Using AI to meet data transparency requirements
Certara, a provider of biosimulation technology, has announced that its regulatory and medical consultancy Synchrogenix has introduced an artificial intelligence enabled solution to meet the data transparency requirements of the clinical drug development market.
The aim of this new technology is to support drug companies’ need to redact and de-identify datasets in their clinical study reports, patient narratives, patient data listings, and submission documents, in order to publish their clinical study information publicly. The details of the requirements were published by the European Medicines Agency (EMA) on March 2, 2016 under Policy 70.
Kelley Kendle, Synchrogenix president, said: ‘Disclosing clinical trial information so that researchers can build upon prior knowledge is an important step in bringing new therapies to patients, and fostering the industry’s commitment to the patients it serves.’
At the same time, we must protect the confidential patient and personal information contained in the myriad clinical reports to be published under Policy 70, which are often hundreds of pages long. In anticipation of these regulations and concerns around protecting patient privacy, Synchrogenix has developed technology that automates the redaction of personally-identifiable information, patient-protected data, and company-confidential information with 99 per cent accuracy.’
The EMA has been working with the industry for several years to develop a set of rules to make clinical trial data more public. In January 2015, the agency released new transparency and disclosure rules related to clinical study reports contained in marketing authorisation applications submitted on or after that date. The first reports are expected to be made publicly available in September 2016. The rules that EMA published earlier this month expand the breadth and depth of the original rules, and provide detailed requirements for companies to follow.
Synchrogenix’s technology is currently the only AI-enabled solution available to the biopharmaceutical industry. Built on natural language processing and recognition, this technology is able to identify individual words, parts of speech, and word and phrasing combinations automatically to enable the software to determine context.