Expert opinion on drug discovery · 2026
Advances in 3D cell culture models for alzheimer's disease drug discovery.
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Valdivia-Aviña Karla, Ochoa-Hugo Sarah Eliuth, Hernández-Sapiéns Mercedes Azucena, Canales-Aguirre Alejandro Arturo
Аннотация
INTRODUCTION: Alzheimer's Disease (AD) affects more than 57 million people, yet drug development faces high failure rates due to the limited translational validity of conventional models. This review analyzes how 3D (three-dimensional) systems provide a technical validation platform that more accurately replicates the human brain microenvironment.
AREAS COVERED: This review analyzes the evolution of 3D culture systems, including spheroids, organoids, hydrogels, and microfluidics. A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and ScienceDirect, covering publications from January 2014 to March 2026. The analysis synthesizes evidence on the capacity of these models to reproduce beta-amyloid (β-amyloid) aggregation and tau hyperphosphorylation, while exploring the integration of artificial intelligence to optimize compound screening.
EXPERT OPINION: 3D models serve as high-fidelity biological filters that complement current preclinical methodologies, improving early identification of toxicities and therapeutic efficacy. Their strategic integration and technical standardization are essential steps to refine predictive accuracy and optimize the transition to clinical trials.
AREAS COVERED: This review analyzes the evolution of 3D culture systems, including spheroids, organoids, hydrogels, and microfluidics. A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and ScienceDirect, covering publications from January 2014 to March 2026. The analysis synthesizes evidence on the capacity of these models to reproduce beta-amyloid (β-amyloid) aggregation and tau hyperphosphorylation, while exploring the integration of artificial intelligence to optimize compound screening.
EXPERT OPINION: 3D models serve as high-fidelity biological filters that complement current preclinical methodologies, improving early identification of toxicities and therapeutic efficacy. Their strategic integration and technical standardization are essential steps to refine predictive accuracy and optimize the transition to clinical trials.