Understanding the Relationship of All Components
There are numerous ways that process and method performance knowledge can be used to monitor and improve overall process and
product quality. In reality, unlimited time and resources are not always available and we should first identify the most critical
components and maintain committed to implement critical improvements before we get lost in too much data. A hypothetical example
is provided to illustrate how acceptance criteria for AMTs and AMMs could be derived with respect to estimated probabilities
for 1A–B and 2A–B. Following from this example, potential improvements are discussed to illustrate what could be done to reduce
the undesirable probabilities for cases 1B and 2A-B.
A potency bioassay is used for downstream in-process and final container testing of a licensed biopharmaceutical drug. This
method is monitored in our AMM program. After months of commercial production, transfer of this analytical method to a different
laboratory for product release testing is needed. If the downstream in-process stage is yielding inconsistent potency results
(Table 2), the following data should be reviewed:
- process development
- process validation
- AMD
- AMV
- historical process
- method performance (assay control) data.
Table 2. Historical process, sampling, assay performance data, and suggested limits for accuracy and (intermediate) precision
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We may want to start with the easiest variance component, the assay variance, as monitored with the assay control. Figure
1 illustrates the relationship between the assay performance and observed recent SPC potency results (last n = 60).
The statistical likelihood for failures now needs to be estimated and a component variance analysis performed to estimate
the contribution of each component. Focus can then be put on how to set limits on post-validation activities from understanding
the potential impact on the likelihood for all cases (1A–2B) to occur. The situation can be most effectively improved by primarily
having in mind patient safety, dosing, and regulatory expectations, but secondarily also the firm as the need to pass specifications
and stay profitable is important. Similar to propagation-of-error calculations, an estimate for the sampling variance (batch-uniformity,
stability, protein adsorption losses, etc.), could allow immediate estimation of the actual (true) process performance for
potency by simply solving for it from Equation 1 (V = variance):
Equation 1: [V
observed for process
]
2
= [V
assay
]
2
+ [V
sampling/batch uniformity
]2 + [V
actual for proces
s]
2
Equation 2: [CV% Potency]
2
= 1/n [ (CV% Inter-assay)
2
+ (CV% Sampling)
2
/(# sampling units) + (CV% Intra-assay)
2
/( # sampling units) x (replicates for each sample)]
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