Digital Evidence Chains for Phytopharmaceutical Claims: Connecting In Silico Evidence, Experimental Validation, Traditional Use, Safety Data, and Regulatory Logic

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Phytopharmaceutical claims often draw on heterogeneous evidence, including botanical identity, chemical characterization, computational prediction, experimental validation, traditional use, safety data, and regulatory reasoning. However, these evidence types do not support the same kinds of claims, and their overextension can lead to unsupported statements about efficacy, mechanism, safety, or regulatory relevance. This article proposes a digital evidence-chain framework for organizing phytopharmaceutical claim support through transparent links among evidence source, method, validation status, uncertainty, claim wording, expert interpretation, and decision boundary. The framework treats in silico prediction, network inference, molecular docking, and ADMET prediction as hypothesis-generating or plausibility-supporting evidence that requires experimental validation before stronger mechanistic or translational claims are made. It also treats traditional use as contextual evidence that may inform historical and ethnopharmacological interpretation but does not establish modern clinical effectiveness or universal safety alone. Safety data, pharmacovigilance signals, adverse-event evidence, herb–drug interaction evidence, and exposure plausibility are positioned as mandatory qualifiers for responsible claim support. Regulatory logic is framed as claim-specific reasoning rather than a universal evidence hierarchy. The proposed model contributes a structured approach for aligning evidence type, claim type, uncertainty, validation gaps, safety qualification, digital provenance, and expert review in phytopharmaceutical research.