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.
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.
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.
Microbial data deviation investigations are notoriously difficult to perform. They are time consuming and labor intensive. Determining the definitive root cause is often challenging. Evidence to support conclusions and decisions takes time to collect, and the available evidence is usually circumstantial and often scanty.
Adapting any manufacturing process to continuous manufacturing requires in-depth chemical and mechanical engineering understanding as well as the right technologies.
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