New FDA binding guidelines force industry switch to digital

From December 2017, the FDA will require all NGA licence applications to be submitted electronically – part of a push towards a digital data pharma industry

The strive to create electronic data submission standards for clinical trials in the USA stretches back to the 1980s, without a great deal of success.

The FDA joined this movement, also with limited success; now it has started introducing several guidelines regarding digital data, a handful of which are binding.

The industry has been reluctant to embrace standardisation in digital data, according to a white paper by global contract research organisation, Chiltern.

It took nearly 12 years for sponsors to embrace SDTM (Study Data Tabulation Model), the FDA’s endorsed standard for tabulation data, when preparing regulatory submissions. Even since it has been widely adopted, 25% of data are still not submitted in SDTM format.

To combat this, FDA started to introduce binding guidelines in December 2016. These guidelines require, rather than merely suggest, the cooperation of companies.


The new guidelines require studies to utilise specific, FDA-endorsed data standards for the collection, analysis and delivery of clinical and nonclinical trial data and results.

Additionally, as of December 2017, new drug applications, abbreviated new drug applications and biologics license applications must be submitted electronically, or they will be rejected.

The FDA currently requires standardised dataset submissions for most applications, except for investigational new drugs. This will change by the end of 2017, when new drugs will also be included.

The white paper speculates that the inconveniences of conforming to a universal, digital data standard had in the past made sponsors and service providers hesitant to adopt these standards, despite the net time and cost benefits.

Being compelled to make changes to data systems could provide a long-term benefit to service providers; they will be able to reallocate resources away from the design and maintenance of data structures into other, more beneficial tasks.

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