MACHINE LEARNING ANALYSIS UNVEIL WITHANIA SOMNIFERA PHYTOCHEMICALS INGIBITION OF GBM THROUGH PDK-1 TARGETING

Abstract

The grade 4 brain tumour glioblastoma multiforme (GBM) is resistant to standard treatments and has a 100% recurrence rate. By blocking the main pathways involved in tumour feeding and development, GBM can be completely eradicated. The AKTmTOR pathway has drawn a lot of attention for GBM therapy because of the higher levels of p AKT (Phosphorylated Protein Kinase B) seen in recurrent GBM. By being phosphorylated by PDK-1 (3-phosphoinositide-dependent kinase-1), AKT is activated. As a result, PDK-1 activity may be targeted and impaired to stop it from activating AKT. This study uses in-silico analysis to look at different phytochemicals that may be able to target PDK-1. Withania somnifera's phytochemical profile is particularly remarkable in this regard. The high antioxidant capacity of withania somnifera is attributed to the presence of carotenoids, tannins, and phenolic chemicals in the plant. As a result, it is viewed as a viable option for the extraction of anticancer medicinal molecules. The study found four compounds with high binding energies (-11.4, -9.4, -9.8, and -10.4 kcal/mol, respectively) that are comparable to the standard inhibitor 2-(5-[2R]-2-amino-3- phenylpropyloxypyridine-3-yl)8,9-dimethoxybenzo[c][2,7]naphthyridine-4-amine (ID 8I1) compound (-10.1 kcal/mol). Machine learning-based investigations were carried out to evaluate the drug similarity, bioactivity, and bioavailability of the chosen phytochemicals in order to confirm the docking results, indicating their potential as therapeutic agents against GBM. further MACCS descriptors analysis is performed to characterize molecular structure and identify key chemical feature for compound comparison and classification.