The translational power of Alzheimer's-based organoid models in personalized medicine: an integrated biological and digital approach embodying patient clinical history.

Abstract

Alzheimer's disease (AD) is a complex neurodegenerative condition characterized by a multifaceted interplay of genetic, environmental, and pathological factors. Traditional diagnostic and research methods, including neuropsychological assessments, imaging, and cerebrospinal fluid (CSF) biomarkers, have advanced our understanding but remain limited by late-stage detection and challenges in modeling disease progression. The emergence of three-dimensional (3D) brain organoids (BOs) offers a transformative platform for bridging these gaps. BOs derived from patient-specific induced pluripotent stem cells (iPSCs) mimic the structural and functional complexities of the human brain. This advancement offers an alternative or complementary approach for studying AD pathology, including β-amyloid and tau protein aggregation, neuroinflammation, and aging processes. By integrating biological complexity with cutting-edge technological tools such as organ-on-a-chip systems, microelectrode arrays, and artificial intelligence-driven digital twins (DTs), it is hoped that BOs will facilitate real-time modeling of AD progression and response to interventions. These models capture central nervous system biomarkers and establish correlations with peripheral markers, fostering a holistic understanding of disease mechanisms. Furthermore, BOs provide a scalable and ethically sound alternative to animal models, advancing drug discovery and personalized therapeutic strategies. The convergence of BOs and DTs potentially represents a significant shift in AD research, enhancing predictive and preventive capacities through precise in vitro simulations of individual disease trajectories. This approach underscores the potential for personalized medicine, reducing the reliance on invasive diagnostics while promoting early intervention. As research progresses, integrating sporadic and familial AD models within this framework promises to refine our understanding of disease heterogeneity and drive innovations in treatment and care.