Lignans: a versatile source of anticancer drugs.

Background: Cancer is considered as the second deadliest disease globally. Plants have continuously offered unique secondary metabolites with remarkable biological applications. Lignans have gained great importance due to their biological activity. Previous studies revealed that the most remarkable bioactivity of lignan class of molecules is anticancer. They are derived from the oxidative dimerization of two phenylpropanoid units. This review covers the isolated anticancer lignans and their mechanistic aspects. Main body: A bibliographic investigation was performed by analyzing the information available on anticancer lignans in the internationally accepted scientific databases including Web of Science, SciFinder, PubMed, Scopus, and Google Scholar. In this review we have tried to sum up the isolated anticancerous lignan, its source, active plant part, extract and various cell lines used to establish different studies. Here we have included a total number of 113 natural lignans. Many studies that mainly performed in human cell lines have reported. Very few plants have been evaluated for their in vivo anticancer activity. Conclusion: It can be concluded that in near future the lignans may be an effective pharmacon for the treatment of cancer. Fruitful areas of future research may be in modifying natural lignans or synthesizing new lignans with structural diversity and potent pharmacological activities. Extensive studies are needed to be done highlighting the mechanism of anticancer action of explored and unexplored plants. The data will definitely attract many researchers to start further experimentation that might lead to the drugs for the cancer treatment.

Molecular insights into a mechanism of resveratrol action using hybrid computational docking/CoMFA and machine learning approach.

A phytoalexin, Resveratrol remains a legendary anticancer drug candidate in the archives of scientific literature. Although earlier wet-lab experiments rendering its multiple biological targets, for example, epidermal growth factors, Pro-apoptotic protein p53, sirtuins, and first apoptosis signal (Fas) receptor, Mouse double minute 2 (MDM2) ubiquitin-protein ligase, Estrogen receptor, Quinone reductase, etc. However, notwithstanding some notable successes, identification of an appropriate Resveratrol target(s) has remained a major challenge using physical methods, and hereby limiting its translation into an effective therapeutic(s). Thus, computational insights are much needed to establish proof-of-concept towards potential Resveratrol target(s) with minimum error rate, narrow down the search space, and to assess a more accurate Resveratrol signaling pathway/mechanism at the starting point. Herein, a brute-force technique combining computational receptor-, ligand-based virtual screening, and classification-based machine learning, reveals the precise mechanism of Resveratrol action. Overall, MDM2 ubiquitin-protein ligase (4OGN.pdb) and co-crystallized quinone reductases 2 (4QOH.pdb) were found two suitable drug targets in the case of Resveratrol derivatives. Indeed, carotenoid cleaving oxygenase together with later twos gave gigantic momentum in guiding the rational drug design of Resveratrol derivatives. These molecular modeling insights would be useful for Resveratrol lead optimization into a more precise science.Communicated by Ramaswamy H. Sarma.