Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction Export

@article{citeulike:3394636, abstract = {Abstract\ \ Quantitative structure–activity relationships (QSAR) methods are urgently needed for predicting ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties to select lead compounds for optimization at the early stage of drug discovery, and to screen drug candidates for clinical trials. Use of suitable QSAR models ultimately results in lesser time-cost and lower attrition rate during drug discovery and development. In the case of ADME/T parameters, drug metabolism is a key determinant of metabolic stability, drug–drug interactions, and drug toxicity. QSAR models for predicting drug metabolism have undergone significant advances recently. However, most of the models used lack sufficient interpretability and offer poor predictability for novel drugs. In this review, we describe some considerations to be taken into account by QSAR for modeling drug metabolism, such as the accuracy/consistency of the entire data set, representation and diversity of the training and test sets, and variable selection. We also describe some novel statistical techniques (ensemble methods, multivariate adaptive regression splines and graph machines), which are not yet used frequently to develop QSAR models for drug metabolism. Subsequently, rational recommendations for developing predictable and interpretable QSAR models are made. Finally, the recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction, including in\ vivo hepatic clearance, in\ vitro metabolic stability, inhibitors and substrates of cytochrome P450 families, are briefly summarized.}, author = {Li, Haiyan and Sun, Jin and Fan, Xiaowen and Sui, Xiaofan and Zhang, Lan and Wang, Yongjun and He, Zhonggui}, citeulike-article-id = {3394636}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10822-008-9225-4}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18574695}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18574695}, citeulike-linkout-3 = {http://www.springerlink.com/content/c1u762x085723133}, day = {1}, doi = {10.1007/s10822-008-9225-4}, journal = {Journal of Computer-Aided Molecular Design}, keywords = {adme, sar}, month = {November}, number = {11}, pages = {843--855}, posted-at = {2009-01-30 08:45:10}, priority = {2}, title = {Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction}, url = {http://dx.doi.org/10.1007/s10822-008-9225-4}, volume = {22}, year = {2008} }

TY - JOUR ID - citeulike:3394636 L3 - citeulike-article-id:3394636 N2 - Abstract  Quantitative structure–activity relationships (QSAR) methods are urgently needed for predicting ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties to select lead compounds for optimization at the early stage of drug discovery, and to screen drug candidates for clinical trials. Use of suitable QSAR models ultimately results in lesser time-cost and lower attrition rate during drug discovery and development. In the case of ADME/T parameters, drug metabolism is a key determinant of metabolic stability, drug–drug interactions, and drug toxicity. QSAR models for predicting drug metabolism have undergone significant advances recently. However, most of the models used lack sufficient interpretability and offer poor predictability for novel drugs. In this review, we describe some considerations to be taken into account by QSAR for modeling drug metabolism, such as the accuracy/consistency of the entire data set, representation and diversity of the training and test sets, and variable selection. We also describe some novel statistical techniques (ensemble methods, multivariate adaptive regression splines and graph machines), which are not yet used frequently to develop QSAR models for drug metabolism. Subsequently, rational recommendations for developing predictable and interpretable QSAR models are made. Finally, the recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction, including in vivo hepatic clearance, in vitro metabolic stability, inhibitors and substrates of cytochrome P450 families, are briefly summarized. IS - 11 JF - Journal of Computer-Aided Molecular Design EP - 855 TI - Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction VL - 22 SP - 843 KW - adme KW - sar AU - Li, Haiyan AU - Sun, Jin AU - Fan, Xiaowen AU - Sui, Xiaofan AU - Zhang, Lan AU - Wang, Yongjun AU - He, Zhonggui PY - 2008/11/01/ UR - http://dx.doi.org/10.1007/s10822-008-9225-4 ER -