Design of Tau Aggregation Inhibitors Using Iterative Machine Learning and a Polymorph-Specific Brain-Seeded Fibril Amplification Assay

The aggregation of tau into amyloid fibrils is associated with Alzheimer's disease (AD) and related tauopathies. Since different tauopathies are characterized by the formation of distinct tau fibril morphologies, it is important to combine the search of tau aggregation inhibitors with the development of in vitro tau aggregation assays that recapitulate aggregation as it may occur in the brain. Here we address this problem by reporting an in vitro tau aggregation assay in which AD brain homogenates are used to seed the generation of first-generation tau fibrils in a polymorph-specific manner under quiescent conditions. These fibrils are then used to create amyloid seed libraries from which second-generation kinetic assays can be readily performed. Using this strategy, we illustrate an iterative machine learning method for the identification of small molecules for the polymorph-specific inhibition of the in vitro formation of tau fibrils. We further show that the small molecules selected by this procedure are potent inhibitors in a <i>Drosophila</i> tauopathy model.

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Publication Details

Journal
Journal of the American Chemical Society
Published
2025-09-18
DOI
https://doi.org/10.1021/jacs.5c12812
Citations
2
Primary Topic
Alzheimer's disease research and treatments
Type
article
Field-Weighted Citation Impact
2.33
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article

Design of Tau Aggregation Inhibitors Using Iterative Machine Learning and a Polymorph-Specific Brain-Seeded Fibril Amplification Assay

Ergina Vourkou, Byron Caughey, Michael A. Metrick, Nicholas C. T. Gallagher et al.
2 citations
Journal of the American Chemical Society
Alzheimer's disease research and treatments
2.33
article

Design of Tau Aggregation Inhibitors Using Iterative Machine Learning and a Polymorph-Specific Brain-Seeded Fibril Amplification Assay

Ergina Vourkou, Byron Caughey, Michael A. Metrick, Nicholas C. T. Gallagher, Dillon J. Rinauro, Katerina Papanikolopoulou, Efthimios M. C. Skoulakis, Z. Faidon Brotzakis, Michele Vendruscolo, Robert I. Horne, Alessia Santambrogio, Sara Snogerup Linse
article en
2 citations

Abstract

The aggregation of tau into amyloid fibrils is associated with Alzheimer's disease (AD) and related tauopathies. Since different tauopathies are characterized by the formation of distinct tau fibril morphologies, it is important to combine the search of tau aggregation inhibitors with the development of in vitro tau aggregation assays that recapitulate aggregation as it may occur in the brain. Here we address this problem by reporting an in vitro tau aggregation assay in which AD brain homogenates are used to seed the generation of first-generation tau fibrils in a polymorph-specific manner under quiescent conditions. These fibrils are then used to create amyloid seed libraries from which second-generation kinetic assays can be readily performed. Using this strategy, we illustrate an iterative machine learning method for the identification of small molecules for the polymorph-specific inhibition of the in vitro formation of tau fibrils. We further show that the small molecules selected by this procedure are potent inhibitors in a <i>Drosophila</i> tauopathy model.

Journal of the American Chemical SocietyVol. 147(39)
University of California, San Francisco (US), Lund University (SE), University of Cambridge (GB), Bioinova (Czechia) (CZ), National Institute of Allergy and Infectious Diseases (US), Institution of Structural Engineers (GB), Institute of Structural and Molecular Biology (GB), Neurological Surgery (US), Institute of Infection and Immunity (CA), Alexander Fleming Biomedical Sciences Research Center (GR)
Openalex Percentile: Top 10%
Alzheimer's disease research and treatments
2.33
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