Translational Approaches in Neurodegenerative Diseases

Abstract

Neurodegenerative diseases -- including Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD) -- collectively affect over 50 million people worldwide, yet their translational pipelines have historically failed at far higher rates than oncology or cardiovascular disease. This study systematically evaluated the translational fidelity of six mechanistic therapeutic targets -- amyloid-beta (Abeta) aggregation inhibition, tau hyperphosphorylation reduction, alpha-synuclein clearance, SOD1 silencing, HDAC6 inhibition, and neuroinflammation blockade via CSF1R inhibition -- across validated cellular models, transgenic murine models, and available human clinical data, assessing translational concordance and identifying species-specific divergence points. Using a 240-data-point matrix (6 targets x 8 model systems x 5 efficacy endpoints), we quantified Pearson correlations between preclinical efficacy and clinical outcomes across 42 completed phase II/III trials. Abeta-targeting therapies showed the lowest translational concordance (r = 0.18; p = 0.31 for preclinical vs clinical efficacy), consistent with the 99.6% phase II/III failure rate of Abeta-directed candidates. SOD1-ASO silencing (tofersen) and HTT-lowering (branaplam) demonstrated the highest concordance (r = 0.84 and 0.79, respectively; p < 0.001), attributable to direct target engagement biomarkers quantifiable in CSF. Neuroinflammation blockade showed intermediate concordance (r = 0.61) but the highest unmet potential based on biomarker-pharmacodynamic coupling. These findings identify target engagement biomarkers, human genetic validation, and disease-relevant transgenic models as the three factors most strongly predicting translational success, providing a quantitative framework for preclinical-to-clinical go/no-go decisions in neurodegeneration drug development.