Raut Janvi A, Dixit Vaibhav A
Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals & Fertilizers, Govt. of India Sila Katamur (Halugurisuk), P.O.: Changsari, Dist: Kamrup 781101 Guwahati Assam India
RSC Med Chem. 2025 May 2. doi: 10.1039/d4md01012d.
Cytochrome P450 1A2 (CYP1A2) metabolizes ∼10-15% of FDA-approved drugs. Available quantitative structure-activity relationship (QSAR) and machine learning methods offer little design insights to reduce CYP1A2 inhibition. We performed matched molecular pair analysis (MMPA) on the CYP1A2 inhibition dataset (ChEMBL3356) and identified key structural transformations. A chemical context-based analysis was performed using Kramer's method to tackle the limitations of the global MMPA. The global MMPA agreed with earlier QSAR studies (influence of H to F, Me, OMe, and OH transformations). Our results show that the effect of these transformations depends on the local chemical environment. The H to Me transformation reduced the inhibition in three pharmacologically important scaffolds (, indanylpyridine). Structure-based analysis (docking) showed that the interaction of the heteroatoms with Heme-Fe is influenced by useful transformations. Overall, this work presents the first context-based analysis of the CYP1A2 dataset and offers novel medicinal chemistry insights useful for lead optimization.
细胞色素P450 1A2（CYP1A2）参与代谢约10-15%的美国食品药品监督管理局（FDA）批准的药物。现有的定量构效关系（QSAR）和机器学习方法在减少CYP1A2抑制方面提供的设计见解很少。我们对CYP1A2抑制数据集（ChEMBL3356）进行了匹配分子对分析（MMPA），并确定了关键的结构转变。使用克莱默方法进行了基于化学背景的分析，以解决全局MMPA的局限性。全局MMPA与早期的QSAR研究结果一致（氢到氟、甲基、甲氧基和羟基转变的影响）。我们的结果表明，这些转变的效果取决于局部化学环境。氢到甲基的转变降低了三种具有重要药理作用的骨架（茚满基吡啶）中的抑制作用。基于结构的分析（对接）表明，杂原子与血红素铁的相互作用受有用转变的影响。总体而言，这项工作首次对CYP1A2数据集进行了基于背景的分析，并提供了有助于先导化合物优化的新颖药物化学见解。
