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Changes Without Prior Approval

Changes Without Prior Approval. How do we get there from here?. Jon Clark Associate Director for Policy Development Office of Pharmaceutical Science Center for Drug Evaluation and Research, FDA ACPS Manufacturing Subcommittee July 21, 2004. Overview.

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Changes Without Prior Approval

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  1. Changes Without Prior Approval How do we get there from here? Jon Clark Associate Director for Policy Development Office of Pharmaceutical Science Center for Drug Evaluation and Research, FDA ACPS Manufacturing Subcommittee July 21, 2004

  2. Overview • The traditional system of approval and change control seems burdensome • There should be a way to protect the public without slowing innovation • Methods and standards for this are available • Need to train ourselves into a new way of thinking and working

  3. Shared Concerns • The pharmaceutical industry has one of the most technically advanced discovery organizations, but remains more conservative when it comes to using "cutting edge" technology in manufacturing. • Concern over how regulatory agencies will react to using knowledge and technology • Agency focus on changes with inconsequential impact on the product quality can result in delay.

  4. Complex Interaction • Commitment to high quality products with • Commitment to most rapid introduction to market

  5. When to Optimize the Process • Optimization before approval • Greatest cost may be time • No baseline for measuring return on investment • Provides immediate benefit to patient • Continuous improvement • Time element minimized • Enables measured improvement • Feed forward data and scope protocols • Inclusion of development data helps, but can not equal knowledge obtained during routine production

  6. Points to Consider • Raw Materials • Process • Measurement • Steering the Process • Variability

  7. Raw Materials • Pharmaceutical raw materials are variable. • Cannot assume that holding the inputs constant will always produce a constant product. • Ergo: Attempting process control through raw material control is futile.

  8. Process • Discovery and design suggest a process model • The model should be designed so that its parameters can be measured in the real world • As the model evolves measurement strategy evolves with it • The effect of change can be better predicted with realistic models • There is a lack of process models in applications

  9. Measurement • Measurement is most effective when used to control the process in “real time” • Traditional approach has been to sample the process and product, then test for compliance with criteria via “Laboratory Determination”

  10. Steering the Process • Change times, speeds, temperatures based on measurement to achieve target value for a product parameter. • Discarding batches or portions of batches reveals failure to steer the process.

  11. Variability • Variability reduction adds value • increases process capability • minimizes the risk of OOS • prerequisite for investigation

  12. Situation Spectrum High Process Understanding and Control Obviated End Product Testing Extensive Product Testing Little Process Understanding Increasing Desirability

  13. Therefore • FDA focus on Laboratory Testing is not ideal for controlling a process • Need to encourage Process Understanding and Engineering • Focus resources on the manufacturing process instead of lab tests and criteria • Avoid trap of “measure it because you can”

  14. Need for Zero Tolerance Limits Increasing Process Understanding and Control Need for Zero Tolerance Limits

  15. Post Approval Regulation Knowledge and Process Understanding Continuous Improvement

  16. Manufacturing Process Locked Process Variables Current Paradigm Variability Raw Material Product

  17. Dynamic System Manufacturing Process Raw Material Product Endpoint Response Input Response Measurement Dependant Process Variables

  18. P A TProcess Analytical Technology Manufacturing Process Raw Material Product Feedback Feed Forward Critical Process Parameter (CPP) adjusted by measurement of Critical Quality Attributes (CQA)

  19. We are not AloneMIL-STD-1916 dated 1996 • “Process controls and statistical control methods are the preferable means of preventing nonconformances, controlling quality, and generating information for improvement.” • “Sampling inspection by itself is an inefficient industrial practice for demonstrating conformance to the requirements of a contract and its technical data package.” • “To the extent that such practices are employed and are effective, risk is controlled and, consequently, inspection and testing can be reduced.”

  20. More • “The objective is to create an atmosphere where every noncompliance is an opportunity for corrective action and improvement rather than one where acceptable quality levels are the ... goals.” • “The goal is to support the movement away from a [product] inspection strategy to … effective prevention-based strategies including a comprehensive quality system, continuous improvement and partnership with Government.”

  21. And More • Process should be focus of quality system • Consistently producing conforming product. • Controlled as far upstream as possible. • Robust to variation…. • Operated to constantly reduce variation. • Utilization of equipment in a way that minimizes variability around target values • Managed for continuous improvement • Designed and controlled using a combination of practices and methods in order to ensure defect prevention and process improvement.

  22. Product Sampling and QualityDr. W. Edwards Deming • "Cease dependence on inspection [laboratory determination] to achieve quality. Eliminate the need for inspection on a mass basis by building quality into the product in the first place." • "Depending on inspection is like treating a symptom while the disease is killing you. The need for inspection results from excessive variability in the process. The disease is variability.” • "Ceasing dependence on inspection means you must understand your processes so well that you can predict the quality of their output from upstream activities and measurements."

  23. Target Critical Quality Attributes CQA Range Process Designed to Limit Product Variability Range of Raw Material and Facility Attributes

  24. Variation ControlAnna Thornton “Variation Risk Management” • Identification • Key Characteristics (KC) • Those that assure achieving CQA • Variation “Flowdown” • Assessment • Which variations put CQA at risk • Mitigation • Eliminate source • Reduce impact

  25. Examples of evidence regarding process understandingMIL-STD-1916 dated 1996 • Process flow charts showing the key control points for action to prevent defective product • Identification of process improvement techniques… • Identification of measures used, e.g., trend analysis • Results of improvements from using these… • Results of experiments that led to reduced variability...

  26. Examples of evidence regarding process control • Identification of the scope of use of process control techniques… • Process control plans, including improvement goals… • Approaches and supporting data used to determine if suppliers have adequate controls… • Descriptions of the required training … • Identification of departmental interrelations • Rationale for establishing subgroups • Identification of key parameters used in lieu of specified characteristics • More...

  27. Examples of evidence regarding process control (continued) • Identification of personnel responsible for process related corrective action. • Proper gage measurement studies showing measurement variations relative to total variation. • Traceability of the product and process corrective action(s) taken when the process went out of control, showing how the root cause was identified and eliminated.

  28. Examples of evidence regarding product conformance • Control chart showing the process in statistical control in accordance with the criteria… • Records of product and process corrective action(s) taken when nonconformances occur. • Process capability studies consisting of correct calculation and interpolation of [attribute measures] • History of product inspection results reinforced by statistical data and analysis. • Results from in-process control methods, such as [automation applications]

  29. Contribution ofExperience and Quality System • Institutionalization of Knowledge is a Quality Concern • Need to apply “solutions” wherever they provide improvement • Prior regulatory approval for every improvement defeats this goal

  30. Application w/o supplements? • What are quality critical attributes, the means of monitoring and controlling them • What are the fundamental scientific mechanisms of the physical changes? • How do formulation and process factors affect product performance? • Control of the operation using mechanistic scientific principles; Directly! • Demonstrate a range of operating ranges, controls and principles • History of manufacturing success with similar drugs or similar operating principles or similar site operations

  31. More Information • Significance of the site location and environment on the quality of the finished product • Drug product specification based on attributes critical to product performance experienced by the patient • Process control relationships to finished product quality

  32. Operational Freedom • This process understanding knowledge leads to greater freedom from narrow operating procedures and allow focus on drug product quality • Provide for use of alternatives to any application requirements • components • manufacturing and packaging procedures • in-process controls • analytical procedures • 21 CFR 314.50 (d)(1)(ii)

  33. Focus on process science and understanding • The FDA wishes to avoid allowing the submission of great operating procedure detail with equipment specifications to create a "safe harbor" for processes that do not consistently result in quality of product suitable for use • Batch records should not be used as manufacturing process control specifications or change control restrictions • Stability analysis is more valuable than raw data • Understanding of degradation mechanism helps predict impact of change

  34. Commercial Batch Research Data • Agency acknowledges concern that process research data may indicate a problem when the product still meets its approved release methods. • FDA began using a "research data exemption" concept in several guidance documents. • Doesn’t protect one that knowingly does harm without attempting corrective action. • This is designed to place this information outside the scope of a “normal” inspection. • Shouldn’t impact on the ability to release products that meet all aspects of the company's current registered quality control strategy.

  35. Situation Spectrum High Process Understanding and Control Obviated End Product Testing Extensive Product Testing Little Process Understanding Increasing Desirability

  36. End Thank you

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