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Over the past five years many advances have enabled better process understanding and a more cost-effective approach to process validation. The application of risk management tools, design of experiments (DoE) for process characterization, equipment that is scalable, and sophisticated analytical tools all have contributed to a more rational approach to downstream process validation. Additionally, platform technologies based on experience with classes of products, such as monoclonal antibodies and DNA plasmids, have simplified validation approaches. Validation is no longer just 3 to 5 consecutive conformance batches; rather, it is a process that begins in development and uses a life-cycle approach for continuous improvements.
Process validation has been described as just another chore to satisfy regulatory authorities. In biotechnology, some of the problematic issues were, and still are, related to differences in worldwide regulatory reviewers' expectations and differences even within a single regulatory agency. Although those differences still exist, it has been demonstrated time and again that a downstream process validation plan complying with worldwide expectations can be developed and implemented.
Validation should not be considered as just 3 to 5 consecutive conformance batches. Validation begins in development and includes a life-cycle approach. Validation can be thought of as a multistep, structured effort that starts in process development with a risk assessment and the use of risk mitigation tools that enable quality by design (QbD).1 After the initial design phase, characterization (also called robustness) studies that use Design of Experiments (DoE) plus further experimental work enable the establishment of ranges in which the process always delivers the requisite active pharmaceutical ingredient (API) quality. Conformance or validation batches confirm that the entire process can be run consecutively at least three times.
Start thinking about validation in early development stages. Understanding the potential risks associated with the host organism, raw materials, processing materials, and product-related impurities enables the design of a validatable downstream process that can mitigate those risks (Table 1). Manufacturing and analytical capabilities should also be evaluated in conjunction with the chromatography design phase.
Table 1. Risks that can be mitigated by downstream processes
Risk assessment and mitigation are described in ICH Q9.2 For biotechnology, Failure Modes and Effects Analysis (FMEA) is probably the most commonly described risk management tool.3–5 All risk management requires that experts from multiple disciplines ask the following questions:
The information obtained from the risk analysis will only be useful, however, if the input is appropriate. For downstream processing, it is essential to have additional input from the upstream processing, manufacturing, and analytical departments. The results from the risk assessment often dictate the number of chromatographic or other downstream steps needed to reduce specific risks to acceptable levels.
Risk management is an iterative process and empirical data gathered in development may alter process design. For example, column load for initial steps often is increased during early development as cell culture conditions are modified to increase productivity. An overloaded column is not likely to provide expected purity levels and can alter the outcome of subsequent downstream process steps. Optimizing column size or even adding another step may be necessary to achieve the expected product quality. Changes during cell culture that alter metabolism and rates of protein expression have been shown to increase the rate of retrovirus production. Such changes may necessitate the need for greater virus clearance capability to be designed into the downstream process to mitigate patient safety risks.6
Designing quality into a purification process also requires considering risks that may arise from the processing materials themselves. Risks include protein ligands, leachables or extractables, and processing additives such as detergents. Also, the process should be designed using only materials suitable for manufacturing according to current good manufacturing practices. One of the most important factors for designing in quality is the ability to clean and sanitize packed columns and ancillary equipment.
Once a suitable process has been identified, the next step toward achieving a validated state is to perform studies to quantify cause–effect relationships from the inputs to the outputs of the process. DoE is a powerful statistical tool for quantifying these relationships, but it is important to point out that any DoE study should be built on a foundation of process know-how and empirical knowledge whenever available.
The final goal of many development studies of chromatographic unit operations is to establish ranges for critical process parameters within which the process outputs meet acceptance limits. Generally, DoE studies leading up to this can be divided into three categories, which are often performed in a sequential manner. The categories are:
Screening studies, in which a large number of process inputs are studied in a systematic way to identify the inputs that have the most significant effects on the process outputs.
Optimization studies, in which the most important process inputs from a screening study are evaluated in more detail in order to quantify the cause–effect relationships between process inputs and outputs.
Robustness studies, in which often a fairly large number of process inputs are studied in a systematic way, but with much smaller variation intervals compared to those used in screening and optimization studies. Typically, the process inputs are varied in a systematic way within their control limits to verify that the resulting process outputs are robust.
DoE studies can be performed at any scale, but due to time and cost restraints, screening studies are commonly performed at laboratory scale, whereas optimization and robustness studies are performed at laboratory or pilot scale, or in some rare cases at production scale. This varies, of course, between different processes and applications.
Data from a DoE study7 on the Capto S cation exchanger (GE Healthcare, Chalfont St. Giles, UK) will be used to illustrate the use of DoE from a validation perspective. The effect from the process inputs residence time (2–6 minutes), conductivity (5–15 mS/cm), and pH (4.5-5.5) on the process output dynamic binding capacity (QB 10%) for a monoclonal antibody (MAb) was studied. A total of 17 experiments were performed to quantify the effect of three process inputs on the process output.
The rather complex model coefficients for the effects of conductivity and pH on the dynamic binding capacity translate into an easily interpretable response surface, as shown in Figure 2.
As shown by Figure 1, it was found that within the investigated ranges, residence time had a small effect compared to conductivity and pH, whereas both conductivity and pH were shown to have significant linear as well as second degree curvature effects on the QB 10% for the MAb. In addition, a significant interaction effect between pH and conductivity was found.
Figure 2 shows the combined effect from variations in conductivity and pH on the QB 10% for the studied MAb at a 95% confidence level. Assuming that a dynamic binding capacity of at least 120 mg/mL is always desired from this process step, it would be reasonable to set the target for pH at 5.1 and the target for conductivity at 6 mS/cm (as illustrated by the red dot) in order to give some room for variation (illustrated by the blue lines) in these parameters and still be able to have a dynamic binding capacity of at least 120 mg/mL.
It is also important to note that, as with any study, some additional runs should be performed in the region of greatest interest to verify the indications from the study. In this example, the final test before proceeding to conformance runs could be a robustness test, centered around the indicated set point (red dot) with narrow variation ranges that are still practical in manufacturing (the blue lines).
A combination of small-scale and manufacturing-scale runs typically are used to validate a downstream process. In chromatography, the scale-down runs are used for prospective resin lifetime studies and clearance studies, where appropriate. For viral clearance studies, it is essential to validate the scale down prior to performing virus spiking studies. Column scale down is best designed and validated at the manufacturer's site, where all the analytical methods are available for demonstrating comparable purity and impurity profiles at both small and production scales.
The selection of analytical methods and their validation is critical for validating a downstream process. For clearance of impurities other than viruses or other hazardous materials, the need to validate a small-scale model will depend on the availability of sensitive analytical methods. For example, Q-PCR sensitivity enables the detection of DNA removal at pilot or even manufacturing scale.
A risk assessment can be conducted to determine whether to perform a clearance study (and the scale of that study) or to include an assay as an in-process test or lot release test. The emphasis today is on applying meaningful, in-process tests with corrective actions enabled by automated systems, in other words process analytical technologies (PAT). The issue of replacing validation with PAT is frequently discussed. At this time, it does not appear that better in-process control will negate the need for formalized process validation (i.e., conformance batches), but it should certainly aid in attaining the goal of process validation (i.e., control of variability).8
Column packing, storage, cleaning of packed columns and multiuse equipment, and resin lifespan studies should all be included in the validation plan.
Column packing procedures at large scale should be validated. Repacking at large scale is expensive. Labor, buffer, and water costs, as well as resin attrition, are all reasons to maintain a well-packed column that delivers the expected process intermediate quality. The packing quality at different scales can vary and measurements to determine how well a column needs to be packed to achieve requisite performance should be established so that the specifications are realistic and achievable. Several companies have recently implemented transitional analyses to determine column packing efficiency. This technique allows routine measurements of column efficiency by measuring a step change that is part of the manufacturing protocol (e.g., a change in conductivity in an ion exchange step).9 Time needs to be allotted to validate column packing at large scale. Up to three months or more may be required for this effort.
Regulatory agencies direct attention to the storage of process intermediates and packed columns. Stability studies for process intermediates should demonstrate control over bioburden, proteolytic degradation, aggregation, and other potential product modifications. The stability of column storage solutions and the removal of storage agents prior to column reuse should be validated. Validated rapid microbiology methods may now enable faster turnaround times and reduce the amount of processing that is carried out at risk while waiting for quality control bioburden results.
Cleaning packed columns should be addressed early in development. Designing a robust cleaning protocol is essential to prevent carryover of impurities and residual target molecules, some of which may be degraded. New developments include the use of in-line total organic carbon, but product-specific or protein assays may also be needed for validation. As with other validation activities, a risk assessment can dictate which assays are most appropriate. Factors that should be evaluated include feedstream impurities; chromatography mechanism (i.e., binding or flow-through); and location of step in purification train (i.e., early capture or final polishing step).
Resin lifespan studies may be more appropriately carried out concurrently, provided the analytical methods are demonstrated to be sufficiently sensitive and appropriate.10 Virus clearance by aged resins has been a topic of discussion for several years. Surrogate measurements that replace the need to evaluate virus clearance after repeated use have been proposed for some types of chromatographic steps (i.e., affinity and flow-through mode anion exchange chromatography used in MAb production).11,12
Once the process is characterized and scale up verified, 3–5 consecutive batches are run at center point to demonstrate manufacturing consistency. The validation effort does not stop here, however. The level of process understanding increases with manufacturing experience (Figure 3).
Better process understanding may lead to changes to improve process control, increase productivity or reduce costs. Often, implementing such changes is delayed due to concerns about validating changes and submitting regulatory filings. The concept of design space, defined by process development, DoE, empirical studies, and experience, and approval of that space may now enable changes to be made within that space without incurring regulatory delays (see ICH Q8).
Although there is no one protocol for process validation for a chromatography step, a strategy and activity plan for validating a cation exchange step in a purification process for a MAb summarizes the key elements (Figure 4 and Table 2).
To validate this step, it is necessary to know why it was designed into the process. Most monoclonal antibodies bind to cation exchangers. This step is used to capture the MAb and remove process impurities such as host cell proteins (HCP), DNA, leached Protein A, other process impurities from cell culture and clarification steps, and product-related impurities. This cation exchange step is also used to enhance overall virus clearance.
Based on the intended use of this cation exchange step, assays are developed and validated. A decision will be made whether to use clearance studies, routine in-process assays, or API testing for removal of HCP, DNA, and Protein A. The choice is dictated by assay sensitivity, the practicality of performing the assay, and relevance of the assay for in-process control.
Table 2. Validation of a cation exchange step in the production of a monoclonal antibody
Antibody titer and purity by high performance liquid chromatography (HPLC) are commonly used methods for assessing product quality. Impurities, such as aggregates and other product modifications, may also be detected by the HPLC assay. Other modes of HPLC may be used to detect glycoforms, and isoelectric focusing (IEF) might also be a useful assay.
Viral clearance studies will be performed in a scaled down model, validated to represent manufacturing scale. Adherence to the ICH guideline for virus validation will be confirmed, which means prospective cation exchanger lifespan studies must be performed.13 In the future, it is possible that the surrogate determinations (i.e., removal of a specific impurity, height equivalent to a theoretical plate (HETP), and backpressure) might be acceptable for assessing column performance for a cation exchanger.
Before performing validation studies and conformance runs, column packing is validated. Ranges for process control parameters (e.g., flow rate, load, pH, conductivity) will be established in characterization studies. Engineering runs will be performed at production scale. Before confirming scale up, it will be necessary to determine if any modifications resulting from scale changes might alter the process control parameters or product critical quality attributes.
Column storage will be validated by evaluating packing integrity (e.g., frontal analysis, removal of storage agents, any residues removed by the cleaning effect of storage, and control over bioburden.
Column cleaning will be validated by a combination of small-scale prospective studies and concurrent in-process analysis at manufacturing scale.
Experience over the last decade in developing and producing biotherapeutics has enabled the development of a structured approach to process validation that begins in development and continues throughout the lifetime of the product. Although process analytical technologies continue to be discussed as a means for achieving better quality and process control, the need for process validation does not appear to be going away. Improvements in analytical methods have improved our understanding of downstream intermediates and final purified products. These analytical tools enhance the value of process validation. Improvements in process validation approaches have resulted in better process understanding that enables better control over variability—the intent of process validation. The use of enhanced analysis and feedback control, process development, statistical analysis, and characterization studies to establish robust processes have led to the ability to define a space in which the process delivers the critical quality attributes.
The Polymerase Chain Reaction (PCR) is covered by patents owned by Roche Molecular Systems and F. Hoffman-LaRoche. A license to use the PCR process for certain research and development activities accompanies the purchase of certain reagents from licensed suppliers.
Gustav Rodrigo and Maria Murby, GE Healthcare Life Sciences R&D, Uppsala, Sweden, are gratefully acknowledged for providing the data from the DoE study on Capto S.
All illustrations are reproduced with the permission of GE Healthcare Bio-Sciences AB, a General Electric Company, Bjorkgatan, Uppsala, Sweden.
Gail Sofer is director of regulatory compliance at the life sciences business unit of GE Healthcare, and a member of BioPharm International’s Editorial Advisory Board, 732.457.8000, email@example.com.
Mattias Ahnfelt is senior research engineer and black belt in Six Sigma at the life sciences business unit of GE Healthcare, +46 18 612 1990, firstname.lastname@example.org.
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