Pharmacophore an International Research Journal
Artificial intelligence models increasingly participate in pharmaceutical activities in which data, scientific assumptions, software dependencies, intended uses, and decision consequences evolve over time. Conventional model reports are usually static: they describe a model at development or publication but rarely preserve the relationships among model version, data provenance, validation scope, uncertainty, limitations, human approvals, subsequent changes, and continuing performance. This creates an evidentiary gap when models are retrained, transferred between development stages, exposed to altered data distributions, or incorporated into decisions different from those originally evaluated. This article proposes the Model Evidence Dossier, a living, version-bound, and queryable evidence-governance architecture for artificial intelligence in pharmaceutical development. The dossier organizes model identity, intended use, data lineage and suitability, technical configuration, validation evidence, uncertainty characterization, known limitations, monitoring signals, change-impact assessments, requalification decisions, and human challenge records as connected evidence objects. Its central principle is that no isolated performance measure can establish pharmaceutical usefulness, scientific validity, continuing fitness, or acceptable reliance. Instead, each claim must remain traceable to the model version, data state, decision context, assessment method, responsible actor, and boundary conditions that support it. The architecture distinguishes monitoring from authorization, updating from requalification, confidence from calibrated uncertainty, data accessibility from suitability, and predictive performance from experimental or clinical confirmation. The proposed dossier is conceptual rather than empirically validated and does not constitute a regulatory submission, deployment protocol, or universal evidence standard. Its value will depend on future ontology development, prospective evaluation, organizational integration, and evidence that dossier use improves scientific review, change control, and pharmaceutical decision quality.
