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Article
Alexandra Durr
Hôpital Pitié-Salpêtrière
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8921-7104
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This work is licensed under a CC BY 4.0 License. Read Full License
We propose a new approach, based on machine learning, to evaluating new treatments for neurodegenerative diseases. Using data from two longitudinal studies of 299 participants with early Huntington Disease, we learned the range of likely trajectories of 15 imaging and clinical biomarkers from the premanifest to manifest disease stages. We positioned independent 11,510 patients on these maps using their baseline data and hence forecast the values of their biomarkers at any timepoint. Applied to trial design, we showed that sample size can be decreased by up to 50% by selecting individuals for whom we predict a significant change for their outcome measures during trial. Reduction occurs whatever the selected outcome measures and the targeted disease stage. This approach does not only select the right patient at the right time for the right trial, but also guides decisions about when to start preventive treatments.
Keywords
Huntington disease, neurodegenerative diseases, preventive treatment
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Yes there is potential Competing Interest. A patent has been filed by INSERM Transfer under the reference PCT/IB2016/052699 (inventors: J.-B. Schiratti, S. Allassonnière, O. Colliot, S. Durrleman). It applies to the use of disease course mapping for selecting patients into clinical trials. The patent has been accepted in the USA and is under investigation in Europe and Japan. SD received a Sanofi iDEA award from Sanofi for a collaborative research project.
This is a list of supplementary files associated with this preprint. Click to download.
- supplementarymaterial.docx
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Article
Alexandra Durr
Hôpital Pitié-Salpêtrière
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8921-7104
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 08 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Neurobiology of Disease
License:
This work is licensed under a CC BY 4.0 License. Read Full License
We propose a new approach, based on machine learning, to evaluating new treatments for neurodegenerative diseases. Using data from two longitudinal studies of 299 participants with early Huntington Disease, we learned the range of likely trajectories of 15 imaging and clinical biomarkers from the premanifest to manifest disease stages. We positioned independent 11,510 patients on these maps using their baseline data and hence forecast the values of their biomarkers at any timepoint. Applied to trial design, we showed that sample size can be decreased by up to 50% by selecting individuals for whom we predict a significant change for their outcome measures during trial. Reduction occurs whatever the selected outcome measures and the targeted disease stage. This approach does not only select the right patient at the right time for the right trial, but also guides decisions about when to start preventive treatments.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Yes there is potential Competing Interest. A patent has been filed by INSERM Transfer under the reference PCT/IB2016/052699 (inventors: J.-B. Schiratti, S. Allassonnière, O. Colliot, S. Durrleman). It applies to the use of disease course mapping for selecting patients into clinical trials. The patent has been accepted in the USA and is under investigation in Europe and Japan. SD received a Sanofi iDEA award from Sanofi for a collaborative research project.
This is a list of supplementary files associated with this preprint. Click to download.
- supplementarymaterial.docx
Supplementary info
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