Optimizing recovery and aggregation in the stream

The previous post demonstrated the ability of Capto Adhere to recover the antibody with minimal losses when used in the weak exchange partitioning mode.  Product quality is also an important criteria for a purification step.  For antibodies, one of the essential product qualities is aggregation.  Aggregation is a major concern in the field and will probably need an entire post (with refs) detailing how these impact the development process as well as the risk to patients.  With that said, the analysis of the aggregation percentages (determined by size exclusion chromatography) using the model for the response surface results in the following ANOVA and parameter estimates, respectively:

ANOVA Results

Source	DF	Sum of Squares	Mean Square	F Ratio
Model	5	0.00002046	4.092e-6	4.6700
Error	4	0.00000350	8.7624e-7	Prob > F
C. Total	9	0.00002397		0.0803

Parameter Estimates

Term		Estimate	Std Error	t Ratio	Prob>|t|
Intercept		0.0078988	0.00054	14.61	0.0001*
Load(93,312)		0.0004262	0.000453	0.94	0.4002
Load cond(10,30)	Biased	-0.000279	0.000415	-0.67	0.5386
Load pH(6,7.5)	Zeroed	0	0	.	.
Load*Load		-0.001182	0.001377	-0.86	0.4389
Load*Load cond	Biased	0.0020644	0.000462	4.47	0.0111*
Load cond*Load cond	Biased	0.000571	0.001144	0.50	0.6439
Load*Load pH	Zeroed	0	0	.	.
Load cond*Load pH	Zeroed	0	0	.	.
Load pH*Load pH	Zeroed	0	0	.	.

The parameter estimates show the only statistically significant model is the pairwise interaction of the load and the load conductivity.  Based upon the chemistry of the Capto Adhere resin, this should make a fair amount of sense.  The antibody is binding through electrostatic interactions when operating in the weak exchange partitioning mode.  As a result, the conductivity is going to play a significant role; however, I wouldn't have expected that the pairwise term would be significant only.  As a result, the final model will have all three terms: the load, the load conductivity and their pairwise term (despite working from the belief that only statistically significant terms should be included in a final model).  The resulting ANOVA shows a reasonably good fit to the data and the parameter estimates continue to show that only the pairwise interaction is statistically significant.

ANOVA Results

Source	DF	Sum of Squares	Mean Square	F Ratio
Model	3	0.00001970	6.5664e-6	9.2358
Error	6	0.00000427	7.1097e-7	Prob > F
C. Total	9	0.00002397		0.0115*

Parameter Estimates

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*

Based upon the actual by predicted, the model's overall fit is quite good.

Fit of Aggregate Results to Model Prediction

Does the model improve using only the pairwise interaction?  Yes!  The F-ratio has increased significantly - primarily as a function of only the single degree of freedom taken by our model!

ANOVA Results using only Statistically Significant Term

Source	DF	Sum of Squares	Mean Square	F Ratio
Model	1	0.00001758	0.000018	22.0356
Error	8	0.00000638	7.979e-7	Prob > F
C. Total	9	0.00002397		0.0016*

Notice that the Mean Square of the Pure Error is 7.979e-7?  The square root of the is value gives an estimate of the pure error in the model.  The result (0.09%) is in pretty good agreement to what I'd estimate is the precision of the SEC assay.  With the model established, the next step is to optimize the process.

Now, JMP allows us to combine the model for the recovery and the aggregate to guide us in our decisions about the process limits.  In this case, a contour plot allows for a good visualization.

The recovery lower limit is set to 95% and the upper limit to the aggregate is 0.8%.  The resulting contour plot then removes the region that wont give 95% recovery (in red) and the less than 0.8% aggregate (green).  The white space represents a first step towards establishing acceptable operating space for the process.  When the results from the CHO HCP ELISA are included, the operating space undergoes even greater definition.

The upper limit to the HCP content is set at 20 ppm with the assumption that a downstream purification step would provide additional clearance.  The process space then becomes restricted to a region centered around the cross hairs.  The contour plot may then be used to provide a scientific justification to the regulatory agencies for the manufacturing control strategy and process validation approach.  In fact, the relationship of this data to the commercial strategy will probably take up several postings in the future!

JMP also allows the exporting of these results as a flash file.  These can be particularly useful when trying to message the results to management.  I'll try to upload a flash file in the future.