Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer

Abstract

Non-small cell lung cancer (NSCLC) remains the leading cause of mortality and morbidity worldwide accounting about 85% of total lung cancer cases. The receptor REarranged during Transfection (RET) plays an important role by ligand independent activation of kinase domain resulting in carcinogenesis. Presently, the treatment for RET driven NSCLC is limited to multiple kinase inhibitors. This situation necessitates the discovery of novel and potent RET specific inhibitors. Thus, we employed high throughput screening strategy to repurpose FDA approved compounds from DrugBank comprising of 2509 molecules. It is worth noting that the initial screening is accomplished with the aid of in-house machine learning model built using IC 50 values corresponding to 2854 compounds obtained from BindingDB repository. A total of 497 compounds (19%) were predicted as actives by our generated model. Subsequent in silico validation process such as molecular docking, MMGBSA and density function theory analysis resulted in identification of two lead compounds named DB09313 and DB00471. The simulation study highlights the potency of DB00471 (Montelukast) as potential RET inhibitor among the investigated compounds. In the end, the half-minimal inhibitory activity of montelukast was also predicted against RET protein expressing LC-2/ad cell lines demonstrated significant anticancer activity. Collective analysis from our study highlights that montelukast could be a promising candidate for the management of RET specific NSCLC. Supplementary Information The online version contains supplementary material available at 10.1007/s12032-022-01924-4.

Orally Administered Drugs and Their Complicated Relationship with Our Gastrointestinal Tract

“…In another study, Brindha et al [ 119 ] successfully implemented machine learning algorithms to predict the efficacy of five drugs based on clinical as well as molecular characteristics of oral squamous cell carcinomas. Furthermore, machine learning has recently been implemented in numerous aspects of oncology, such as drug target prediction [ 120 ], drug repurposing [ 121 ], and prognostic profiles for immunotherapy [ 122 ].…”