QSAR Model of Unbound Brain-to-Plasma Partition Coefficient, Kp,uu,brain: Incorporating P-glycoprotein Efflux as a Variable

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• 作者：Elena Dolgikh ; Ian A. Watson ; Prashant V. Desai ; Geri A. Sawada ; Stuart Morton ; Timothy M. Jones ; Thomas J. Raub
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2016
• 出版时间：November 28, 2016
• 年：2016
• 卷：56
• 期：11
• 页码：2225-2233
• 全文大小：469K
• ISSN：1549-960X

文摘

We report development and prospective validation of a QSAR model of the unbound brain-to-plasma partition coefficient, K_p,uu,brain, based on the in-house data set of ∼1000 compounds. We discuss effects of experimental variability, explore the applicability of both regression and classification approaches, and evaluate a novel, model-within-a-model approach of including P-glycoprotein efflux prediction as an additional variable. When tested on an independent test set of 91 internal compounds, incorporation of P-glycoprotein efflux information significantly improves the model performance resulting in an R² of 0.53, RMSE of 0.57, Spearman’s Rho correlation coefficient of 0.73, and qualitative prediction accuracy of 0.8 (kappa = 0.6). In addition to improving the performance, one of the key advantages of this approach is the larger chemical space coverage provided indirectly through incorporation of the in vitro, higher throughput data set that is 4 times larger than the in vivo data set.