Redefining Preclinical Neuroscience: AI-Driven in-Silico Models as Ethical and Efficient Alternatives to Animal Testing in Alzheimer’s Nanomedicine Research

The growing ethical issues and translational limits of animal models in brain research have led to the development of improved computer systems for simulating disease pathophysiology and treatment responses. This study proposes a novel integrative strategy that uses artificial intelligence (AI)-driven in silico models to replace and improve traditional animal experimentation in the preclinical evaluation of Alzheimer’s disease (AD) nanomedicines. Machine learning models were trained using multi-omics datasets, quantum chemical descriptors, and physicochemical parameters of polymer-encapsulated ursolic acid (UA) nanoformulations to predict neuroprotective efficacy, target binding affinity (AChE, amyloid-β, tau), and potential toxicity profiles. Furthermore, virtual brain organoid simulations combined with deep learning-based connectome analytics allowed for the dynamic mapping of UA nanoparticle interactions in AD-relevant neuronal circuits. A comparative investigation demonstrated significant connections between AI-predicted results and presumed in vivo data, supporting the computational workflow. This paradigm shift not only shortens the drug development timescale, but it also adheres to the 3Rs (Replacement, Reduction, and Refinement) ethical framework, providing a scalable, replicable, and humane alternative to animal testing. Our findings highlight AI’s transformational potential in developing precision nano-neurotherapeutics for neurodegenerative diseases.