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Published on: November 16, 2016
A Rapid Assessment Model for Liver Toxicity of Macrolides and an Integrative Evaluation for Azithromycin Impurities
Miao-Qing Zhang1, Jing-Pu Zhang1, Chang-Qin Hu2
1Key Laboratory of Biotechnology of Antibiotics, The National Health Commission (NHC), Beijing Key Laboratory of Antimicrobial Agents, Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Abstract:
Impurities in pharmaceuticals of potentially hazardous materials may cause drug safety problems. Macrolide antibiotic preparations include active pharmaceutical ingredients (APIs) and different types of impurities with similar structures, and the amount of these impurities is usually very low and difficult to be separated for toxicity evaluation. Our previous study indicated that hepatotoxicity induced by macrolides was correlated with c-fos overexpression. Here, we report an assessment of macrolide-related liver toxicity by ADMET prediction, molecular docking, structure-toxicity relationship, and experimental verification via detection of the c-fos gene expression in liver cells. The results showed that a rapid assessment model for the prediction of hepatotoxicity of macrolide antibiotics could be established by calculation of the -CDOCKER interaction energy score with the FosB/JunD bZIP domain and then confirmed by the detection of the c-fos gene expression in L02 cells. Telithromycin, a positive compound of liver toxicity, was used to verify the correctness of the model through comparative analysis of liver toxicity in zebrafish and cytotoxicity in L02 cells exposed to telithromycin and azithromycin. The prediction interval (48.1∼53.1) for quantitative hepatotoxicity in the model was calculated from the docking scores of seven macrolide antibiotics commonly used in clinics. We performed the prediction interval to virtual screening of azithromycin impurities with high hepatotoxicity and then experimentally confirmed by liver toxicity in zebrafish and c-fos gene expression. Simultaneously, we found the hepatotoxicity of azithromycin impurities may be related to the charge of nitrogen (N) atoms on the side chain group at the C5 position via structure-toxicity relationship of azithromycin impurities with different structures. This study provides a theoretical basis for improvement of the quality of macrolide antibiotics.
Insights
This study developed a rapid model to predict macrolide antibiotic hepatotoxicity using computational methods and gene expression. The model accurately identified toxic impurities, improving macrolide antibiotic safety.
Area of Science:
- Pharmacology
- Toxicology
- Computational Chemistry
Background:
- Pharmaceutical impurities pose safety risks, particularly in macrolide antibiotics where toxic compounds are present in low, difficult-to-measure amounts.
- Previous research linked macrolide-induced hepatotoxicity to c-fos overexpression, suggesting a potential biomarker.
Purpose of the Study:
- To develop and validate a predictive model for macrolide antibiotic-induced hepatotoxicity.
- To identify potentially hazardous macrolide impurities and understand their structure-toxicity relationships.
Main Methods:
- Utilized ADMET prediction, molecular docking (specifically -CDOCKER interaction energy with FosB/JunD bZIP domain), and structure-toxicity relationship analysis.
- Experimentally verified predictions through c-fos gene expression in L02 liver cells and hepatotoxicity assessments in zebrafish.
- Validated the model using telithromycin and azithromycin, followed by virtual screening of azithromycin impurities.
Main Results:
- Established a rapid assessment model for macrolide hepatotoxicity prediction, confirmed by c-fos gene expression.
- Identified a prediction interval (48.1–53.1) for quantitative hepatotoxicity based on docking scores of seven clinical macrolide antibiotics.
- Discovered that hepatotoxicity of azithromycin impurities may correlate with the charge of nitrogen atoms at the C5 position's side chain.
Conclusions:
- The developed model provides a reliable method for predicting macrolide antibiotic hepatotoxicity.
- This study offers a theoretical foundation for enhancing the quality and safety of macrolide antibiotic preparations.
- Identified specific structural features of impurities linked to liver toxicity, guiding future drug development.
