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A data-driven strategy can assess the quality of legacy drugs developed before 2011 process-validation requirements were established.
The pharmaceutical regulatory landscape and pharmaceutical development have been reshaped by International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Harmonised Guidelines. ICH guidelines Q8–Q12 (1–5), and those in development such as ICH Q14 (6), have applied science, risk management, and quality systems to enhance process and product quality. In 2011, FDA’s revised process validation guidance (7) extended ICH’s concepts to pharmaceutical product lifecycle, an approach that is now being applied to cleaning validation and other areas of manufacturing, as well as to clinical trials (8).
Legacy drug products developed before any these guidelines were established, however, have not undergone the same rigor during development as newer products. As a result, companies must categorize legacy products as being in Stage 1, 2, or 3 of the lifecycle and make a data-driven assessment to determine whether remediation or one of the following plans must be developed:
This article proposes an independent structured review process for legacy products—those pharmaceuticals that predate FDA’s updated process validation and quality by design [QbD] guidance. This approach uses data to develop insights into product sustainability and risks. It focuses on the evaluation of intra- and inter-batch variability for uniformity and other critical quality attributes (9–10) and can be applied to small-molecule manufacturing processes.
This review process involves comprehensive process assessment (i.e., historical data review and the gathering of end-to-end manufacturing process data); extensive process monitoring, and characterization exercises that enable lifecycle quality gaps to be addressed.
By allowing sound pharmaceutical manufacturing science principles to be applied, as well as data analysis, this assessment permits legacy product quality to be on par with that of any products developed by applying QbD approaches. Going through this assessment process results in a heightened level of product and process knowledge, while substantially reducing the risk of potential failure.
Based on the 2011 FDA process validation guidance and relevant ICH guidelines, this assessment method requires the use of analytical and interpretive methods based on standard statistical and data visualization tools. The approach allows results to be verified at any time by both product owner and regulator, utilizing standard statistical tools. All data sets prepared, transposed, and analyzed should be subject to data verification procedures to assure data accuracy and integrity.
Documents required for the assessment include the following:
The assessment uses current regulatory standards for validation (e.g., from FDA, the European Medicines Agency, Health Canada, and the World Health Organization). It also uses tools and methods such as cause-and-effect Ishikawa diagram, scientific rationale checklists, decision trees, as well as supporting analytical and statistical tools such as Minitab or JMP software.
The assessment focuses on analyzing the information and knowledge gained from the three stages of the process validation lifecycle: process design, process performance qualification, and continued process verification during commercial manufacturing. It is performed in the following two parts:
A comprehensive compliance review is designed to summarize and confirm compliance with the FDA process validation guidance and ICH and other emerging guidance, based on observation and the available data. The recent ICH Q12 document allows use of lifecycle guidelines for seamless post-approval change management. Adherence to lifecycle guidance, therefore, becomes more important for ongoing operational flexibility and compliance.
Stage I, process design assessment, involves the following steps:
Stage 2, process performance qualification review, covers aspects such as:
Stage 3, continued process verification of the product, includes the following:
The seven steps involved in Part B are as follows:
Identify sources of variability (Step 1).
Variability is caused by contributing factors associated with materials, methods, machine, measurement, manpower, and mother-nature (12). In pharmaceutical manufacturing, this relates to attributes of material, formulation and associated processing parameters including scale-up factors, processing equipment, test method, personnel, and facility/utilities (Figure 1).
The overall critical quality attribute (CQA) variability is a function of individual component variability.
A cause-and-effect technique is utilized to identify underlying factors that could have an impact on CQA that would affect patients (i.e., label claim CQAs). The assessment includes factors and attributes of materials, formulation, processing parameters and scale-up, processing equipment, method and analytical, personnel, and facility/utilities as shown in Figure 2.
Criticality assignment (Step 2).
The rationale for the criticality assignment is discussed qualitatively (Figure 3). Upon identifying the risk factors through cause-and-effect techniques, a risk-rating decision tree (Figure 4) is used to analyze the legacy process upon risk assessment. The criticality assignment will be commensurate with the product/formulation characteristics. In addition to any product-specific CQAs that would affect the patient, the review should, at a minimum, assess the impact of variability factors on CQAs (i.e., assay, uniformity, weight variation, dissolution, and related substances). Process variability may affect the assay of the drug product, thus the impact of sources of variability on assay should be evaluated. Variability in uniformity and weight variation can affect safety and efficacy.
Formulation, process, and other variables may impact content uniformity and weight variation. Thus, the impact of sources of variability on content uniformity should also be evaluated. Related substances can impact safety and must be controlled based on FDA, compendia, or ICH requirements. Formulation, process, and other variables can impact related substances. Risk assessment must cover all the formulation and process attributes as shown in the below example for uniformity.
The decision tree with a data-driven semi-quantitatively risk-rating system (Figure 4) is used to prioritize the unexamined influencing factors labeled in yellow (Figure 3) identified for each element. Note that the example provided in Figure 3 pertains to factors affecting one quality attribute only (uniformity). Additional review is warranted when the rating value is higher.
Gap assessment (Step 3).
The decision tree-based assessment determines whether the established current control strategy for each attribute is supported by the cumulative documented data from relevant Stage 1 studies (e.g., design of experiments; ranging studies, laboratory or pilot batches). The control strategies established to control and mitigate the identified risk factors are then reviewed to determine the missing parts.
This gap assessment also evaluates whether the established current control strategies are derived based on objective information/rationale and data from original and subsequent process design studies. The need for establishing additional controls, including manufacturing process controls, is determined at the end of the gap assessment.
The gap assessment also determines whether the established current control strategy is supported by the cumulative data from all relevant studies (e.g., designed experiments; laboratory, pilot, and PPQ batches). The control strategies established to control and mitigate the identified risk factors are reviewed to arrive at the conclusions. The residual risks and the need for establishing additional controls, including manufacturing process controls, are then identified to initiate a heightened sampling and testing Stage 3a plan for the product.
Lifecycle review and Stage 3a monitoring batch execution (Step 4).
The lifecycle review should determine, based on available lifecycle data, whether the current process is operating consistently within the originally established control strategy. The control strategy gap assessment step also may identify the need for additional controls or identify gaps in supporting data. The lifecycle review will determine if any commercial product data supports the identified gaps. The data from all three stages of the process validation lifecycle (e.g., design of experiments, manufacturing trials, statistical analysis, annual product review, and commercial batch data) may enable the development of a risk-based control strategy that would suitably minimize sources of variability. Data from previous credible experience with sufficiently similar products and processes may also be utilized to support the control strategy.
Data gaps for legacy products at Stage 1 QbD, Stage 2 equipment/facility/utility qualification and PPQ, and Stage 3 continued process verification require that a sampling and testing plan be developed under a Stage 3a protocol, to further enable prediction with adequate statistically significant data sets.
The type of Stage 3a sampling/testing plan developed is based on the product/process gaps that have been identified. The Stage 3a study focuses on processes that have not been characterized, or for which variability has not been determined. Extensive data must be collected and tested, and a monitoring protocol executed by the technical operations team responsible for Stage 1 lifecycle of the product. The Stage 3a lifecycle monitoring data that are collected supports the identified gaps or enables closure of existing Stage 1 data gaps for the product.
Data-driven, scientifically sound Stage 3a monitoring enables the following:
ICH Q10 recommends management of product and process knowledge from development through commercialization and until discontinuation. The monitoring batches add to the body of knowledge on the product. FDA’s process validation guidelines allow for use of credible experience with sufficiently similar products, hence a bracketing approach for monitoring may be applied and conclusions can be used for control strategy enhancement of similar products.
The Stage 3a data collection protocol (13) developed after gap assessment enables in-depth statistical assessment of the legacy product. The Stage 3a assessment will also provide a baseline for continual Stage 3b program for the product. The approach supports the organization’s ability to make science- and risk-based continual process improvement decisions for the product, as well as to identify areas where QbD development should focus for similar manufacturing processes or products. The Stage 3a monitoring measures are developed in line with upcoming revisions to ICH Q2/Q14 and the proposed I General Chapter <1220> framework for analytical QbD, which requires identification of adverse trends, allowing proactive measures and facilitation of continued improvements and change control through continued monitoring.
Legacy data collection (Step 5).
The extensive data collection step includes batch data from development, process performance qualification, and commercial manufacturing. The new database will include potential variables that can impact the CQAs. This will include, but will not be limited to, API and excipient material attributes, process parameters, equipment parameters, hold times, operational variables, operation/analytical personnel, instrumentation, documented weights/time, facility/utility parameters, and incidents. This step will involve extensive data set collection, data set review, and data preparation for the subsequent statistical analysis.
As a result, the database must be developed so that it maximizes the utilization of existing product/process data for multivariate statistical analysis such as correlations. The database is created for variables that can impact CQAs. The database is utilized as a starting point for Stage 3b trending and ongoing signal detection of the legacy formulations that are determined to be robust post assessment.
Statistical analysis (Step 6).
The data collected from Stage 3a heightened sampling and the historical batches are statistically analyzed to identify patterns and insights into the impact of potential sources of variability on the CQAs. Statistical software tools such as Minitab and JMP may be utilized. The analysis determines the adequacy and suitability of the collected data sets to support the current control strategy. The analysis first attempts to determine whether the original control strategy developed for the legacy product adequately meets the current standards.
Enhancement requirements are then determined, based on the correlations and trends observed from batch data across commercial manufacturing. This step includes an assessment of within-batch and between-batch variability, and the associated controls available to minimize the variability.
The statistical assessment can identify patterns and insights showing the impact of potential sources of variability on the CQAs. The correlation between the uniformity results, such as blend uniformity (BU) or content uniformity (CU), can suggest whether adequate mixing is happening and therefore justify continuing stratified CU sampling in place of error-prone and disruptive BU sampling. A further statistical criterion can also be developed using the assessment: removing the CU sampling once adequate uniformity data have been generated. To augment the historical data, for example, the variability of uniformity is determined with Stage 3a monitoring batch data generated (14). Correlation of process parameters are conducted to determine the degree of their influence on the CQAs at manufacturing stages.
The statistical assessment tries to address each of the attribute gaps identified though Stage 3a heightened monitoring and analysis. The rating table is then updated based on the newly identified and generated data, as shown in Table I.
Interpretation of results (Step 7).
Interpretation generates actionable recommendations based on the statistical analysis. The data-driven estimations allow for in-depth product/process understanding, proving product robustness and are in line with regulators’ expectations that science and data be used for product and process decision making. The determination is based on data signals identified through assessment of the historical data and the generated heightened results from Stage 3a monitoring batches.
When adequate data are available, the signals seen (i.e., CQAs, key performance parameters, and CPPs), can be categorized based on risk. All signals are not created equal, and different action plans are required for the yellow flags. The assessment suggests actions based on practical relevance and the statistical strength of the signal (15). As a result, this review process enables organizations to make data-driven justification on the product sustainability while implementing continuous improvement remediation for legacy drug products.
The protocol-driven legacy product assessment closely dissects the manufacturing process to determine the current state of control, to comprehend and address Stage 3a gaps for completeness of validation status. The exercise also determines the process robustness, allowing for business decision making. The review provides clear data-driven input for one of three actions:
The seven-step legacy product transformation plan serves as the first concrete step in upgrading legacy drug products. The combination of the substantial lifecycle data and the newly generated Stage 3a data allows for multivariate analysis, and establishes a design space for the product. The transformation plan substantially lowers the regulatory risks of legacy products and hence can be considered an exercise contributing towards business continuity and a source of great value for organizations. The QbD transformation plan is critical for legacy products for which there was no opportunity to apply ICH Q8 concepts.
1. ICH, Q8 (R2) Pharmaceutical Development, Step 4 version (2009).
2. ICH, Q9 Quality Risk Management, Step 4 version (2005).
3. ICH, Q10 Pharmaceutical Quality Systems, Step 4 version (2008)
4. ICH, Q11 Development and Manufacture of Drug Substance, Step 4 version (2012).
5. ICH, Q12 Technical and Regulatory Considerations for Pharmaceutical Product Lifecycle Management, Step 4 version (2019).
6. ICH, Q14 Analytical Procedure Development and Revision of ICH Q2 (R1) Analytical Validation, Final Concept Paper (2014).
7. FDA, Guidance for Industry, Process Validation: General Principles and Practices (CDER, January 2011).
8. ICH, E8 (R1) General Considerations for Clinical Studies, Step 2b version (May 2019).
9. A. Pazhayattil, et. al., Solid Oral Dose Process Validation, Volume Two: Lifecycle Approach Application (Springer, 2019).
10. ISPE, Good Practice Guide: Practical Implementation of the Lifecycle Approach to Process Validation (2019).
11. ICH, Q13 Continuous Manufacturing of Drug Substances and Drug Products, Step 1 version, (June 2018).
12. L. Liliana, “A New Model of Ishikawa Diagram for Quality Assessment,” presentation at the 20th Innovative Manufacturing Engineering and Energy Conference Series (2016).
13. S. Sharma, et al., Journal of Validation Technology (2021).
14. N. Sayeed-Desta, et. al., AAPS PharmSciTech 19, 1483–1492 (2018).
15. A. Pazhayattil, et. al., PDA Letter (Nov. 8, 2020).
Ajay Babu Pazhayattil is lead consultant, specializing in process validation, with Validant; Sanjay Sharma is vice-president and head of technology transfer at Lupin Pharmaceuticals; Amol Galande is senior manager of the process development laboratory at Lupin Pharmaceuticals; Marzena Ingram is senior consultant at Validant; Robert Rhoades is managing partner at Validant.
Vol. 45, No. 7
When referring to this article, please cite it as A. Pazhayattil, et. al., “Assessing Legacy Drug Quality,” Pharmaceutical Technology, 45 (7) 2021.