TO FILL IN THE GAPS IN ALZHEIMER'S DISEASE MEDICATIONS THAT PENETRATE THE BLOOD-BRAIN BARRIER BY PUTTING FORWARD A TRANSLATIONAL PREDICTION

Despite significant investment in drug discovery, Alzheimer's disease (AD) continues to be a significant unmet medical need. The effectiveness of CNS drugs is severely hampered by the blood-brain barrier (BBB), and many potential compounds fall short because of poor ADMET characteristics or insufficient brain exposure. Key research gaps on BBB-crossing compounds for AD are identified in this review: A strong dependence on in vitro or in silico predictors that do not generalize well in vivo; A limited integration of robust BBB permeability prediction with ADMET profiling, A translational gap between computational predictions and experimental/clinical validation; inadequate multi-target and mechanism-aware evaluation in repurposing studies. We point out antipsychotic repurposing candidates (like benperidol) as examples that lack thorough BBB/ADMET and free-energy or dynamics studies but have encouraging docking or preclinical signals. In order to identify candidates with genuine translational potential, we suggest a useful, integrated workflow that combines multi-task machine-learning BBB models, orthogonal in-vitro assays, ADMET pipelines, and conventional MD/MM-PBSA benchmarking. Closing these gaps will improve the success rate of AD treatments by speeding up the selection of agents with both target engagement and consistent brain exposure.