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Medidata collab sheds light on rare diseases, the value of precision medicine

By Melissa Fassbender

06-Dec-2018 - Last updated on 11-Dec-2018 at 17:48 GMT

Medidata’s machine learning-based solution revealed novel biomarkers for Castleman disease among other discoveries that could help accelerate diagnosis and treatment.

The research was presented by the Castleman Disease Collaborative Network (CDCN) at the 60th Annual Meeting of the American Society of Hematology (ASH).

Medidata collaborated with CDCN to discover six new patient subgroups based on previously unknown proteomic signatures using its machine-based learning solution, Rave Omics.

According to the company, the discoveries provide novel insights into treatment response and potential new drug targets.

“As with many rare diseases, Castleman researchers have access to few data points,” said David Lee, chief data officer, Medidata. However, CDCN gained access to what Lee said is the largest collection of data for a form of Castleman disease, idiopathic multicentric Castleman disease (iMCD), and related diseases.

Yet, CDCN did not have the analytical and technological resources to integrate, clean, and analyze this data, Lee told us. “Medidata partnered with CDCN to satisfy their needs, and to demonstrate that Medidata Rave Omics can maximize the speed and scientific value of their integrated proteomic and clinical data,” he added.

According to Lee, Rave Omics was able to reduce CDCN’s timelines from “many months to just a few days,” for many of the analytical results shared at the ASH meeting.

A step closer to personalized medicine

Siltuximab is currently the only US Food and Drug Administration (FDA) approved drug for the treatment of iMCD. The drug was effective in approximately 34% of patients in the randomized trial, Lee explained.

“One of the newly discovered subsets had a response rate to siltuximab of 65%, almost double the overall response rate in the trial,” said Lee.

Understanding that this subset exists with double the response rate is “one major step” to realizing the goal of personalized medicine – which is to provide more effective treatments to the right patients, explained Lee.

“Also the clarity that new drug discoveries are still desperately needed for the other subtypes (19% response rate) increases the odds of finding better treatments for those very underserved subsets,” he added.

“Together, these outcomes bring the group of iMCD patients closer to the personalized medicine goal of providing more e effective treatments to the right patients.”

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