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Simulating the outcome of amyloid treatments in Alzheimer's disease from imaging and clinical data

ABSTRACT: Abstract In this study, we investigate SimulAD, a novel quantitative instrument for the development of intervention strategies for disease-modifying drugs in Alzheimer's disease. SimulAD is based on the modeling of the spatio-temporal dynamics governing the joint evolution of imaging and clinical biomarkers along the history of the disease, and allows the simulation of the effect of intervention time and drug dosage on the biomarkers' progression. When applied to multi-modal imaging and clinical data from the Alzheimer's Disease Neuroimaging Initiative the method enables to generate hypothetical scenarios of amyloid lowering interventions. The results quantify the crucial role of intervention time, and provide a theoretical justification for testing amyloid modifying drugs in the pre-clinical stage. Our experimental simulations are compatible with the outcomes observed in past clinical trials, and suggest that anti-amyloid treatments should be administered at least 7 years earlier than what is currently being done in order to obtain statistically powered improvement of clinical endpoints. In this study, Abi Nader et al. presented a computational model of Alzheimer’s disease progression that allows to create scenarios of drug intervention. They estimated that statistically powered improvement of clinical endpoints could be obtained if amyloid was blocked at least seven years before a dementia diagnosis is made. Graphical Abstract Graphical Abstract

SUBMITTER: Abi Nader C

PROVIDER: S-EPMC8168944 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Changing the course of Alzheimer's disease: anti-amyloid disease-modifying treatments on the horizon.

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| S-EPMC1894844 | biostudies-other

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