A qualitative approach to bioprocess monitoring and decision making with NIR-Spectroscopy and MVDA.

Transcription

1 A qualitative approach to bioprocess monitoring and decision making with NIR-Spectroscopy and MVDA. Dan Kopec - Field Marketing Manager Sartorius Stedim Biotech N.A.

2 Sartorius and PAT Ready to use Solutions Data Information Process Understanding Demo of Understanding Process Control Sensors & Analytical Systems SCADA System / MFCS DoE Develop Design Space MDVA Monitor Design Space

3 BioPAT Solutions BioPAT Integration Pilot / Manufacturing Page 58

4 Agenda Quantitative vs Qualitative Approach Why use Qualitative approach Online MVDA - Classic Sensors vs NIR Spectra Application Examples - CC and MO studies Why NIR technology - comparison of technologies

5 Bioprocess Cell Cultivation / MO Fermentation Critical Process Parameters (CPP) Nutrients Glucose Sucrose Glutamin Fructose Metabolites Lactate Glutamate Ammonia Acetate Acetoin Cell parameters: Total Cell Count dry mass Viability OD600 microbial contamination Product (CQA): Antibody Spores Vaccines Enzymes Reactor parameters Oxygen, CO 2 ph Temperature

6 Quantitative Analysis - how is a model developed? Quantitative: spectral data + reference needed! Cell Culture Spectra Reference Data Analysis (PLS) Analytes January 2015 Page 20

7 Applications Scope of CPP s Qualitative

8 Qualitative Analysis Qualitative: only spectral data needed! General OOS Contamination Cell Culture Spectra Data Analysis (PCA) Batch Trajectories Process Profiles Important Trends Classification End Point Media January 2015 Page 19

9 Application Note BioPAT Spectro - NIR system 100% spectral record of Batch (CMO s) Saving batches by reacting quicker Guided Sampling - as needed sampling versus scheduled sampling. Process Visibility Simpler information sharing with Stakeholders Process Knowledge - Process Understanding

10 MVDA-online Standard Definition Vs. High Definition Inclusion of more information in MVDA plots and trajectories give a clearer picture of Online MVDA with NIR Spectra Online MVDA with Classic Sensors Page 10

11 Case Study BioPAT Spectro - NIR system Evaluation in Cooperation with TCI Hannover

12 Case Study BioPAT Spectro NIR system Evaluation in Cooperation with TCI Hannover Cell Cultivation Cultivation of suspension cell-line CHO-K1 1 batch cultivation for process parameter optimization 8 batch cultivations in 7.5 L scale 4 high performance cultivation additional substrate feeds in late deceleration growth phase to minimize analyte correlations (glucose, glutamine) 4 cultivation runs with oxygen limitation 2 glucose feed control

13 NIR Spectra Interpretation describes the whole chemical composition broad absorption bands overlap of information separation of information needed => MVDA (Chemometrics Chemometrics) January 2015 Page 7

14 NIR - Spectroscopy measuring principle 1 absorption of light (small molecules) Glucose, Lactate, Glutamine, Product 2 scattering of light (cells) Cell parameter Cell count, Viability Contamination

15 MVDA-online Process Monitoring & Control Today and in the Future Summarizing the data during the evolution of good batches results in a few new variables Data from all relevant process parameters are concentrated to a few highly informative graphs Enhanced process safety by easy understandable graphics as well as simplified analysis and interpretation becomes Many variables of one batch are plotted All the variables of same batch plotted, but now as the first SUMMARY t1. Page 10

16 Multivariate Statistical Process Monitoring (MSPC) The Batch-Trajectory Score 1 Comparison to golden batch No Offline analytics needed Alteration within batches can be observed in process trajectories Ability to detect similarities and variations before real deviations occur

17 Guided sampling The Batch-Trajectory Contamination Take a sample! Analysis identifies reason for deviation Early glucose limitation reduce complexity of analyzes by guided sampling in real-time

18 Application Note BioPAT Spectro - NIR system

19 Application Note BioPAT Spectro NIR system Bacillus Spore Production 4 fermentation runs 50K L scale Offline reference measurements Full recorded spectra used for qualitiative model

20 Multivariate Statistical Process Monitoring (MSPC) Classification - Media composition 0,03 0,02 0,01 0-0,01-0,02-0,03 1-m02 1-m01 4-m01 3-m01 3-m02 4-m02 4-m03 3-m03 1-m03-0,04-0,2-0,15-0,1-0,05 0 0,05 0,1 0,15 t[1] tps[2] Building the model with high performance batches compare new batches with model significant deviations detected

21 Qualitative Process Monitoring visualizing phases 1 End of lag phase NIR Score 2 NIR Score Nutrient 1 exhausted All nutrients exhausted Metabolites exhausted Production phase January 2015 Page 15

22 Inline Analytics (NIR) Why NIR? Robust process analyzer is needed! January 2015 Page 4

23 Why NIR Spectroscopy for Bioprocess Comparison of Technologies Selectivity Sensitivity Costs Robustness Raman UV / VIS MIR NIR

24 Look into the process NIR spectroscopy for a closer look! Contactless Nondestructive Fast Versatile January 2015 Page 3

25 Technology Water absorption band Fixed slit constant volume Established technology Robust process analyzer design, proven in F&B processes January 2015 Page 6

26 Spectro - Sensor BioPAT Spectro - Freebeam NIR system Sensor Design / Adaptation Process Robust Ingold / Sanitary Port adaptations Adaptable to most SS Bioreactors to 50K+ liters Fiber free system No fragile fiber cables Eliminates issues with fiber variation/ length Integrated Electronics/ Sensor Single Cable for power and communications Solid State Diode - Array Sensor Proven Robust in Process Environments nm (accessible nm) spectrometer No Moving Parts IP65 CIP/SIP ready cgmp Contact materials / Interface

27 Summary NIR-Spectroscopy for Bioprocess Monitoring & Control Quantitative Analysis NIR Spectroscopy Qualitative Analysis Process Knowledge Process Understanding January 2015 Page 29

28 Thank you! January 2015 Page 30

29 Literature ASTM International. E Standard Guide for Verification of Process Analytical Technology (PAT) Enabled Control Systems. (2011). European Medicines Agency. Guideline on the use of Near Infrared Spectroscopy (NIRS) by the pharmaceutical industry and the data requirements for new submissions and variations. (2012). European Medicines Agency. Addendum to EMA/CHMP/CVMP/QWP/17760/2009 Rev 2: Defining the Scope of an NIRS Procedure. (2014). Henriques J, Buziol S, Stocker E, Voogd A, Menezes JC. Monitoring mammalian cell cultivations for monoclonal antibody production using near-infrared spectroscopy. In: Optical Sensor Systems in Biotechnology. Rao G (Ed.). Springer Berlin Heidelberg, (2010). Sandor M, Rüdinger F, Bienert R, Grimm C, Solle D, Scheper T. Comparative study of non-invasive monitoring via infrared spectroscopy for mammalian cell cultivations. J. Biotechnol. 168(4), (2013). Clavaud M, Roggo Y, Daeniken R Von, Liebler A, Schwabe J. Talanta Chemometrics and in-line near infrared spectroscopic monitoring of a biopharmaceutical Chinese hamster ovary cell culture : Prediction of multiple cultivation variables. Talanta. 111, (2013). Bienert R and Grimm C, Zellfabriken unter ständiger Beobachtung Ein NIR-Spektroskopischer Einblick in industrielle Bioprozesse, Nachrichten aus der Chemie 61, (2013). Shaobin Lu et. al., Modern IR-Spectroscopy for Bioprocess Monitoring, Genetic Engineering & Biotechnology News, under revision Alves-Rausch J, Bienert R, Grimm C, Bergmaier D, Real time in-line monitoring of large scale Bacillus fermentations with nearinfrared spectroscopy, Journal of Biotechnology, in press (DOI: /j.jbiotec ) Hoehse M, Alves-Rausch J, Prediger A, Roch P, Grimm C, Review: Near-Infrared Spectroscopy in Upstream Bioprocesses, Pharmaceutical Bioprocessing, submitted