Critical or Non-Critical

My previous post sparked me to pose a question within LinkedIn and the resulting conversation has been really helpful.  What I want to do next is analyze what the guidelines mean in relation to a possible example of data that would be used in a filing.  In a previous post, I used data from an evaluation of CaptoAdhere to do some DOE analyses and illustrations.  I'm going to use that data to work through a thought experiment.  Let's start with some assumptions:

1. The data are for the last step in the fine purification prior to final filtration and formulation
2. The preceeding steps were a protein A capture, low pH viral inactivation, and an anion exchange column
3. The CHO HCP assay represents a cell-line specific assay
4. A risk analysis has identified both the aggregate and CHO HCP as critical quality attributes (CQA) for the product.
5. Characterization experiments were completed with a qualified laboratory-scale model of the manufacturing scale process and the results are to be used in supporting the process characterization section, the validation section and the manufacturing description of the regulatory filing
6. Finally, there exists limited manufacturing scale data, both analytical and process related, for the CaptoAdhere process.  Let's say...10 lots were produced by the time the commercial filing was submitted.

The model for the aggregate has found that the main effects of load amount and load conductivity are statistically insignificant; however, their pairwise interaction is statistically significant.

Parameter Estimates for Aggregate in the Eluate

Term		Estimate	Std Error	t Ratio	Prob>|t|
Intercept		0.0075547	0.000268	28.23	<.0001*
Load(93,312)		0.0005093	0.000353	1.44	0.1989
Load cond(10,30)		-0.000359	0.000363	-0.99	0.3614
Load*Load cond		0.0019552	0.000401	4.88	0.0028*

In contrast, the CHO HCP is dependent upon the load amount, the load conductivity and their pairwise interaction.

Parameter Estimates for CHO HCP in the Eluate

Term		Estimate	Std Error	t Ratio	Prob>|t|
Intercept		16.175041	0.876267	18.46	<.0001*
Load(93,312)		8.1260841	1.155147	7.03	0.0004*
Load cond(10,30)		-2.534245	1.18887	-2.13	0.0770
Load*Load cond		-3.252868	1.312414	-2.48	0.0479*

The prediction profiler was then used to simulate 5000 points using the following input:

1. Load: imagine the load standard deviation is 10 g/L and reflects the lot-to-lot variation in the process
2. Load conductivity: the conductivity is controlled fairly precisely at the manufacturing scale through precise, and accurate, measurements of the buffer constituents.  The result is a standard deviation of 0.5 mS/cm
3. SEC results:  The assay precision could be demonstrated in the validation of the method.  Based upon my experience, I'd be comfortable with a value of 0.04%

Simulating Manufacturing Scale Data

Based upon the model, as long as the process parameters are controlled to the precision indicated the aggregate percentage is essentially unchanging.  Based upon this data (I'll get to the CHO HCP data in the next post), would the load and load conductivity be critical or non-critical process parameters?   

Relationship of Upper Tolerance Limit to Prediction Profile

Argument against CPPs:  if the process parameter data from the manufacturing runs match, or are better, than the estimates used in the prediction profiler then the process parameters could be considered to be non-critical because within the normal manufacturing process variation, there is no statistically significant change in the critical quality attribute. 

Argument for CPPs: guidelines clearly state that a CPP is "a process parameter whose variability has an impact on a critical quality attribute and therefore should be monitored or controlled to ensure the process produces the desired quality."