An Artificial-Intelligence-Driven Biochemical Framework for Linking Environmental Pollutant Exposure to Oxidative Stress and DNA Damage

Olatunde Adesoji Aladegba; Moshood Ayinde

doi:10.56201/rjpst.vol.9.no7.2026.pg13.35

📄 Download Paper

An Artificial-Intelligence-Driven Biochemical Framework for Linking Environmental Pollutant Exposure to Oxidative Stress and DNA Damage

Olatunde Adesoji Aladegba, Moshood Ayinde

Abstract

Environmental pollutant exposure is a pervasive and modifiable contributor to the burden of cancer in the United States, yet the causal pathway linking a chemical insult in the environment to a malignant transformation in a target tissue remains fragmented across disciplines. Exposure science characterizes what enters the body; biochemistry describes how reactive intermediates are generated and buffered; molecular toxicology enumerates the lesions inflicted on the genome; and epidemiology observes disease at the population scale. These communities work with different variables, timescales, and inferential traditions, and the resulting knowledge is rarely assembled into a single quantitative object that can be estimated, validated, and interrogated. This manuscript proposes an artificial-intelligence-driven biochemical framework that renders the intermediate steps of the exposure-to-cancer chain explicit and measurable, thereby converting a qualitative narrative into a predictive, falsifiable model. The central abstraction is a three-layer architecture. The Exposure layer (X) encodes pollutant identity, congener and speciation profiles, external and internal dose, and the co-exposure context. The Biochemical-response layer (B) captures oxidative stress as a measurable intermediate phenotype through oxidative damage markers such as 8-hydroxy-2'-deoxyguanosine (8-OHdG), malondialdehyde, and F2-isoprostanes, alongside antioxidant-capacity indicators including reduced glutathione , superoxide dismutase , catalase, and total antioxidant capacity. The DNA-Damage layer (D) represents oxidized bases, single- and double-strand breaks, γ-H2AX foci, micronucleus frequency, comet- assay metrics, and emergent mutational signatures. The framework learns two mechanistically constrained mappings, f: X→B and g: B→D, together with their composition g∘f, using machine- learning methods ranging from regularized regression and gradient-boosted trees to neural networks and mechanism-guided hybrids. We argue that making the biochemical intermediate an explicit, supervised target (rather than a hidden nuisance variable marginalized away in exposure- disease regressions) yields three decisive advantages: mechanistic interpretability through monotonic dose-response priors and antioxidant capacity as an effect modifier; statistical efficiency by exploiting intermediate ground truth; and translational value for early, non-invasive detection of environmentally induced cancer risk. We detail model inputs and outputs, candidate learning algorithms, the incorporation of mechanistic priors and feature-attribution methods, a layered validation strategy spanning internal, external, and prospective designs, data-integration requirements, and uncertainty quantification through conformal and Bayesian approaches. The framework is offered as a conceptual scaffold to align disparate measurement programs toward a shared, decision-relevant prediction target of pressing public-health importance.

Keywords

oxidative stress; 8-hydroxy-2'-deoxyguanosine; environmental pollutants; DNA damage; machine learning; exposure biomarkers; reactive oxygen species; mechanistic priors; cancer risk prediction; antioxi

References

Abah, M. A., Okwah, M. N., Oladosu, M. A., Yohanna, N. R., & Blessing, C. T. (2025a). Advances in the management of hypertension: A review of current guidelines, pharmacological therapies, and lifestyle interventions. Drug and Pharmaceutical Science Archives, 5(4), 35- 41. https://doi.org/10.47587/DAP.2025.5401 Abah, M. A., Okwah, M. N., Oladosu, M. A., Yohanna, N. R., Blessing, C. T., & Vanessa, O. I. (2025b). The impact of lifestyle modifications on cardiovascular health: A review of diet, exercise, and stress reduction intervention. Drug and Pharmaceutical Science Archives, 5(3), 26-34. https://doi.org/10.47587/DAP.2025.5301 Alexandrov, L. B., Kim, J., Haradhvala, N. J., Huang, M. N., Tian Ng, A. W., Wu, Y., ... Stratton, M. R. (2020). The repertoire of mutational signatures in human cancer. Nature, 578(7793), 94–101. https://doi.org/10.1038/s41586-020-1943-3 Alexandrov, L. B., Nik-Zainal, S., Wedge, D. C., Aparicio, S. A. J. R., Behjati, S., Biankin, A. V., ... Stratton, M. R. (2013). Signatures of mutational processes in human cancer. Nature, 500(7463), 415–421. https://doi.org/10.1038/nature12477 Amadi, C. E., Akanbi, P. O., Okwah, M. N., Mbakwem, A. C., & Ajuluchukwu, J. N. (2026a). Triglyceride-glucose index and reduced kidney function in hypertensive Nigerian adults: Independent association within the cardio-renal continuum. BMC Cardiovascular Disorders. Advance online publication. https://doi.org/10.1186/s12872-026-05985-5 Amadi, C. E., Ale, O. K., Okorafor, U. C., Okwah, M. N., Akanbi, P. O., Mbakwem, A. C., & Ajuluchukwu, J. N. (2026b). Unmasking the contemporary burden of hypertension in Lagos, Nigeria: Insights from an opportunistic screening. BMC Public Health. Advance online publication. https://doi.org/10.1186/s12889-026-26310-x Amadi, C. E., Okorafor, U. C., Okorafor, C. I., Okwah, M. N., Akanbi, P. O., Okam, O. V., Udenze, C. I., Mbakwem, A. C., & Ajuluchukwu, J. N. (2025). Association between triglyceride-glucose index and estimated 10-year risk of cardiovascular disease in Nigerian non-diabetic hypertensives: A cross-sectional study. PLOS Global Public Health, 5(7), e0004760. https://doi.org/10.1371/journal.pgph.0004760 Angelopoulos, A. N. & Bates, S. (2023). Conformal prediction: a gentle introduction. Foundations and Trends in Machine Learning, 16(4), 494–591. Arowoogun, J., Akanni, O. O., Adefisan, A. O., Owumi, S. E., Tijani, A. S., & Adaramoye, O. A. (2021). Rutin ameliorates copper sulfate-induced brain damage via antioxidative and anti- inflammatory activities in rats. Journal of Biochemical and Molecular Toxicology, 35(1), e22623. https://doi.org/10.1002/jbt.22623 Ayala, A., Muñoz, M. F., & Argüelles, S. (2014). Lipid peroxidation: Production, metabolism, and signaling mechanisms of malondialdehyde and 4-hydroxy-2-nonenal. Oxidative Medicine and Cellular Longevity, 2014, 360438. https://doi.org/10.1155/2014/360438 Collins, A. R. (2004). The comet assay for DNA damage and repair: Principles, applications, and limitations. Molecular Biotechnology, 26(3), 249–261. https://doi.org/10.1385/MB:26:3:249 Cooke, M. S., Evans, M. D., Dizdaroglu, M., & Lunec, J. (2003). Oxidative DNA damage: Mechanisms, mutation, and disease. The FASEB Journal, 17(10), 1195–1214. https://doi.org/10.1096/fj.02-0752rev David, F. T., Abah, M. A., Oladosu, M. A., Agida, O. D., Clive, N. O., Adebukola, A. M., Nnabuko, O. M., & Maryanne, O. U. (2025). The impact of plant-based diets on cardiovascular health: A comprehensive review. Plant Science Archives, 10(4), 51-59. https://doi.org/10.51470/PSA.2025.10.4.51 Falcioni, M. L., Nasuti, C., Bergamini, C., Fato, R., Lenaz, G., & Gabbianelli, R. (2010). The primary role of glutathione against nuclear DNA damage of striatum induced by permethrin in rats. Neuroscience, 168(1), 2-10. https://doi.org/10.1016/j.neuroscience.2010.03.024 Fenech, M. (2007). Cytokinesis-block micronucleus cytome assay. Nature Protocols, 2(5), 1084– 1104. https://doi.org/10.1038/nprot.2007.77 Halliwell, B. (2007). Oxidative stress and cancer: Have we moved forward? Biochemical Journal, 401(1), 1–11. https://doi.org/10.1042/BJ20061131 Hoeijmakers, J. H. J. (2001). Genome maintenance mechanisms for preventing cancer. Nature, 411(6835), 366–374. https://doi.org/10.1038/35077232 Jackson, S. P., & Bartek, J. (2009). The DNA-damage response in human biology and disease. Nature, 461(7267), 1071–1078. https://doi.org/10.1038/nature08467 Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics- informed machine learning. Nature Reviews Physics, 3(6), 422–440. https://doi.org/10.1038/s42254-021-00314-5 Kasai, H. (1997). Analysis of a form of oxidative DNA damage, 8-hydroxy-2'-deoxyguanosine, as a marker of cellular oxidative stress during carcinogenesis. Mutation Research/Reviews in Mutation Research, 387(3), 147–163. https://doi.org/10.1016/S1383-5742(97)00035-5 Kensler, T. W., Wakabayashi, N., & Biswal, S. (2007). Cell survival responses to environmental stresses via the Keap1-Nrf2-ARE pathway. Annual Review of Pharmacology and Toxicology, 47, 89–116. https://doi.org/10.1146/annurev.pharmtox.46.120604.141046 Klaunig, J. E. & Kamendulis, L. M. (2004). The role of oxidative stress in carcinogenesis. Annual Review of Pharmacology and Toxicology, 44, 239–267. Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. In Advances in Neural Information Processing Systems 30 (pp. 6402–6413). Curran Associates. Luechtefeld, T., Marsh, D., Rowlands, C., & Hartung, T. (2018). Machine learning of toxicological big data enables read-across structure activity relationships outperforming animal test reproducibility. Toxicological Sciences, 165(1), 198–212. https://doi.org/10.1093/toxsci/kfy152 Lundberg, S. M. & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. Ma, Q. (2013). Role of Nrf2 in oxidative stress and toxicity. Annual Review of Pharmacology and Toxicology, 53, 401–426. https://doi.org/10.1146/annurev-pharmtox-011112-140320 Milne, G. L., Musiek, E. S., & Morrow, J. D. (2005). F2-isoprostanes as markers of oxidative stress in vivo: An overview. Biomarkers, 10(Suppl. 1), S10–S23. https://doi.org/10.1080/13547500500216546 Milne, G. L., Yin, H., Hardy, K. D., Davies, S. S., & Roberts, L. J. (2011). Isoprostane generation and function. Chemical Reviews, 111(10), 5973–5996. https://doi.org/10.1021/cr200160h Nebert, D. W., & Dalton, T. P. (2006). The role of cytochrome P450 enzymes in endogenous signalling pathways and environmental carcinogenesis. Nature Reviews Cancer, 6(12), 947–960. https://doi.org/10.1038/nrc2015 Oakley, G. G., Devanaboyina, U., Robertson, L. W., & Gupta, R. C. (1996). Oxidative DNA damage induced by activation of polychlorinated biphenyls : Implications for PCB- induced oxidative stress in breast cancer. Chemical Research in Toxicology, 9(8), 1285– 1292. https://doi.org/10.1021/tx960103o Obi, L. I., Dogbanya, G., Awah, L. C., Tawose, O. M., Ubani, C., Ayo-ige, A. B., Odedele, M., & Edafeadhe, O. (2025). A systematic analysis of effectiveness of nurse-led dementia care interventions on health outcomes among community-dwelling older adults. Journal of Pharma Insights and Research, 3(5), 24-33. https://doi.org/10.69613/3f5cq717 Ogunlakin, A. D., Ojo, O. A., Iyobhebhe, M., Ajisafe, T. L., Adeoye, E. O., Ayokunle, D. I., Sonibare, M. A., Ambali, O. A., Adebodun, G. O., Ajayi-Odoko, O. A., Adetunji, T. L., & Oguntibeju, O. O. (2024). Sodium 3-phenylpropanoate alleviates oxidative stress and iron- induced testicular toxicity in Wistar rats. Journal of Applied Pharmaceutical Science, 14(3), 1-7. Ojo, O. A., Oladepo, F. S., Ogunlakin, A. D., Ayokunle, D. I., Odugbemi, A. I., Babatunde, D. E., et al. (2024). Spilanthes filicaulis (Schumach. & Thonn.) C. D. Adam leaf extract prevents assault of streptozotocin on liver cells via inhibition of oxidative stress and activation of the Nrf2/Keap1, PPARγ, and PTP1B signalling pathways. PLOS ONE, 19(6), e0306039. https://doi.org/10.1371/journal.pone.0306039 Okwah, M. N. (2022). Integrating triglyceride-glucose index and echocardiographic parameters for improved cardiovascular risk stratification in Sub-Saharan Africa. International Journal of Cardiology, 1(2), 1-16. https://doi.org/10.34218/IJC_01_02_001 Okwah, M. N., Abah, M. A., Oladosu, M. A., Blessing, T. B., Anku, A. I., & Agida, O. D. (2026). Anti-inflammatory effects of omega-3 fatty acids: Evidence from circulating biomarkers and inflammatory gene-expression studies. African Research Reports, 2(4), 365-382. https://doi.org/10.65221/0167 Omo Enabulele, A. B., Oyebamiji, I., Ikubanni, O., Okwah, M. N., Babalola, W. S., Azeez, O. M., & Olaniyi, A. O. (2025). A coordinated project-management approach to multisite implementation of motor-rehabilitation programs for children with autism spectrum disorder in United States healthcare systems: A narrative review. Cureus, 17(12), e100513. https://doi.org/10.7759/cureus.100513 Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707. https://doi.org/10.1016/j.jcp.2018.10.045 Rappaport, S. M. & Smith, M. T. (2010). Environment and disease risks. Science, 330(6003), 460– 461. Reuter, S., Gupta, S. C., Chaturvedi, M. M., & Aggarwal, B. B. (2010). Oxidative stress, inflammation, and cancer: How are they linked? Free Radical Biology and Medicine, 49(11), 1603–1616. https://doi.org/10.1016/j.freeradbiomed.2010.09.006 Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). 'Why should I trust you?': Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778 Rogakou, E. P., Pilch, D. R., Orr, A. H., Ivanova, V. S., & Bonner, W. M. (1998). DNA double- stranded breaks induce histone H2AX phosphorylation on serine 139. Journal of Biological Chemistry, 273(10), 5858–5868. https://doi.org/10.1074/jbc.273.10.5858 Rudin, C. (2019). Stop explaining black-box machine-learning models for high-stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1, 206–215. Sies, H. (2015). Oxidative stress: A concept in redox biology and medicine. Redox Biology, 4, 180–183. https://doi.org/10.1016/j.redox.2015.01.002 Valavanidis, A., Vlachogianni, T. & Fiotakis, C. (2009). 8-Hydroxy-2′-deoxyguanosine (8- OHdG): a critical biomarker of oxidative stress and carcinogenesis. Journal of Environmental Science and Health, Part C, 27(2), 120–139. Valko, M., Rhodes, C. J., Moncol, J., Izakovic, M., & Mazur, M. (2006). Free radicals, metals and antioxidants in oxidative stress-induced cancer. Chemico-Biological Interactions, 160(1), 1–40. https://doi.org/10.1016/j.cbi.2005.12.009 Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., ... Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477. https://doi.org/10.1038/s41573-019-0024-5 Wild, C. P. (2005). Complementing the genome with an 'exposome'. Cancer Epidemiology, Biomarkers & Prevention, 14(8), 1847–1850. Yusuf, I. A., Badero, O. J., Ihesiulo, A., Okwah, M. N., Oshodin, A., & Asikong, E. (2026). Aldosterone synthase inhibitors in uncontrolled and resistant hypertension: A phenotype- stratified systematic review and network meta-analysis of randomized trials. PLOS ONE, 21(6), e0349932. https://doi.org/10.1371/journal.pone.0349932

An Artificial-Intelligence-Driven Biochemical Framework for Linking Environmental Pollutant Exposure to Oxidative Stress and DNA Damage

Abstract

Keywords

References

More Articles from RESEARCH JOURNAL OF PURE SCIENCE AND TECHNOLOGY