Statistical Questions from CPV Monitoring of Bioreactor Data bernier... · perfusion in bioreactor Upstream Production Process 7 Stage 1: Preculture and expansion ... sample average

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Statistical Questions from CPV Monitoring of Bioreactor Data

Craig Bernier

Principal StatisticianDesign to Value and Quality Engineering

Janssen Pharmaceutical Companies of Johnson and Johnson


CPV Statistics

• Much support around developing procedures, teaching control charts, and providing backup to more complex questions. As per the 2011 Guidance:

“The data should be statistically trended and reviewed by trained personnel.” ….

…“We recommend that a statistician or person with adequate training in statistical process control techniques develop the data collection plan and statistical methods and procedures used in measuring and evaluating process stability and process capability.”

1

252321191715131197531

102

100

98

96

94

92

Observation

Ind

ivid

ual

Valu

e

_X=97.21

UCL=101.50

LCL=92.92

Assay Control Chart ExampleTrending of Typical CQA of a Product


Common Statistical Questions for CPV

• Data failed the Normality test

– ‘Do I have to transform the Data?’

– ‘How should I transform the data?’ or ‘ What transformation do I use?’

• What Shewhart tests to apply?

• Long Term vs Short Term sd

• ‘Can I use Levey Jennings Chart’?

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Common Statistical Question for CPV

• For dissolution do I just chart the averages? Or Individuals?

– For individuals do I use:

• Individuals chart?

• X-bar / R, or X-bar / S?

• Should we use the I-MR R/S (Between / Within) charts?

• Do I have to calculate Process Capability?

– How often?

– Use Cpk or Ppk?

– Confidence intervals?

3


Statistically Related Questions

• If I see a point outside the control limits, do I have to open an investigation?

• The control limits are to 3 decimals, but the spec is one decimal?

• The data is rounded to same precision as the specification. Do I need to ask for ‘unrounded data’?

4


Somewhat Statistically Related Questions

• How often do I need to trend / report?

– Why?

• How to evaluate risks, prioritize actions from CPV reports?

• Do I need to trend Yields?

• Do I need to trend

– CMA (Critical Material Attributes)?

– CPP (Critical Process Parameters)?

• Is Minitab/JMP/R ‘Validated’?

5


More Complex Statistical Questions

For Example:

• How to monitor longitudinal data from the bioreactor during CPV?

– Multiple CPV parameters (e.g. Viable Cell Density, Viability, IgG content, pH)

– Daily offline measurements for each batch

• Unique data profile across culture period

• Up to 60 days depending on product

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Stage 2:Production by

continuous perfusion in bioreactor

Upstream Production Process

7

Stage 1:Preculture and

expansion

continuous perfusion bioreactor


Bioreactor Data

8


Problem

• How to evaluate cell growth data over time for trending purposes.

• When is a batch acting differently than usual – what tool to use to detect special cause variability?


Problem Continued

• Initial solution from our practitioners.

• This type of trending shows a sample average and some expected variability around that model. Essentially mean +/-3sd at each day

• A very nice solution to be able to detect unusual results and potential special cause variability

• Some challenges though…


Problem Continued

• We do not have the ability to monitor the process over time as we usually desire in CPV

– Is process trending up / down?

• Another challenge is that this approach allows for multiple chances for a batch to signal.

– What to do about a batch that has a single point or a couple points outside the range but in general follows a typical pattern


Alternate approach

•Trending of residuals from average at each day

– This approach captures the deviations from average across time as the current approach. But also allows us to then trend the batches over time potentially using typical control chart methods.


Plot Individuals

Autocorrelation exists due to the nature of the data


Plot Averages and sd

Batch 45Batch 40Batch 35Batch 30Batch 25Batch 20Batch 15Batch 10Batch 5Batch 1

2

1

0

-1

-2

Batch

Mean


2.0

1.5

1.0

0.5

0.0

Batch

StD

ev

Time Series Plot of Residuals Mean

Time Series Plot of StDev


Charting of Average Residuals


1

0

-1

-2

Batch

Ind

ivid

ual

Valu

e

_X=0.010

UCL=1.327

LCL=-1.307


1.50

1.25

1.00

0.75

0.50

Batch

Sam

ple

StD

ev

_S=0.830

UCL=1.093

LCL=0.567

1

1

1

1

1

I chart of Residuals Means

Batch 31 omitted from calculations

S Chart of SRES1

*Standard X-bar chart shows variability more than within batch variability. We use I-MR R/S type approach


Batches of interest identified

Control Charts signaled at Batch 12, Batch 21, and Batch 31. (as well as Batch 6)


Trend Data by Test Date

• Potential additional benefit of evaluating the residuals is that we could also plot the data by test date.

– Are test results unusually high / low on a particular day indicating some lab variability?

918273645546372819101

0.50

0.25

0.00

-0.25

-0.50

-0.75

-1.00

-1.25

Date

Sam

ple

Mean

__X=-0.000

UCL=0.586

LCL=-0.586

1

1

1

Xbar Chart of SRES by Test Date_Qtr 20xx

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References

Montgomery, Douglas C. Introduction to Statistical Quality Control 7th edition, Wiley, 2013.

*Note: Montgomery text page 461-495 discusses various works with some examples on monitoring autocorrelated data as well as profile monitoring.

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Questions

Thanks to:

Tim Overkleeft

Wendy de Wit

Kevin Pipkins

Clemens Haerder

David Enck

Documents

Statistical Questions from CPV Monitoring of Bioreactor Data bernier... · perfusion in bioreactor Upstream Production Process 7 Stage 1: Preculture and expansion ... sample average