Ensuring Real-Time Quality In Manufacturing

A few years back, US drug authority Food and Drug Administration (FDA) published a guidance work that introduced the concept of Process
Analytical Technology (PAT) and redefined quality assurance and pharmaceutical manufacturing for the future. In the same guidance a
concept known as "real-time release," was introduced, which is defined as "the ability to assess and certify the suitable quality of final
and/or in-process product based on process data".
PAT enables the introduction of innovative technologies into manufacturing in the pharmaceutical industry. In today's competitive
environment, firms must strive to enhance pharmaceutical quality control while lowering production costs. PAT’s goal is to improve
understanding and regulate the manufacturing process, which is as per the FDA's existing drug quality system. CGMPs provide for systems
that assure proper design, monitoring, and control of manufacturing processes and facilities, and these days the importance of
cGMP manufacturing cannot be neglected as well.

Real-time Quality Assurance Pharma with PAT
Structured process and product development using experimental design and with process analyzers to gather data in real-time, can
offer better understanding and insight for optimization, process development, scale-up, control, and technology transfer. Process
understanding subsequently remains in the production stage when other variables are encountered. So, continuous learning over the
product lifecycle and implementing those learnings is important.
                

Benefits of Process Analytical Technology
PAT offers the pharma industry a framework for reforming its R&D and manufacturing businesses, to create value for both patients and
themselves. The major benefits linked to PAT for companies are as follows:

 Strong product supply.
Fewer work-in-progress, raw materials, and finished goods inventories through lean cGMP manufacturing processes
A better movement toward a real-time product release.
Real-time validation and quality assurance.
Reduced waste, greater production asset utilization, and right-first-time manufacturing.

Quality Control in the Pharmaceutical Industry and the Transformation of Drug Development
FDA supported the inclusion of Quality Risk Management (QRM) within the process and product design. This is the fundamental
principle behind industry best-practice guidance. Until ICH Q10 was launched, the industry wanted to carry out an agenda that made
a distinction between the development organization from the quality organization, placing quality as the final decision-making authority. 
However, Q10 required process and quality development to be integrated if an organization achieves both control and performance.
This leads to an organization grounded in scientific comprehension and risk management, with a strong foundation in statistical
analysis.
Good manufacturing practice (GMP) is a system for ensuring that products are consistently produced and controlled according to quality
standards. The term CGMP Manufacturing refers to the Current Good Manufacturing Practice regulations that are generally being enforced
by the FDA in adherence to the CGMP regulations that actually assures for identity, strength, quality, and purity of drug products by requiring
that manufacturers of medications adequately focus on controlling Manufacturing operations.
        
Quality experts must understand how stage-appropriate data helps in making decisions about quality. This distinction exemplifies the key
difference between ensuring product quality by just ensuring QMS adherence, specification compliance, and traceability vs scientific
understanding.
Since the FDA issued its “compliance through science” guidance, business performance and drug quality have improved. The major changes
carried out 10 years back allow industry and regulators to make the most of the innovations we expect to see in healthcare. Today we are shifting
toward a process that will help us acquire and handle data. 

If solution providers focus more on the problems facing the industry today, they will not find any difficulty in transitioning to new predictive,
self-aware technology and architectures with little disruption. However, if the core issues of cybersecurity and data integrity are ignored and
relegated to the background, the advantages of a new age of efficiency and safety promised will remain out of reach.

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