Importance of structural information in predicting human acute toxicity from in vitro cytotoxicity data.
In this study, we tried to assess the utility of the structural information of drugs for predicting human acute toxicity from in vitro basal cytotoxicity, and to interpret the informative quality and the pharmacokinetic meaning of each structural descriptor. For this, human acute toxicity data of 67 drugs were taken from literature with their basal cytotoxicity data, and used to develop predictive models. A series of multiple linear regression analyses were performed to construct feasible regression models by combining molecular descriptors and cytotoxicity data. We found that although the molecular descriptors alone had only moderate correlation with human acute toxicity, they were highly useful for explaining the discrepancy between in vitro cytotoxicity and human acute toxicity. Among many possible models, we selected the most explanatory models by changing the number and the type of combined molecular descriptors. The results showed that our selected models had high predictive power (R(2): between 0.7 and 0.87). Our analysis indicated that those successful models increased the prediction accuracies by providing the information on human pharmacokinetic parameters which are the major reason for the difference between human acute toxicity and cytotoxicity. In addition, we performed a clustering analysis on selected molecular descriptors to assess their informative qualities. The results indicated that the number of single bonds, the number of hydrogen bond donors and valence connectivity indices are closely related to linking cytotoxicity to acute toxicity, which provides insightful explanation about human toxicity beyond cytotoxicity.
Lee
Soyoung
S
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, South Korea.
Park
Keunwan
K
Ahn
Hee-Sung
HS
Kim
Dongsup
D
D003603:Cytotoxins / Q000633:toxicity*; D006801:Humans; D020128:Inhibitory Concentration 50; D016014:Linear Models; D008954:Models, Biological; D011237:Predictive Value of Tests; D013329:Structure-Activity Relationship
D003603:Cytotoxins