Perturbations of pathway co-expression network identify a core network in metastatic breast cancer

Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets

Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.

Benzothiazole derivatives as p53-MDM2 inhibitors: in-silico design, ADMET predictions, molecular docking, MM-GBSA Assay, MD simulations studies

Breast cancer stands as the most prevalent malignancy among the female populace. One of the pivotal domains in the therapeutic landscape of breast cancer revolves around the precise targeting of the p53-MDM2 inhibitory pathway. The advent of p53-MDM2 inhibition in the context of developing treatments for breast cancer marks a significant stride. In the quest for enhancing the efficacy of p53-MDM2 inhibition against breast cancer, a new series of benzothiazole compounds (B1-B30) was designed through in-silico methodologies in the present work. Using Schrodinger Maestro, the compounds underwent molecular docking assessments against the p53-MDM2 target (PDB: 4OGT). Compared to reference compounds, B25 and B12 exhibited notably elevated glide scores. Extensive in-silico studies, including ADMET and toxicity evaluations, were performed to predict pharmacokinetics, drug likeness, and toxicity. All compounds adhered to Lipinski criteria, signifying favorable oral drug properties. The MM-GBSA analysis indicated consistent binding free energies. Molecular dynamics simulations for B25 over 200 ns assessed complex stability and interactions. In summary, these compounds exhibit potential for future cancer therapy medication development.Communicated by Ramaswamy H. Sarma.

Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data

The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.

Integration of Molecular Docking and In Vitro Studies: A Powerful Approach for Drug Discovery in Breast Cancer

Molecular docking in the pharmaceutical industry is a powerful in silico approach for discovering novel therapies for unmet medical needs predicting drug–target interactions. It not only provides binding affinity between drugs and targets at the atomic level, but also elucidates the fundamental pharmacological properties of specific drugs. The purpose of this review was to illustrate newer and emergent uses of docking when combined with in vitro techniques for drug discovery in metastatic breast cancer. We grouped the selected articles into five main categories; namely, systematic repositioning of drugs, natural drugs, new synthesized molecules, combinations of drugs, and drug latentiation. We focused on new promising drugs that have a good affinity with their targets, thus inducing a favorable biological response. This review suggests that the integration of molecular docking and in vitro studies can accelerate cancer drug discovery showing a good consistency of the results between the two approaches.

A protein interaction map identifies existing drugs targeting SARS-CoV-2

BACKGROUND

Severe acute respiratory syndrome coronavirus (SARS-CoV-2), an emerging Betacoronavirus, is the causative agent of COVID-19. Angiotensin converting enzyme 2 (ACE2), being the main cell receptor of SARS-CoV-2, plays a role in the entry of the virus into the cell. Currently, there are neither specific antiviral drugs for the treatment or preventive drugs such as vaccines.

METHODS

We proposed a bioinformatics analysis to test in silico existing drugs as a fast way to identify an efficient therapy. We performed a differential expression analysis in order to identify differentially expressed genes in COVID-19 patients correlated with ACE-2 and we explored their direct relations with a network approach integrating also drug-gene interactions. The drugs with a central role in the network were also investigated with a molecular docking analysis.

RESULTS

We found 825 differentially expressed genes correlated with ACE2. The protein-protein interactions among differentially expressed genes identified a network of 474 genes and 1130 interactions.

CONCLUSIONS

The integration of drug-gene interactions in the network and molecular docking analysis allows us to obtain several drugs with antiviral activity that, alone or in combination with other treatment options, could be considered as therapeutic approaches against COVID-19.