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

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 (GSH), superoxide dismutase (SOD), 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, candidatelearning 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.