Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, I-7, Sector-39, Noida-201301, India.
Med Chem. 2020;16(1):52-62. doi: 10.2174/1573406415666190206204853.
EGFR is a clinically approved drug target in cancer. The first generation tyrosine kinase inhibitors targeting L858R mutated EGFR are routinely used to treat non-small cell lung cancer (NSCLC). However, the presence of a secondary mutation (T790M) tenders these inhibitors ineffective and thus results in the relapse of the disease.
New reversible inhibitors are required, which act against T790M/L858R (TMLR) double mutants and overcome resistance.
In the present study, various Fragment based QSAR (G-QSAR) models along with interaction terms have been studied for amino-pyrimidine derivatives having biological activity against TMLR mutant enzyme.
The G-QSAR models developed using partial least squares regression via stepwise forward- backward variable selection technique showed the best results. The model showed a high correlation coefficient (r² = 0.86), cross-validation coefficient (q² = 0.81) and predicted correlation (predicted r² = 0.62), which indicated that the model is robust and predictive. Based on the model, it was revealed that at R1 position increasing saturated carbon (number of -CH atom connected with 3 single bonds i.e. SsssCHcount) and retention index (chi3) is desired for the enhancement of bioactivity. Additionally, at the R2 position, increasing lipophilic character (slogp) and at site R3, the polarizability of compound need to be increased for better inhibitory activity. We further studied the contribution of interactions among significant descriptors in enhancing the activity of the compounds. It revealed that the presence of Sum((R1-SsssCHcount, R2-slogp) and Mult(R1-chi3, R3-polarizabilityAHC) are the most significantly influencing descriptors. We further compared the variation in the most and least active compounds which established that retention of the above properties is essential for imparting significant inhibitory activity to these molecules.
The study provides site specific information wherein chemical group variation influences the inhibitory potency of TMLR amino-pyrimidine inhibitors, which can be used for designing new molecules with the desired activity.
表皮生长因子受体(EGFR)是癌症中一种经临床批准的药物靶点。针对 L858R 突变 EGFR 的第一代酪氨酸激酶抑制剂通常用于治疗非小细胞肺癌(NSCLC)。然而,存在继发性突变(T790M)会使这些抑制剂失效,从而导致疾病复发。
需要新的可逆抑制剂,其针对 T790M/L858R(TMLR)双突变体并克服耐药性。
在本研究中,针对具有针对 TMLR 突变酶的生物活性的氨基嘧啶衍生物,研究了各种基于片段的定量构效关系(G-QSAR)模型以及相互作用项。
通过逐步向前-向后变量选择技术的偏最小二乘回归开发的 G-QSAR 模型显示出最佳结果。该模型显示出较高的相关系数(r²=0.86)、交叉验证系数(q²=0.81)和预测相关系数(预测 r²=0.62),表明该模型稳健且具有预测性。基于该模型,揭示了在 R1 位置增加饱和碳原子(与 3 个单键相连的-CH 原子的数量,即 SsssCHcount)和保留指数(chi3)对于提高生物活性是有利的。此外,在 R2 位置,增加疏水性(slogp),在 R3 位置,需要增加化合物的极化率以提高抑制活性。我们进一步研究了显著描述符之间相互作用对增强化合物活性的贡献。结果表明,Sum((R1-SsssCHcount, R2-slogp) 和 Mult(R1-chi3, R3-polarizabilityAHC) 的存在是最显著的影响描述符。我们进一步比较了最活跃和最不活跃化合物的变化,确定了保留上述性质对于赋予这些分子显著抑制活性是必不可少的。
该研究提供了特定于部位的信息,其中化学基团的变化会影响 TMLR 氨基嘧啶抑制剂的抑制效力,这可用于设计具有所需活性的新分子。