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DOI 10.1007/s11095-019-2582-y

Медицинская библиотека · Pharmaceutical research · 2019

Analyzing the Mechanisms Behind Macrolide Antibiotic-Induced Liver Injury Using Quantitative Systems Toxicology Modeling.

Woodhead Jeffrey L, Yang Kyunghee, Oldach David, MacLauchlin Chris, Fernandes Prabhavathi, Watkins Paul B, Siler Scott Q, Howell Brett A

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Аннотация

PURPOSE: Macrolide antibiotics are commonly prescribed treatments for drug-resistant bacterial infections; however, many macrolides have been shown to cause liver enzyme elevations and one macrolide, telithromycin, has been pulled from the market by its provider due to liver toxicity. This work seeks to assess the mechanisms responsible for the toxicity of macrolide antibiotics.

METHODS

Five macrolides were assessed in in vitro systems designed to test for bile acid transporter inhibition, mitochondrial dysfunction, and oxidative stress. The macrolides were then represented in DILIsym, a quantitative systems pharmacology (QST) model of drug-induced liver injury, placing the in vitro results in context with each compound's predicted liver exposure and known biochemistry.

RESULTS

DILIsym results suggest that solithromycin and clarithromycin toxicity is primarily due to inhibition of the mitochondrial electron transport chain (ETC) while erythromycin toxicity is primarily due to bile acid transporter inhibition. Telithromycin and azithromycin toxicity was not predicted by DILIsym and may be caused by mechanisms not currently incorporated into DILIsym or by unknown metabolite effects.

CONCLUSIONS

The mechanisms responsible for toxicity can be significantly different within a class of drugs, despite the structural similarity among the drugs. QST modeling can provide valuable insight into the nature of these mechanistic differences.

Для цитирования: Woodhead Jeffrey L, Yang Kyunghee, Oldach David, MacLauchlin Chris, Fernandes Prabhavathi, Watkins Paul B, Siler Scott Q, Howell Brett A. Analyzing the Mechanisms Behind Macrolide Antibiotic-Induced Liver Injury Using Quantitative Systems Toxicology Modeling.. Pharmaceutical research. 2019. DOI: 10.1007/s11095-019-2582-y.

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