Application of Rules-Based QRM Approach for Selection of Number of PPQ Batches

Mike Long- Process Engineering and Packaging, BioPharmaceuticals Development, R&D- AstraZeneca, Boston, MA

Background

In January 2023, ICH Q9 Quality Risk Management was updated. Managing and minimizing subjectivity within quality risk management (QRM) was a major addition. Section 5.3 of the guidance notes that subjectivity can impact the effectiveness of decisions made from QRM activities.¹

An approach to both manage and minimize subjectivity is to use a rules-based application of QRM. A rules-based approach to QRM is a risk-based approach that embraces standardization. These approaches generally have more formality, can be proceduralized and help manage and minimize subjectivity.²

Industry is encouraged to use these risk-based approaches and QRM throughout the Process Validation (PV) Lifecycle.^3,4,5 There are many potentials for specific rules-based application of QRM, including process performance qualification (PPQ), and continuous process verification (CPV). This paper will discuss rules-based selection for the number of PPQ batches.

Method

A standard assessment method for the selection of an acceptable number of PPQ batches should be based upon process, product, and site knowledge. An important tenet of this specific application of QRM is as knowledge increases, the potential starting point for the number of batches decreases. Though a systematic application of risk tools is required. The recent ICH Q9 R (1) update states:

“Subjectivity cannot be completely eliminated from quality risk management activities, it may be controlled by addressing bias and assumptions, the proper use of quality risk management tools and maximizing the use of relevant data and sources of knowledge”.⁶

To minimize bias and subjectivity, the approach described within makes use of a decision-tree or checklist approach. These approaches employ Yes/No questions to guide the decision-making process, which reduces approach/assessment subjectivity bias. Many firms use risk approaches requiring an estimation of the amount of knowledge on a scale leading to an overestimation of knowledge which ultimately underestimates residual risk. The rules-based approach asks simple clear questions. Essentially, do we have the information or knowledge or do we not.

Justification Of the Baseline (N=3)

The justification of the overall QRM approach and rules-based evaluation is based upon current industry and health authority guidance. Risk-based approaches and application of Quality Risk Management to the process validation lifecycle are noted in health authority guidance(s) including the FDA and EU.^3,4,5

As stated, prior, the determination of the number of batches should be based upon QRM principles as well as product and process knowledge. The more knowledge one possesses (with proof) the relative risk to move into commercial production is reduced, and the number of batches required within a PPQ campaign may be lessened.

All guidances state a risk-based approach must be used. FDA does not point to a specific number of batches as a requirement. Annex 15 provides additional support for the use of N=3 as a starting point for the evaluation table used in this approach.

• “The number of batches manufactured, and the number of samples taken should be based on quality risk management principles, allow the normal range of variation and trends to be established and provide sufficient data for evaluation within the PPQ report”

• “It is generally considered acceptable that a minimum of three consecutive batches manufactured under routine conditions could constitute a validation of the process”.

• “An alternative number of batches may be justified taking into account whether standard methods of manufacture are used and whether similar products or processes are already used at the site”.

Based upon this we update the baseline number of batches to N=3, (see Table 2).⁷

It is important to note in this approach increasing the number of batches does not occur when there is low knowledge and high risk. This may be counterintuitive. Increasing the number of batches does not lower risk, it increases it for the PPQ. If the knowledge level places us at the high-risk level, we would expect some risk control activities to be executed to lower risk. Including more work to increase product knowledge, or in cases like pandemic vaccines, get leadership approval to initiate PPQ. This will come with an acknowledgement of risk, including a risk benefit analysis (RBA).

High Level Approach

A high-level summary view of the approach is provided below. In practice, expect a multi-page risk check list or a very detailed decision tree. Figure 1 is for visualization purposes only, however it should be noted this approach has been used on Aseptically Filled Drug Product, ATMPs, and Biologics Drug Substances. There are three basic steps to the rules-based approach as shown in Figure 1.

Step 1: Determine the baseline number of batches using product and process knowledge.

• The level of product and process knowledge are used to determine the minimum number of batches (or baseline) that need to be produced during PPQ.

• Answers to the questions provide guidance to one of three rules-based starting points for “N” number of batches. As product and process knowledge increases the potential starting point for the selection of the number of batches decreases. A risk evaluation table is used to guide the determination (Table 1).

Step 2: Determine if any additional controls, activities, or technical rationales are required.

• This is our risk control step.

• Step 2 helps to confirm the selection of the number of batches from Step 1 and determines specific site level knowledge.

• Risk control activities determine if additional studies or batches need to be performed/executed.

Step 3: Determine if there are any related risks/sources of knowledge such as, business, technical, or strategic justifications for the addition of batches above that set in steps 1 and 2.

• Additional rounds of questions may be asked to determine if the number of batches needed to support PPQ should be increased based upon business or strategy, in addition to risk.

• These should be rules-based as well.

• After Step 3, the number of batches, N, is provisionally set.

Let us apply two high-level cases to the process outlined in Figure 1.

Case 1: A firm is producing a new mAb drug product (DP) on a new isolator fill line. It will be released in a single strength pre-filled syringe (PFS). The firm is planning on doing full scale engineering runs with the new formulation on the new isolator. This product has yet to be approved. The rules-based selection of PPQ batches for this case will be N=3. The justification is provided visually in Figure 2 and summarized in Table 2.

Case 2: A fi rm is transferring an existing approved mAb drug product (DP) within an existing facility to an existing isolator fill line due to increased demand. It will be released in a single strength PFS. The firm is planning on doing confirmatory engineering runs of the DP on the existing isolator. This product has an approved BLA. The rules-based selection of PPQ batches for this case can be N ≤ 3. The justification is provided visually in Figure 3 and summarized in Table 3. The two cases provided illustrate the rules based QRM concept as applied to the selection of “N” PPQ batches. In practice there will be more questions in the process and evidence for decisions made will be required (report numbers, etc). SOPs and work instruction(s) will need to be written to create standard ways of working. White papers may need to be written to justify the baseline number of batches. Filtering questions may also be required. For example, is the product to be marketed in a country that requires a minimum of N=3 PPQ batches.

Utilization of risk-check lists and decision trees in a formal procedural manner helps to reduce and manage subjectivity when selecting the number of batches for a PPQ campaign by using a simple binary method rather than a subjective scale of knowledge.

References

1. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use. ICH Harmonised Guideline: Quality Risk Management Q9 (R1): 9.18 January 2023.
2. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use. ICH Harmonised Guideline: Quality Risk Management Q9 (R1): 9.18 January 2023
3. U.S. Food and Drug administration. FDA Guidance for Industry: Process Validation: General Principles and Practices: 1-7.2011 
4. EudraLex Volume 4: EU Guidelines for Good Manufacturing Practice for Medicinal Products for Human and Veterinary Use Annex 15: Qualification and Validation: 2-3.2015
5. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use. ICH Harmonised Guideline: Quality Risk Management Q9 (R1):1.18 January 2023.
6. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use. ICH Harmonised Guideline: Quality Risk Management Q9 (R1): 9.18 January 2023.
7. EudraLex Volume 4: EU Guidelines for Good Manufacturing Practice for Medicinal Products for Human and Veterinary Use Annex 15: Qualification and Validation:8.2015

Author Biography

Mike Long has over 25 years of pharma, device, and combination product and process development. He is currently a Sr. Director with AstraZeneca within their Biopharmaceuticals Development Group. Mike is active within industry including being a past member of PDA’s Science Advisory Board and has also instructed graduate courses on topics such as Data Analysis and Pharma and Device Quality Systems. Mike is a master black belt with a BS from WPI, an MS from Tufts University and doctorate from Northeastern University

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