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A Machine Learning-Driven Pathophysiology Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations.
Limbu, S.; Glasgow, E.; Block, T.; Dakshanamurthy, S. A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations. Toxics2024, 12, 481.
Limbu, S.; Glasgow, E.; Block, T.; Dakshanamurthy, S. A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations. Toxics 2024, 12, 481.
Limbu, S.; Glasgow, E.; Block, T.; Dakshanamurthy, S. A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations. Toxics2024, 12, 481.
Limbu, S.; Glasgow, E.; Block, T.; Dakshanamurthy, S. A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations. Toxics 2024, 12, 481.
Abstract
Environmental chemicals, including PFAS (per- and polyfluoroalkyl substances), pesticides, industrial chemicals, and consumer products, commonly exist as mixtures. These substances are frequently exposed or co-exposed in varying concentrations, leading to potentially hazardous health effects such as cancer in humans. Thus, understanding the dose-dependent toxicity of chemical mixtures is important for assessing health risks. In this context, comprehensive methods for assessing the toxicity and identifying the mechanisms of harmful chemical mixtures are currently lacking. Here, the dose-dependent toxicity assessments of chemical mixtures are performed in three methodologically distinct phases. In the first phase, we evaluated our machine learning method (AI-HNN) and pathophysiology method (CPTM) for predicting toxicity. In the second phase, we integrated AI-HNN and CPTM to establish a comprehensive new approach method (NAM) framework called AI-CPTM, targeted at refining prediction accuracy and providing a comprehensive understanding of toxicity mechanisms. The third phase involved experimental validations of the AI-CPTM predictions. Initially, we developed binary, multiclass classification, and regression models to predict binary, categorical toxicity, and toxic potencies, using nearly a thousand experimental mixtures. This empirical dataset was expanded with virtual mixtures compensating the lack of experimental data and broadening the scope of the dataset. For comparison, we also developed additional machine learning models based on RF, Bagging, AdaBoost, SVR, GB, KR, DT, KN, and Consensus methods. The models achieved overall accuracies of over 80% with AUC values exceeding 90%. The regression models achieved an R2 >0.88. In the second phase, we innovatively integrated HNN-derived toxicity predictions with Z-scores from CPTM, resulting method called AI-CPTM. In the final phase, we demonstrated the superior performance of AI-CPTM through rigorous literature and statistical performance validations. Additionally, the predictive capability of AI-CPTM, including for PFAS mixtures and their interaction effects, was demonstrated by experimental validations using dose-response zebrafish embryo toxicity assays. Overall, the AI-CPTM approach significantly improves upon the limitations of standalone models and has shown extensive enrichments in the identification of toxic chemicals and mixtures. Further experimental studies involving human cell models, patient-derived xenografts, and investigations into the toxicity of multiple mixtures are currently underway.
Keywords
Environmental chemicals; mixtures; PFAS; NAM method; Toxicity; zebrafish toxicity
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
