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| Title |
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| A comprehensive approach for in silico risk assessment of impurities and degradants in drug products | Pranas Japertas, Kiril Lanevskij, Liutauras Juska, Justas Dapkunas, Andrius Sazonovas, Remigijus Didziapetris (ACD/Labs) | Download Poster |
Event Information
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Lecture Schedule
A comprehensive approach for in silico risk assessment of impurities and degradants in drug products
Lecture #: 931
Session: Poster Session 1
Date: Monday, August 29, 2011
Time: 7:30 AM –6:30 PM
Authors: P. Japertas, K. Lanevskij, L. Juska, J. Dapkunas, A. Sazonovas, R. Didziapetris
Abstract: View Abstract
Purpose:
According to FDA Guidance for Industry, assessment of genotoxicity/carcinogenicity by computational methods is sufficient for impurities
in drug products that are present at levels below the ICH qualification thresholds. The aim of this study was to develop a comprehensive
in silico approach to aid this assessment.
Methods:
The overall evaluation of genotoxic and/or carcinogenic potential is based on four predictive models reflecting different mechanisms of
hazardous activity. These include two probabilistic models and a knowledge-based expert system that identifies potentially hazardous structural
fragments that could be responsible for carcinogenic activity of the test molecule. The probabilistic models estimate the compounds’ mutagenic
potential in the Ames test, and the likelihood of causing endocrine system disruption due to interactions with estrogen receptor alpha (ER-α).
Results:
The list of alerting structural fragments was compiled from various literature sources and refined by analyzing their performance on data from
different assays (Ames test, chromosomal aberrations, micronucleus test, mouse lymphoma assay). Sensitivity of the expert system was further
improved using carcinogenicity data obtained from FDA. The final list contained 67 alerting groups, 53 of which accounted for point mutational
and/or clastogenic mechanisms of DNA damage, while the remaining 14 substructures ensured detection of carcinogens acting by non-genotoxic mechanisms.
Together with Ames test and ER-a binding predictors the expert system was able to recognize >90% of compounds marked as potent carcinogens in FDA
database. The proposed approach may serve as a valuable tool for rapid genotoxicity/carcinogenicity profiling of impurities and degradants.
