Computerized System Assurance (CSA) is a modern approach to validating automated systems, particularly in the life sciences industry. It emphasizes a risk-based methodology, focusing on product quality and patient safety, rather than the traditional, more burdensome Computer System Validation (CSV) approach [1], [2]. This article delves into the intricacies of CSA, highlighting the dos and
The use of artificial intelligence and machine learning in the pharmaceutical industry holds immense promise for overcoming drug shortages. By leveraging predictive analytics, real-time demand forecasting, quality control, regulatory compliance, inventory management, and early warning systems, AI and ML can help ensure that patients have reliable access to the medications they need.
N-nitrosamine impurities, a class of potentially carcinogenic substances, have become a significant concern in the pharmaceutical industry. These compounds can form during the drug manufacturing process or due to degradation.
Choosing the right QMS is an investment in your organization’s future. By carefully considering these factors, you can select a system that empowers your quality goals, streamlines operations, and sets you on the path to continuous improvement.
As technology continues to advance, the integration of artificial intelligence (AI) into GMP environments promises enhanced efficiency, productivity, and quality control. However, alongside these benefits come unique challenges and considerations, particularly concerning regulatory compliance.
