Data integrity and data quality are critical success factors for artificial intelligence (AI) and machine learning (ML) solutions in life sciences. Simply performing computer system validation or managing computer systems under CGMP conditions is not enough to ensure data integrity and data quality for data sets where AI/ML is intended to be applied.
Measurable benefits of off-site module construction are becoming more evident, and it is emerging as a viable method for delivering a greater degree of predictability. Furthermore, market constraints driven by shortages of craft labor, lagging productivity, project complexity, and competitive forces are contributing to biopharma’s move toward modularization.
How can the industry improve the quality of its products? The first tactic could be to increase the frequency of vendor inspections, using internal quality control programs to inspect suppliers to assure compliance with the necessary supply agreements and adherence to appropriate manufacturing practices, including GMPs.
The number of new chemical entities gaining FDA approval more than doubled from 2017 to 2018. Global R&D spending increased 3.5%. The pipeline is strong, but how many innovations are actually reaching patients?
FDA enforcement for failures in data integrity and data governance began almost 20 years ago, and these areas remain as enforcement initiatives of the FDA and other global health authorities to this day. In 2018, however, we may have turned a corner.
In this first of what will be a two-part article, USP's Fouad Atouf highlights the challenges presented by the FDA’s newest guidance while remaining optimistic that the large amount of data recommended today will open doors to more efficient development in the (hopefully) near future.
Upstream bioprocessing at large and commercial scales is projected to be the fastest growing segment of the single-use equipment market as products now in development using SUS move up to commercial manufacturing. In addition, as cellular and gene therapies emerge, we will likely see SUS technologies created and adapted explicitly for these personalized applications.
In this sixth installment on the selection and use of quality improvement tools in your organization, Ben Locwin wraps up the series with a treatise on the 7 Tools of Quality.
Despite the importance of the process the FDA is outlining in the guidance, I’ve surprisingly heard little chatter — positive or negative — about what the agency is now outlining and what this may mean for biosimilars and the biosimilar regulatory paradigm moving forward. Here are a couple of the biggest takeaways to note.
While many may conceptually understand change control for equipment and facilities, most lack the knowledge to apply this control to highly sophisticated equipment and the utilities to which they connect.
|
|
|
|
|
|
|
|
|