Unlocking the potential of approved drugs for the allosteric inhibition of tropomyosin-receptor kinase A using molecular docking and molecular dynamics studies - Mukhtar, Rua M.; Abdelmoniem, Nihal; Elrufaie, Hisham A.; Edris, Alaa; Ghaboosh, Hiba; Mahgoub, Mohanad A.; Garelnabi, Elrashied A. E.; Osman, Wadah; Sherif, Asmaa E.; Ashour, Ahmed; Ghazawi, Kholoud F.; Samman, Waad A.; et al. (Frontiers in Chemistry, 2023)
Tropomyosin-receptor kinase A (TrkA) is the primary isoform among the tropomyosin-receptor kinases that have been associated with human cancer development, contributing to approximately 7.4% of all cancer cases. TrkA represents an attractive target for cancer treatment; however, currently available TrkA inhibitors face limitations in terms of resistance development and potential toxicity. Hence, the objective of this study was to identify new allosteric-approved inhibitors of TrkA that can overcome these challenges and be employed in cancer therapy. To achieve this goal, a screening of 9923 drugs from the ChEMBL database was conducted to assess their repurposing potential using molecular docking. The top 49 drug candidates, exhibiting the highest docking scores (-11.569 to -7.962 kcal/mol), underwent MM-GBSA calculations to evaluate their binding energies. Delanzomib and Tibalosin, the top two drugs with docking scores of -10.643 and -10.184 kcal/mol, respectively, along with MM-GBSA dG bind values of -67.96 and -50.54 kcal/mol, were subjected to 200 ns molecular dynamic simulations, confirming their stable interactions with TrkA. Based on these findings, we recommend further experimental evaluation of Delanzomib and Tibalosin to determine their potential as allosteric inhibitors of TrkA. These drugs have the potential to provide more effective and less toxic therapeutic alternatives. The approach employed in this study, which involves repurposing drugs through molecular docking and molecular dynamics, serves as a valuable tool for identifying novel drug candidates with distinct therapeutic uses. This methodology can contribute to reducing the attrition rate and expediting the process of drug discovery.