A Multifaceted Framework for Assessing Nanocarriers from Formulation Development to Therapeutic Readiness: The Translational CNS Nanomedicine Index (TCNI)

Introduction: Evaluation of central nervous system (CNS) nanomedicines requires evidence extending beyond physicochemical characterisation or total brain accumulation to include released, unbound drug exposure and pharmacodynamic relevance. Objective: This framework-development study introduces the Translational CNS Nanomedicine Index (TCNI) to organise evidence across the development and translational pathway. Methodology: The framework distinguishes five assessment domains: physicochemical and colloidal performance; systemic exposure and drug–carrier dissociation; neurovascular transport and unbound brain availability; parenchymal distribution and cellular localisation; and pharmacodynamic activity and safety. Directional normalisation maps heterogeneous endpoints to bounded domain scores, which are aggregated using a prespecified weighted geometric mean. Monte Carlo resampling is applied to characterise uncertainty, while unmeasured domains remain explicitly unscored. The computational pipeline was illustrated using physicochemical and pharmacokinetic inputs drawn from two published ursolic acid formulation studies. Results: The pipeline processed available inputs and generated provisional assessments and uncertainty distributions despite incomplete data. The source studies represented different experimental settings and did not provide a matched dataset covering unbound brain exposure, parenchymal distribution and target engagement. Consequently, this exercise demonstrates computational functionality rather than experimental validation, predictive accuracy or comparative therapeutic performance. Conclusion: TCNI provides a structured approach for identifying evidence gaps, reporting domain-specific findings and prioritising further experiments. Its predictive utility and relevance to CNS efficacy require independent experimental assessment using complete, appropriately matched datasets.