From Organ-on-Chip Signals to Human Pharmacology through Mechanistic Translation, Uncertainty Propagation, and Multiscale Modeling Download PDF

Organ-on-chip systems can generate human-relevant observations of tissue exposure, transport, metabolism, injury, and pharmacodynamic response, yet these observations do not translate directly into predictions of organism-level pharmacology. A measured chip signal remains conditional on device geometry, materials, flow, cell provenance, tissue maturity, exposure history, sampling procedures, and assay interpretation. This article proposes a Mechanistic Translation and Uncertainty Architecture for connecting organ-on-chip evidence with human pharmacology without treating platform performance, biological resemblance, or model fit as sufficient evidence of translational validity. The architecture comprises a structured representation of device and tissue states, a measurement-interpretation layer, mechanistic interfaces linking cellular and tissue processes with physiologically based pharmacokinetic, pharmacokinetic–pharmacodynamic, and quantitative systems pharmacology models, a multimodal calibration layer, an uncertainty ledger, and a context-of-use qualification gate. Its central principle is that translation is a sequence of conditional transformations rather than a direct endpoint mapping. Uncertainty must therefore be identified at its source, propagated across interfaces, and interpreted relative to the intended prediction or decision. Clinical pharmacology observations, quantitative imaging, biomarkers, and omics measurements are positioned as complementary constraints that may expose model discrepancy as well as refine parameters. The proposed architecture is conceptual and has not been empirically validated, clinically qualified, or accepted for regulatory use. Its applicability will depend on organ system, compound properties, biological context, available evidence, and decision consequence. By separating observation from biological state, local mechanism from whole-body behavior, calibration from confirmation, and prediction from decision fitness, the architecture provides a structured basis for evaluating when organ-on-chip signals may contribute to defensible human-pharmacology inference.