Process Characterization –It’s Not A Science...

BioProcess Technology Consultants

www.bptc.com

CONFIDENTIAL

Process Characterization – It’s Not A Science Project

March 1, 2017

Why Do Process Characterization? What’s the Goal?

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Standard answers from clients

• Increase process knowledge

― A correct, if esoteric answer. It begs the question, “Then what?”

• Establish Proven Acceptable Ranges (PARs)

― Better, but for what immediate and ultimate purpose?

• Design of Experiments

• Creating mathematical models relating process variables to Critical Quality Attributes

It’s Not A Science Project!

Why Do Process Characterization? What’s the Goal?

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From PDA Technical Report #60: Process Validation: A Life Cycle Approach, 2013 has the answer

• From Section 3.7

― (What PC is): “Process characterization is a set of documented studies in which operational parameters are purposefully varied to determine their effect on product quality and process performance”

― (Almost there!): “The approach uses the knowledge and information from risk assessments to determine a set of process characterization studies to examine proposed ranges and acceptance criteria”

― (Here’s the Goal or Application of PC): “The resulting information is used to define the PPQ ranges and acceptance criteria”

From BPTC Standard Templates for PPQ Protocols

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PC studies needed to ensure that operation at the NORs specified in PPQ protocol (and at values somewhat outside of the NOR to account for measurement error and possible deviations) will not impact CQAs and process performance.


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In addition to historical process data from full scale GMP manufacturing lots, PC studies are required to specify acceptable CQA limits for PPQ campaign.


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In addition to historical process data from full scale GMP manufacturing lots, PC studies are required to specify acceptable performance for PPQ campaign.

With Goal Now Defined, How Do We Design PC Program?

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What variables do we study?

• Those that are at the greatest risk of screwing up your PC campaign

What variables would those be?

• Those that were ranked the highest during the risk assessment (e.g., the RPN score from an FMEA)

OK, we have the variables we want to study, what ranges do we study for each variable

• Study values a little bit outside of the Normal Operating Ranges (NORs) to account for measurement error and deviations

How do we determine what the NORs are?

• …….

What is an NOR and How Do We Establish Them?

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One definition of NOR

• From TR60: “a defined range, within (or equal to) the Proven Acceptable Range, specified in the manufacturing instructions as the target and range at which a process parameter is controlled, while producing unit operation material or final product material meeting release criteria and CQAs”

― NOR < PAR (That’s the only guidance TR60 gives)

Not a helpful definition. For example …..

• Suppose we have data to know that a temperature between 34 and 39 oC has no impact on product quality or process performance (i.e., the PAR is 34 to 39). How does this help us define NOR?

― Is it 34 – 39? 35 – 38? 36.5 – 37.5? ???????

― Key term in NOR is “normal”. Where does the process normally run? What is a reasonable range within which to control a process parameter regardless of PAR?

Establishing Normal Operating Ranges

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Another, more useful, definition of NOR

• “A defined range within (or equal to) the Proven Acceptable Range, specified in the manufacturing instructions as the target and range at which a process parameter is controlled, and that can be achieved with a high degree of assurance and reproducibility”.

• One way to establish NOR is from historical data

― Use process data from historical batches to calculate 95%/99% tolerance intervals

Example: 30 batches are shown and plotted. 95%/99% tolerance interval is 6.77 to 7.22. Thus, NOR is 6.77 –7.22 (as long as PAR is wider).

6.65

6.75

6.85

6.95

7.05

7.15

7.25

7.35

0 5 10 15 20 25 30

Bio

Rx

pH

or

Elu

tio

n p

H o

r ??

?

Run # or Sample #

99%/99%

95%/99%

Establishing Normal Operating Ranges

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Best way to establish NORs is from historical at-scale data

• Use process data from historical batches to calculate 95%/99% tolerance intervals

But what if only a few historical lots have been manufactured, how does one establish NOR?

• Same idea as previous slide – propose ranges where you suspect process can operate 90% or 95% of the time (regardless of impact on process or product as that will be discovered during PC)

• Idea is to minimize the need to execute process deviations and investigations during commercial manufacturing

• Example: for process buffer pH, given the equipment, procedures and instrument tolerances, propose a pH range for the process buffer that can be met 90% or 95% of the time (regardless of impact on process or product as that will be discovered during PC)

We’ve Established NORs: What’s Next for PC Program?

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We know what variables we want to study

We know the NORs for the variables

What ranges do we study for each variable?

• Study values a little bit outside of the Normal Operating Ranges (NORs) to account for measurement error and deviations

How far outside the NORs should be studied in our DOEs?

• One answer: Potentially very far outside because we want to see an impact on CQA and process attributes so that the DOE results generate a mathematical model relating process variables to …….

It’s Not A Science Project!

Mathematical Models “Don’t Do No Harm” ………

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The need for mathematical models reminds me of a line from the movie Western “Unforgiven”

• Gene Hackman as Little Bill Daggett

― “Look son, being a good shot, being quick with a pistol, that don't do no

harm, but it don't mean much next to being cool-headed. A man who will keep his head and not get rattled under fire, like as not, he'll kill ya”

• Having mathematical models relating process variables to CQAs and performance attributes “don’t do no harm”, but it is NOT the point of the PC program.

• The point of the PC program is to:

― Provide experimental support for the NORs specified in the PPQ protocols;

― Provide some wiggle room to operate in the event the NORs are exceeded (i.e., deviations) & to account for measurement variability

We’ve Established NORs: What’s Next for PC Program?

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So, how far outside the NORs should be studied in our DOEs

• One answer: Potentially very far outside because we want to see an impact on CQA and process attributes so that the DOE results generate a mathematical model relating process variables to …….

• Far enough to account for measurement variability & to provide wiggle room to operate in the event the NORs are exceeded so as to provide valuable information for investigations to deviations

• Example: If NOR for pH of column load is 6.7 to 7.3

― Explore 6.5 to 7.5 during PC program

6.7 7.3pH

NOR

6.5 7.5Range studied for PC

to establish PAR

What is an PAR and How Do We Establish Them?

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One definition of PAR

• From TR60: “A characterized range of a process parameter for which the operation within this range, while keeping other parameters constant, will result in producing a material meeting relevant quality criteria.”

NORNOR

Range of acceptable performance• “…. producing

material meeting relevant quality criteria”. What is relevant quality criteria?

• Again, can rely on historical performance of clinical batches

• Graph shows historical clinical batches

Establishing Proven Acceptable Ranges

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Example

• Characterization data shows all experiments studied met all criteria of acceptable performance defined as that seen in clinical batches

• Thus, PAR is the entire range studied

• Note: No mathematical models required!

― No need to look at wider ranges to see an impact on performance

Process Characterization Results

NOR

PAR

Manufacturing Scale Results

NOR

PAR


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Another Example

• Characterization data shows some experiments failed at values less than the lowest boundary of the NOR

• Thus, PAR is range for which parameter gave acceptable results

― Data shown could be actual data or “data” obtained from a model fit

• Lower end of NOR in danger of providing unacceptable quality

― Need more data at lower end

Process Characterization Results Manufacturing Scale Results

NOR

PAR


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Another Example

• Characterization data shows some experiments failed at values less than the lowest boundary of the NOR

• Thus, PAR is range for which parameter gave acceptable results

― Data shown could be actual data or “data” obtained from a model fit

• Lower end of NOR in danger of providing unacceptable quality

― Need more data at lower end; seems like critical process parameter

Process Characterization Results Manufacturing Scale Results

PAR

NOR


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PC studies needed to ensure that operation at the NORs specified in PPQ protocol (and at values somewhat outside of the NOR to account for measurement error and possible deviations) will not impact CQAs and process performance.

Note: Often times PARs are presented in PPQ protocols as well. PARs provide helpful documentation in the event NORs are exceeded as the PARs in the PPQ protocol can be cited during the investigation and review of the deviation.

What’s Next?

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We know what variables we want to study

We know the NORs for the variables

We know the ranges we want to study for each variable

We have a strategy for determining PARs

• Mine historical data to determine acceptable performance

Execute the studies: DOEs, OFATs

Advice regarding DOE execution

• Don’t fall in love with JMP software

• JMP say tell you can study 7 variables with 8 experiments – DON”T BELIEVE IT!

DOE Example

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7 variables with 8 experiments. The design is as follows:

• Suppose the average result is 10 and the first variable has a 20% impact and the second variable has a 12% impact and the results are ideal with no variance.

Run # Var 1 Var 2 Var 3 Var 4 Var 5 Var 6 Var 7

1 1 1 1 1 1 1 1

2 -1 1 1 -1 -1 1 -1

3 1 -1 1 -1 -1 -1 1

4 -1 -1 1 1 1 -1 -1

5 1 1 -1 -1 1 -1 -1

6 -1 1 -1 1 -1 -1 1

7 1 -1 -1 1 -1 1 -1

8 -1 -1 -1 -1 1 1 1

• If process results, effect of variable 1 would be exactly 2 and the impact of variable 3 would be exactly 1.2. All other variables are zero.

Run # Var 1 Var 2 Var 3 Var 4 Var 5 Var 6 Var 7 Result

1 1 1 1 1 1 1 1 13.2

2 -1 1 1 -1 -1 1 -1 9.2

3 1 -1 1 -1 -1 -1 1 13.2

4 -1 -1 1 1 1 -1 -1 9.2

5 1 1 -1 -1 1 -1 -1 10.8

6 -1 1 -1 1 -1 -1 1 6.8

7 1 -1 -1 1 -1 1 -1 10.8

8 -1 -1 -1 -1 1 1 1 6.8

2 0 1.2 0 0 0 0

DOE Example

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• Now assume some random variance


1 1 1 1 1 1 1 1 13.5

2 -1 1 1 -1 -1 1 -1 9.0

3 1 -1 1 -1 -1 -1 1 13.0

4 -1 -1 1 1 1 -1 -1 9.4

5 1 1 -1 -1 1 -1 -1 11.2

6 -1 1 -1 1 -1 -1 1 6.7

7 1 -1 -1 1 -1 1 -1 10.5

8 -1 -1 -1 -1 1 1 1 6.6

2.0625 0.1125 1.2375 0.0375 0.1875 -0.088 -0.038

• Still get essentially the same result. The most important variables are #1 and #3. The values for the rest are due to experimental variance.


1 1 1 1 1 1 1 1 13.2

2 -1 1 1 -1 -1 1 -1 9.2

3 1 -1 1 -1 -1 -1 1 13.2

4 -1 -1 1 1 1 -1 -1 9.2

5 1 1 -1 -1 1 -1 -1 10.8

6 -1 1 -1 1 -1 -1 1 6.8

7 1 -1 -1 1 -1 1 -1 10.8

8 -1 -1 -1 -1 1 1 1 6.8

2 0 1.2 0 0 0 0

DOE Example

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• Now suppose for 1 variable – just one – you get an unusually high variance …….Run # Var 1 Var 2 Var 3 Var 4 Var 5 Var 6 Var 7 Result

1 1 1 1 1 1 1 1 13.5

2 -1 1 1 -1 -1 1 -1 9.0

3 1 -1 1 -1 -1 -1 1 13.0

4 -1 -1 1 1 1 -1 -1 9.4

5 1 1 -1 -1 1 -1 -1 11.2

6 -1 1 -1 1 -1 -1 1 6.7

7 1 -1 -1 1 -1 1 -1 10.5

8 -1 -1 -1 -1 1 1 1 6.6

2.0625 0.1125 1.2375 0.0375 0.1875 -0.088 -0.038

• Now the story becomes a little muddled: is variable #5 significant? Are variables 2 and 4? The story becomes muddled all because of 1 bad point.


1 1 1 1 1 1 1 1 13.5

2 -1 1 1 -1 -1 1 -1 9.0

3 1 -1 1 -1 -1 -1 1 10.1

4 -1 -1 1 1 1 -1 -1 9.4

5 1 1 -1 -1 1 -1 -1 11.2

6 -1 1 -1 1 -1 -1 1 6.7

7 1 -1 -1 1 -1 1 -1 10.5

8 -1 -1 -1 -1 1 1 1 6.6

1.7 0.475 0.875 0.4 0.55 0.275 -0.4

DOE Advice

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Even if only had 3 variables and 8 experiments, one bad point can make it appear as if you have interacting effects when you may not

Here is my advice:

• Critical: Ensure that your experimental systems and assays are reliable before executing DOEs. One bad point can muddle your results and at worse, mislead you!!!

• Critical: Know your experimental variance before executing DOEs so that you know what level of effects are significant compared to variance.

• Helpful: Run center-points along with the DOE as controls to provide a check that experiment ran appropriately

DOE Advice

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Here is my advice:

• Helpful: Run duplicates if you can. Even running duplicates for just some of the runs can be helpful.

• Helpful: If the results indicates that you have many 2-factor interactions or if all the effects are relatively close to one another and no one effect stands out, be suspicious that one or two bad points may have muddled the results

• Critical: Leave time available to re-run experiments or to execute more experiments to explore a variable space with higher resolution.

Summary

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From FMEA, determine what variables you want to study

• Prioritize variables with high RPNs

Establish NORs for these variable from historical runs and/or equipment tolerances

Determine the ranges you wish to study

• Ranges a tad larger than the NORs

Determine a strategy for establishing PARs

• Mine historical data to determine acceptable performance

Execute the studies: DOEs, OFATs

• Don’t fall in love with JMP software

• Ensure your experimental systems are reliable & know the variance

Process Characterization –It’s Not A Science...

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