Graphical methods for data from a fermentation process. Antje Christensen Per Rexen Novo Nordisk A/S

Transcription

1 Graphical methods for data from a fermentation process Antje Christensen Per Rexen Novo Nordisk A/S

2 Agenda The Process The Project Data Graphs Slide No October 2002 Fall Technical 2002

3 The Process: Fermentation of a Pharmaceutical product: FVIIa ( activated factor seven ), a blood clotting agent producer: genetically modified mammalian cells Slide No October 2002 Fall Technical 2002

4 Process steps Working cell bank ampoule Raw materials Cell culture Fermentation Purification Formulation of finished product Slide No October 2002 Fall Technical 2002

5 Cell culture cell bank ampoule cell factory seed fermentor I seed fermentor II production fermentor Slide No October 2002 Fall Technical 2002

6 Fermentation: Draw and Fill cell growth phase: 10 days production phase: up to 48 days harvest every 24 hours several fermentors at each step no fixed coupling between seed and production fermentors Slide No October 2002 Fall Technical 2002

7 Purification chromatography 4 purification steps one column per step purpose: volume reduction removal of impurities activation harvests from two days are pooled column step 1 column step 4 Slide No October 2002 Fall Technical 2002

8 The Project purpose discovery - new knowledge from existing data optimization of product yield description of a normal state of production prediction of an individual fermentation s yield at an early stage team specialists from fermentation specialists from purification statistician Slide No October 2002 Fall Technical 2002

9 Data Observations 26 fermentation batches up to 24 purification batches per fermentation batch two days per purification + days during cell culture fermentation purification purification purification day day day day day day day day Slide No October 2002 Fall Technical 2002

10 Data Time Intervals Between Observations one figure per fermentation batch eg cell number in cell bank ampoule one figure per purification batch eg product yield in mg one figure per 24 hour period from samples eg cell concentration, laboratory measured ph virtually continuous data from sensors eg temperature, ph Slide No October 2002 Fall Technical 2002

11 Data Variables: Product Yield yield in mg before and after each purification step yield in % across each purification step concentration during cell culture and fermentation different measurement methods chromatographical (HPLC) immunological (ELISA) Slide No October 2002 Fall Technical 2002

12 Data Variables: Adjustable Input temperature ph / amount of added soda glucose concentration (not adjusted in data collection period) Slide No October 2002 Fall Technical 2002

13 Data Variables: Cells cell numbers during cell culture and fermentation growth rate number of cells detached from the carrier proportion of dead cells among detached cells viability score in cell bank ampoule Slide No October 2002 Fall Technical 2002

14 Data Variables: Product Variations and Product Related Impurities fermentation related various incomplete molecules (eg propeptide still attached) various molecules with different posttranslational structure (eg glycosylation) purification related dimers, oligomers, polymers various degradation products degree of activation (purification step 2-4) Slide No October 2002 Fall Technical 2002

15 Data Variables: Side Products and Product Unrelated Impurities side products (fermentation related) ammonium lactate impurities (purification related) cell proteins antibodies introduced during affinity chromatography Slide No October 2002 Fall Technical 2002

16 Data Variables: Non-Adjustable Physical Parameters volume of decanted liquid conductivity of decanted liquid load on purification columns time on purification columns Slide No October 2002 Fall Technical 2002

17 Graphical methods univariate time series distributions bivariate scatter plots follow groups of data points from one graph to another multivariate Slide No October 2002 Fall Technical 2002

18 Where are differences in yield generated? Yield per purification batch after step 1 and after completed purification total yield in mg same analytical method! directly comparable coeff. of correlation 0,84 for yield optimization, concentrate on the fermentation process and purification step 1 yield in mg after purification step 1 Slide No October 2002 Fall Technical 2002

19 Influence of purification step 1 on yield Yield per purification batch before and after step 1 yield in mg after step 1 yield in mg in decanted liquid different analytical methods after step 1: HPLC in decanted liquid: ELISA (much higher analytical variation) still a clear correlation for yield optimization, concentrate on the fermentation process Slide No October 2002 Fall Technical 2002

20 Development of yield over time Total yield in mg for individual purifications per fermentation facility rebuilt total yield in mg fermentation!any other parameters that change upon rebuilding? Slide No October 2002 Fall Technical 2002

21 Other parameters that change upon rebuilding I Heavy chain degradation Single chain 1,00 heavy chain degradation single chain enkk 0,75 0,50 0,25 0, fermentation fermentation Both phenomena are a result of higher concentration, as FVII has autocatalytic properties. Slide No October 2002 Fall Technical 2002

22 Other parameters that change upon rebuilding II Conductivity per fermentation conductivity fermentation Slide No October 2002 Fall Technical 2002

23 The Conductivity Story Average yield in mg per purification vs. average conductivity average yield in mg recent fermentations show high yield and low conductivity graph is based on fermentation averages average conductivity yellow dots: after facility rebuilding Slide No October 2002 Fall Technical 2002

24 The Conductivity Story Contd. yield in mg Yield in mg vs. conductivity next graph is based on individual purifications negative correlation vanishes conductivity yellow dots: after facility rebuilding Slide No October 2002 Fall Technical 2002

25 Some Parameters Vary Systematically with the Fermentation s Age Residual glucose Gla-35 residual glucose Gla day purification Slide No October 2002 Fall Technical 2002

26 Normal Curves I: Limits for Individual Observations Residual glucose residual glucose day mean curve: mean per purification/day spline parametrical curve limits: mean curve ± 2 s, s 2 = s 2 residual + s 2 between ferm. from ANOVA with purification/day fixed and fermentation random Slide No October 2002 Fall Technical 2002

27 Normal Curves II: Limits for Curves Residual glucose residual glucose mean curve: as for individual observations limits: mean curve ±δ with minimum δ such that the limit curve is significantly different from the historical data at 2,5% level day Slide No October 2002 Fall Technical 2002

28 Normal Curves III: Limits for Parameters intercept Individual range charts for Gla-35 slopes and intercepts slope fermentation 25 UCL Avg= LCL= UCL Avg= LCL If the profile is modelled by a parametrical curve: Shewhart charts for parameters Multivariate charts for correlated parameters If the profile is not modelled: Shewhart charts for principal components fermentation Slide No October 2002 Fall Technical 2002

29 Handling Parameters with a Profile for Optimization Analysis on purification or day basis: Use residuals to normal curve rather than original observations Analysis on fermentation basis: Use parameters of parametrical curves or principal components Slide No October 2002 Fall Technical 2002

30 Tracing Groups of Data Points Distribution of lactate based on days in fermentor yellow white blue lactate day Slide No October 2002 Fall Technical 2002

31 Lactate: The Top Branch lactate analytical run day most samples in the branch come from the same fermentation and are analyzed in the same analytical run additional lactate can be produced in the sample if it is not sterile presumably a problem of sample handling Slide No October 2002 Fall Technical 2002

32 Lactate: The Bottom Branch Lactate Ammonium per l lactate ammonium per l day day Slide No October 2002 Fall Technical 2002

33 Lactate: The Bottom Branch Contd. Cell concentration FVII concentration by ELISA cell concentration FVII conc. / ELISA day day Slide No October 2002 Fall Technical 2002

34 Do Higher Temperatures Cause Lower Lactate Concentrations? Temperature setpoint temperature 36,7 36,6 36,5 36,4 36,3 36,2 36, day temperature setpoint was varied in various experiments Why is there a distinguished shift in lactate when comparing normal operation and experiments, but not between experiments? Slide No October 2002 Fall Technical 2002

35 The Root Cause: A Process Change Lactate by fermentation lactate fermentation growth medium composition was changed after fermentation 5 no temperature experiments were conducted after fermentation 5 lactate and ammonium measurements were discontinued after fermentation 15 Slide No October 2002 Fall Technical 2002

36 PCA for Discovering Covariances dataset based on fermentations variables: fermentation parameters averages of purification parameters averages of daily measurements a rough-and-ready analysis of all available data Slide No October 2002 Fall Technical 2002

37 Score Plot of the First Two Principal Components Slide No October 2002 Fall Technical 2002

38 Loading Plot of the First Two Principal Components medium medium Slide No October 2002 Fall Technical 2002

39 PLS for Modelling Yield same dataset as for PCA all yield measures as Y model based on fermentations validate model on the other fermentations Slide No October 2002 Fall Technical 2002

40 PLS for Modelling Yield Total yield in mg model basis total yield in mg fermentation observed predicted Slide No October 2002 Fall Technical 2002

41 Conclusion I: Graphical Methods univariate graphs for process monitoring time series along fermentations, across purifications/days time series along purifications/days, across fermentations distributions control charts bivariate graphs for visualizing correlations scatter plots follow groups of data points from one graph to another multivariate methods for discovering correlations and for prediction score plots loading plots overlay PLS-predicted and observed values Slide No October 2002 Fall Technical 2002

42 Conclusion II: The Role of Graphs Graphs facilitate discoveries in data Graphs facilitate communication of discoveries Graphs can give a feel for the process Graphs can be misleading choose your graphs carefully! Slide No October 2002 Fall Technical 2002