FDA Releases New Draft Guidance on CSA

Computer Software Assurance for Production and Quality System Software

The FDA has recognized that current perceptions and approaches to computer system validation (CSV) are a significant barrier to Digital Transformation and Manufacturing Modernization.  Today, the Agency released the long anticipated Draft Guidance on Computer Software Assurance for Production and Quality System Software.

This guidance discusses specific risk considerations, acceptable testing methods, and efficient generation of objective evidence for production or quality system software.

FDA is issuing the draft guidance to provide recommendations on computer software assurance
for computers and automated data processing systems used as part of medical device production
or the quality system. This guidance describes “computer software assurance” as a risk-based approach to establish confidence in the automation used for production or quality systems, and identify where additional rigor may be appropriate. It also describes various methods and testing activities that may be applied to establish computer software assurance and provide objective evidence to fulfill regulatory requirements, such as computer software validation requirements in 21 CFR part 820 (Part 820).

This guidance document has been prepared by the Center for Devices and Radiological Health (CDRH) and the Center for Biologics Evaluation and Research (CBER) in consultation with the Center for Drug Evaluation and Research (CDER), Office of Combination Products (OCP), and Office of Regulatory Affairs (ORA).

The guidance has been on the FDA’s A-List for released since FY 2019 and we have been publishing content on the topic since 2018. See all related CSA articles, eBooks and webinars.

Contact research@axendia.com to schedule an Analyst Inquiry on this topic.

The opinions and analysis expressed in this post reflect the judgment of Axendia at the time of publication and are subject to change without notice. Information contained in this post is current as of publication date. Information cited is not warranted by Axendia but has been obtained through a valid research methodology. This post is not intended to endorse any company or product and should not be attributed as such.

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