Khan Naveed, Halim Sobia Ahsan, Khan Waqasuddin, Zafar Syed Kashif, Ul-Haq Zaheer
H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.
Natural and Medical Sciences Research Center, University of Nizwa, Nizwa, 616, Oman.
J Mol Graph Model. 2019 Jun;89:199-214. doi: 10.1016/j.jmgm.2019.03.016. Epub 2019 Mar 17.
Obesity is the fifth primary hazard for mortality in the world; hence different therapeutic targets are explored to overcome this problem. Endocannabinoid is identified as the emerging target for the treatment of obesity as Cannabinoid 1 (CB1) receptor over-activation resulted in abdominal obesity. Potent antagonists or inverse agonists for CB1 receptor are the new strategies to develop anti-obesity drugs. Here, ligand-based 3D-QSAR studies was performed on 100 analogues belonging to a class of 1,2,4-tirazole containing diarylpyrazolylcarboxamide as CB1 receptor antagonists. We developed three CoMFA models using different charge schemes, AM1BCC, Gasteiger-Huckle and MMFF. These models produced almost similar statistical results (q = 0.725, 0.692, 0.719 and r = 0.929, 0.924, 0.928 for AM1BCC, Gasteiger-Huckle and MMFF, respectively). The said models were validated through 20 external test set compounds which resulted in significant r values (r = 0.747, 0.743 and 0.745 for AM1BCC, Gasteiger-Huckle and MMFF, respectively). Comparatively, AM1BCC model provided slightly better statistics among all three tested charges scheme models, hence AM1BCC model was further utilized to generate CoMSIA models considering different field combinations. The best selected CoMSIA model also produced substantial q = 0.788, r = 0.916 and r = 0.836 values. Furthermore, two new molecules were designed by modifying the same scaffolds on the basis contour map analysis. The activities of newly designed molecules were predicted through obtained CoMFA model ranked as better than their parent molecules. Moreover, these newly designed compounds were successfully docked on the complex crystal structure of CB1 receptor (PDB ID: 5XRA). The docked conformation of these newly designed inhibitor interacted with Ser173, His178, Lys192, Thr197 and Ser383 mainly by hydrophobic and pi-pi stacking interactions. The obtained results signify the potential of the developed model; suggesting that the models can be useful to test and design potent novel CB1 receptor antagonists or inverse agonists prior to the synthesis.
肥胖是全球第五大主要死亡风险因素；因此，人们探索了不同的治疗靶点来克服这一问题。内源性大麻素被确定为肥胖治疗的新兴靶点，因为大麻素1（CB1）受体过度激活会导致腹部肥胖。CB1受体的强效拮抗剂或反向激动剂是开发抗肥胖药物的新策略。在此，对100种属于含二芳基吡唑基羧酰胺的1,2,4-三唑类的类似物进行了基于配体的3D-QSAR研究，这些类似物作为CB1受体拮抗剂。我们使用不同的电荷方案AM1BCC、Gasteiger-Huckle和MMFF开发了三个CoMFA模型。这些模型产生了几乎相似的统计结果（AM1BCC、Gasteiger-Huckle和MMFF的q分别为0.725、0.692、0.719，r分别为0.929、0.924、0.928）。上述模型通过20种外部测试集化合物进行了验证，得到了显著的r值（AM1BCC、Gasteiger-Huckle和MMFF的r分别为0.747、0.743和0.745）。相比之下，AM1BCC模型在所有三种测试电荷方案模型中提供了稍好的统计数据，因此AM1BCC模型被进一步用于生成考虑不同场组合的CoMSIA模型。最佳选择的CoMSIA模型也产生了可观的q = 0.788、r = 0.916和r = 0.836值。此外，通过基于等高线图分析对相同支架进行修饰，设计了两个新分子。通过获得的CoMFA模型预测新设计分子的活性，结果显示其优于母体分子。此外，这些新设计的化合物成功对接在CB1受体的复杂晶体结构上（PDB ID：5XRA）。这些新设计抑制剂的对接构象主要通过疏水和π-π堆积相互作用与Ser173、His178、Lys192、Thr197和Ser383相互作用。所得结果表明所开发模型的潜力；表明这些模型在合成之前可用于测试和设计强效的新型CB1受体拮抗剂或反向激动剂。
