Using Design of Experiments in Validation part 1

The statistics-based method called Design of Experiments (DoE) has a long history when applied to optimising a product or a process. This technique is well suited for running robustness trials as part of validation. A shift in emphasis from discovery to verification is all it takes. DoE techniques should improve the efficiency and the effectiveness of trials. A thorough validation is essential if a company wants to introduce a new product or process and not spend a year in troubleshooting. DoE is a powerful tool that gives early warning of potential problems. If a process can pass a validation carried out with DoE, then the number of problems in volume manufacture will be reduced.

DoE techniques ordinarily are used to optimize a product or process, where they are an efficient way of identifying factors that improve performance or save money. The methods and statistical tools are described in many books on the topic. Their application to validation is less common but examples in electronics and mechanical engineering have been published.^1,2 The pharmaceutical industry probably avoids trying DoE in validation, for two reasons:

• Regulatory requirements have encouraged people to use tried and tested methods.
• Some companies prefer to follow the minimum route of Installation Qualification (IQ), Operational Qualification (OQ), and Process Qualification (PQ), which will satisfy an auditor although it is not a reliable indicator of robustness in service.

FDA has proposed a new approach to validation that may further encourage the use of designed experiments.³ The advantages are simply better results for less work:

• When compared to traditional one-at-a-time experiments, which force factors to the extremes they are likely to encounter, the number of trials is typically halved and potentially reduced to one tenth.
• The DoE approach will identify the presence of unwelcome interactions between any two factors, something that one-at-a-time methods will always miss.

PURPOSE OF VALIDATION A validation test demonstrates that a product or process is fit for its intended purpose. A thorough test is a necessary stage-gate between development and production and it may be called validation, verification, qualification, acceptance, release or some other name, which is the common industry jargon. A receiving company, department, or individual desires a validation that is thorough and complete because this reduces the risk that the product or process is in some way deficient. A failed validation is grounds for rejection and rightly puts the burden of fixing the problem on the supplier.

If validation is incomplete and the product passes despite its hidden faults, then the vendor may have made a sale, but the purchaser will ultimately be dissatisfied and unlikely to buy again. This even applies within a company where a development department creates a process that works in a laboratory but does not provide the same yields when it is scaled up.

The first experimental stage in validation is to show that a process (or product) works as intended when the factors that can affect performance are set to nominal or target levels. Typical factors are running speed, dimensions of a component, strength of a solution, and room temperature. This is the basis of an OQ and is often used to release a design from development to production. The risk is that variation in some of these factors will have a significant effect on performance. A good validation will run all the factors that could affect performance through their ranges of expected values.

A simplistic OQ + PQ approach relies mostly on random, natural variation to allow the factors to travel to their limits. The minimal OQ test is one run at nominal conditions although sometimes a "worst case" combination is also required.⁴ The PQ phase is to make a minimum of three batches, with the underlying assumption that any natural variation which is ever likely to occur will reveal itself. This is an optimistic view and as an unintended consequence, many products or processes require modifications and adjustments long after they enter service.

Advances in the Validation of Chromatographic Processes part3

Resin Lifespan Studies

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

Conformance Batches and Beyond

Figure 3

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).

Validation Strategy and Activities for A Cation Exchange Step

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.

Process-related Impurities

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.

Product-related Assays

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

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.

Summary

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.

Acknowledgements

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, gail.sofer@ge.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, mattias.ahnfelt@ge.com.

References

1. International Conference on Harmonization. Q8, pharmaceutical development. Geneva, Switzerland; 2005.

2. International Conference on Harmonization. Q9, quality risk management. Geneva, Switzerland; 2005.

3. Seely JE, Seely RJ. A rational, step wise approach to process characterization. BioPharm Int. 2003;16(8): 24–34.

4. Murtagh J. Development of a bio-derived drug. Contract pharma. 2006;8(3):70–75.

5. Mollah AH. Application of FMEA for process risk assessment. 2005;3(10):12–22.

6. Brorson K, et al. Impact of cell culture process changes on endogenous retroviral expression. Biotechnol. bioeng. 2002;80(3):257–67.

7. Application Note 28-4078-17 AA, Capto S cation exchanger for post-protein A purification of monoclonal antibodies. Chalfont St. Giles, UK; 2006.

8. US Food and Drug Administration. Guidance for industry. General principles of process validation. Rockville, MD; May 2006.

9. Rathore A. Efficiency measurements for chromatography columns. BioPharm Int. 2005;18(8):58–64.

10. US Food and Drug Administration. Therapeutic compliance guide. 7341.001.

11. Norling L, et al. Impact of multiple re-use of anion-exchange chromatography media on virus removal. J Chrom A. 2005;1069(1):79–89.

12. Brorson K, et al. Identification of protein A media performance attributes that can be monitored as surrogates for retrovirus clearance during extended re-use. J Chrom A. 2003;989(1):153–163.

13. International Conference on Harmonization. Q 5A, viral safety evaluation of biotechnology products derived from cell lines of human or animal origin. Geneva, Switzerland; 1997.

Advances in the Validation of Chromatographic Processes part2

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).

Process Validation

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

Not-to-be-Forgotten Downstream Processing Validation Issues

Column packing, storage, cleaning of packed columns and multiuse equipment, and resin lifespan studies should all be included in the validation plan.

Column Packing

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.

Storage of Columns and Process Intermediates

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 Validation

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).

Advances in the Validation of Chromatographic Processes part1

ABSTRACT

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.

Designing Validation and Quality into a Chromatographic Process

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.

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:

• What can go wrong?
• What is the likelihood of its going wrong?
• What is the severity if it goes wrong?

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.

Characterization and Robustness Studies

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.

Introduction to Validation of Biopharmaceuticals

Synthetic drugs can be well characterized by established analytical methods. Biologics on the other hand are complex, high-molecular-weight products, and analytical methods have limited abilities to completely characterize them and their impurity profiles. Regulation of biologics includes not only final product characterization but also characterization and controls on raw materials and the manufacturing process. FDA has defined process validation as "establishing documented evidence which provides a high degree of assurance that a specific process will consistently produce a product meeting its predetermined specifications and quality attributes." This involves supporting product and manufacturing process claims with documented scientific studies. Protocols, results with statistical analysis, authorizations, and approvals must be available to regulatory inspectors. Process validation is part of current good manufacturing practices (cGMP) and is required in the US and EU for a manufacturing license.

In addition to process validation, biopharmaceutical firms must conduct analytical method validation, expression system characterization, facility and equipment validation, software validation, and cleaning validation. Final product quality is assured when these elements are combined with other elements of cGMP, including lot release testing, raw material testing, vendor quality certifications, and vendor audits.

Expression system characterization is performed before Phase I studies in humans to insure safety. Concerns include the presence of contaminating organisms, tumorigenic cells, proteins, nucleic acids, retroviruses, or other pathogens. Taking tissue culture as an example, characterization includes the source, raw materials used, selection methods, number of generations, transfection or fusion methods used, procedures for establishing working cell banks, facilities, identity, homogeneity, absence of contaminating pathogens, tumorigenicity, and stability.

Analytical methods measure product characteristics important for therapeutic safety and efficacy during preclinical and early Phase I studies. Additional tests are developed for final product release and in-process sampling of the final manufacturing process. These measure characteristics such as molecular identity, purity, potency, and safety. The number of tests should be sufficient to show manufacturing consistency and the impact of manufacturing changes. Once a test is made a formal part of the manufacturing process, it is almost impossible to remove. Test methods are evaluated for different attributes such as accuracy, precision, range, selectivity, recovery, calibration (detection and quantitation limits), assay sampling, robustness, and stability.

Test method validation is needed to conduct clinical trials. Specifications should start off wide for Phase 1 and narrow to tighter values in the license application. Relaxing established specifications is very difficult.

Process validation involves the identification, monitoring, and control of sources of variation that can contribute to changes in the product. It starts with process characterization studies using scale-down models for optimization, operating range specification, extractables and leachables characterization, and clearance studies. Such work depends on validated assays and representative scale-down models.

Process development normally involves identifying critical variables, defining setpoints for each unit operation, and establishing operating ranges (deviations from the setpoint). Maximum operating range (MOR) limits are typically set during Phase II or III. If they are exceeded, an investigation is necessary to determine if product quality remains acceptable.

Normal operating range (NOR) limits are determined by run-to-run reproducibility with scale-down models and trending with control charts at production scale. NOR limits lie within MOR limits, which must allow for normal variability while maintaining acceptable operation.

Facility and equipment validation is normally divided into design qualification (DQ), installation qualification (IQ), operational qualification (OQ), and performance qualification (PQ). Equipment validation begins with pilot production of clinical materials for Phase II.

DQ provides documented evidence that the proposed design of the facilities, equipment, and systems are suitable for the intended purpose. DQ must compare the design to a set of well-defined user requirements relating to product safety, identity, strength, purity, and quality.

IQ provides documented evidence that the system is assembled, installed, plumbed, and wired according to the user's design specifications, vendor recommendations, and appropriate codes and standards. Vendors typically provide much of the hardware documentation.

OQ provides documented evidence that the system performs as expected throughout its intended operating ranges, including all the system's different functions and all its components (hardware, monitoring instruments, controls, alarms, and recorders). Elements of OQ testing and documentation may be part of the factory acceptance test at the vendor's site. Integration with plant utilities and component installation must be verified at the factory. Hardware cleanliness must also be assessed after cleaning.

PQ is documented by processing actual feedstock by trained operators using buffers and utilities at the factory. Full-scale process validation includes testing the consistency of batch production.

Software validation operates under the principle that quality should not be diminished if a manual process is replaced with an automated process. Software must be developed and tested under a quality system with defined user requirements, change-control procedures, provisions for authorization of operators for data entry and data checking, data archiving, software backup, provisions for system crashing, and procedures for monitoring and correcting software problems. 21 CFR 11 defines requirements for maintaining the integrity of data and software and handling electronic signatures for traceability.

Cleaning validation demonstrates the ability of cleaning procedures to permit reuse of processing components and equipment without a concomitant deterioration of product quality. Batch-to-batch carryover is of particular concern in multi-use plants making more than one product.

Consistency of product quality is demonstrated by showing operating consistency and product quality from batch-to-batch, processing with only buffer (blank runs) with assays for contaminants, examination of cleaned surfaces and materials, and extended scale-down clearance studies on reused materials. Disposable processing components that eliminate the need for cleaning validation are increasingly used at small scale. Herb Lutz is strategic marketing manager at Millipore Corporation, 80 Ashby Road, Bedford, MA 01730, 781.533.2366, herb_lutz@millipore.com