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Regulatory, analytical, and process concerns must be taken into account.
Process validation (PV) is the collection and evaluation of data from the process design stage through commercial production that establishes scientific evidence that a manufacturing process is capable of effectively and consistently delivering quality product (1, 2). The manufactured drug substance must meet its predetermined acceptance criteria and/or specification for safety, quality, identity, purity, strength, and efficacy (SQIPSE). PV integrates a variety of disciplines from process development (PD), engineering, industrial pharmacy, analytical development (AD), microbiology, statistics, manufacturing, regulatory affairs (RA), quality control (QC), and quality assurance (QA).
In this article, the authors share opinions and strategies for a monoclonal antibody (mAb) PV from a RA, AD, QC, PD, and manufacturing perspective. Figure 1 outlines PV activities across three stages. The first is the prerequisite stage. Stage 1 focuses on raw material qualification, cell line development, fermentation, purification PD and characterization, analytical method development and qualification, risk assessment, documentation management, and equipment and facility fit. Stage 2 involves validation of process performance and product SQIPSE. Stage 3 includes post-validation activities such as continuous process verification.
Regulatory agencies such as FDA and the European Medicines Agency (EMA) have issued regulations and guidance for PV (1,2). The biopharmaceutical industry is recommended to follow the highest standards per Parenteral Drug Association (PDA) technical guidance as possible (3–7). For example, PDA’s TR60 (7) can be used as a guide to plan and execute key PV activities, such as shown in Figure 1.
In addition, the International Council for Harmonization (ICH) brings the regulatory authorities and the manufacturing industry together with a published series of guidelines on viral safety (Q5A R1) (8), cell line development (Q5B) (9), stability testing of drug substance)/drug product (Q5C) (10), the concept of quality by design (Q8 R2) (11), risk management (Q9) (12), quality systems and knowledge management (Q10) (13), and drug substance development and manufacture (Q11) (14) to support PV.
In 2011, FDA published Process Validation: General Principles and Practices that outlines the general PV principles and approaches for the manufacture of human and animal drug and biological products (1). It guides a manufacturing validation campaign (i.e., three consecutive production batches from industrial application) at commercial scale to demonstrate process performance qualification (PPQ). PPQ uses the pre-set operating parameters, controls, equipment, and facilities established after process characterization (PC) and early and late-stage clinical batch production data. The aim of the PPQ campaign is to effectively and repeatedly manufacture consistent drug substance, which should meet the predefined specifications and quality attributes.
However, unavoidable challenges are often faced by sponsors and their contract manufacturing organizations (CMOs) during process validation. Challenges may arise from slight differences in production scale, facilities, and/or equipment between clinical and commercial manufacture. For example, commercial facilities from a selected CMO may have multiple large-scale bioreactors, but each bioreactor may have subtle differences from one another in configuration and/or design. What is the PPQ campaign strategy to validate the bioreactors? Would the campaign consist of multiple runs in each bioreactor to enable the product to be commercially manufactured in any one of them? One approach would be to perform three batches in one bioreactor and a confirmatory run in each of the other bioreactors if there are subtle differences between bioreactors. A justification should be made to determine the bioreactor utilization strategy for commercial production based on the PPQ data. It is recommended that sponsors confirm their strategy with the applicable health authorities through a meeting or written correspondence before filing a marketing application.
Method validation is performed to confirm that the analytical procedure employed for a specific test is suitable for its intended use; the methods for in-process samples need to be qualified at a minimum, and DS release tests need to be validated.
ICH Q2 (R1) provides traditional guidance for analytical procedure validation of chemical and biological drugs (15). ICH is also working on Q14 Analytical Procedure Development (16), which is expected to be available in 2021. FDA published guidance on analytical procedures and methods validation in 2015 (17) providing recommendations on how method validation should be performed and the data that should be submitted when filing a new drug application, abbreviated new drug application, or biologics license application. The United States Pharmacopeia (USP) general chapters describe method validation (USP <1225>) (18), method verification (USP <1226>) (19), and method transfer (USP <1224>) (20), and address portions of the lifecycle but do not consider it holistically. Based on three stages of process validation, the USP Council of Experts in 2016 proposed a new general chapter <1220> for lifecycle management of analytical procedures to address the procedure lifecycle (21).
It is highly recommended that a systematic method robustness study be performed during the development stage. The procedures should be in sufficient detail so that a competent analyst can reproduce the necessary conditions and obtain results within the acceptance criteria. For biological testing, a reference material (RM) is required. The primary RM should be from lot(s) representative of production materials and needs to be characterized extensively for qualification. The working RM should be calibrated against the primary RM (i.e., a two-tier approach). The primary RM and drug substance that are representative of commercial material are usually used for method validation. For each analytical procedure, there should be a continual assessment of the system suitability test results to verify the method is suitable for use.
At the PV stage, all analytical procedures should already be validated. After validation, the analytical procedure lifecycle management should begin using method monitoring (see Figure 2 and Table I). Figure 2 illustrates an individual-moving range (IR) control chart for a cell-based potency assay that is used to assess method performance. The RM value is tracked against its nominal assigned value, which is set as “0.0”. Figure 2 demonstrates how RM data fluctuate during routine stability testing.
In Figure 2, the violation of rules 1, 2, and 5 starting from data point 42 is the direct result of a RM requalification where the protein concentration was approximately 5% higher than the previous qualified value. The root cause was reference material being diluted approximately 5% less for each analysis, and subsequent values all increased accordingly. Issues identified help to establish a robust analytical lifecycle management program from Stage 1 to Stage 3.
PC plays an important role for PV. Pre-characterization focuses on historical data review, risk assessment, qualification of the scale-down model, fermentation, and AD method qualification. PC studies concentrate on impurity clearance, design of experiment (DoE) studies for key and critical process parameters (KPP, CPP), and critical quality attribute (CQA) assessment. CQAs will define the CPPs, which directly impact the quality of the product. Figure 3 illustrates the relationship between a program’s target product profile (TPP), quality TPP (qTPP), and CQAs.
A cell-line development effort is the foundation of any biologics manufacturing program. The decisions made during this stage, listed in Table II, have far-reaching effects on further development options and on validation plans.
The choice of production host, cell line generation, and raw materials used in the process is critical. It affects how straightforward the future validation process will be. The authors recommend using a proven Chinese hamster ovary (CHO) cell line (e.g., CHO-K1, CHO-S, or DG-44) from a supplier with a well-documented clone lineage and safety test records, as noted in Table II. Chemically defined and/or animal-origin free media should be used to reduce the burden of safety testing and improve batch consistency. A medium or feed from a supplier that has submitted a Type II Drug Master File would be ideal as the details of media composition, raw materials, and manufacturing are available. It is worth the effort to properly isolate a production cell line and generate direct evidence of monoclonality to avoid a complicated statistical argument, or for remedial characterization work of an MCB later, as noted in Table II.
The upstream PD (USPD) and manufacturing are not only for process development and PC but also for data trending from historical manufacturing batch production. A key point is using the qualified scale-down model to demonstrate relevant and/or predictive behavior at commercial scale with a risk-based approach in mind (see Table III).
Failure mode and effects analysis (FMEA) is adopted to rank process inputs by integrating PC and production data for process criticality based on a risk-based approach (ICH Q9) (12). This analysis systematically assesses risks based on severity, occurrence, and detectability from raw materials, the process, product quality, manufacturing, equipment, and business practices. FMEA exercises also help to determine process targets and ranges, such as non-key process parameters (nKPPs), KPPs, CPPs, normal operating ranges (NORs), and proven-acceptable ranges (PARs). A nKPP is a parameter that may not impact process performance or product quality, while a KPP is a parameter that influences process performance, but many not impact product quality. A CPP is a parameter that impacts quality attributes and must be well controlled.
Downstream process validation strategies are also focused on raw material selection, process consistency, process control, safety, product purity, and quality (see Table IV). After PD, PC, and clinical-stage batch production, process set-point, operating range, and in-process control strategy can be evaluated and established based on product SQIPSE. Detailed PV activities are listed in Table IV, such as performing at-scale buffer and intermediate mixing and hold time studies to ensure the solution homogeneity, stability, and expiry setting; intermediate quality and drug substance content uniformity should be thoroughly evaluated and determined. It is a common practice to validate viral clearance and resin lifetime from a qualified scale-down model. It is crucial to validate manufacturing utilities, facility, equipment, and automation at commercial scale from late-stage clinical trials.
For a mAb process and manufacturing PV, the authors recommend that a cross-functional team be formed including project management, PD, AD, manufacturing, manufacturing science and technology, QC, QA, RA, and counterparts from CMOs if CMOs will perform commercial production. Historical data from PD, PC, and clinical manufacturing batch production play a crucial role in PV design and execution. The ultimate goal is that the validated manufacturing process consistently produces high quality drug substance that meets the product SQIPSE.
1. FDA, Guidance for Industry: Process Validation: General Principles and Practices (January 2011).
2. EMA, EMA/CHMP/CVMP/QWP/70278/2012 Rev. 1, Guideline on Process Validation (Nov. 21, 2016).
3. PDA, Technical Report No. 14, Validation of Column-Based Chromatography Processes for the Purification of Protein (2008).
4. PDA, Technical Report No. 15, Validation of Tangential Flow Filtration in Biopharmaceutical Applications (2009).
5. PDA, Technical Report No. 42, Process Validation of Protein Manufacturing (2005).
6. PDA, Technical Report No. 49, Points to Consider for Biotechnology Cleaning Validation (2010). 7. PDA, Technical Report No. 60, Process Validation: A Lifecycle Approach (2013)
8. ICH Q5A (R1) Quality of Biotechnological Products: Viral Safety Evaluation of Biotechnology Products Derived from Cell Lines of Human or Animal Origin Step 4 (1999).
9. ICH Q5B Quality of Biotechnological Products: Analysis of the Expression Construct In Cells Used for Production of R-DNA Derived Protein Products Step 4 (1995).
10.ICH Q5C Quality of Biotechnological Products: Stability Testing of Biotechnological/Biological Products Step 5 (1995).
11. ICH, Q8 (R2) Pharmaceutical Devlopment Step 4 (2009).
12. ICH Q9 Quality Risk Management Step 4 (2005).
13. ICH Q10 Pharmaceutical Quality System Step 4 (2008).
14. ICH Q11 Development and manufacture of drug substances (chemical entities and biotechnological/biological entities) Step 4 (2012).
15. ICH Q2 (R1) Validation of Analytical Procedures: Text and Methodology Step 5 (1995).
16. ICH Q14 Analytical Procedure Development and Revision of Q2(R1) Analytical Validation, Final Concept Paper (2018).
17. FDA, Guidance for Industry: Analytical Procedures and Methods Validation for Drugs and Biologics (July 2015).
18. USP, USP <1225>, “Validation of Compendial Procedures,” (2012).
19. USP, USP <1226>, “Verification of Compendial Procedures,” (2012).
20. USP, USP <1224>, “Transfer of Analytical Procedures,” (2012).
21. USP, USP Proposed General Chapter <1220>, “The Analytical Procedure Lifecycle,” (2016).
Yanhuai (Richard) Ding*, PhD, is director, Downstream Process Development and Manufacturing; Margaret (Peggy) Marino, PhD, is senior vice-president, Program Management; Kevin Zen, PhD, is executive director, Analytical Method Development and QC; Joe Sheffer is senior program manager, CMC; Nick Almaguer is manager, Analytical Development; Kim Caddy is associate director, CMC; and Alex Praseuth, PhD, is associate director, Formulation and Drug Product Development, all at AnaptysBio.
*Address all correspondence to Richard Ding, AnaptysBio, 10421 Pacific Center Court, Suite 200, San Diego, CA92121; +1 (858) 362-6349, rding@Anaptysbio.com.
Vol. 44, No. 8
When referring to this article, please cite it as Y. Ding, et. al, “Considerations for Monoclonal Antibody Bioprocess and Manufacturing Validation,” Pharmaceutical Technology 44 (8) 2020.