"AI—the Apollo Guidance Computer of the Exposome Moonshot"

Fenna SilleThomas Hartung^*

• Biology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, United States

The Exposome—the totality of environmental exposures across a lifetime—remains one of the most significant challenges in understanding and preventing human disease. Translating its vast, heterogeneous data streams into actionable knowledge requires artificial intelligence (AI) integrated with human-relevant experimental systems. We propose a unifying vision in which Microphysiological Systems (MPS) and multi-omics platforms generate high-quality, context-specific data that iteratively calibrate AI models, enabling the creation of digital twins of organs, individuals, and ultimately populations. This "Exposome Moonshot" parallels the Apollo program in ambition, with MPS as the rocket, multi-omics as the lunar module, and AI as the guidance computer. Early applications demonstrate that deep learning can already outperform canonical animal tests for several toxicological endpoints, while reducing cost and time to decision. Realizing the full potential of exposome intelligence will require expanding the applicability domain of models, implementing robust data security, and prioritizing transparent, interpretable algorithms. By linking predictive AI with experimental feedback, we can move toward a prevention-driven, personalized paradigm for human health and regulatory science.