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Managing risk in biopharmaceutical operations is of utmost importance for patient protection.
Managing risk in biopharmaceutical operations is of utmost importance for patient protection, ensuring that only the highest quality products are developed and distributed. A quality risk-management program systematically identifies and analyzes the risks associated with a product or process, mitigates those risks deemed unacceptable, and monitors the overall risk profile as conditions change. These programs facilitate more informed decision-making within a company regarding a product's quality and provide greater assurance to a company's stakeholders of the ability to deliver the highest quality product to patients. In this paper, the authors describe risk-assessment tools used in change control.
According to the International Conference on Harmonization (ICH) Q9 guidance, Quality Risk Management, all manufacturing processes carry certain, inherent risks (1). It is, therefore, essential that these risks are assessed and mitigated throughout the product lifecycle. Risk assessment is especially critical when changes are made to validated processes or systems to ensure the integrity of the product is preserved as the risk profile evolves. Not all risks pose a concern; it is important to distinguish between risks that are problematic and require mitigation efforts and those that do not. Thus, an effective risk assessment will ensure that maximal resources are directed towards products, equipment, and processes deemed high risk and minimal resources towards those deemed low risk.
Less-formal tools for managing change control
Risk management tools provide the necessary means by which risk can be successfully understood and controlled, making the entire process both efficient and consistent. While there are several well-known formal tools for risk assessment, such as failure mode effect analysis (FMEA), fault tree analysis (FTA), hazard operability analysis (HAZOP), and hazard analysis and critical control points (HACCP), ICH Q9 notes that the use of formal tools is not always appropriate or necessary to manage risk. It is, therefore, important to select the appropriate tool based on the objective and scope the assessment. The greater the risk and complexity of the system (or process) under review, the greater the level of formality and detail is required of the risk tool (see Figure 1). Less-formal tools, such as the comparison matrix (CM) and the risk estimation matrix (REM), which are designed to be easily implemented and broadly applied, are useful when assessing simple or well-understood systems or changes. Less-formal tools can also be used to make preliminary decisions about whether to stop or advance a given project or to employ more formal risk assessment methodologies.
Figure 1: Risk assessment tool formality. (FIGURE 1 COURTESY OF AUTHOR.)
There are two primary goals in the assessment of risk when managing change: to assure that a company is not taking on additional risk by making the change, and to ensure the success and effectiveness of the change through the identification of risk mitigation activities to be implemented in parallel with the change. The risk tools selected to assess changes should also be simple enough to use in a fast-paced manufacturing environment and clearly communicate the scope and impact of the change to all stakeholders. CM and REM are two such tools.
Both the CM and REM have a foundation in critical parameters—that is, categories of attributes that are deemed critical to the proper functioning of a system and must be considered to fully characterize the implications of a given change. Critical parameters are system-specific and should capture such elements as critical quality attributes (CQAs), critical or key process parameters (CPPs/KPPs), critical aspects (CAs) of equipment, system capacity, process capability, raw materials, and product-contact materials. These critical parameters will serve as the input into the risk assessment process.
The CM is a less-formal risk tool used to compare two different states in an effort to understand what the differences mean from a risk-based perspective. The primary objective of the CM is to determine if, overall, the change will lead to more or less risk exposure for the process or system. The CM is particularly helpful when making "go/no-go" decisions regarding individual change requests.
The process for the CM is as follows:
1. Identify critical parameters for the system under review.
2. Populate the CM with details for each critical parameter, for both the current and proposed states.
3. Determine what the differences between the current and proposed states mean from a risk-based perspective (i.e., the change to overall risk profile for each critical parameter).
4. Evaluate whether changes to overall risk profile are acceptable.
A hypothetical change request, for example, related to scaling up the production of saline solution may identify the following attributes as critical parameters: bioburden specifications, environmental exposure, vessel type, and vessel capacity. Once the CM is populated with details on how the current and proposed states fulfill each of these critical parameters, the potential impact of each change on the overall risk profile is assessed (see Table I). This assessment must take into consideration the nature (i.e., types of risk or potential failures), the gravity (i.e., frequency or severity of a failure), and the pervasiveness (i.e., where the failure might occur or what downstream impact it might have) of each risk.
Table I: Comparison matrix: hypothetical scale-up of saline solution.
The overall risk profile may be increased if the proposed change increases variability, reduces reproducibility or robustness, introduces a variable that is not well understood (such as a new technology), or cannot be quantified. Conversely, the exposure to overall risk may be reduced if the change decreases variability, improves reproducibility or robustness, or upgrades an element of the system in a way that is well-understood and controlled. Overall risk may remain the same if the change does not affect that particular critical parameter or if it is proven or expected to be equivalent to the current system. As with any risk assessment, available data should be cited as justification for the conclusions drawn.
The final step in the CM process is to assess whether the change is acceptable from a risk-based perspective. In general, the proposed change is acceptable if the overall risk profile has not changed or has been reduced for the majority of critical parameters. If the overall risk profile, however, has increased for the majority of critical parameters that were assessed, the proposed change should not be accepted until additional analyses are conducted or risk mitigation measures are pursued.
To continue the hypothetical example in Table I, the overall reduction of the risk profile suggests that it is appropriate to move forward with this change. The critical parameter surrounding the introduction of a new product-contact material, however, increases risk and should be examined more thoroughly.
Although the CM illustrates whether a given change should be pursued, individual risks associated with the proposed state (change) are not thoroughly explored through this tool. These individual risks are best assessed through another less-formal tool, the risk estimation matrix (REM).
Risk estimation matrix
REM is a simple risk assessment tool that assumes failure of each critical parameter and uses the likelihood and severity of that failure to determine the overall risk. REM is based on a 3 x 3 matrix, similar to a heat map. Like CM, REM is limited in that it is not a formal risk assessment tool; hence, it does not have the level of detail and rigor that more complex systems and processes may require.
The process for REM is as follows:
1. Determine qualitative scales for likelihood and severity rankings. Develop an action level table (see Table II).
Table II: Risk action level.
2. Identify critical parameters for the system under review.
3. Brainstorm potential failures for each critical parameter.
4. Rank each potential failure for likelihood and severity using the criteria established in Step 1.
5. Determine overall risk using the risk matrix (see Table III). Propose mitigation for unacceptable risks.
Table III: Risk matrix.
In order to preserve objectivity and ensure consistency of the risk assessment to follow, the first step in the REM methodology involves the establishment of risk ranking scales. Two qualitative scales will be developed, each containing three potential scores. The likelihood scale addresses how likely is it that the failure will occur, given the current controls in place. This scale includes options ranging from remote (unlikely) through average (likely) to certain (very likely or unknown). The severity scale addresses the question: If that failure did occur, how severe would the consequences be? The severity scale ranges from minor (insignificant impact) through moderate (moderate impact) to critical (significant impact).
The final scale that must be established is an action level table that dictates the acceptability for overall risk, including whether mitigation measures are required. Low-risk items may not require any mitigation activities or resource expenditure, whereas high-risk items will require additional risk control measures to reduce risk to an acceptable level.
Returning to the hypothetical saline solution scale-up, the risk team would first brainstorm potential failures associated with each critical parameter for the saline solution process. For example, the batch could fail the bioburden specification, the closed aseptic system could be breached, the new material may not be biocompatible, or the vessel capacity may be insufficient for production needs (see Table IV). Each of these potential failures is then ranked for likelihood and severity and the overall risk identified using the risk matrix in Table III.
Table IV: Risk estimation matrix: hypothetical scale-up of saline solution.
Focusing on the new product-contact material, it may be difficult to assign a likelihood score if there is no available data on the biocompatibility or extractable/leachable profile of this material. In such cases, it is best to take a conservative approach and assign a likelihood score of "certain" to the lack of biocompatibility. Based on the potential patient impact of this failure, the severity would be given a score of "critical." The intersection of "certain" and "critical" in the risk matrix shows this risk to be high. Thus, the risk of changing the vessel type to a new material is not acceptable, and additional risk control measures must be taken. Because in this example the overall risk is driven primarily by a lack of data, mitigation efforts would focus on biocompatibility testing to better understand the implications of the new material on the product. Once this action is taken, it is expected that overall risk would then be reduced to an acceptable level.
To ensure that the quality system and associated processes remain in control over time, every company must understand how their risk exposure is affected as validated systems evolve. The application of quality risk management principles and tools facilitate this understanding, allowing for more comprehensive strategy development and informed decision-making. It is not always, however, necessary to perform lengthy, formal risk assessments to reach these goals. For simple systems and processes as well as for changes that are well understood, less-formal tools such as the comparison matrix and risk estimation matrix provide a comprehensive picture of the associated risk in an easily applied format. The consistent use of these tools can enable the pharmaceutical industry to prioritize resource expenditure and provide only the highest quality products to patients.
Kelly Waldron is principal continuous process improvement analyst, Global Quality Risk Management, at Genzyme, a Sanofi company, firstname.lastname@example.org. Marissa Gray is field marketing manager, Sterile Filtration at EMD Millipore, Marissa.Gray@Merckgroup.com.
Submitted: July 6, 2012. Accepted: July 18, 2012.
1. ICH, Q9 Quality Risk Management (Nov. 2005).