Comprehensive computational analysis via Adverse Outcome Pathways and Aggregate Exposure Pathways in exploring synergistic effects from radon and tobacco smoke on lung cancer.

Lung cancer remains the leading cause of cancer mortality worldwide, with tobacco smoke and radon exposure being the primary risk factors. The interaction between these two factors has been described as sub-multiplicative, but a better understanding is needed of how they jointly contribute to lung carcinogenesis. In this context, a comprehensive analysis of current knowledge regarding the effects of radon and tobacco smoke on lung cancer was conducted using a computational approach. Information on this co-exposure was extracted and clustered from databases, particularly the literature, using the text mining tool AOP-helpFinder and other artificial intelligence (AI) resources. The collected information was then organized into Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathways (AOP) models. AEPs and AOPs represent analytical concepts useful for assessing the potential risks associated with exposure to various stressors. AOPs provide a structured framework to organize knowledge of essential Key Events (KEs) from a Molecular Initiating Event (MIE) to an Adverse Outcome (AO) at an organism or population level, while AEPs model exposures from the initial source of the stressor to the internal exposure site within the target organism, situated upstream of the AOP. Combining these frameworks offered an integrated method for knowledge consolidation of radon and tobacco smoke, detailing the association from the environment to a mechanistic level, and highlighting specific differences between the two stressors in DNA damage, mutational profiles, and histological types. This approach also identified gaps in understanding joint exposure, particularly the lack of mechanistic studies on the precise role of certain KEs such as inflammation, as well as the need for studies that more closely replicate real-world exposure conditions. In conclusion, this study demonstrates the potential of AI and machine learning tools in developing alternative toxicological models. It highlights the complex interaction between radon and tobacco smoke and encourages collaboration among scientific communities to conduct future studies aiming to fully understand the mechanisms associated with this co-exposure.