Gao Feng, Johnson Diane L, Ekins Sean, Janiszewski John, Kelly Kevin G, Meyer R Daniel, West Michael
Pfizer Global Research and Development, Groton, CT, USA.
J Biomol Screen. 2002 Aug;7(4):373-82. doi: 10.1177/108705710200700410.
Drug-drug interactions involving cytochrome P(450) (CYP) are an important factor in whether a new chemical entity will survive through to the development stage. Therefore, the identification of this potential as early as possible in vitro could save considerable future unnecessary investment. In vitro CYP interaction screening data generated for CYP2C9, CYP2D6, and CYP3A4 were initially analyzed to determine the correlation of IC(50) from 10- and 3-point determinations. A high correlation (r = 0.99) prompted the further assessment of predicting the IC(50) by a single value of percent inhibition at either 10, 3, or 1 microM. Statistical analysis of the initial proprietary compounds showed that there was a strong linear relationship between log IC(50) and percent inhibition at 3 microM, and that it was possible to predict a compound's IC(50) by the percent inhibition value obtained at 3 microM. Additional data for CYP1A2, CYP2C19, and the recombinant CYP2D6 were later obtained and used together with the initial data to demonstrate that a single statistical model could be applicable across different CYPs and different in vitro microsomal systems. Ultimately, the data for all five CYPs and the recombinant CYP2D6 were used to build a statistical model for predicting the IC(50) with a single point. The 95% prediction boundary for the region of interest was about +/- 0.37 on log(10) scale, comparable to the variability of in vitro determinations for positive control IC(50) data. The use of a single inhibitor concentration would enable determination of more IC(50) values on a 96-well plate and result in more economical use of compounds, human liver or expressed enzyme microsomes, substrates, and reagents. This approach would offer the opportunity to increase screening for CYP-mediated drug-drug interactions, which may be important given the challenges provided by the generation of orders of magnitude more new chemical entities in the field of combinatorial chemistry. In addition, the algorithmic approach we propose would obviously be applicable for other in vitro bioactivity and therapeutic target enzyme and receptor screens.
涉及细胞色素P(450)(CYP)的药物相互作用是新化学实体能否进入研发阶段的一个重要因素。因此，尽早在体外鉴定这种可能性可以节省未来大量不必要的投资。最初对为CYP2C9、CYP2D6和CYP3A4生成的体外CYP相互作用筛选数据进行分析，以确定10点和3点测定的IC(50)的相关性。高度相关性(r = 0.99)促使进一步评估通过在10、3或1微摩尔处的单一抑制百分比值预测IC(50)。对最初的专利化合物进行统计分析表明，log IC(50)与3微摩尔处的抑制百分比之间存在很强的线性关系，并且可以通过在3微摩尔处获得的抑制百分比值预测化合物的IC(50)。后来获得了CYP1A2、CYP2C19和重组CYP2D6的其他数据，并与初始数据一起使用，以证明一个单一的统计模型可以适用于不同的CYP和不同的体外微粒体系统。最终，所有五种CYP和重组CYP2D6的数据被用于建立一个单点预测IC(50)的统计模型。在对数(10)尺度上，感兴趣区域的95%预测边界约为+/- 0.37，与阳性对照IC(50)数据的体外测定变异性相当。使用单一抑制剂浓度将能够在96孔板上测定更多的IC(50)值，并更经济地使用化合物、人肝或表达酶微粒体、底物和试剂。这种方法将提供增加对CYP介导的药物相互作用进行筛选的机会，鉴于组合化学领域中数量级更多的新化学实体的产生所带来的挑战，这可能很重要。此外，我们提出的算法方法显然适用于其他体外生物活性以及治疗靶点酶和受体筛选。
