Abstract
Recent failures of clinical trials in Alzheimer’s Disease underline the critical importance of identifying optimal intervention time to maximize cognitive benefit. While several models of disease progression have been proposed, we still lack quantitative approaches simulating the effect of treatment strategies on the clinical evolution. In this work, we present a data-driven method to model dynamical relationships between imaging and clinical biomarkers. Our approach allows simulating intervention at any stage of the pathology by modulating the progression speed of the biomarkers, and by subsequently assessing the impact on disease evolution. When applied to multimodal imaging and clinical data from the Alzheimer’s Disease Neuroimaging Initiative our method enables to generate hypothetical scenarios of amyloid lowering interventions. Our results show that in a study with 1000 individuals per arm, accumulation should be completely arrested at least 5 years before Alzheimer’s dementia diagnosis to lead to statistically powered improvement of clinical endpoints.
Significance Statement In this study, we model the interactions at stake between multimodal processes during Alzheimer’s Disease, by combining state-of-the-art machine learning methods with the analysis of imaging and clinical biomarkers. Inferring such relationships allows us to simulate the effect of disease modifiers on the pathology progression. As current clinical trials lack of quantitative guidelines in order to better hypothesize the effect of intervention time or drug dosage, our computational framework provides key insights to establish a roadmap for investigation of certain drugs. In particular, we show in this work that antiamyloid treatments should be administered earlier than what is currently done in clinical trials, in order to obtain statistically powered improvement of clinical outcomes.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Clément Abi Nader and Marco Lorenzi designed the method. Implementation was carried out by Clément Abi Nader. The manuscript was written by Clément Abi Nader with support from Marco Lorenzi, Nicholas Ayache, Giovanni B. Frisoni, and Philippe Robert.
The authors declare no competing interests.
↵2 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.