Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.
The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates.
Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore.
Shangying
S
Chen
Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore.
Peng
P
Zhang
Shanghai Applied Protein Technology Co. Ltd, Research Center for Proteome Analysis, Institute of Biochemistry and cell Biology, Shanghai Institutes for Biological Sciences, Shanghai, 200233, China.
Xin
X
Liu
Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore.
Chu
C
Qin
Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore.
Lin
L
Tao
Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore.
Cheng
C
Zhang
State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan, China.
Sheng Yong
SY
Yang
Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore. Electronic address: phacyz@nus.edu.sg.
Yu Zong
YZ
Chen
Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore. Electronic address: phacwk@nus.edu.sg.
Wai Keung
WK
Chui
D000927:Anticonvulsants / Q000627:therapeutic use*; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results; D060388:Support Vector Machine; D000074389:Therapeutic Index, Drug
D000927:Anticonvulsants
Anticonvulsant; Protective index; Quantitative structure-index relationship; Quantitative structure–activity relationship; Support vector regression