Senior quality leaders and the FDA agree quality organizations supporting pharma manufacturing often fall short of providing the support necessary to maintain and increase regulatory compliance and product quality. At the same time, some do not understand their actions and believe they are obstructionist and reflective of a strict compliance mentality with little regard for the business and its success. What would cause business partners to look at quality in such a way?
This is the second of two articles, this part focuses on the importance of cybersecurity and software maintenance, how time scales and methods of execution differ from those in traditional pharma, how agile software development works, and how to avoid potential pitfalls.
As biopharmaceutical companies develop and market more medical devices and combination products with digital health components, the FDA and industry experts caution about regulatory and business risks.
Amgen is piloting a process using artificial intelligence (AI) that has the potential to greatly enhance its ability to trend and find patterns in manufacturing deviations and to prevent their recurrence. The AI tool will look across large data sets and find correlations between obscure signals and events which the previous system could have missed.
Continuously learning systems (CLS) are artificial intelligence (AI) algorithms that constantly and automatically update themselves as they recognize patterns and behaviors from real-world data — enabling companies to become predictive, rather than reactive, with quality assurance.
The FDA recently revamped the methods it uses to determine which foreign and domestic drug manufacturing sites warrant inspection or other types of surveillance and at what frequency.
Learn what U.K. MHRA Senior GMDP Inspector Tracy Moore had to say about MHRA’s efforts in the areas of drug/device supply chain security and international efforts on harmonization of data integrity.
FDA officials and leaders in the pharma and medical device spaces agree artificial intelligence (AI) tools could enable a step change in quality management in those industries. Areas that could be impacted include supply chain management, lot release, manufacturing, compliance operations, clinical trial end points, and drug discovery, among others.