ABSTRACT
Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.
ABSTRACT
In this study, a polygalacturonase (PGase) producing bacterial strain was isolated and identified as Pseudomonas sp. 13159349 from fruit market soils, and TLC analysis confirmed its pectinolytic activity. Additionally, SSF, Plackett-Burman design (PB), and response surface methodology (RSM) were used to optimize the production of this thermostable and alkalophilic PGase. Wheat bran demonstrated the highest activity (60.13 ± 3.39 U/gm) among the various agricultural wastes used as solid substrates. To further enhance the enzyme production, statistical optimization of media components was investigated using the PB design. Among the 11 variables tested, pH (p < 0.0001), inoculum size (p < 0.0001), incubation time (p < 0.0001), and temperature (p < 0.0041) were found to have a positive effect on the production. The interaction and concentration of the selected factors were examined by RSM, which demonstrated the optimal conditions for maximum production (315.65 U/gm) of the enzyme using wheat bran as the solid substrate were pH 10.5, 61-66 h of incubation, and 6-7.5% inoculum size. The model was highly significant, with a p-value of <0.0001, an F-value of 95.33, and a low CV of 2.31. The RSM model was validated by a laboratory-scale experiment showing 30600 ± 400.32 U/100 gm PGase activity. Thus, SSF and the statistical design of media components resulted in a significant 5.2-fold increase in PGase output solely by using agro waste and optimizing the physical parameters, making this a highly cost-effective bioprocess.
