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Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
Published on: July 25, 2020
Identification of active or inactive agonists of tumor suppressor protein based on Tox21 library
Bingxin Gui1, Chen Wang1, Xiaotian Xu1
1State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, 2555 Jingyue Street, Changchun 130117 Jilin, PR China.
Abstract:
Exposure of cells to xenobiotic human-made products can lead to genotoxicity and cause DNA damage. It is an urgent need to quickly identify the chemicals that cause DNA damage, and their toxicity should be predicted. In this study, recursive partitioning (RP), binary logistic regression, and one machine learning approach, namely, random forest (RF) classifier, were used to predict the active and inactive compounds of a total 5036 data based on the assay conducted by a β-lactamase reporter gene under control of the p53 response element (p53RE) from Tox21 library. Results show that the binary logistic regression model with a threshold of 0.5 has a high accuracy rate (83%) to distinguish active and inactive compounds. The RF classifier method has satisfactory results, with an accuracy rate (84.38%) approximately higher than that of binary logistic regression. The models established can identify compounds that induce DNA damage and activate p53, and provide a scientific basis for the risk assessment of organic chemicals in the environment.
Insights
Identifying chemicals causing DNA damage is crucial. Machine learning models, including random forest, accurately predict genotoxicity, aiding environmental chemical risk assessment.
Area of Science:
- Toxicology
- Computational Chemistry
- Genetics
Background:
- Xenobiotic compounds can induce genotoxicity and DNA damage.
- Rapid identification and toxicity prediction of DNA-damaging chemicals are essential.
- The p53 pathway is a critical cellular defense against DNA damage.
Purpose of the Study:
- To develop and compare machine learning models for predicting chemical genotoxicity.
- To identify compounds that activate the p53 response element (p53RE).
- To provide a basis for environmental risk assessment of organic chemicals.
Main Methods:
- Utilized a dataset of 5036 compounds from the Tox21 library.
- Employed recursive partitioning (RP), binary logistic regression, and random forest (RF) classifier.
- Assessed compound activity using a β-lactamase reporter gene assay under p53RE control.
Main Results:
- Binary logistic regression achieved 83% accuracy in distinguishing active and inactive compounds.
- The random forest classifier demonstrated higher accuracy at 84.38%.
- Both models effectively identified compounds inducing DNA damage and activating p53.
Conclusions:
- Machine learning models, particularly RF, can accurately predict chemical genotoxicity.
- These models aid in identifying DNA-damaging agents and assessing environmental chemical risks.
- The study provides a foundation for proactive chemical safety evaluations.
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