AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial
Abstract Alzheimer’s Disease (AD) drug discovery has been hampered by patient heterogeneity, and the lack of sensitive tools for precise stratification. Here, we demonstrate that our robust and interpretable AI-guided tool (predictive prognostic model, PPM) enhances precision in patient stratificati...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61355-3 |
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| author | Delshad Vaghari Gayathri Mohankumar Keith Tan Andrew Lowe Craig Shering Peter Tino Zoe Kourtzi |
| author_facet | Delshad Vaghari Gayathri Mohankumar Keith Tan Andrew Lowe Craig Shering Peter Tino Zoe Kourtzi |
| author_sort | Delshad Vaghari |
| collection | DOAJ |
| description | Abstract Alzheimer’s Disease (AD) drug discovery has been hampered by patient heterogeneity, and the lack of sensitive tools for precise stratification. Here, we demonstrate that our robust and interpretable AI-guided tool (predictive prognostic model, PPM) enhances precision in patient stratification, improving outcomes and decreasing sample size for a AD clinical trial. The AMARANTH trial of lanabecestat, a BACE1 inhibitor, was deemed futile, as treatment did not change cognitive outcomes, despite reducing β-amyloid. Employing the PPM, we re-stratify patients precisely using baseline data and demonstrate significant treatment effects; that is, 46% slowing of cognitive decline for slow progressive patients at earlier stages of neurodegeneration. In contrast, rapid progressive patients did not show significant change in cognitive outcomes. Our results provide evidence for AI-guided patient stratification that is more precise than standard patient selection approaches (e.g. β-amyloid positivity) and has strong potential to enhance efficiency and efficacy of future AD trials. |
| format | Article |
| id | doaj-art-a1441c0fb36c4cab95a8d33c17b25df8 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-a1441c0fb36c4cab95a8d33c17b25df82025-08-20T04:03:02ZengNature PortfolioNature Communications2041-17232025-07-0116111210.1038/s41467-025-61355-3AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trialDelshad Vaghari0Gayathri Mohankumar1Keith Tan2Andrew Lowe3Craig Shering4Peter Tino5Zoe Kourtzi6Department of Psychology, University of CambridgeCentre for AI, Data Science & Artificial Intelligence, BioPharmaceuticals R&D, AstraZenecaNeuroscience, BioPharmaceuticals R&D, AstraZenecaNeuroscience, BioPharmaceuticals R&D, AstraZenecaNeuroscience, BioPharmaceuticals R&D, AstraZenecaSchool of Computer Science, University of BirminghamDepartment of Psychology, University of CambridgeAbstract Alzheimer’s Disease (AD) drug discovery has been hampered by patient heterogeneity, and the lack of sensitive tools for precise stratification. Here, we demonstrate that our robust and interpretable AI-guided tool (predictive prognostic model, PPM) enhances precision in patient stratification, improving outcomes and decreasing sample size for a AD clinical trial. The AMARANTH trial of lanabecestat, a BACE1 inhibitor, was deemed futile, as treatment did not change cognitive outcomes, despite reducing β-amyloid. Employing the PPM, we re-stratify patients precisely using baseline data and demonstrate significant treatment effects; that is, 46% slowing of cognitive decline for slow progressive patients at earlier stages of neurodegeneration. In contrast, rapid progressive patients did not show significant change in cognitive outcomes. Our results provide evidence for AI-guided patient stratification that is more precise than standard patient selection approaches (e.g. β-amyloid positivity) and has strong potential to enhance efficiency and efficacy of future AD trials.https://doi.org/10.1038/s41467-025-61355-3 |
| spellingShingle | Delshad Vaghari Gayathri Mohankumar Keith Tan Andrew Lowe Craig Shering Peter Tino Zoe Kourtzi AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial Nature Communications |
| title | AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial |
| title_full | AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial |
| title_fullStr | AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial |
| title_full_unstemmed | AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial |
| title_short | AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial |
| title_sort | ai guided patient stratification improves outcomes and efficiency in the amaranth alzheimer s disease clinical trial |
| url | https://doi.org/10.1038/s41467-025-61355-3 |
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