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

Jeffrey L Woodhead1, Kyunghee Yang2, David Oldach3

  • 1DILIsym Services, Inc., a Simulations Plus Company, 6 Davis Drive, PO Box 12317, Research Triangle Park, North Carolina, 27709, USA. jwoodhead@dilisym.com.

Pharmaceutical Research
|February 9, 2019
PubMed
Abstract

Insights

Macrolide antibiotics can cause liver injury through different mechanisms, such as mitochondrial dysfunction or bile acid transporter inhibition. Quantitative systems pharmacology modeling helps reveal these distinct toxicity pathways for drug-induced liver injury (DILI).

Area of Science:

  • Pharmacology
  • Toxicology
  • Drug-induced liver injury (DILI)

Background:

  • Macrolide antibiotics are crucial for treating bacterial infections but can cause liver enzyme elevations.
  • Telithromycin, a macrolide, was withdrawn due to liver toxicity, highlighting the need to understand macrolide-induced hepatotoxicity.

Purpose of the Study:

  • To investigate the underlying mechanisms of liver toxicity associated with various macrolide antibiotics.
  • To differentiate the specific pathways leading to toxicity within the macrolide class.

Main Methods:

  • In vitro assays were used to evaluate bile acid transporter inhibition, mitochondrial dysfunction, and oxidative stress for five macrolides.
  • Quantitative systems pharmacology (QST) modeling (DILIsym) integrated in vitro data with predicted liver exposure to assess DILI risk.

Main Results:

  • Solithromycin and clarithromycin toxicity were linked to mitochondrial electron transport chain (ETC) inhibition.
  • Erythromycin toxicity was primarily associated with bile acid transporter inhibition.
  • DILIsym did not predict toxicity for telithromycin and azithromycin, suggesting potential unmodeled mechanisms or metabolite effects.

Conclusions:

  • Mechanisms of drug-induced liver injury vary significantly among structurally similar macrolide antibiotics.
  • Quantitative systems pharmacology (QST) modeling is a valuable tool for elucidating these mechanistic differences in drug toxicity.

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