Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies

Cancer is heterogeneous and dynamic in nature, and its drug resistance, due to high vulnerability to point mutations, and its aberrant pathways are making it quite challenging to efficiently address and manage. Natural products have always been a mainstream source of anticancer drugs due to the modulation of multiple hallmark traits of cancer. Nevertheless, the anticancer drugs available today are not efficient in treating patients with advanced-stage cancers and also exert quite serious side effects. One thing that clinicians and researchers have thoroughly understood so far is that highly specific drugs with only a single mechanism of action are not a prime choice. Thus, polypharmacologically active drugs with detailed knowledge about the genes and pathways that they modulate are what researchers and clinicians are presently looking for. Gene and pathway knowledge will also help us to understand the possible side effects of any drugs in advance since most genes are not specific to a particular location or responsible only for a particular disease. Most genes influence other genes as they are connected through the related pathways. Cheminformatics- and bioinformatics-based studies such as network-pharmacology-based studies, ADMET prediction, molecular docking, and molecular dynamics simulations are quite important in aiding and expediting research towards the translational level.

Translational aspects of preclinical studies to clinical level are of utmost importance for natural products to progress from bench to bedside.