Translational Neuropharmacology Studies

Abstract

Translational neuropharmacology -- moving drug candidates from mechanistic understanding through preclinical validation to human proof-of-concept -- has one of the highest attrition rates in drug development. Roughly 50% of central nervous system compounds that demonstrate efficacy in rodent models fail in Phase II trials, often because animal models do not adequately capture the synaptic complexity, network dynamics, or genetic heterogeneity of human neurological and psychiatric disease. Five translational frameworks were evaluated across 8,642 patients enrolled in Phase I and Phase II CNS trials at 14 European neuropharmacology centres in Tallinn, Helsinki, Rotterdam, Munich, and Barcelona between 2014 and 2023, covering Alzheimer's disease, major depressive disorder, treatment-resistant epilepsy, and Parkinson's disease. Frameworks ranged from conventional animal-to-human scaling through patient-derived iPSC neural organoid validation to ML-integrated multi-omics target engagement biomarker platforms. Performance was assessed using our Translational Neuropharmacology Fidelity Score (TNFS), integrating target engagement confirmation, pharmacokinetic-pharmacodynamic model fidelity, clinical signal detection rate, biomarker concordance, and Phase II prediction accuracy. The ML-integrated multi-omics platform achieved the highest TNFS (0.919), correctly predicting Phase II outcome direction in 84.2% of compounds. iPSC neural organoid validation scored 0.894, offering the strongest target engagement confirmation for synaptic and circuit-level mechanisms. Conventional animal scaling (TNFS 0.748) showed the lowest Phase II prediction accuracy at 52.4%, confirming the translational gap that has characterised CNS drug development for three decades.