Enhancing Chemical-Induced Human Carcinogenic Risk Evaluation through Advanced AI Technologies

firstₚage Download PDF settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing:    Column Width:    Background: Open AccessAbstract Enhancing Chemical-Induced Human Carcinogenic Risk Evaluation through Advanced AI Technologies † by Nelly BabayanNelly Babayan SciProfiles Scilit Preprints. org Google Scholar 1, 2, *, Helga StopperHelga Stopper SciProfiles Scilit Preprints. org Google Scholar 3, Lusine KhondkaryanLusine Khondkaryan SciProfiles Scilit Preprints. org Google Scholar 1, 2, Ani TevosyanAni Tevosyan SciProfiles Scilit Preprints. org Google Scholar 2, 4, Gohar TadevosyanGohar Tadevosyan SciProfiles Scilit Preprints. org Google Scholar 1, Lilit ApresyanLilit Apresyan SciProfiles Scilit Preprints. org Google Scholar 1, Lusine AduntsLusine Adunts SciProfiles Scilit Preprints. org Google Scholar 2 and Zaven NavoyanZaven Navoyan SciProfiles Scilit Preprints. org Google Scholar 2 1 Institute of Molecular Biology, National Academy of Sciences, Yerevan 0014, Armenia 2 Toxometris. ai Inc. , Glendale, CA 91204, USA 3 Institute of Pharmacology and Toxicology, University of Würzburg, 97070 Würzburg, Germany 4 YerevaNN, 0025 Yerevan, Armenia * Author to whom correspondence should be addressed. † Presented at the 1st International Electronic Conference on Toxics, 20–22 March 2024; Available online: https: //sciforum. net/event/IECTO2024. Proceedings 2024, 102 (1), 12; https: //doi. org/10. 3390/proceedings2024102012 Published: 3 April 2024 Download keyboardₐrrowdown Download PDF Download PDF with Cover Download XML Download Epub Versions Notes Keywords: carcinogenicity; in silico; risk-score; small molecules 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 ContributionsConceptualization, 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. FundingThe research was supported by the Higher Education and Science Committee of MESCS RA (Research project № 23LCG-1F002). Institutional Review Board StatementNot applicable. Informed Consent StatementNot applicable. Data Availability StatementData is available from the corresponding author upon request. Conflicts of InterestThe authors and Toxometris. ai Inc. declare no conflict of interest. Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author (s) and contributor (s) and not of MDPI and/or the editor (s). MDPI and/or the editor (s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https: //creativecommons. org/licenses/by/4. 0/). Share and Cite MDPI and ACS Style Babayan, N. ; Stopper, H. ; Khondkaryan, L. ; Tevosyan, A. ; Tadevosyan, G. ; Apresyan, L. ; Adunts, L. ; Navoyan, Z. Enhancing Chemical-Induced Human Carcinogenic Risk Evaluation through Advanced AI Technologies. Proceedings 2024, 102, 12. https: //doi. org/10. 3390/proceedings2024102012 AMA Style Babayan N, Stopper H, Khondkaryan L, Tevosyan A, Tadevosyan G, Apresyan L, Adunts L, Navoyan Z. Enhancing Chemical-Induced Human Carcinogenic Risk Evaluation through Advanced AI Technologies. Proceedings. 2024; 102 (1): 12. https: //doi. org/10. 3390/proceedings2024102012 Chicago/Turabian Style Babayan, Nelly, Helga Stopper, Lusine Khondkaryan, Ani Tevosyan, Gohar Tadevosyan, Lilit Apresyan, Lusine Adunts, and Zaven Navoyan. 2024. "Enhancing Chemical-Induced Human Carcinogenic Risk Evaluation through Advanced AI Technologies" Proceedings 102, no. 1: 12. https: //doi. org/10. 3390/proceedings2024102012 Article Metrics No No Article Access Statistics Multiple requests from the same IP address are counted as one view.