11/16/01 batch.ppt 1 (6)

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Pharmaceutical Production -- Batch Processes

• Quality Control today made after batch completion;Upsets are found too lateNo correction is possible

• Real Time Quality Control made during batch evolutionUpsets found earlierCorrections possible

• Based on summarizing adequate data measured on a representative set of good batches; control limits based on average batch trace ± 3

(measured properties)

11/16/01 batch.ppt 2 (6)

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Real Time Quality Control (Batch monitoring)

• Models the evolution of representative good batches• Take all the variables and their correlation into account • Use control charts for display, with limits computed from

traces of good batches• New batches monitored as they are evolving• Problems detected and interpreted on the fly• Culprit variables in problem batches clearly identified• Quality of the whole batches predicted as it is evolving and

at completion• Based on multidimensional informative data measured

during the batch evolution + multivariate analysis

11/16/01 batch.ppt 3 (6)

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Batch Control Charts of a mixing process (pharm)Two summary traces of good batch (green) ± 3 (red)

Good batches must evolve inside these limits

11/16/01 batch.ppt 4 (6)

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Monitoring the evolution of new Batches (On line)

Bad batch (black) as it is evolving, seen outside the limitsDeviations are interpreted with Contribution Plots

Bad batch

11/16/01 batch.ppt 5 (6)

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Control chart of the culprit variable

11/16/01 batch.ppt 6 (6)

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Benefits B-SPC; same principles as MSPC

• Multivariate batch modeling

brings the analysis of 3-way batch data

to the same framework as continuous process data

• Allows interference as batch is evolving,

and prediction of final quality

• Applies to

evolving batches (individual observations),

and whole batches as units

• Results shown as easily interpretable control charts

• Makes batch quality control more reliable, and faster

• Facilitates compliance inspection