A Novel Chemometric Method for the Prediction of Human Oral Bioavailability

• Main Authors: Jinyou Duan, Yang Ling, Yonghua Wang, Hua Yu, Chao Huang, Yan Li, Wuxia Zhang, Xue Xu
• Format: Article
• Language: English
• Published: MDPI AG 2012-06-01
• Series: International Journal of Molecular Sciences
• Subjects: oral bioavailability; quantitative structure activity relationship; cytochrome P4503A4 and P4502D6; P-glycoprotein
• Online Access: http://www.mdpi.com/1422-0067/13/6/6964

Orally administered drugs must overcome several barriers before reaching their target site. Such barriers depend largely upon specific membrane transport systems and intracellular drug-metabolizing enzymes. For the first time, the P-glycoprotein (P-gp) and cytochrome P450s, the main line of defense by limiting the oral bioavailability (OB) of drugs, were brought into construction of QSAR modeling for human OB based on 805 structurally diverse drug and drug-like molecules. The linear (multiple linear regression: MLR, and partial least squares regression: PLS) and nonlinear (support-vector machine regression: SVR) methods are used to construct the models with their predictivity verified with five-fold cross-validation and independent external tests. The performance of SVR is slightly better than that of MLR and PLS, as indicated by its determination coefficient (<em>R</em><sup>2</sup>) of 0.80 and standard error of estimate (SEE) of 0.31 for test sets. For the MLR and PLS, they are relatively weak, showing prediction abilities of 0.60 and 0.64 for the training set with SEE of 0.40 and 0.31, respectively. Our study indicates that the MLR, PLS and SVR-based <em>in silico</em> models have good potential in facilitating the prediction of oral bioavailability and can be applied in future drug design.