As mentioned, when beginning a large investigation (e.g., bioreactor failure, change in cell culture productivity, contamination),
certain variables are selected while others are not – and this selection process comes down largely to intuition (experience)
on the part of the investigators. We have, in our industry been a part of myriad process investigations, and many times this
experiential intuition makes us want a certain answer to come out of the data, or think that a certain failure mode is at
work before the data are there to support the conclusion – rather than letting the data alone guide us to the answer. Should
this occur, the very real potential exists for a CAPA to be assigned that doesn't necessarily correct the real issue nor set
up effective preventive measures against its recurrence. This can lead to a new CAPA being assigned to the same issue later
because the initial assessment was insufficient, wasting both time and money. And should the original (insufficient) CAPA
have been assigned to resolve a serious process issue, there is a high level of risk associated with continued operations.
In the biopharmaceutical industry, this risk could manifest itself as: increased potential of production failure; an increase
in process deviations generating more investigative work, creating an additional investment of time and money; or failure
to comply with regulatory requirements, perhaps preventing the manufactured product's release. It is therefore imperative
to determine CAPA based on good science and coherent reasoning, and treat this corrective or preventive action as an important
process intervention, not merely a requirement for regulatory compliance.
The least we can do in our industry to ensure scientific integrity in our pursuit towards effective CAPA is to have our data
peer reviewed. The peer review is part of the systematic approach we employ when analyzing a process issue and determining
an appropriate CAPA. The importance of a peer review group cannot be underestimated — it acts, literally, to fulfill the verification
aspect of the scientific method. Working in most cases against impressive time pressures, peer review quickly builds or refutes
the case for a CAPA's effectiveness by subjecting it to additional rigor which forms the basis of good science – striving
towards an unassailable deductive hypothesis for an observed set of data. The CAPA can then be implemented with a very high
likelihood for success as a corrective or preventive measure.
Benjamin Locwin is a Process Technologist for Lonza Biologics, Inc., 101 International Drive, Portsmouth, NH 03801, 603.610.4682, firstname.lastname@example.org
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