In Silico Study of a Small Bioactive Molecule Targeting Topoisomerase II and P53-MDM2 Complex in Triple-Negative Breast Cancer.
Vishal SinghSuman VermaFiza FatimaSintu Kumar SamantaPritish Kumar VaradwajAmaresh Kumar SahooPublished in: ACS omega (2023)
Treatment of triple-negative breast cancer (TNBC) is very challenging as only few therapeutic options are available, including chemotherapy. Thus, a constant search for new and effective approaches of therapy that could potentially fight against TNBC and mitigate side effects is "turn-on". Recently, multitarget therapy has come up with huge possibilities, and it may possibly be useful to overcome several concurrent challenges in cancer therapy. Herein, we proposed the inhibition of both Topoisomerase II enzyme and p53-MDM2 (p53 cavity in MDM2) protein complex by the same bioactive molecules for multitarget therapy. RNA-seq analysis was performed to get a network of essential proteins involved in the apoptosis pathway by considering the triple-negative breast cancer cell line (MDA-MB-231). All of the untreated duplicate sample data were retrieved from NCBI (GSC149768). Further, via in silico screening, potent bioactive molecules were screened out to target both Topo II and the p53-MDM2 complex. The results of ligand-based screening involving docking, MMGBSA, ADME/T, MD simulation, and PCA suggested that resveratrol, a plant bioactive molecule, showed more potential binding in the same cavity of target proteins compared with doxorubicin for Topo IIα (5GWK) and etoposide for the second protein target (p53-MDM2 complex; 4OQ3) as the control drug. This is also evident from the in vitro validation in case of triple-negative breast cancer cell lines (MDA-MB-231) and Western blotting analysis. Thus, it paves the scope of multitargeting against triple-negative breast cancer.
Keyphrases
- cancer therapy
- rna seq
- molecular docking
- single cell
- drug delivery
- protein protein
- cell cycle arrest
- molecular dynamics
- oxidative stress
- binding protein
- breast cancer cells
- cell death
- squamous cell carcinoma
- endoplasmic reticulum stress
- machine learning
- small molecule
- risk assessment
- electronic health record
- mesenchymal stem cells
- radiation therapy
- artificial intelligence
- signaling pathway
- quantum dots
- combination therapy
- deep learning
- cell therapy
- drug induced
- chemotherapy induced