Pharmacophore-based virtual screening for the identification of the novel Src inhibitor SJG-136 against lung cancer cell growth and motility.

Aberrant elevated Src activity is related to lung cancer growth and metastasis. Therefore, the development of potent small molecule inhibitors to target Src kinase is a potential therapeutic strategy for lung cancer. This study aimed to develop a computational model for the in silico screening of Src inhibitors and then assess the suppressive effect of candidate compounds on cellular functions. A 3D-quantitative structure-activity relationship (QSAR) pharmacophore model consisting of two hydrogen bond acceptors and two hydrophobic regions was constructed by using 28 structurally diverse compounds with IC values spanning four orders of magnitude. A National Cancer Institute (NCI) compound dataset was employed for virtual screening by applying the pharmacophore model and molecular docking. Candidate compounds were chosen from the top 20% of scored hits. Among these compounds, the suppressive effects of 30 compounds available in the NCI on Src phosphorylation were validated by using an enzyme-linked immunosorbent assay. Among these compounds, SJG-136, a pyrrolobenzodiazepine dimer, showed a significant inhibitory effect against Src activity in a dose-dependent manner. Further investigations showed that SJG-136 can inhibit lung cancer cell proliferation, clonogenicity, invasion and migration and tumour growth . Furthermore, SJG-136 also had an inhibitory effect on Src-related signaling pathways, including the FAK, paxillin, p130Cas, PI3K, AKT, and MEK pathways. In conclusion, we have established a pharmacophore-based virtual screening approach to identify novel Src inhibitors that can inhibit lung cancer cell growth and motility through suppressing Src-related pathways. These findings may contribute to the development of targeted drugs for lung cancer treatment, such as lead compounds.