3D QSAR studies, molecular docking and ADMET evaluation, using thiazolidine derivatives as template to obtain new inhibitors of PIM1 kinase. (June 2018) - Aouidate, Adnane; Ghaleb, Adib; Ghamali, Mounir; Ousaa, Abdellah; Choukrad, M'barek; Sbai, Abdelouahid; Bouachrine, Mohammed; Lakhlifi, Tahar
Graphical abstract: Highlights: A 3D QSAR study of thiazolidine derivatives as PIM1 inhibitors is reported. The interaction between the studied compounds and PIM1 kinase was carried out using the molecular docking study. New potent PIM1inhibitors were designed based on the 3D-QSAR and molecular docking studies. The newly designed thiazolidine derivatives were evaluated for their in silico ADMET. Abstract: Proviral Integration site for Moloney murine leukemia virus-1 (PIM1) belongs to the serine/threonine kinase family of Ca 2+ -calmodulin-dependent protein kinase (CAMK) group, which is involved in cell survival and proliferation as well as a number of other signal transduction pathways. Thus, PIM1 is regarded as a promising target for treatment of cancers. In the present paper, a three-dimensional quantitative structure activity relationship (3D-QSAR) and molecular docking were performed to investigate the binding between PIM1 and thiazolidine inhibitors in order to design potent inhibitors. The comparative molecular similarity indices analysis (CoMSIA) was developed using twenty-six molecules having pIC50 ranging from 8.854 to 6.011 (IC50 in nM). The best CoMSIA model gave significant statistical quality. The determination coefficient (R 2 ) and Leave-One-Out cross-validation coefficient (Q 2 ) are 0.85 and 0.58, respectively. Furthermore, the predictive ability of this model was evaluated by external validation((n = 11, R 2 test  = 0.72, and MAE = 0.170 log units). The graphical contour maps could provide structural features to improve inhibitory activity. Furthermore, a good consistency between contour maps and molecular docking strongly demonstrates that the molecular modeling is reliable. Based on these satisfactory results, we designed several new potent PIM1 inhibitors and their inhibitory activities were predicted by the molecular models. Additionally, those newly designed inhibitors, showed promising results in the preliminary in silico ADMET evaluations, compared to the best inhibitor from the studied dataset. The results expand our understanding of thiazolidines as inhibitors of PIM1 and could be of great help in lead optimization for early drug discovery of highly potent inhibitors.