| Abstrakt: |
Background: The Critical Path for Alzheimer's Disease (CPAD) consortium's primary objective is to promote, support, and manage pre‐competitive data sharing from Alzheimer disease (AD) clinical trials to allow quantification of Alzheimer's Disease progression across the AD continuum. A quantitative understanding of disease dynamics will drive scientific discovery provided by aggregated and standardized primary clinical trial data. This will provide solutions to optimize the design of clinical trials of AD drugs intended for regulatory review in support of marketing approval. CPAD is a nonprofit, pre‐competitive consortium (https://c‐path.org/programs/cpad/) of the Critical Path Institute (C‐Path) and convenes diverse stakeholders from academia, advocacy groups, industry, and regulators. Method: As of January 2020, CPAD's database (www.codr.c‐path.org) contains data contains 38 studies, representing 14,853 individual records and more than 420,000 data points, standardized to the AD CDISC (Clinical Data Interchange Standards Consortium) standards. CPAD's objective is to collaborate with industry and regulators to leverage their wealth of drug development knowledge by enabling pre‐competitive data sharing from clinical trials in AD. Analysis of datasets (at meta‐data and patient levels) enables generation of tools involving comprehensive disease progression models across the AD continuum, to support successful drug development. Result: CPAD continues ongoing efforts towards expansion of the CPAD database with contemporary datasets containing patient‐level data from both control and active arms of clinical trials in AD. By aggregating such datasets into our rich clinical trial repository, CPAD will fill existing gaps in our models and generate of quantitative solutions for drug development in AD, in collaboration with the Quantitative Medicine team. Key focus of CPAD in 2020 includes completion of the pre‐dementia model (which has received a Letter of Support from the EMA) and generation of the comprehensive disease progression model. This effort will be based on an evolving workflow initiating with dataset exploration at the level of meta‐data and patient level data and determination of model specifications, resulting in model validation, simulations and regulatory submissions. Conclusion: Pre‐competitive sharing of contemporary clinical trial data will allow us to develop a comprehensive understanding of the disease continuum in AD, enable a fully informed trial design and advance effective drug development in AD. [ABSTRACT FROM AUTHOR] |
