APR May/June 2023 - 15

« MANUFACTURING
Table 2. Case 1: Summary information and
rules-based N batches output.
Table 3. Case 2: Summary information and rules-based
N batches output.
Case 1 New Product
* New Formulation
* New Fill Line
* First PPQ
* Single presentation & Strength
* Engineering / at scale batches to be executed pre PPQ
* Step 1 and 2 questions: N=3
* Step 3 Questions: No
N = 3
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 firm 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.
Case 2 Existing Product
* Existing Formulation
* Existing Validated Line
* Secondary Tech Transfer
* Single presentation & Strength
* Engineering / at scale batches to be executed pre PPQ
* Step 1 and 2 Questions: N≤3
* Step 3 Questions: No
N ≤ 3
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.
2.
3.
4.
5.
6.
7.
International Council for Harmonization of Technical Requirements for Pharmaceuticals
for Human Use. ICH Harmonised Guideline: Quality Risk Management Q9 (R1): 9.18
January 2023.
International Council for Harmonization of Technical Requirements for Pharmaceuticals
for Human Use. ICH Harmonised Guideline: Quality Risk Management Q9 (R1): 9.18
January 2023
U.S. Food and Drug administration. FDA Guidance for Industry: Process Validation: General
Principles and Practices: 1-7.2011
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
International Council for Harmonization of Technical Requirements for Pharmaceuticals
for Human Use. ICH Harmonised Guideline: Quality Risk Management Q9 (R1):1.18
January 2023.
International Council for Harmonization of Technical Requirements for Pharmaceuticals
for Human Use. ICH Harmonised Guideline: Quality Risk Management Q9 (R1): 9.18
January 2023.
EudraLex Volume 4: EU Guidelines for Good Manufacturing Practice for Medicinal Products
for Human and Veterinary Use Annex 15: Qualification and Validation:8.2015
Figure 3. Case 2 Existing Product: Rules-based QRM selection of
Number of Batches (N ≤ 3)
Author Biography
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
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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APR May/June 2023

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