Empirical Regioselectivity Models for Human Cytochromes P450 3A4, 2D6, and 2C9

Cytochromes P450 3A4, 2D6, and 2C9 metabolize a large fraction of drugs. Knowing where these enzymes
will preferentially oxidize a molecule, the regioselectivity, allows medicinal chemists to plan how best to
block its metabolism. We present QSAR-based regioselectivity models for these enzymes calibrated against
compiled literature data of drugs and drug-like compounds. These models are purely empirical and use only
the structures of the substrates, in contrast to those models that simulate a specific mechanism like hydrogen
radical abstraction, and/or use explicit models of active sites. Our most predictive models use three substructure
descriptors and two physical property descriptors. Descriptor importances from the random forest QSAR
method show that other factors than the immediate chemical environment and the accessibility of the hydrogen
affect regioselectivity in all three isoforms. The cross-validated predictions of the models are compared to
predictions from our earlier mechanistic model (Singh et al. J. Med. Chem. 2003, 46, 1330−1336) and
predictions from MetaSite (Cruciani et al. J. Med. Chem. 2005, 48, 6970−6979).

细胞色素P450（Cytochromes P450）3A4、2D6及2C9可代谢绝大多数临床药物。明确这些酶对药物分子的优先氧化位点，即区域选择性（regioselectivity），有助于药物化学家制定最优策略以阻断该药物的代谢进程。本研究针对上述三种酶，构建了基于定量构效关系（Quantitative Structure-Activity Relationship, QSAR）的区域选择性预测模型，模型以已汇编的药物及类药化合物的文献数据进行校准。与那些模拟氢自由基攫取等特定代谢机制、或采用显式活性位点模型的方法不同，本研究构建的模型仅以底物分子结构为输入，且完全基于经验推导。本研究中预测性能最优的模型采用了3种子结构描述符与2种物理性质描述符。通过随机森林QSAR方法得到的描述符重要性分析结果显示，除底物的局部化学环境与氢原子可及性外，其他因素同样会影响三种CYP同工酶（isoforms）对底物的区域选择性。本研究将模型的交叉验证预测结果，与我们此前开发的机制性模型（Singh等，《药物化学杂志》，2003年，第46卷，1330−1336页）以及MetaSite软件的预测结果进行了对比。