Nathan Brown Quoted by Medtech Insight on FDA Pre-Certification Program for Medical Software

Akin Gump health care and life sciences partner Nathan Brown has been quoted by Medtech Insight for its article “Pre-Cert Program Will Start Off Slow With De Novo Framing, But Big Questions Remain,” on the Food and Drug Administration’s (FDA) pre-certification program for software-as-a-medical-device (SaMD) products.

The article notes that FDA has been working in a pilot collaboration with nine companies to craft the pre-certification approach through which companies making SaMD products will be evaluated for operational excellence in a bid to qualify for reduced pre-market review requirements for future products. The agency recently announced a new regulatory framework for the phase of the program that follows this year’s launch, along with a test plan. Under this plan, FDA will test how well the pre-certification appraisal process can substitute for some portion of pre-market submission requirements by using its de novo pathway.

The de novo pathway is an alternate pathway to “classify novel medical devices that had automatically been placed in Class III after receiving a ‘not substantially equivalent’ determination in response to a premarket notification [510(k)] submission,” according to the agency.

Brown, a former FDA lawyer and Senate Health, Education, Labor and Pensions Committee advisor, noted, “I think this reflects what FDA believes it can do by being creative within the existing de novo and 510(k) legal constructs. This will be the beginning of a process to see how well that works, compared to the option of adding different tools legislatively, both in terms of accommodating rapid innovation and providing transparency and confidence about what is being cleared.”

Discussing the de novo pathway as an approach for products combining software and hardware, he said, “The question will be whether FDA can find a way to grant a prospective clearance through its de novo process that allows for the dynamic evolution of, for example, a machine learning-based platform, while providing an assurance of safety and effectiveness—can they overcome the paradigm of re-review of each iterative modification of significance?”