Potential P-glycoprotein (P-gp) inhibitors from SuperDRUG2 database toward reversing multidrug resistance in cancer treatment: Database mining, molecular dynamics, and binding energy estimations

Abstract

P-glycoprotein (P-gp) transporter is included in the failure of various carcinoma chemotherapeutics because of the multidrug resistance (MDR) phenomenon, in which the chemotherapeutic drugs are eliminated from target cells. Consequently, inhibiting P-gp transporter function is a prospective strategy for cancer treatment. In the current study, the SuperDRUG2 database containing >4600 pharmaceutical compounds was virtually screened toward the P-gp transporter utilizing the docking predictions. For inhibitors with a docking score lower than −10.5 kcal/mol, molecular dynamics (MD) simulations were performed, accompanied by binding energy evaluations using the MM-GBSA approach. In accordance with the MM-GBSA//100 ns MD, angiotensin amide (SD003508), terlipressin (SD002603), argipressin (SD002535), and lanreotide (SD001365) exhibited potential binding affinities against the P-gp transporter with ΔG_binding < −120.0 kcal/mol. The outstanding consistency of the investigated inhibitors inside the P-gp binding pocket was shown by the post-dynamics analyses. Additionally, MD simulations of the inhibitor-P-gp complexes in a POPC membrane environment were conducted to mimic the physiological conditions. These results demonstrated that angiotensin amide, terlipressin, argipressin, and lanreotide are promising P-gp inhibitors and deserve additional in-vitro/in-vivo studies.