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Process Validation - A Lifecycle Approach
U.S. Food and Drug AdministrationGrace E. McNally (Senior Policy Advisor)
May 6, 2011
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Of special interest are:
Page 3:
2. Update Guidance based on regulatory experience since 1987.
– Emphasis on process design elements and maintaining process control during commercialization
– Communicate that PV is an ongoing program and align process validation activities with product lifecycle
– Emphasize the role of objective measures and statistical tools and analyses.
– Emphasize knowledge, detection, and control of variability.
Lifecycle approach is more rational, scientific and can improve control and assurance of quality.
2. Update Guidance based on regulatory experience since 1987.
– Emphasis on process design elements and maintaining process control during commercialization
– Communicate that PV is an ongoing program and align process validation activities with product lifecycle
– Emphasize the role of objective measures and statistical tools and analyses.
– Emphasize knowledge, detection, and control of variability.
Lifecycle approach is more rational, scientific and can improve control and assurance of quality.
Page 16:
• “Focusing exclusively on qualification efforts without also understanding the manufacturing process and associated variations may not lead to adequate assurance of quality.”
• Poor quality drugs on the market, evidenced by recalls, complaints and other indicators, from supposedly “validated” processes pointed to a lack of process understanding and adequate process control. This was an impetus for revising the 1987 Guideline.
• “Focusing exclusively on qualification efforts without also understanding the manufacturing process and associated variations may not lead to adequate assurance of quality.”
• Poor quality drugs on the market, evidenced by recalls, complaints and other indicators, from supposedly “validated” processes pointed to a lack of process understanding and adequate process control. This was an impetus for revising the 1987 Guideline.
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• Follow up unexpected, unexplained information during early design studies
• Understand multivariate interactions and scale factors
• Consider cumulative effects of tolerance stacking
• Anticipate and plan for greater input variability at commercial scale from Operators, Equipment, Manufacturing instructions, Environment, APIs and Excipients, Measurement Systems
• Revisit process design if current process is not robust
• Revisit and update earlier risk assessments
• Re-assess original specifications, i.e., - are they appropriate?
• Conduct in-depth Root Cause Analysis
• Follow up unexpected, unexplained information during early design studies
• Understand multivariate interactions and scale factors
• Consider cumulative effects of tolerance stacking
• Anticipate and plan for greater input variability at commercial scale from Operators, Equipment, Manufacturing instructions, Environment, APIs and Excipients, Measurement Systems
• Revisit process design if current process is not robust
• Revisit and update earlier risk assessments
• Re-assess original specifications, i.e., - are they appropriate?
• Conduct in-depth Root Cause Analysis
Page 21:
• Protocol(s) include
– “Criteria and process performance indicators that allow for a science- and risk-based decision about the ability of the process to consistently produce quality products.”
– “A description of the statistical methods to be used in analyzing all collected data (e.g., statistical metrics defining both intra-batch and inter-batch variability).”
• Protocol(s) include
– “Criteria and process performance indicators that allow for a science- and risk-based decision about the ability of the process to consistently produce quality products.”
– “A description of the statistical methods to be used in analyzing all collected data (e.g., statistical metrics defining both intra-batch and inter-batch variability).”
Page 22:
• Manufacturer must scientifically determine suitable criteria and justify it.
• Objective measures, where possible.
• Manufacturer must scientifically determine suitable criteria and justify it.
• Objective measures, where possible.
Page 31:
• In order to detect process drift, normal (common cause) variability has to be understood and measured where possible.
• In order to detect process drift, normal (common cause) variability has to be understood and measured where possible.
Page 32, "Stage 3- Continued Process Verification":
• A strategy for trending and monitoring.
– What is the goal?
– For example, determining machine-to-machine
variability? within a machine? Batch to batch variability for certain attributes?
– May need to tailor approaches, use different tools, for different products and processes.
• Obtain expertise applying statistical tools and analysis to manufacturing data.
• Further refine the control strategy.
• A strategy for trending and monitoring.
– What is the goal?
– For example, determining machine-to-machine
variability? within a machine? Batch to batch variability for certain attributes?
– May need to tailor approaches, use different tools, for different products and processes.
• Obtain expertise applying statistical tools and analysis to manufacturing data.
• Further refine the control strategy.