A COMPARATIVE FRAMEWORK BETWEEN NEW PRODUCT & LEGACY PRODUCT PROCESS VALIDATION

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1 A COMPARATIVE FRAMEWORK BETWEEN NEW PRODUCT & LEGACY PRODUCT PROCESS VALIDATION By Mark Mitchell, Principal Consultant, Process and Engineering, Pharmatech Associates, Inc. PHARMATECH WHITE PAPER.DOCX Pharmatechassociates.net Page 1

2 ABSTRACT This paper is a comparative analysis between application of the FDA Process Validation Guidance (2011) to new products and legacy products. While the three stages of process validation (design, qualification, and continued monitoring) provide a clear framework for the development, scale- up, qualification, and manufacturing of new commercial products, the application of these process validation principles to legacy products is only briefly mentioned in the FDA guidance. A practical approach, which applies analysis of historical data, risk assessment, and statistical principles, is outlined for legacy products in order to assess whether these existing processes require further supporting experimental and qualification activities. INTRODUCTION The current FDA Process Validation Guidance 1 introduces a new paradigm for the validation of pharmaceutical process. The concept of Process Validation is no longer defined as a study of three consecutive manufacturing lots, which must pass in order to allow the start of commercial production. Process Validation is now defined as a lifecycle, which starts at the development of the commercial process; continues through the qualification of equipment, facilities, and other systems; proceeds with the qualification of the process itself (previously referred to as the process validation study); and finally, an ongoing monitoring phase, which continues until the product is no longer marketed. Page 1

3 Figure 1 shows the Process Validation Lifecycle as it is defined in the FDA Guidance with three stages: Process Design, Process Qualification, and Continued Process Verification. A New Product enters the lifecycle at Stage 1, Process Design and follows the Lifecyle through drug product commercialization. The underlying principles of this Lifecycle are also referred to as Quality by Design (QbD) as per ICH Q8/9/10 2,3,4. Legacy Products, which are produced by previously qualified processes prior to the introduction of the current FDA Guidance, are unlikely to have been developed using QbD principles. However, it is necessary for a pharmaceutical company to align all products, both New Products and Legacy Products, to a common approach to the Process Validation Lifecycle. It is neither feasible nor required to restart the process validation of a Legacy Product with Stage 1 experimental studies as if it was a New Product. A more practical method is to assess existing historical data (i.e., Stage 3) to determine how to apply the Process Validation Lifecycle approach to Legacy Products. Figure 1. Process Validation Lifecycle per the FDA Guidance (2011) Page 2

4 INCORPORATING LEGACY PRODUCTS INTO A LIFECYCLE Per Figure 1, Legacy Products are incorporated into to the Process Validation Lifecycle at Stage 3, Continued Process Verification. The FDA Process Validation Guidance refers to Legacy Products in this manner: Manufacturers of legacy products can take advantage of the knowledge gained from the original process development and qualification work as well as manufacturing experience to continually improve their processes. Implementation of the recommendations in this guidance for legacy products and processes would likely begin with the activities described in Stage 3. Per the FDA Guidance, the primary activities of Stage 3, Continued Process Verification (CPV), involve the continued monitoring and sampling of process parameters and quality attributes and should include relevant process trends and quality of incoming materials or components, in- process material, and finished product. Legacy Products are already subject to trending and analysis through Annual Product Reviews (APR), but this only assesses the product trends, i.e., quality attributes, not the process trends of process parameters, in- process controls, nor critical material attributes. Additionally, the frequency of APRs may not be sufficient to allow for early detection, and thereby correction, of processes, which are not in statistical control. A comprehensive CPV program should include the following elements: Collated process parameter data and in- process control results from Master Batch Records. Collated quality attribute data from finished product testing (may be obtains from LIMS databases, where available). Collated material attribute data from incoming raw material testing. Defined data analysis emphasis and frequency, which is based on process parameters and material attributes with the highest risk to product quality, also known as criticality. Defined application of quantitative statistical tools and other qualitative methods used to detect unintended process variability. Scrutiny of intra- batch as well as inter- batch variation should be made. Selection of such tools should be done with the involvement of a statistician or a person adequately trained in application of statistical process control. Defined action plan for investigation of root causes of unintended process variability. Defined action plan for when additional monitoring or sampling is required. Page 3

5 Supportive data used in supporting CPV root cause analysis from other elements of the quality system: complaints and adverse events; process deviations; process change control; equipment and facility qualification, calibration, and preventive maintenance frequency or status changes. Analysis and interpretation of CPV data should be performed by not only quality personnel and trained statisticians, but also process subject matter experts from production and/or engineering and personnel from the quality control laboratory. SO MANY PRODUCTS AND SO MUCH DATA Companies with a sufficiently large number of Legacy Products may be quickly overwhelmed by the shear size of implementing a comprehensive CPV program. A timeline for implementing the Process Validation Lifecycle approach to Legacy Product should be developed by justifying the priority of each product. This is documented in a process validation plan or as part of the site s master validation plan. For example, the product priority could be set by a risk assessment including factors such as: history of lot failures, history of process deviations, number of lots produced per time period, time from last process validation study, success/failure of previous validation studies, stability history, history of adverse events or complaints, and date product was approved (newly approved or near obsolesce). This priority plan should include provisions for increasing the priority of a Legacy Product if a change to any of these factors may occur such as sudden increase in lot failures or a stability failure. Product data, such as from a LIMS or other electronic database, may be more easily collated than process data from non- electronic sources such as master batch records. The CPV database must be evaluated for both computer systems validation and CFR 21, Part 11 (Electronic Records) compliance. This includes both the data entry method and verification and the application of control charting and other statistical tools. New Products that have gone through both Stage 1 and Stage 2 of the Process Validation Lifecycle have an advantage going into Stage 3 over Legacy Products. For New Products, the criticality of process parameters (and material attributes) and their relationship to Critical Quality Attributes (CQA) are well understood and the overall Process Control Strategy (PCS) has been qualified with a successful Process Performance Qualification. This allows for the CPV program to focus the statistical analyses on those process parameters and material attributes, which have the highest impact on CQAs. Legacy Products may or may not have process parameters with a defined criticality supported by development data; this may lead to a fairly large number of process parameters being analyzed during the early phases of CPV. The CPV program for Legacy Products should be sufficiently resourced such that data from newly produced lots are entered and analyzed within a reasonable timeframe without creating a backlog. Page 4

6 Historical lots are added working backward from present lots with a defined minimum target for the number of lots. For statistical control chart purposes, more data is always better, but to establish meaningful control limits, a target of at least 25 to 30 lots is reasonable. ASSESS DEVELOPMENT HISTORY AND THE PROCESS CONTROL STRATEGY For New Products Stage 1 Process Design involves the application of first principles and prior knowledge (also called the Knowledge Space) combined with risk assessment to determine a statistically designed set of experimental studies, Design of Experiments (DOE). The resulting data along with the risk assessment allows for the determination of criticality of process parameters and material attributes relative to their impact on quality attributes. This is called the Design Space and it consists of modeled relationships between parameters and attributes where known combinations of process parameters and material attributes will produce acceptable product quality, i.e., critical quality attributes within their acceptance limits. These combinations of ranges for critical process parameters and critical material attributes are frequently called their Proven Acceptable Range (PAR). Parameters may have narrower ranges of control (or even fixed set point) to ensure the highest quality. These narrower ranges of control are called the Normal Operating Range (NOR). The combination of all NORs for a process is called the Control Space. The culmination of all risk assessments, models, PARs, NORs, etc. is documented as the Process Control Strategy (PCS). It is the PCS that is qualified during Process Performance Qualification. Few Legacy Products will have a documented PCS. A gap analysis will assist in compiling the necessary information in order to define the PCS for a Legacy Product as it currently exists. Some of the sources for this gap analysis include: Existing development reports for the commercial process (including those which may have occurred after the transfer to commercial manufacturing) Process change controls affecting set points or ranges or parameters Process descriptions or process flow diagrams Previous process validation studies Documentation of the criticality of quality attributes (note: quality attributes are not only finished product specifications, but also may include in- process controls) Documentation justifying the criticality of process parameters (data, first principles, or prior knowledge) Page 5

7 Process risk assessments such as FMEA 5 Incoming material specifications (for Critical Material Attributes) As part of this analysis, how criticality is defined for process parameters, material and quality attributes should be thoroughly reviewed, documented and uniformly applied to all products both new and legacy. ICH Q8 defines as follows: Critical Quality Attribute (CQA): A physical, chemical, biological or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality. Critical Process Parameter (CPP): A process parameter whose variability has an impact on a critical quality attribute and therefore should be monitored or controlled to ensure the process produces the desired quality. These definitions are useful starting points but additional enhancement is usually necessary. From the definition for CPP the phrase has an impact is vague. Any process parameter may have an impact at some extreme conditions; therefore, it is more relevant to understand what reasonable range this applies to (such as the PAR or NOR). Many companies find it useful to re- evaluate the criticality of parameters using a risk assessment in order to evaluate how the variation in the parameter can impact on product quality. For example, process parameters, which are essential fixed set points without any variation or have very tight control, may be considered to be low risk or even non- critical since they cannot impact the variation of product quality. This allows a focused effort in CPV on the high risk critical process parameters since it is known that within their PAR or NOR there is an impact on the variation of product quality. Completing a documented PCS for a Legacy Product allows for identification of possible gaps in the process understanding and the ability to control the process effectively (Note: this is the purpose of Stage 1). Quantitative evaluation of the control strategy for a Legacy Product is the focus of the next section. EVALUATE CPV HISTORICAL LOTS One of important elements of a Legacy Product s process understanding is how well the current process is controlled. In Statistical Process Control (SPC) process variation is defined as either Common Cause or Special Cause. Common causes occur in all processes since there is always some variation of control from lot- to- lot, and from day- to- day. Common causes are the culmination of the PCS and will have a statistically predictable outcome over a number of lots. Special causes are events, which are Page 6

8 unexpected changes in the process. Special causes are not necessarily bad, however, they are a bellwether that the statistical nature of the process has changed. Since the root cause of special causes is not immediately known or predictable, the process may become unpredictable in how it produces product quality. The process may be out of control. By generating control charts and process capability histograms of the CQAs, CPPs, CMAs, etc. from the historical lots of a Legacy Product, statistical analysis can be made on the capability and the state of control of the process. First, parameters and attributes should be assessed for control using control charts such as X- bar/r, Moving Range, etc. With sufficient number of lots (20-30 usually) control limits for the charts are calculated (usually ± 3 times sigma, the process standard deviation). Control charts using statistical rules (e.g., Western Electric or Nelson) can be used to identify Special Causes. To properly use these statistical rules requires interpretations from personnel trained in SPC and those with specific knowledge of the process, measurement, and analytical method. The rules will flag unexpected statistical differences, but further investigation is required to understand if this difference is undesirable. Example 1: a control chart of tablet hardness shows a shift in the process average; the root cause is a different hardness tester was used which produced an acceptable, but statistically measureable, difference in average hardness. Example 2: analysis of a capsule filling machine shows a see- saw type pattern in the control chart for fill weight and two peaks in its histogram; the root cause is that there are two filling stations on the filling machine and their average and variability is statistically different. Even if the process parameter or quality attribute is determined to have an acceptable level of control, it may not be capable to its acceptance limits. Capability is a measure of how well a process performs to its limits: NOR in the case of CPPs and specification limits in terms of CQAs. Here, Ppk or long- term capability is used since there are a large number of historical lots. The histogram will generally appear as a normal distribution. There are some examples where the distribution may not be normal, however: operators are arbitrarily setting different set points for a CPP in a fixed range, or cases of titrating to a ph limit or drying to a target limit. The Ppk should be 1.33 or better. If the Ppk is significantly lower, than even an in control process is statistically likely to exceed its limit. The batch record limits (essentially, the NOR) for Legacy Product high- risk CPP may not have supporting development data to show that product quality is achieved throughout the range. This may be due to missing or incomplete development data. The PAR (and therefore the narrower NOR) for a high- risk CPP should be justified by data. Additional development studies may be not feasible or even necessary to justify a wide NOR. The author recommends a statistical evaluation of the range of historical stage (such as tolerance intervals) to justify the NOR for the CPP since this historical range has presumably produced acceptable product quality. Page 7

9 PROCESS CHANGES AND WHEN PPQ IS NEEDED Evaluation of historical data may determine the product quality variability is either unacceptable or requires ongoing improvements. In these cases a process change may be justified that will improve the control of CPPs or CMAs to ensure product quality. These changes may be minor such as tightening the NOR of a critical parameter (as described above), or improving sampling technique. However, changes may be significant such as adding new CPPs, new control technology or equipment, or even revising an entire unit operation. Significant process changes will require revised risk assessments and new experimental data (new DOEs). Effectively, the process has now returned to Stage 1, Process Design, of the Lifecycle. These data may create the need for new CPPs or updated PAR and NOR for existing CPPs. Since this is a revision of the PCS, its documentation must be revised and the new control strategy must be qualified by PPQ. The scope of the PPQ, including number of batches and sampling plans, is commensurate with the level of risk of the process change. It may be a PPQ focused only on affected unit operation and affected downstream process or it may cover the entire process. Acceptance criteria for this PPQ should reflect the required reduction in variability that the process change is designed to achieve. The FDA PV Guidance does not mention the concept of revalidation. Periodic, such as annual, revalidation of process has been replaced with CPV. Any new PPQ is driven by process changes, which are in turn driven by ongoing evaluation of process and product data. After a CPV program is fully implemented for a Legacy Product, the author recommends that periodic process qualification studies be discontinued. CONCLUSION Table 1 provides a summary of the application of the three stages of the Process Validation Lifecycle to both New Products and Legacy Products. New Products follow a straightforward path from Stage 1 to Stage 2 to Stage 3. Legacy Product starts with creating a Stage 3 CPV program and evaluating gaps in the PCS (Stage 1). Data from Stage 3 will determine if the process is in control and capable. If a process (either New Product or Legacy) is determined to have too much variability, a process change may be required. Minor changes may not require a new PPQ (Stage 2) only additional monitoring and increased sampling (Stage 3). However, significant changes to the process may require experimental studies (Stage 1, Process Design) to support an improvement to the PCS and a new PPQ (Stage 2) to qualify the change. Page 8

10 Table 1. Comparative Framework between New and Legacy Product Process Validation Process Validation Stage Purpose New Products Legacy Product 1 Process Design Through process understanding determine Process Control Strategy (PCS) Apply first principles, risk assessment, and design of experiments to design small- scale and full- scale experimental studies. Risk assessments and study data used to establish PCS. Evaluate level of process understanding and control through review of PCS, development history, historical data (see stage 3), previous process validation studies, deviations, lot failures, etc. Close any identified gaps in PCS documentation. If process is not well controlled, a significant process change may be required with supporting experimental studies. 2 Process Qualification Qualify equipment, facilities, cleaning, methods, etc. Qualify process control strategy using PPQ New products require PPQ. Use process design data and risk analysis to determine number of lots, acceptance criteria, and sampling requirements. PPQ required only if significant process (or PCS) change is needed. Use historical data and level of change to determine number of lots, acceptance criteria and sampling requirements. 3 Continued Process Verification Ongoing verification of process control and identify any new risk to product quality Collect data on CPP, CQA, CMA, etc. Action and control limits are determined after a number (e.g ) of commercial lots are produced. Collect data based on existing CPP, CQA, CMA, etc. (or new risk assessment) from existing production records. Sufficient lot history to establish action and control limits usually exists. References: 1. FDA, Guidance for Industry, Process Validation: General Principles and Practices, January 2011, Revision 1 2. ICH Harmonized Tripartite Guideline, Pharmaceutical Development, Q8 (R2), August ICH Harmonized Tripartite Guideline, Quality Risk Management, Q9, June ICH Harmonized Tripartite Guideline, Pharmaceutical Quality System, Q10, April IEC 60812, Analysis Techniques for System Reliability Procedure for Failure Mode and Effects Analysis (FMEA), Edition 2.0, Page 9