Introduction: Efficient cancer risk assessment is vital for sustainable practices in pharma, agriculture, and environmental protection. Traditional animal tests for chemical carcinogenicity are time- and cost-consuming. Ongoing efforts focus on pioneering alternative approaches to improve accessibility and reliability in cancer risk assessment.
Objectives: This study aimed to develop a scoring function that can rank chemical compounds based on their potential human carcinogenicity through in silico methods.
Materials and methods: An ensemble of diverse AI/ML models, including Boosting Machines, Graph Neural Networks, and Large Language Models, was used to predict endpoints associated with carcinogenicity, including in vitro mutagenicity, in vitro and in vivo clastogenicity/aneugenicity, and rodent carcinogenicity. A risk score function was developed by applying a weighting strategy to every endpoint. Datasets of human carcinogenic and non-carcinogenic chemicals were used to evaluate the performance of the risk score; the p-value was estimated to indicate the significance of the difference.
Results: It was shown that the mean risk score values differed significantly (p < 0.0001) between human carcinogens and non-carcinogens. Human carcinogens were able to be predicted with an accuracy rate of 73%, which was slightly lower than the 76% accuracy achieved in experimental carcinogenicity studies in mice and significantly surpassed the 65% accuracy obtained in studies with rats.
Conclusion: The devised risk score evaluates the potential of chemicals to induce cancer in humans in silico by integrating information from diverse cancer-related test results, providing an approach that is nearly as accurate as in vivo experiments. Due to its speed and efficiency, the approach developed can effectively be employed for screening large quantities of chemicals. The risk score developed focuses on genotoxic carcinogens. It is anticipated to enhance the versatility and applicability of the approach through the inclusion of additional endpoints associated with non-genotoxic carcinogenesis, as well as the implementation of more sophisticated AI/ML technologies, such as multi-task learning.
Author Contributions
Conceptualization, N.B. and Z.N.; methodology, A.T. and L.A. (Lusine Adunts); software, A.T.; validation, L.K. and Z.N.; formal analysis, G.T. and L.A. (Lilit Apresyan); investigation, G.T. and L.A. (Lilit Apresyan); resources, Z.N.; data curation, L.K.; writing—original draft preparation, N.B.; writing—review and editing, H.S.; visualization, A.T.; supervision, N.B. and H.S.; project administration, N.B.; funding acquisition, N.B. All authors have read and agreed to the published version of the manuscript.
Funding
The research was supported by the Higher Education and Science Committee of MESCS RA (Research project № 23LCG-1F002).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data is available from the corresponding author upon request.
Conflicts of Interest
The authors and Toxometris.ai Inc. declare no conflict of interest.
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