Recognition of specific types of drug-induced liver injury using random forest algorithm: the importance of individual serum bile acid level

doi:10.5246/jcps.2022.09.057

Abstract

Abstract:

Drug-induced liver injury (DILI) is associated with an imbalance in the homeostasis of bile salts (BAs). However, a clear connection between BAs and different types of DILI remains to be established. In the present study, random forest (RF) machine learning prediction systems were deployed with 17 individual BAs for categorizing DILI. BAs were analyzed via LC-MS/MS in the serum using the model of seven known hepatotoxins (isoniazid, acetaminophen, bendazac, 17α-ethinylestradiol, 1-naphthylisothiocyanate, tetracycline, and ticlopidine), which caused cholestasis, steatosis, and necrosis in rats. The RF model was validated via leave-one-out cross-validation. The importance of each individual BA with respect to prediction ability was determined. The RF model achieved the best prediction performance, producing accuracy values of 0.98, 0.97, and 1.00 for leave-one-out cross-validation, the training set, and the external test set, respectively. The order of descriptor’s importance was obtained, which was TUDCA > GUDCA > TCA > THDCA. The specificity values for necrosis, cholestasis, and steatosis were 0.94, 1.00, and 1.00, respectively. The results indicated the potential value of individual BA level in serum for categorizing DILI. The RF model in the present work was an inexpensive and readily available tool for categorizing DILI.

Key words: Drug-induced liver injury, Bile acids, Random forest

Supporting:

Yongwen Jin, Lili Xi, Yuhui Wei, Xinan Wu. Recognition of specific types of drug-induced liver injury using random forest algorithm: the importance of individual serum bile acid level[J]. Journal of Chinese Pharmaceutical Sciences, 2022, 31(9): 677-688.

Figures/Tables 8

Table 1. The abbreviations of BAs.

Table 2. Hepatotoxins and their doses used in this study.

Table 3. Summary of toxicant and the clinical chemistry. The results are shown as mean ± SD (n = 8).

Table 4. The performance of the built RF model based on internal and external validation.

Table 5. The performance of the best RF model.

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