Preparing for characterization of the process space

With the FMEA completed and the process parameters identified, the next step is to qualify the scale down model.  One question that I've been considering is, "does the qualification lend itself to phase appropriate strategies?"

During efforts that carry a program from a research molecule to IND, there will most likely be a limited number of lots manufactured that lead to a limited process experience.  Further, for biologics the process can be expected to evolve under the pressures of cell culture optimization.  With such a dynamic process in place, a classic strategy for the purification team would be to leverage the knowledge from 1.5 cm and 10 cm column performance for establishing the scale-down model.  At these scales, the drug substance being generated is typically for pre-clinical research and is completed within the development lab.  As a result, the team has a clear picture of the scale comparability.  For example, the tubing type, plumbing length, pumps, on-line meters, etc., can all be evaluated along with the change in column scale (a 44x scale-up in this case).   When combined with the chromatographic performance and process performance (elution volume, recovery, etc) at each scale, the team can document the starting point of the process development history.  This also presents an opportunity to leverage process knowledge from the literature into the scale-down documentation and create a foundational document for future process development activities.

As the program moves into pivotal phase 2 status, the push is to deliver material to the clinic in sufficient quantities to support the clinical program.  With a bit of luck and great planning, the cell culture process has been completed to the point that they can be scaled-up.  For companies working with their CMOs, the path to scale-up becomes a bit more challenging as the purification equipment to support an 80 cm column is much different than that of the earlier models: the scale difference is 2844 between an 80 cm and 1.5 cm column.  The plumbing becomes stainless steel, the hold up volumes increase, water quality, and the differences continue and all will need to be documented.  Looping back to a previous post, the qualification of the laboratory-scale model presents a good opportunity to initiate (or revisit) the FMEA to prioritize the scale differences with the greatest potential to impact the process and product quality attributes.  To bridge the scale performance back to the early phase results, data from the 80 cm columns has to be compared with data from the laboratory-scale models.  To maximize the opportunity for success, the ideal situation would be to use process eluate retains from the clinical manufacturing scale in the laboratory-scale models whose process parameters are run as close as possible to those at clinical manufacturing scale.  An important point to consider is that there are relatively few raw material lots being used in the cell culture process during this stage of the program.  Consequently, the anticipated variation in the product quality will be relatively modest and this will need to be included in the discussion of the scale-dependent, or independent, process performance or product quality differences.

What are the metrics establishing the scale down model?  Based upon the above discussion, the argument can be made that these metrics will change, and become more robust, with the phase of the program (see links at the start of this entry).  Documenting, and providing scientific justification, through the drug development stages will establish the basis of the QbD program because the laboratory-scale model qualification links the design space knowledge to the manufacturing scale.  The importance of this link cannot be under appreciated when the time comes to file the commercial process because the sponsor will have to provide a scientifically sound justification that the results obtained in laboratory-scale models are predictive of the process at manufacturing scale.

Handling the FMEA

In the previous posts, JMP was used to illustrate how a fishbone diagram could be used to sketch the how the different elements of a chromatography process could affect the product quality and how there needed to be sufficient quality systems in place to enable efficient conversion of this data into knowledge.

The FMEA should enable the reviewer to see a clear connection between the process parameters and the critical quality attributes (CQAs) of the product.  The next step is a bit trickier.  The FMEA should also provide a roadmap for developing the process knowledge relating the effect of these process parameters to the CQAs.  What I really liked about the paper by Xu, et al was the use of a Pareto chart to establish the number of factors that would be studied in their process characterization work.  The Pareto plot allows the visualization of the risk priority number (RPN) as well as the cumulative effect of the number of factors.  By setting the bar at 90% of the cumulative effect, the argument may be made to the regulators that the majority of the risk to the product quality has been accounted for in the process characterization work.

Pareto Plot of RPN Score by Operating Parameter (Adapted from Xu, et al)

Setting the bar at 90% means there will be seven factors that need to be studied for the process characterization work.  These can be readily handled with a screening design to identify which have the most significant effect and then augment the design for refinement of the operating space.  A Plackett-Burman would need only 12 experiments to find the main effects!  Nice.  Before tackling these experiments, a justifiable scale-down model has to be in place for comparison with the manufacturing scale.  In the next entry, I'll be talking about strategies for justification of a scale down model.

Putting the Quality in QbD

In the previous post, I laid out an example of how to begin documenting the QbD process.  Over at LinkedIn, there's a discussion about the challenges of bringing a QbD/PAT program into existence that is really worth the read.  My plan for this entry is to outline some of the pathways that need to be in place for QbD to be effective and deliver on its promises.

I believe the important, and obvious, starting point is in the Q of QbD.  The agency's have clearly articulated their expectations through the guidelines (see link to the right).  What are the practical implications of these?  Paperwork.  We have to work with the quality systems to enhance, or build, the infrastructure to support a QbD approach.  For example, there are lots of examples of risk analysis in the literature; however, the approach has to be coded into the quality system to ensure that people are appropriately trained in the methodology and the results documented, reviewed and filed within the quality system for reference.  For pharmaceuticals and biopharmaceuticals, the risk assessment has to be phase appropriate and continuous: what is done for phase 1 is not what is done for phase 3/validation and the risk analysis should evolve as a program pushes through from phase 1 to phase 3.  Oh, the QbD process also has to be flexible to handle in-licensing of a product.  All of this, and more, represent the foundation of a solid QbD program.

What does this mean on the day to day?  The lines of communication to the various quality groups have to be reinforced every day.  Conversations over a cup of coffee, formal strategy meetings, and more all serve to develop those relationships that will enable the organization to leverage its knowledge effectively and efficiently.  

At the end of a long day...

I'm flattered so many of you, from so many different countries, have been visiting.  Thanks so much and I look forward to your next visit!

Analysis of Visits to "In the Process Stream"

CEX Chromatography and QbD

I was going through my previous posts on the media selection process last night while listening the to the Bruins game.  Those last two minutes were heartbreaking. Anyways, using the augmenting process to build up a body of knowledge for the model led to a different conclusion than that proposed by the authors despite using the same data set.  Which is more correct?  What's the best way to process a CCD data set?  I'm a big proponent of starting simple and then adding complexity.  With a CCD data set, I'll start with the full-factorial model to identify the statistically significant effects.  From there, the addition of center points allows a check for curvature.  If there's none, then the analysis is complete!  If the center points are in alignment with the full-factorial model, including the axial components becomes an important step in identifying what's driving higher order behavior.  Last, tying the results back to the underlying chemistry will become a vital link when writing up the work for archiving.  Being able to have traceability and sound scientific understanding of process behavior is SO important when the time comes from authoring the IND or, if you're lucky, the BLA.

The next article of merit is from Genor Biopharma in Shanghai.  The authors take an integrated approach to developing a cation exchange step using QbD principles.  One of their first steps is to define a fish-bone diagram of the factors affecting the process.  JMP allows you to do this quite easily as a quick sketch up of the process.

Start by making a two-column file in JMP (see below).  In this case, I've labeled them Parent and Child to make it easy to remember the order.  In every case, the child belongs to the parent and this would be repeated until you had all the children accounted for each parent in your process.

Setting up for a Fishbone diagram

In my version of JMP, I go to Graph and then diagram:

Next, put the categories into the appropriate placing within the GUI

and the result follows!

I like these diagrams throughout the design process.  They provide an easy reference for the preliminary FMEA that prioritizes the experiments and for documenting along the way which has the greatest impact on the process.

In the next posting, I'll take a deeper look at their QbD approach.

Last round of Media Analysis

I spent a fair amount of time noodling around with the problem and then did a quick visualization to confirm my thoughts.  When the points of the full-factorial are shown with the center points, notice they all fall at the origin.  As a result, there's no way to figure out which term is the leading to quadratic behavior.  I'm embarrassed to say the amount of time I spent reaching that conclusion!
Distribution of Coded Points within the Design

What to do?  We're at 35 experiments (32 for the full factorial, 3 for the center points).  What I decided to do was augment, yes, again, but this time I'll add axial points.  However, for which factors?  I decided to stick with the main effects that had the greatest statistical significance: Glutamine, NEAA, and ITS.  In the augmentation, I also included an additional 3 center point runs.  The experimental total is now at 44!

Fitting to a full factorial model that includes square terms for Glutamine, NEAA, and ITS gives some good results.  The ANOVA results aren't spectacular, but the model fits the data better than normal variations.  More importantly, the square term behavior is coming from the NEAA and ITS.
ANOVA for Augmented Design with Axial Components

Source	DF	Sum of Squares	Mean Square	F Ratio
Model	9	296.45290	32.9392	8.2079
Error	34	136.44500	4.0131	Prob > F
C. Total	43	432.89790		<.0001*

Parameter Estimates from Fit

Term		Estimate	Std Error	t Ratio	Prob>|t|
Intercept		10.829294	0.785856	13.78	<.0001*
Glutamine		-0.985914	0.320352	-3.08	0.0041*
NEAA		1.7462562	0.319869	5.46	<.0001*
ITS		0.4912445	0.320681	1.53	0.1348
Glutamine*NEAA		0.2518681	0.35905	0.70	0.4878
Glutamine*ITS		0.2831327	0.357962	0.79	0.4345
NEAA*ITS		-1.104809	0.358561	-3.08	0.0041*
Glutamine*Glutamine		-0.110353	0.392928	-0.28	0.7805
NEAA*NEAA		-1.504103	0.392928	-3.83	0.0005*
ITS*ITS		-1.241603	0.392928	-3.16	0.0033*

After simplifying the model to just the statistically significant terms, the ANOVA improves as well as nearly all the factors are highly relevant.  The outlier factor is the main effect of ITS (p>0.05).  I can rationalize an argument for including and excluding it in the final model to the data; in the end, you'll have to decide which way you'd take the model.

ANOVA from Simplified Model Fit to Augmented Design with Axial Components

Source	DF	Sum of Squares	Mean Square	F Ratio
Model	6	291.39175	48.5653	12.6985
Error	37	141.50615	3.8245	Prob > F
C. Total	43	432.89790		<.0001*

Parameter Estimates from Simplified Model Fit to Augmented Design

Term		Estimate	Std Error	t Ratio	Prob>|t|
Intercept		10.681397	0.599025	17.83	<.0001*
Glutamine		-1.002546	0.311948	-3.21	0.0027*
NEAA		1.7072543	0.309969	5.51	<.0001*
ITS		0.4520094	0.310588	1.46	0.1540
NEAA*ITS		-1.125011	0.348986	-3.22	0.0026*
NEAA*NEAA		-1.485901	0.376752	-3.94	0.0003*
ITS*ITS		-1.223401	0.376752	-3.25	0.0025*

An interesting observation is that my approach leads to a different conclusion about the model that fits the data than the conclusion presented in the paper.  I'll discuss the implications of this in the next, and final, entry about these experiments.

Prediction Profiler from Simplified Model Fit to Augmented Design

Closing in on a Media Selection

In the last post, the augmented design gave some results that provided deeper insight into how the different media components were affecting the viable cell density.  Presently, the model for the VCD is made of both main effects and pairwise interactions.
Parameter Estimates from Fit to Full-Factorial Model

Term		Estimate	Std Error	t Ratio	Prob>|t|
Intercept		7.5838154	0.228814	33.14	<.0001*
Glutamine		-0.572075	0.209098	-2.74	0.0170*
EAA		0.0998006	0.27464	0.36	0.7222
NEAA		1.2100318	0.331635	3.65	0.0029*
ITS		0.5629417	0.202966	2.77	0.0158*
Lipids		-0.727668	0.368208	-1.98	0.0697
Glutamine*EAA		-1.04495	0.268457	-3.89	0.0019*
EAA*NEAA		0.8313522	0.344822	2.41	0.0314*
EAA*Lipids		1.2371315	0.334539	3.70	0.0027*
NEAA*ITS		-1.070604	0.260279	-4.11	0.0012*
NEAA*Lipids		0.840536	0.276571	3.04	0.0095*

The ANOVA results indicate the model is significantly better at fitting the data than normal process variations.
ANOVA Results of Fit to Full-Factorial Model

Source	DF	Sum of Squares	Mean Square	F Ratio
Model	10	237.42417	23.7424	26.6759
Error	13	11.57041	0.8900	Prob > F
C. Total	23	248.99458		<.0001*

The resulting prediction profiler shows linear responses (no square terms used at this point), and at the coded point 0, the middle of the ranges explored, the expected viable cell density is 7.58x10^6.  Note that the exponents were omitted from the plot to improve visualization.
Prediction Profiler for Fit to Full-Factorial Model

Stepping back for a minute, let's recall the purpose of this exercise: I wanted to find the minimum number of experiments needed to explain how the different media components affected the viable cell density.  I reasoned that this could be achieved in fewer than the 54 experiments that were used in the cited paper.  At the moment, I'm 24 experiments into the analysis and most of the effects that were observed in the results from the CCD analysis are present.  My current problem is that when the center point data is included, the full factorial model is unable to predict the measured values (red triangles).

Actual by Predicted with Center Points Shown but Excluded from Fit to Model

Despite the 24 experiments, the process space has yet to be fully explored from a full-factorial perspective - as a result, trying to include square terms to the model would be useless because they continue to be aliased against each other.  The only path forward is to augment the data further to see if the results allow model refinement.

Augmenting the experiment to a total of 32 now and selecting only the statistically significant terms from a fit to the full-factorial model gives the same results as obtained from the previous analysis.  The key differences are the reduction in the F ratio and the loss of the pairwise interaction of NEAA and EAA.

ANOVA Results from Fit of Augmented Data to Full Factorial Model

Source	DF	Sum of Squares	Mean Square	F Ratio
Model	9	228.02583	25.3362	15.8153
Error	22	35.24409	1.6020	Prob > F
C. Total	31	263.26992		<.0001*

Parameter Estimates from Fit of Augmented Data to Full Factorial Model

Term		Estimate	Std Error	t Ratio	Prob>|t|
Intercept		7.5813733	0.228694	33.15	<.0001*
Glutamine		-0.560817	0.22865	-2.45	0.0226*
EAA		0.170159	0.241575	0.70	0.4886
NEAA		1.3289597	0.23971	5.54	<.0001*
ITS		0.4425618	0.226401	1.95	0.0634
Lipids		-0.191069	0.228817	-0.84	0.4127
Glutamine*EAA		-1.2019	0.238782	-5.03	<.0001*
EAA*Lipids		0.9275438	0.24142	3.84	0.0009*
NEAA*ITS		-0.775146	0.240365	-3.22	0.0039*
NEAA*Lipids		0.7488445	0.239475	3.13	0.0049*

The model is still unable to predict the VCD at the midpoint of the range explored (solid red triangles).  In spite of completing enough runs for a full factorial (32), the square terms of each of the main effects remain indistinguishable from each other!  What to do next?  Stay tuned!

Actual versus Predicted of Augmented Design

Singularity Details 

Glutamine*Glutamine = EAA*EAA = NEAA*NEAA = ITS*ITS = Lipids*Lipids