Continuous Process Verification/Validation Maintenance

Continued Process Verification/Validation Maintenance
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Continuous Process Verification/Validation Maintenance
Regulations and guidelines
Today, professional quality and process data trending is of major importance for science-based development and manufacturing of medicinal products. Of late, the European Medicines Agency (EMA) and the U.S Food and Drug Administration (FDA) provided revised process validation guidance for the purposes of enforcing frequent analysis of data as a regulatory chief prerequisite (Subramanian 2015). Periodic process and product monitoring, commonly referred to as Process Verification, is regarded as a vital aspect of process validation, aimed at demonstrating product compliance as well as process robustness for the duration of the entire life cycle. Even though this new guidance actually signifies a consequence improvement of the well-known processes, it actually has the potential of revolutionizing the everyday business of the pharmaceutical industry (Rommerskirchen & Ingelheim, 2015).
Validation is understood as corroborating or confirming; that is, to give official confirmation or legal force to. Verification means to establish the correctness of a fact or theory. Process validation entails giving guarantee that the production of medicinal products has the capacity to constantly deliver the required quality (Allison, Cain & Cooney 2014). During process validation, the focus should be on preventing product quality and processing problems by applying good science and risk-based approaches. There should be focus on product and process knowledge through entire product lifecycle, which include using risk-based methods. The crucial quality attributes and critical process parameters for the process should be defined. Fundamental to this is the development and maintenance of good quality management system. To monitor and control processes, current technology for instance Process Analytical Technology can be employed. Using this modern technology facilitates processing adjustments and is of help in minimizing output variation, which actually makes real time release a possibility (Levenson & Chatterjee 2013).
U.S. Food and Drug Administration Guidelines
The three-stage US Food and Drug Administration (FDA) lifecycle approach can be employed in process validation. Stage 1: Design Stage – this stage covers building as well as capturing process knowledge and understanding; and creating a strategy for process control. Process definition commences in the initial phases of development where critical process parameters, equipment, materials, as well as critical quality attributes are identified initially. All process parameters are evaluated and varied within operating ranges and their influence on product quality established and recognized (Alsmeyer & Pazhayattil, 2014). Stage 2: Process Performance Qualification – this stage includes qualification of equipment and utilities, as well as design of a facility. It also includes process performance qualification (PPQ) or process validation (PV). An organization should prove that it can always make pharmaceutical products at commercial scale. An organization also needs to demonstrate reproducibility in its capacity to control process parameters and product characteristics (Ryan, Greene & Calnan 2013).
Stage 3: Continued Verification – in this third stage, according to the U.S FDA guidance 2011, the goal is to repeatedly assure that the process stays in a state of control – the validated state – during the commercial manufacture (Alsmeyer & Pazhayattil 2014). To achieve this goal, it is important to have a system that will detect unplanned deviations from the process as designed. Compliance with the requirements stipulated by the CGMP, particularly the gathering and assessment of information and data regarding the performance of the process, would allow the undesired process variability to be detected (Cahill 2014).
At present, cGMP necessitates a system to gather and evaluate product data, and in particular, to detect process drift or shift. According to the guideline, the gathered data needs to comprise pertinent process trends and quality of incoming components or materials, in-process materials, as well as finished goods. The data needs to be statistically trended and evaluated by people who are trained in statistical techniques to verify that crucial quality characteristics are properly controlled (Ryan, Greene & Calnan 2013). Moreover, procedures have to describe the way calculations and trending are performed.
Companies in the pharmaceutical industry are advised to employ statistical techniques to establish variation, characterize it, and recognize origin causes. The scrutiny of intra-batch and inter-batch variation is also recommended. In addition, cGMP recommends continued sampling and/or monitoring at PQ levels until adequate data is obtainable to produce enough variability estimates. After that, sampling could be changed to a level that is statistically apt (Rommerskirchen & Ingelheim 2015). Other areas that should be monitored continually include process deviation reports, OOS findings, product complaints, batch records, process yield variations, adverse reports, and incoming raw material records. CGMP also recommends that feedback should be obtained from quality staff and production line operators.
Moreover, operator errors need to be tracked in order to measure the quality of training programs, to identify issues pertaining to operator performance; to search f...