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.
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.
Automating digital data management in biopharmaceutical manufacturing is a process of using digital technologies such as cloud, artificial intelligence, data lakes, and wearables to collect, store, analyze, and share data across the entire development lifecycle.
Validation by design thinking in the pharmaceutical industry, often referred to as “Validation 4.0”, is a shift in approach that adapts to the evolution of the industry.
