C-Path and Replica Analytics Collaboration Aims to Accelerate Rare Disease Research
Critical Path Institute (C-Path) and Replica Analytics, an Aetion company, announced a new partnership that involves leveraging synthetic data to further catalyze the generation of actionable solutions to accelerate drug development for rare diseases.
Replica Analytics will help generate synthetic datasets across rare and orphan indications in which patient-level datasets are often quite small, which heightens considerations regarding data privacy and accessibility.
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Together with real data, these synthetic data will help maximize the utility of C-Path’s Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP), which provides a centralized and standardized infrastructure to support and accelerate rare disease characterization targeted to accelerate drug development. Additionally, the platform includes a framework that supports the rigorous conduct of natural history studies, with attention to established data quality standards, to be most useful to clinical trial design and regulatory review. It includes a robust, integrated database and analytics hub that allows for the aggregation of rare disease data from various sources and the efficient and effective interrogation of that data. Once added to RDCA-DAP, the synthetic data will also represent valuable real-world, electronic health record (EHR) based data available on the platform, opening doors to modeling and research based on longitudinal focused analyses.
“Our collaboration with Replica Analytics is important because synthetic data can add value to real-world data while observing data privacy considerations, which will help accelerate overall rare disease drug development,” said RDCA-DAP Scientific Director Alexandre Betourne, Pharm.D., Ph.D. “The goal of RDCA-DAP is to provide a centralized and standardized infrastructure to support and accelerate rare disease characterization and therapy development, this collaboration is in line with our efforts.”
Synthetic data generation (SDG) is a privacy enhancing technology that has been gaining rapid adoption, particularly in the life sciences sector. SDG uses AI to create machine learning models that learn the statistical patterns and properties of real datasets to generate data that retain the same characteristics as the original dataset, but with no one-to-one mapping back to an identifiable person. SDG can help amplify small datasets, simulate virtual patients to augment patients in existing datasets, and optimize the design of small sample clinical trials.
“We are certainly seeing a growing opportunity to partner with organizations like C-Path for generating datasets that are fit-for-purpose,” says Khaled El Emam, Ph.D., SVP and GM of Replica Analytics. “Synthetic data, which preserves the integrity and utility of source data, as well as being privacy-protective, can be a very valuable tool to enable this collaboration.”
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