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Formation of Covalent DNA Adducts by Enzymatically Activated Carcinogens and Drugs In Vitro and Their Determination by 32P-postlabeling
Published on: March 20, 2018
Designing safer drugs: (Q)SAR-based identification of mutagens and carcinogens
Romualdo Benigni1, Romano Zito
1Laboratory of Comparative Toxicology and Ecotoxicology, Istituto Superiore di Sanita, Viale Regina Elena 299-00161 Rome, Italy. rbenigni@iss.it
Abstract:
Mutagenicity and carcinogenicity are chronic effects of primary concern for human health. A unifying approach to their mechanistic understanding is the recognition that many chemicals provoke both effects by electrophilic attack to the biological macromolecules, as such or after metabolism (genotoxic carcinogenicity). QSARs of individual classes of genotoxic carcinogens have contributed to the elucidation of the chemical determinants of this activity. Little work has been done on the epigenetic carcinogens, acting through non-genotoxic, very specific mechanisms. However, the existing QSARs for individual chemical classes are too few to be of real usefulness in the screening of masses of candidate drugs. Models for predicting the carcinogenicity of "any type" of chemicals have been proposed: prospective prediction exercises pointed to the serious limitations of most of these approaches. The best alternative is provided by panels of human experts. The above prediction exercises considered samples of general chemicals, thus we specifically addressed in this paper the issue of pharmaceutical drugs. We applied our expert knowledge to a database of drugs whose carcinogenicity/noncarcinogenicity status was known. Whereas most of the noncarcinogens were correctly identified, our prediction of carcinogens was less successful than with the general chemicals. Several carcinogenic drugs did not show recognized structural alerts, and supposedly acted by epigenetic mechanisms. Whereas the contribution of human experts is highly valuable in this phase (e.g. priority setting), more work is necessary on: a) epigenetic carcinogens; b) efficient computerized models.
Insights
Predicting drug carcinogenicity is challenging, especially for epigenetic mechanisms. Expert knowledge aids in identifying non-carcinogenic drugs but requires further development of computational models for accurate carcinogen prediction.
Area of Science:
- Toxicology
- Computational Chemistry
- Drug Safety
Background:
- Mutagenicity and carcinogenicity are significant chronic health concerns.
- Genotoxic carcinogenicity involves electrophilic attack on macromolecules, with Quantitative Structure-Activity Relationships (QSARs) aiding understanding.
- Epigenetic carcinogens act via non-genotoxic mechanisms, with limited QSAR development.
Purpose of the Study:
- To evaluate the effectiveness of expert knowledge in predicting the carcinogenicity of pharmaceutical drugs.
- To identify limitations in current prediction models for both genotoxic and epigenetic carcinogens within drug development.
- To highlight the need for improved computational models and further research into epigenetic carcinogens.
Main Methods:
- Application of expert knowledge to a curated database of drugs with known carcinogenicity/non-carcinogenicity status.
- Analysis of prediction accuracy for both carcinogenic and non-carcinogenic drugs.
- Identification of drugs lacking structural alerts, suggesting potential epigenetic mechanisms.
Main Results:
- Expert knowledge accurately identified most non-carcinogenic drugs.
- Prediction of carcinogenic drugs was less successful compared to general chemicals.
- Several carcinogenic drugs lacked recognized structural alerts, indicating possible epigenetic modes of action.
Conclusions:
- Human expert input is valuable for drug carcinogenicity assessment, particularly for prioritizing compounds.
- Current prediction models and expert systems show limitations in identifying all carcinogenic drugs, especially those acting epigenetically.
- Further research is crucial for developing efficient computational models for epigenetic carcinogens and improving overall drug safety prediction.
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