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.