Prediction of milk/plasma drug concentration (M/P) ratio using support vector machine (SVM) method.
OBJECTIVE
Development of reliable computational models to predict/classify milk-to-plasma (M/P) drug concentration ratio remains a challenging object. Support vector machine (SVM) method, as a new algorithm, was constructed to distinguish the potential risk of drugs to nursing infants.


METHODS
Each drug was represented by a large pool of descriptors, of which five were found to be most important for constructing the predictive models. Next, two classification models, linear discriminant analysis (LDA) and SVM, were developed with bootstrapping validation based on the selected molecular descriptors.


CONCLUSIONS
The classification accuracy of training set and test set for SVM was 90.63 and 90.00%, respectively. The total accuracy for SVM was 90.48%, which was higher than that of LDA (77.78%). Comparison of the two methods shows that the performance of SVM was better than that of LDA, which implies that the SVM method is an effective tool in evaluating the risk of drugs when experimental M/P ratios have not been investigated.
Zhao
Chunyan
C
Department of Chemistry, Lanzhou University, Lanzhou, 730000, China.
Zhang
Haixia
H
Zhang
Xiaoyun
X
Zhang
Ruisheng
R
Luan
Feng
F
Liu
Mancang
M
Hu
Zhide
Z
Fan
Botao
B
D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D016014:Linear Models; D008895:Milk, Human / Q000737:chemistry*; D015233:Models, Statistical; D004364:Pharmaceutical Preparations / Q000032:analysis* / Q000378:metabolism; D010599:Pharmacokinetics; D010949:Plasma / Q000737:chemistry*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D013329:Structure-Activity Relationship; D009626:Terminology as Topic
D004364:Pharmaceutical Preparations