Cancer is a multifactorial disease that continues to increase. Lignans are known to be important anticancer agents. However, due to the structural diversity of lignans, it is difficult to associate anticancer activity with a particular subclass. Therefore, the present study sought to evaluate the association of lignan subclasses with antitumor activity, considering the genetic profile of the variants of the selected targets. To do so, predictive models were built against the targets tyrosine-protein kinase ABL (ABL), epidermal growth factor receptor erbB1 (EGFR), histone deacetylase (HDAC), serine/threonine-protein kinase mTOR (mTOR) and poly [ADP-ribose] polymerase-1 (PARP1). Then, single nucleotide polymorphisms were mapped, target mutations were designed, and molecular docking was performed with the lignans with the best predicted biological activity. The results showed more anticancer activity in the dibenzocyclooctadiene, furofuran and aryltetralin subclasses. The lignans with the best predictive values of biological activity showed varying binding energy results in the presence of certain genetic variants.
Keywords: cancer; lignans; single nucleotide polymorphisms; virtual screening.