سامانه مدیریت نشریات علمی دانشگاه علوم پزشکی مشهد

Shaki, Fatemeh, Amirkhanloo, Mohamadsadegh, Chahardori, Milad. (1402). The Future and Application of Artificial Intelligence in Toxicology. , 13(1), 21-28. doi: 10.22038/apjmt.2024.78877.1449
Fatemeh Shaki; Mohamadsadegh Amirkhanloo; Milad Chahardori. "The Future and Application of Artificial Intelligence in Toxicology". , 13, 1, 1402, 21-28. doi: 10.22038/apjmt.2024.78877.1449
Shaki, Fatemeh, Amirkhanloo, Mohamadsadegh, Chahardori, Milad. (1402). 'The Future and Application of Artificial Intelligence in Toxicology', , 13(1), pp. 21-28. doi: 10.22038/apjmt.2024.78877.1449
Shaki, Fatemeh, Amirkhanloo, Mohamadsadegh, Chahardori, Milad. The Future and Application of Artificial Intelligence in Toxicology. , 1402; 13(1): 21-28. doi: 10.22038/apjmt.2024.78877.1449

The Future and Application of Artificial Intelligence in Toxicology

Asia Pacific Journal of Medical Toxicology
دوره 13، شماره 1، خرداد 2024، صفحه 21-28    اصل مقاله (381.82 K)
نوع مقاله: Review Article
شناسه دیجیتال (DOI): 10.22038/apjmt.2024.78877.1449
نویسندگان
Fatemeh Shaki1؛ Mohamadsadegh Amirkhanloo2؛ Milad Chahardori* 3
1Pharmaceutical Sciences Research Center, Hemoglobinopathy Institute, Mazandaran University of Medical Sciences, Sari, Iran
2Ph.D. in Private Law, Mehrandish Educational Law Institute, Gorgan, Iran
3Student Research Committee, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran
چکیده
Background: Toxicology is a critical field that is of significant importance to various industries, including pharmaceuticals, environmental protection, and consumer product safety. It's a multidisciplinary science that often involves time-consuming and expensive toxicity tests, which can delay the development of new products and pose significant risks to public health and the environment. Therefore, there is an ever-growing demand for faster and more efficient toxicity evaluations. Artificial Intelligence (AI) has emerged as a promising solution to address these pressing challenges. By enabling the development of machine learning models that can analyze vast amounts of data. This review article focuses on the potential impact of AI in toxicology and its applications in different areas, such as predictive toxicology, development of toxicity screening assays, assessment of chemical mixtures, interpretation of toxicological data, and forensic toxicology.
Methods: This review was done a comprehensive literature search across multiple scientific databases. Searches were conducted in Medline/PubMed, Google Scholar and Web of Science to identify relevant publications. The search terms used included combinations of "artificial intelligence", "toxicology", "toxicity", and related keywords. The final set of articles selected provided a comprehensive overview of the current state of research on the applications of AI techniques in toxicology and chemical risk assessment.
Results: The review highlighted a growing body of research exploring the potential role of AI in accelerating and enhancing various aspects of toxicity assessment and chemical risk evaluation. The reviewed studies demonstrate how AI models can be trained on large datasets of chemical structures, in vitro assay results, and toxicological outcomes to predict the toxicity of novel compounds and other fields such as forensic toxicology. On the other hand, legal and ethical aspects of using AI was investigated.
Conclusion: Overall, the findings of this review highlight this fact that AI can enable faster, more cost-effective, and more accurate toxicity assessments and ultimately leading to improved chemical safety and risk management practices. potential role of AI in accelerating and enhancing various aspects of toxicity assessment and chemical risk evaluation. The reviewed studies demonstrate how AI models can be trained on large datasets of chemical structures, in vitro assay results, and toxicological outcomes to predict the toxicity of novel compounds and other fields such as forensic toxicology. On the other hand, legal and ethical aspects of using AI was investigated.
کلیدواژه‌ها
Toxicology؛ Artificial Intelligence؛ Risk Assessment؛ Predictive Toxicology؛ Legal
مراجع
 

 

  1. Shukla SJ, Huang R, Austin CP, Xia M. The future of toxicity testing: a focus on in vitro methods using a quantitative high-throughput screening platform. Drug discovery today. 2010;15(23-24):997-1007.
  2. Tan H, Jin J, Fang C, Zhang Y, Chang B, Zhang X, et al. Deep Learning in Environmental Toxicology: Current Progress and Open Challenges. ACS ES&T Water. 2023.
  3. Ebrahimnejad P, Amilrkhanloo M, Shaki F. Applications of Artificial Intelligence in Pharmacy Education: Legal and Ethical Challenges. Journal of Mazandaran University of Medical Sciences. 2023;33(227):174-86.
  4. Shaki F, Ashari S, Ahangar N. Melatonin can attenuate ciprofloxacin induced nephrotoxicity: Involvement of nitric oxide and TNF-α. Biomedicine & Pharmacotherapy. 2016;84:1172-8.
  5. Togo MV, Mastrolorito F, Ciriaco F, Trisciuzzi D, Tondo AR, Gambacorta N, et al. Tiresia: An eXplainable artificial intelligence platform for predicting developmental toxicity. Journal of Chemical Information and Modeling. 2022;63(1):56-66.
  6. Hasselgren C, Myatt GJ. Computational toxicology and drug discovery. Computational Toxicology: Methods and Protocols. 2018:233-44.
  7. Shokrzadeh M, Abdi H, Asadollah-Pour A, Shaki F. Nanoceria attenuated high glucose-induced oxidative damage in HepG2 cells. Cell Journal (Yakhteh). 2016;18(1):97.
  8. Sadegh H, Mazloumbilandi M, Chahardouri M. Low-cost materials with adsorption performance. Handbook of ecomaterials. 2017;2.
  9. Romano JD, Hao Y, Moore JH, Penning TM. Automating Predictive Toxicology Using ComptoxAI. Chemical research in toxicology. 2022;35(8):1370-82.
  10. Tran TTV, Surya Wibowo A, Tayara H, Chong KT. Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives. Journal of Chemical Information and Modeling. 2023;63(9):2628-43.
  11. Khaleseh F, Chahardori M, Samadi M. Methylnitrosourea. 2023.
  12. Chahardori M, Haghi-Aminjan H, Samadi M. Methyl disulfide. 2023.
  13. Luechtefeld T, Hartung T. Computational approaches to chemical hazard assessment. Altex. 2017;34(4):459.
  14. Moukheiber L, Mangione W, Moukheiber M, Maleki S, Falls Z, Gao M, et al. Identifying protein features and pathways responsible for toxicity using machine learning and tox21: Implications for predictive toxicology. Molecules. 2022;27(9):3021.
  15. Kurwadkar S, Mandal PK, Soni S. Dioxin: environmental fate and health/ecological consequences: CRC Press; 2020.
  16. Mohapatra S, Griffin D, editors. AI-assisted chemical reaction impurity prediction and propagation. AI for Accelerated Materials Design NeurIPS 2022 Workshop; 2022.
  17. Wu Y, Wang G. Machine learning based toxicity prediction: from chemical structural description to transcriptome analysis. International journal of molecular sciences. 2018;19(8):2358.
  18. Cavasotto CN, Scardino V. Machine learning toxicity prediction: Latest advances by toxicity end point. ACS omega. 2022;7(51):47536-46.
  19. Kavlock R, Dix D. Computational toxicology as implemented by the US EPA: providing high throughput decision support tools for screening and assessing chemical exposure, hazard and risk. Journal of Toxicology and Environmental Health, Part B. 2010;13(2-4):197-217.
  20. Gardiner L-J, Carrieri AP, Wilshaw J, Checkley S, Pyzer-Knapp EO, Krishna R. Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity. Scientific reports. 2020;10(1):9522.
  21. Wu Q, Achebouche R, Audouze K. Computational systems biology as an animal-free approach to characterize toxicological effects of persistent organic pollutants. ALTEX-Alternatives to animal experimentation. 2020;37(2):287-99.
  22. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug discovery today. 2021;26(1):80.
  23. Jeong J, Choi J. Artificial intelligence-based toxicity prediction of environmental chemicals: future directions for chemical management applications. Environmental Science & Technology. 2022;56(12):7532-43.
  24. Perez Santin E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, et al. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. Wiley Interdisciplinary Reviews: Computational Molecular Science. 2021;11(5):e1516.
  25. Ciallella HL, Zhu H. Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity. Chemical research in toxicology. 2019;32(4):536-47.
  26. Fedra K, Weigkricht E. Integrated information systems for technological risk assessment. Computer supported risk management: Springer; 1995. p. 213-32.
  27. Ahmad F, Mahmood A, Muhmood T. Machine learning-integrated omics for the risk and safety assessment of nanomaterials. Biomaterials science. 2021;9(5):1598-608.
  28. Kar S, Leszczynski J. Exploration of computational approaches to predict the toxicity of chemical mixtures. Toxics. 2019;7(1):15.
  29. Moingeon P, editor Applications of artificial intelligence to new drug development. Annales Pharmaceutiques Francaises; 2021.
  30. Burgoon LD. The AOPOntology: a semantic artificial intelligence tool for predictive toxicology. Applied In Vitro Toxicology. 2017;3(3):278-81.
  31. Corradi MP, de Haan AM, Staumont B, Piersma AH, Geris L, Pieters RH, et al. Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies. Biomaterials and biosystems. 2022;7:100061.
  32. Yamagata Y, Yamada H, Horii I. Current status and future perspective of computational toxicology in drug safety assessment under ontological intellection. The Journal of Toxicological Sciences. 2019;44(11):721-35.
  33. Lysenko A, Sharma A, Boroevich KA, Tsunoda T. An integrative machine learning approach for prediction of toxicity-related drug safety. Life science alliance. 2018;1(6).
  34. Cowden J. Computational Toxicology and Risk Assessment.
  35. Fournier M, Vroland C, Megy S, Aguero S, Chemelle JA, Defoort B, et al. In silico genotoxicity prediction by similarity search and machine learning algorithm: optimization and validation of the method for High Energetic Materials. Propellants, Explosives, Pyrotechnics. 2023;48(4):e202200259.
  36. Singh AV, Bansod G, Mahajan M, Dietrich P, Singh SP, Rav K, et al. Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment. ACS omega. 2023.
  37. Crǎciun MV, Neagu DC, König C, Bumbaru S, editors. A study of aquatic toxicity using artificial neural networks. Knowledge-Based Intelligent Information and Engineering Systems: 7th International Conference, KES 2003, Oxford, UK, September 2003. Proceedings, Part II 7; 2003: Springer.
  38. Chassang G, Thomsen M, Rumeau P, Sèdes F, Delfin A. An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020). arXiv preprint arXiv:210503192. 2021.
  39. Wille SM, Elliott S. The future of analytical and interpretative toxicology: where are we going and how do we get there? Journal of Analytical Toxicology. 2021;45(7):619-32.
  40. Ivan DL, Manea T. AI Use in Criminal Matters as Permitted Under EU Law and as Needed to Safeguard the Essence of Fundamental Rights. International Journal of Law in Changing World. 2022;1(1):17-32.
  41. Srinivasan A, King RD, Bristol DW, editors. An assessment of submissions made to the predictive toxicology evaluation challenge. IJCAI; 1999: Citeseer.
  42. Tanwar N, Meena J, Hasija Y, editors. Explicate Toxicity By eXplainable Artificial Intelligence. 2022 International Conference on Industry 4.0 Technology (I4Tech); 2022: IEEE.
  43. Wankhade TD, Ingale SW, Mohite PM, Bankar NJ, Wankhade T, Ingale S, et al. Artificial intelligence in forensic medicine and toxicology: The future of forensic medicine. Cureus. 2022;14(8).
  44. Lee SY, Lee ST, Suh S, Ko BJ, Oh HB. Revealing unknown controlled substances and new psychoactive substances using high-resolution LC–MS-MS machine learning models and the hybrid similarity search algorithm. Journal of Analytical Toxicology. 2022;46(7):732-42.
  45. Dhingra V, Pandey J. Some Pitfalls in Forensic Toxicological Analysis of Autopsy Materials in Drug Related Deaths.
  46. Heilinger J-C. The ethics of AI ethics. A constructive critique. Philosophy & Technology. 2022;35(3):61.
  47. Turner OC, Aeffner F, Bangari DS, High W, Knight B, Forest T, et al. Society of toxicologic pathology digital pathology and image analysis special interest group article*: opinion on the application of artificial intelligence and machine learning to digital toxicologic pathology. Toxicologic Pathology. 2020;48(2):277-94.
  48. Idakwo G, Luttrell J, Chen M, Hong H, Zhou Z, Gong P, et al. A review on machine learning methods for in silico toxicity prediction. Journal of Environmental Science and Health, Part C. 2018;36(4):169-91.
  49. Sharma M, Sehgal ML. The Revolutionary Computer Technology Artificial Intelligence that is Changing Our World & it's Impact on Different Jobs.
  50. Cremer J, Medrano Sandonas L, Tkatchenko A, Clevert D-A, De Fabritiis G. Equivariant graph neural networks for toxicity prediction. Chemical Research in Toxicology. 2023;36(10):1561-73.
  51. Dilmaghani S, Brust MR, Danoy G, Cassagnes N, Pecero J, Bouvry P, editors. Privacy and security of big data in AI systems: A research and standards perspective. 2019 IEEE International Conference on Big Data (Big Data); 2019: IEEE.
  52. Habli I, Lawton T, Porter Z. Artificial intelligence in health care: accountability and safety. Bulletin of the World Health Organization. 2020;98(4):251.
آمار
تعداد مشاهده مقاله: 324
تعداد دریافت فایل اصل مقاله: 724