Vol 20, No 4 (2024)

Computer-based modelling and simulation are developing as effective tools for supplementing biological data processing and interpretation. It helps to accelerate the creation of dosage forms at a lower cost and with the less human effort required to conduct the work. This paper aims to provide a comprehensive description of the different computer simulation models for various drugs along with their outcomes. The data used are taken from different sources, including review papers from Science Direct, Elsevier, NCBI, and Web of Science from 1995-2020. Keywords like - pharmacokinetic, pharmacodynamics, computer simulation, whole-cell model, and cell simulation, were used for the search process. The use of computer simulation helps speed up the creation of new dosage forms at a lower cost and less human effort required to complete the work. It is also widely used as a technique for researching the structure and dynamics of lipids and proteins found in membranes. It also facilitates both the diagnosis and prevention of illness. Conventional data analysis methods cannot assess and comprehend the huge amount, size, and complexity of data collected by in vitro, in vivo, and ex vivo experiments. As a result, numerous in silico computational e-resources, databases, and simulation software are employed to determine pharmacokinetic (PK) and pharmacodynamic (PD) parameters for illness management. These techniques aid in the provision of multiscale representations of biological processes, beginning with proteins and genes and progressing through cells, isolated tissues and organs, and the whole organism.

Background:Bladder cancer (BCa) is the most common malignancy of the urinary system. Inflammation is critical in the occurrence and development of BCa. The purpose of this study was to identify key genes and pathways of inflammatory bowel disease in BCa through text mining technology and bioinformatics technology and to explore potential therapeutic drugs for BCa.

Methods:Genes associated with BCa and Crohn's disease (CD) were detected using the text mining tool GenClip3, and analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape, and modular analysis was performed using the Molecular Complex Detection plugin (MCODE). Finally, the genes clustered in the first two modules were selected as core genes, and the drug-gene interaction database was used to discover potential therapeutic drugs.

Results:We identified 796 genes shared by \"Bladder cancer\" and \"Crohn's disease\" by text mining. Gene function enrichment analysis yielded 18 enriched GO terms and the 6 most relevant KEGG pathways. A PPI network with 758 nodes and 4014 edges was constructed, and 20 gene modules were obtained using MCODE. We selected the top two gene clusters as core candidate genes. We found that 3 out of 55 selected core genes could be targeted by 26 existing drugs.

Conclusions:The results indicated that CXCL12, FGF2 and FSCN1 are potential key genes involved in CD with BCa. Additionally, 26 drugs were identified as potential therapeutics for BCa treatment and management.

Background:Breast cancer (BC) is the second-leading cause of cancer-related fatalities in women after lung cancer worldwide. The development of BC is significantly influenced by estrogen receptors (ERs). The problem with current cancer treatments is selectivity, target specificity, cytotoxicity, and developing resistance. Thiazole scaffolds are gaining popularity in drug discovery due to their broad range of biological activity. It has the extraordinary capacity to control a variety of cellular pathways, and its potential for selective anticancer activity can be explored.

Objectives:Synthesis and in-silico studies of 4-Phenyl thiazol-2-amine derivatives as anti-breast cancer agents and molecular docking was used to assess the compounds’ capacity to bind ER-α protein target.

Methods:In this study, 4-Phenylthiazol-2-amine derivatives (3a-j) have been synthesized, and using Schrodinger software, molecular docking and ADME studies of the compounds were conducted.

Results:Most of the synthesized compounds have shown dock scores ranging from -6.658 to - 8.911 kcal/mol, which is better than the standard drug tamoxifen (-6.821 kcal/mol). According to molecular docking, all compounds fit in the protein’s active site and have the same hydrophobic pocket as the standard drug tamoxifen. Further, all of the compounds’ ADME properties are below acceptable limits.

Conclusion:Compound 3e showed the best docking score of -8.911. All compounds’ ADME properties are within acceptable limits, and their p/o coefficients fall within a range, suggesting they will all have sufficient absorption at the site of action. These compounds can be evaluated invitro and in-vivo in the future.

Background:Non-small-cell lung cancer (NSCLC) is one of the most prevalent malignancies and poses a significant threat to human health. Qing-Jin-Hua-Tan (QJHT) decoction is a classical herbal remedy that has demonstrated therapeutic effects in various diseases, including NSCLC, and can improve the quality of life of patients with respiratory conditions. However, the mechanism underlying the effect of the QJHT decoction on NSCLC remains unclear and requires further investigation.

Methods:We collected NSCLC-related gene datasets from the GEO database and performed differential gene analysis, followed by using WGCNA to identify the core set of genes associated with NSCLC development. The TCMSP and HERB databases were searched to identify the active ingredients and drug targets, and the core gene target datasets related to NSCLC were merged to identify the intersecting targets of drugs and diseases for GO and KEGG pathway enrichment analysis. We then constructed a protein-protein interaction (PPI) network map of drug diseases using the MCODE algorithm and identified key genes using topology analysis. The disease-gene matrix underwent immunoinfiltration analysis, and we analyzed the association between intersecting targets and immunoinfiltration.

Results:We obtained the GSE33532 dataset that met the screening criteria, and a total of 2211 differential genes were identified using differential gene analysis. We performed GSEA analysis and WGCNA analysis for a crossover with differential genes, resulting in 891 key targets for NSCLC. The drug database was screened to obtain 217 active ingredients and 339 drug targets of QJHT. By constructing a PPI network, the active ingredients of QJHT decoction were intersected with the targets of NSCLC, resulting in 31 intersected genes. Enrichment analysis of the intersection targets showed that 1112 biological processes, 18 molecular functions, and 77 cellular compositions were enriched in GO functions, and 36 signaling pathways were enriched in KEGG pathways. Based on immune-infiltrating cell analysis, we found that the intersection targets were significantly associated with multiple infiltrating immune cells.

Conclusion:Our analysis using network pharmacology and mining of the GEO database revealed that QJHT decoction can potentially treat NSCLC through multi-target and multi-signaling pathways, while also regulating multiple immune cells.

Background:Diabetes mellitus is a metabolic disorder where insulin secretion is compromised, leading to hyperglycemia. DPP-4 is a viable and safer target for type 2 diabetes mellitus. Computational tools have proven to be an asset in the process of drug discovery.

Objective:In the present study, tools like structure-based virtual screening, MM/GBSA, and pharmacokinetic parameters were used to identify natural terpenoids as potential DPP-4 inhibitors for treating diabetes mellitus.

Methods:Structure-based virtual screening, a cumulative mode of elimination technique, was adopted, identifying the top five potent hit compounds depending on the docking score and nonbonding interactions.

Results:According to the docking data, the most important contributors to complex stability are hydrogen bonding, hydrophobic interactions, and Pi-Pi stacking interactions. The dock scores ranged from -6.492 to -5.484 kcal/mol, indicating robust ligand-protein interactions. The pharmacokinetic characteristics of top-scoring hits (CNP0309455, CNP0196061, CNP0122006, CNP0 221869, CNP0297378) were also computed in this study, confirming their safe administration in the human body. Also, based on the synthetic accessibility score, all top-scored hits are easily synthesizable. Compound CNP0309455 was quite stable during molecular dynamic simulation studies.

Conclusion:Virtual database screening yielded new leads for developing DPP-4 inhibitors. As a result, the findings of this study can be used to design and develop natural terpenoids as DPP-4 inhibitors for the medication of diabetes mellitus.