BioTx Pharmaceutical Sciences Movement within the design space with a robust control strategy Jon Coffman, Ph.D. Principal Engineer III BioTherapeutic Pharmaceutical Science July 19, 2010

2 Overview Non-CPPs do not have an “infinite design space” –Quality system, including change control, for Non- CPPs prevents an infinite design space –“Variability” and “impact” from the ICH Q8 definition of critical process parameter (CPP) must be defined –We are investigating the quantification of “variability” and “impact” in our quality systems Design space verification, periodic testing, and a robust control strategy

3 Control of non-critical process parameters (NCPP) “Variability” is not defined “Impact” is not defined Critical Process Parameter: 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. --ICH Q8 We are investigating the quantification of “variability” and “impact”

4 An example of typical elements considered for the protein A step FMEA “Variability” of process parameter considered for CPP definition in ICH Q8 Becomes non- critical at this point, and range is documented for reference Goes into DOE

5 Impact of PP on HMW level Contour of HMW% Over the range shown, these parameters impact a CQA (i.e. HMW level) Bed height has no impact >5%

6 An example of typical elements considered for the protein A step FMEA “Variability” of process parameter considered for CPP definition in ICH Q8 Becomes non- critical after DOE, and range is documented for reference

7 One method to quantify “impact” We are investigating the quantification of impact Impact can be quantified in relation to the acceptable range of the CQA The numerical value of “impact” may differ for each CQA X can be: A percentage of the allowable range LRV for impurity removal

8 How do we define impact: examples Contour of HMW% >5% From 3% to 4% HMW 20% impact  Critical “Impact” is relevant to the control of the CQA Impact depends upon the acceptable range of the CQA Consider if we had studied only the area in the blue box For this case, we could envision wanting to control PP that changed HMW by 20% of the allowable range

9 Impact examples Contour of HMW% >5% 20% impact  Critical 10% impact Non-critical

10 Impact examples Contour of HMW% >5% 20% impact 10% impact 10% impact Non-critical Critical

11 Define “impact” of a PP on a CQA “Impact” does not mean just “statistically significant” –There can be unimportant, yet statistically significant, impact on a CQA –“Impact” should be relevant to control of the CQA Not just a relationship that causes the CQA to exceed the specification or safe limit –This definition alone is not sufficient since we would want to monitor and control a parameter that brought the CQA just to the edge of failure—this would be critical –We would want to understand and control the parameters that can be meaningfully adjusted to control the level of the CQA A statistically significant relationship of greater than X% of the CQA range or exceeds safe limit –X might be on a log scale for some impurities –X might be different for each CQA, but can be defined numerically Argue about the value of “X” or propose a new rigorous definition

12 Potential control strategy for non-critical process parameters: e.g. Protein A bed height Bed Height MBR Control Range Change Control includes SOP for NCPP New Control Range Evaluate new range. If outside prior assessment, redo QRM, considering multivariable interactions DOE experimentation if required Not reportable if the CQAs are not impacted (i.e. remains non-critical) 20cm35cm QRM range (i.e. knowledge space or FMEA range) as filed in 3.2.S.2.6 or 3.2.P.2.3

13 A control strategy for non-critical process parameters “Variability” and “impact” can be quantified, defined, and recorded Non-critical process parameters do not have an infinite operational space –We can assure regulators that they will not impact the variability of the CQA –The range of Non-CPP can be filed in development sections –Changing the operating range of non-CPPs requires change control with a QRM

14 Design space verification, periodic testing, and a robust control strategy Consider the value of verifying design space model at scale “off-center” verses demonstrating process control

15 Design space is unlikely to ever be confirmed across whole area at large scale NOR Design Space Edge The multidimensional universe is a big place

16 Verify the design space “Down the Middle” This strategy begins the process of getting data for SPC NOR Design Space Edge

17 Consider an example IgG1 6000L production scale Standard mAb process Eight GMP batches Prior approval process verification options –Verify design space by varying CPPs, including near edges of design space –Run process down the middle

18 Limited value of verifying model off center A seven dimensional design space can confirm model main effects with only eight runs at large scale. Does not confirm interaction terms. Verifying the small scale model off-center may have less value than demonstrating statistical process control with those same eight runs

19 Eight runs “down the middle” at scale indicate significant process control: High C pk values

20 Eight runs “down the middle” at scale indicate significant process control glycoforms 12  99 OOS OOS

21 Out of specification rate vs. Cpk Periodic testing warrants investigation Introduction to Statistical Quality Control, D.Montgomery,

22 One element under investigation for shifting to periodic testing: Cpk>1.5* –OOS rate for a Cpk of 1.5 is ~3 ppm for a process that remains in control –With a Cpk of 1.5, we can demonstrate with 95% certainty that the process mean remains within limits with just one test –In order to demonstrate process consistency, one must have significant number of batches (>20-30) *This proposal not generally agreed upon, but illustrates the rational

23 Periodic or no testing may be warranted for some CQAs Batch CQA Testing Every Batch Periodic Testing Based on Cpk HCP Protein A, DNA—often done already Glycoforms—Cpk >1.5 HMW—Cpk was 16

24 Verify the design space “down the middle” NOR Design Space Edge

25 Change control within the design space NOR Determine SPC of new area Confirm Cpk is acceptable if required by QRM Change Control: Risk Assessment NOR Assure product quality is not impacted by the change

26 Change control with QRM that indicates heightened testing Batch CQA Periodic Testing Testing Every Batch for period of time Change within DS

27 Summary Non-CPPs do not have an infinite design space, but one that is controlled by the Quality System –The range of Non-CPP can be filed in development sections –Changing the range of non-CPPs requires change control with a QRM Defining, quantifying and documenting “variability" of process parameters and “impact” on quality attributes may be key to determining CPPs NOR-centered verification of the design space model at scale can provide important statistical process control data and may allow periodic testing of CQAs while running safe, effective large scale processes with the appropriate control strategy

28 Acknowledgements Stefanie Pluschkell Graham Cook Ranga Godavarti Maureen McLaughlin Leslie Bloom Ken Green Nora Slattery Jeff Doyle Bruce Tangarone Tim Charlebois Marta Czupryn Tom Last Jose Gomes Tim Iskra Bonnie German

29 Abstract Robust control strategies include quality elements that control critical process parameters and non- critical process parameters. Quantification of the words “variable” and “impact” in the ICH Q8 definition of “critical process parameter” may represent a means of articulating and controlling these parameters. Robust control strategies could also use process consistency as one element in determining the testing frequency of certain CQAs. In such cases, heightened testing may be required when a operating range is moved under a change control to a new area within the design space.