Model-informed drug development (MIDD) now supports decisions spanning regulatory interaction, dose selection, population extrapolation, exposure–response assessment, trial design, formulation bridging, and individualized dosing. Yet evidence is often organized by modeling technique rather than by the decision a model is intended to support. This can obscure differences in validation requirements, population transportability, measurement dependence, and acceptable inferential strength. To map contemporary MIDD evidence across regulatory and clinical decision contexts and determine how model class, evidence maturity, transferability, and decision consequence jointly constrain interpretation. An evidence-mapping review was conducted across literature published from 2017 through 2026. Four reproducible PubMed search packages identified 67 records. Nine duplicates were removed, leaving 58 unique records for title/abstract screening. Twenty-two were excluded, 36 proceeded to detailed eligibility assessment, and six were excluded at that stage, yielding 30 eligible evidence units. Evidence was coded by decision context, model type, evidence class, population or product boundary, validation status, and the decision the model was used to inform. The mapped literature showed that identical model classes can occupy materially different evidentiary states depending on the question being addressed. Regulatory programs increasingly use MIDD within structured interactions, but jurisdictional and procedural expectations remain context dependent. Model qualification was particularly sensitive to intended use, external performance, structural-model uncertainty, and transfer to new populations or data environments. Early-development examples demonstrated integration of pharmacokinetic, pharmacodynamic, efficacy, and safety information for dose selection, whereas special-population applications required explicit assessment of physiological and exposure–response comparability rather than automatic extrapolation. MIDD evidence is more usefully interpreted as a decision-conditioned landscape than as a hierarchy of modeling technologies. The proposed mapping separates model identity from evidentiary adequacy and treats validation, transferability, measurement integrity, and reassessment as distinct requirements whose importance changes with the clinical or regulatory consequence of the decision.
IJPRAS
