatives, benzene and substituted derivatives, and diazine. Four pathologies were also discovered on the basis of the Mpro PPI network. CONCLUSION: Results demonstrated the efficiency of LBVS combined with MD. This combined strategy provided positive evidence showing that the top screened drugs, including CCX-140, which had the lowest MD score, can be reasonably advanced to the in vitro phase. This combined method may accelerate the discovery of therapies for novel or orphan diseases from existing drugs.

Identification of potential targets of the curcumin analog CCA-1.1 for glioblastoma treatment : integrated computational analysis and in vitro study.

The treatment of glioblastoma multiforme (GBM) is challenging owing to its localization in the brain, the limited capacity of brain cells to repair, resistance to conventional therapy, and its aggressiveness. Curcumin has anticancer activity against aggressive cancers, such as leukemia, and GBM; however, its application is limited by its low solubility and bioavailability. Chemoprevention curcumin analog 1.1 (CCA-1.1), a curcumin analog, has better solubility and stability than those of curcumin. In this study, we explored potential targets of CCA-1.1 in GBM (PTCGs) by an integrated computational analysis and in vitro study. Predicted targets of CCA-1.1 obtained using various databases were subjected to comprehensive downstream analyses, including functional annotation, disease and drug association analyses, protein-protein interaction network analyses, analyses of genetic alterations, expression, and associations with survival and immune cell infiltration. Our integrative bioinformatics analysis revealed four candidate targets of CCA-1.1 in GBM: TP53, EGFR, AKT1, and CASP3. In addition to targeting specific proteins with regulatory effects in GBM, CCA-1.1 has the capacity to modulate the immunological milieu. Cytotoxicity of CCA-1.1 was lower than TMZ with an IC50 value of 9.8 µM compared to TMZ with an IC50 of 40 µM. mRNA sequencing revealed EGFR transcript variant 8 was upregulated, whereas EGFRvIII was downregulated in U87 cells after treatment with CCA-1.1. Furthermore, a molecular docking analysis suggested that CCA-1.1 inhibits EGFR with various mutations in GBM, which was confirmed using molecular dynamics simulation, wherein the binding between CCA-1.1 with the mutant EGFR L861Q was stable. For successful clinical translation, the effects of CCA-1.1 need to be confirmed in laboratory studies and clinical trials.

MDM2 is a Potential Target Gene of Glycyrrhizic Acid for Circumventing Breast Cancer Resistance to Tamoxifen: Integrative Bioinformatics Analysis.

BACKGROUND: Tamoxifen is the drug of choice for treating breast cancer, particularly the estrogen receptor-positive luminal A subtype. However, the increased occurrence of Tamoxifen resistance highlights the need to develop an agent to enhance the effectiveness of this drug. OBJECTIVE: Although glycyrrhizic acid (GA) is known to exhibit cytotoxic effects on Michigan Cancer Foundation-7 cells, the specific gene targets and pathways it employs to overcome Tamoxifen resistance are incompletely understood. Therefore, the goal of the present research is to discover the potential targets and pathways of GA by using a bioinformatics approach. METHODS: Differentially expressed genes (DEGs) were identified in the Gene Expression Omnibus NCBI database using microarray data from GSE67916 and GSE85871. Further analyses were performed on these DEGs by using DAVID v6.8, STRING-DB v11.0, and Cytoscape v3.8.0. Analysis of gene alterations was performed using cBioPortal for target validation, and the relevant interaction process was examined via the molecular docking method. RESULTS: Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses identified the PI3K-AKT signaling as the potential target mechanism. Construction of the protein-protein interaction network and analysis of hub genes identified the top 25 hub genes. Genetic alterations were observed in six potential target genes, such as CDK2, MDM2, NF1, SMAD3, PTPN11, and CALM1. Molecular docking analysis demonstrated that the docking score of GA is lower than that of the native ligand of p53. More importantly, 3n the PI3K-AKT signaling pathway is a potential target for overcoming Tamoxifen resistance in breast cancer. CONCLUSION: MDM2 may be a potential gene target of GA and the PI3K-AKT signaling may be a prospective mechanism for overcoming Tamoxifen resistance in breast cancer cells. Additional research is required to validate the findings of this study.

Comprehensive Computational Analysis of Honokiol Targets for Cell Cycle Inhibition and Immunotherapy in Metastatic Breast Cancer Stem Cells.

Breast cancer stem cells (BCSCs) play a critical role in chemoresistance, metastasis, and poor prognosis of breast cancer. BCSCs are mostly dormant, and therefore, activating them and modulating the cell cycle are important for successful therapy against BCSCs. The tumor microenvironment (TME) promotes BCSC survival and cancer progression, and targeting the TME can aid in successful immunotherapy. Honokiol (HNK), a bioactive polyphenol isolated from the bark and seed pods of Magnolia spp., is known to exert anticancer effects, such as inducing cell cycle arrest, inhibiting metastasis, and overcoming immunotherapy resistance in breast cancer cells. However, the molecular mechanisms of action of HNK in BCSCs, as well as its effects on the cell cycle, remain unclear. This study aimed to explore the potential targets and molecular mechanisms of HNK on metastatic BCSC (mBCSC)-cell cycle arrest and the impact of the TME. Using bioinformatics analyses, we predicted HNK protein targets from several databases and retrieved the genes differentially expressed in mBCSCs from the GEO database. The intersection between the differentially expressed genes (DEGs) and the HNK-targets was determined using a Venn diagram, and the results were analyzed using a protein-protein interaction network, hub gene selection, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, genetic alteration analysis, survival rate, and immune cell infiltration levels. Finally, the interaction between HNK and two HNK-targets regulating the cell cycle was analyzed using molecular docking analysis. The identified potential therapeutic targets of HNK (PTTH) included CCND1, SIRT2, AURKB, VEGFA, HDAC1, CASP9, HSP90AA1, and HSP90AB1, which can potentially inhibit the cell cycle of mBCSCs. Moreover, our results showed that PTTH could modulate the PI3K/Akt/mTOR and HIF1/NFkB/pathways. Overall, these findings highlight the potential of HNK as an immunotherapeutic agent for mBCSCs by modulating the tumor immune environment.

Functional network analysis of p85 and PI3K as potential gene targets and mechanism of oleanolic acid in overcoming breast cancer resistance to tamoxifen.

BACKGROUND: Tamoxifen resistance in estrogen receptor positive (ER+) breast cancer therapy increases, which is the leading cause of cancer treatment failure, as it can impair patients' prognoses, cause cancer recurrence, metastasis, and death. Combination therapy with compounds is needed to overcome tamoxifen resistance. Oleanolic acid (OA) was known to increase tamoxifen sensitivity in tamoxifen-resistant breast cancer; however, the molecular mechanism of OA and its involvement in overcoming tamoxifen resistance remain unknown and need further investigation. This study was conducted to identify the potential gene targets and molecular mechanisms of OA in overcoming tamoxifen resistance. RESULTS: A bioinformatic approach for functional network analysis was used in silico by utilizing secondary data in the Gene Expression Omnibus (GEO) database and analyzing them with GEO2R to obtain data on differentially expressed genes (DEGs). The DEG data were further examined with Database for Annotation, Visualization, and Integrated Discovery (DAVID), STRING, cBioPortal website, and Cytoscape with its plugin CytoHubba. Molecular docking was performed to predict the binding properties of OA on the protein encoded by the potential gene. CD44, FGFR2, PIK3R1, and MDM2 were designated as potential target genes (PTGs), and PIK3R1 was suspected as the potential gene for OA to overcome tamoxifen resistance. Molecular docking confirms that OA can inhibit p85 activation. PIK3R1 is suggested to be the potential gene for OA in overcoming tamoxifen resistance in breast cancer therapy. CONCLUSION: The predicted molecular mechanism of OA in overcoming tamoxifen resistance involves inhibiting p85 activation, leading to the inhibition of the downstream activity of the PI3K signaling pathway, causing breast cancer to respond to tamoxifen therapy once again. Results of this study need to be validated by further studies, including in vitro and in vivo.

Identification of potential target genes of honokiol in overcoming breast cancer resistance to tamoxifen.

Background: Honokiol (HON) inhibits epidermal growth factor receptor (EGFR) signaling and increases the activity of erlotinib, an EGFR inhibitor, in human head and neck cancers. In this study, using a bioinformatics approach and in vitro experiments, we assessed the target genes of HON against breast cancer resistance to tamoxifen (TAM). Materials and methods: Microarray data were obtained from GSE67916 and GSE85871 datasets to identify differentially expressed genes (DEGs). DEGs common between HON-treated and TAM-resistant cells were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and protein-protein interaction (PPI) networks were constructed. Selected genes were analyzed for genetic alterations, expression, prognostic value, and receiver operating characteristics (ROC). TAM-resistant MCF-7 (MCF-7 TAM-R) cells were generated and characterized for their resistance toward TAM. A combination of HON and TAM was used for cytotoxicity and gene expression analyses. Molecular docking was performed using the Molecular Operating Environment software. Results: PPI network analysis revealed that FN1, FGFR2, and RET were the top three genes with the highest scores. A genetic alteration study of potential target genes revealed MMP16 and ERBB4 as the genes with the highest alterations among the breast cancer samples. Pathway enrichment analysis of FGFR2, RET, ERBB4, SOX2, FN1, and MMP16 showed that the genetic alterations herein were likely to impact the RTK-Ras pathway. The expression levels of RET, MMP16, and SOX2 were strongly correlated with prognostic power, with areas under the ROC curves (AUC) ​​of 1, 0.8, and 0.8, respectively. The HON and TAM combination increased TAM cytotoxicity in MCF-7 TAM-R cells by regulating the expression of potential target genes ret, ERBB4, SOX2, and FN1, as well as the TAM resistance regulatory genes including HES1, VIM, PCNA, TP53, and CASP7. Molecular docking results indicated that HON tended to bind RET, ErbB4, and the receptor protein Notch1 ankyrin domain more robustly than its native ligand. Conclusion: HON could overcome breast cancer resistance to TAM, potentially by targeting FGFR2, RET, ERBB4, MMP16, FN1, and SOX2. However, further studies are required to validate these results.

Integrative Bioinformatics Study of Tangeretin Potential Targets for Preventing Metastatic Breast Cancer.

Agents that target metastasis are important to improve treatment efficacy in patients with breast cancer. Tangeretin, a citrus flavonoid, exhibits antimetastatic effects on breast cancer cells, but its molecular mechanism remains unclear. Tangeretin targets were retrieved from PubChem, whereas metastatic breast cancer regulatory genes were downloaded from PubMed. In total, 58 genes were identified as potential therapeutic target genes of tangeretin (PTs). GO and KEGG pathway enrichment analyses of PTs were performed using WebGestalt (WEB-based Gene SeT AnaLysis Toolkit). The PPI network was analyzed using STRING-DB v11.0 and visualized by Cytoscape software. Hub genes were selected on the basis of the highest degree score as calculated by the CytoHubba plugin. Genetic alterations of the PTs were analyzed using cBioPortal. The prognostic values of the PTs were evaluated with the Kaplan-Meier plot. The expression of PTs across breast cancer samples was confirmed using GEPIA. The reliability of the PTs in metastatic breast cancer cells was validated using ONCOMINE. Molecular docking was performed to foresee the binding sites of tangeretin with PIK3Cα, MMP9, PTGS2, COX-2, and IKK. GO analysis showed that PTs participate in the biological process of stimulus response, are the cellular components of the nucleus and the membrane, and play molecular roles in enzyme regulation. KEGG pathway enrichment analysis revealed that PTs regulate the PI3K/Akt pathway. Genetic alterations for each target gene were MTOR (3%), NOTCH1 (4%), TP53 (42%), MMP9 (4%), NFKB1 (3%), PIK3CA (32%), PTGS2 (15%), and RELA (5%). The Kaplan-Meier plot showed that patients with low mRNA expression levels of MTOR, TP53, MMP9, NFKB1, PTGS2, and RELA and high expression of PIK3CA had a significantly better prognosis than their counterparts. Further validation of gene expression by using GEPIA revealed that the mRNA expression of MMP9 was significantly higher in breast cancer tissues than in normal tissues, whereas the mRNA expression of PTGS2 showed the opposite. Analysis with ONCOMINE demonstrated that the mRNA expression levels of MMP9 and NFKB1 were significantly higher in metastatic breast cancer cells than in normal tissues. The results of molecular docking analyses revealed the advantage of tangeretin as an inhibitor of PIK3CA, MMP9, PTGS2, and IKK. Tangeretin inhibits metastasis in breast cancer cells by targeting TP53, PTGS2, MMP9, and PIK3CA and regulating the PI3K/Akt signaling pathway. Further investigation is needed to validate the results of this study.