3D QSAR studies, molecular docking and ADMET evaluation, using thiazolidine derivatives as template to obtain new inhibitors of PIM1 kinase

被引:14
作者
Aouidate, Adnane [1 ]
Ghaleb, Adib [1 ]
Ghamali, Mounir [1 ]
Ousaa, Abdellah [1 ]
Choukrad, M'barek [1 ]
Sbai, Abdelouahid [1 ]
Bouachrine, Mohammed [2 ]
Lakhlifi, Tahar [1 ]
机构
[1] Moulay Ismail Univ, Sch Sci, MCNSL, Meknes, Morocco
[2] Moulay Ismail Univ, High Sch Technol, Meknes, Morocco
关键词
QSAR; Molecular docking; PIM1; Drug design; Thiazolidine; in silico ADMET; PREDICTIVE ABILITY; REGRESSION-MODELS; VALIDATION; POTENT;
D O I
10.1016/j.compbiolchem.2018.03.008
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Proviral Integration site for Moloney murine leukemia virus-1 (PIM1) belongs to the serine/threonine kinase family of Ca2+-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 pIC(50) 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 vahdation((n = ll, R(tes)(2)t=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. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:201 / 211
页数:11
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