Spectroscopy? 1 Spectroscopy: using light-matter interaction to get information about samples Spectroscopy? 2 Near Infrared? 3 Monitoring & Control? 4 Advanced Bioprocess PDA: A Global Monitoring by Means of NIR Spectroscopy Association Christian Grimm Marek Höhse José Alves-Rausch Process Risks Cell culture process 6 Process Risks Failed Batches 7 Process Risks Malfunctions 8 Process Risks Contamination 9 Process Risks RISK 10 PAT / QbD approach Risk Analysis • CQA‘s + CPP‘s DoE • Link CPP‘s CQA‘s Process Analyzers • Sensors Spectrometers Process Control Tools • Scada Multivariate Data Analysis •Process Knowledge 11 PAT / QbD approach Risk mitigation? 12 Cell Cultivation: CQA / CPP Reactor Parameter Product Cell parameter Nutrients Metabolites: 13 Look into process Spectroscopy is: Contactless Nondestructive Use inline NIR spectroscopy! Fast & Versatile 14 Inline Analytics (NIR) Analyzer Requirements? Robust device! 15 NIR – How to use? 1 absorption (molecules) 2 scattering (cells) Glucose, Lactate, Glutamine, Product Cell parameter Cell count, Viability Contamination Technology Limitations Water band • Fixed slit constant volume Low sensitivity • Low absorption large volume Low selectivity • Sum parameter analysis Old technology • Robust design, proven in F&B 17 NIR – measuring principle Spectra interpretation whole chemical composition broad absorption bands Information overlap Extract information => MVDA Qualitative Analysis Qualitative: only spectral data! Cell Culture Spectra Data Analysis (PCA) Process Monitoring 19 Qualitative Analysis - general PCA – Score Plot (main spectral variation) 1 Batch start 2 Batch end 3 Glutamin exhausted PC1 Cell count PC2 Metabolic state PC2 3 PC1 2 1 General trends visible! Qualitative Process Monitoring score contamination low performer (oxygen limitation) process time “golden batch” process trajectories observe alteration detect variations before real deviations occur Guided Sampling Guided sampling event based process control 22 Score 1 Qualitative Process Monitoring Score 2 1 2 3 4 5 1 End of lag phase 2 Nutrient 1 exhausted 3 All nutrients exhausted 4 Metabolites exhausted 5 Production phase Qualitative Analysis Information from spectra only! General OOS • Contamination Cell Culture Spectra Data Analysis (PCA) Batch Trajectories • Process Profiles • Important Trends Classification • End Point • Media 24 Quantitative Analysis Quantitative: spectral data + reference! Spectra Cell Culture Reference Data Analysis (PLS) Analytes 25 Calibration / Validation Model 1 2 Calibration set n Data Analysis (PLS) Model error SEC SECV 26 Calibration / Validation Prediction Model 1 n Validation set: calibration independent batches!!! Validation error SEP Reference 27 Calibration / Validation prediction (g/l) Example: Glucose reference (g/l) Model Glucose (g/l) Concentration Range 0–7 Algorithm PCs PLS 5 Validation Testset 1 batch Error of Prediction (SEP) 0.24 Inline Prediction 6,5 6 5,5 5 4,5 4 3,5 3 2,5 2 1,5 Glucose Reference 29 Inline Prediction 18 16 14 12 10 8 6 4 2 0 TCD Reference 30 Inline Prediction 110 100 90 80 70 60 50 40 30 Viability Reference 31 viability (offline analysis) viability Inline Prediction - Viability Score3 (spectral variations ) score !strong correlation! Inline Prediction Titer Reference 33 Quantitative Analysis Analytes • Glucose • Sum Signals of Nutrients & Metabolites Spectra Cell Culture Reference Data Analysis (PLS) Cell Parameter • Total Cell Count • Viability Product • mAb Titer 34 Calibration Transfer Transfer Reactor Type A NIR Device A Transfer possible? Reactor Type B NIR Device B 35 Calibration Transfer 9 8 7 6 5 4 3 2 1 Trend correct – values biased! 0 Glucose Reference 36 Calibration Transfer 9 Adaption: 3 samples from batch start 8 7 6 5 4 3 2 1 After adaption: Trend + values correct! 0 Glucose Reference 37 Near Infrared Spectroscopy? 38 Summary NIR Spectroscopy Robust Device Spectra Multivariate Analysis Process Trajectories Control Parameters Risk mitigation 39 Thank you! 40 How do you control your process in the future? 41 Spectroscopy? 42 Additional Information 43 End point determination Endpoint-spectra of batch End point determination optimal endpoint of current batch Alternatively: Using NIR prediction of cell count viability titer instead of scores Endpoint from several batches design space for optimal endpoint current batch real time data of current batch Media classification 2-m01 1-m01 1-m02 4-m01 3-m01 3-m02 4-m02 4-m03 3-m03 2-m02 2-m03 1-m03 Build model with high performance batches compare new batches significant deviations detected prediction (Mio cells / ml) Quantitative Analysis reference (Mio cells / ml) Model Total Cell Count Cell count Mio/ml Concentration Range 0 – 16 Algorithm PLS PCs 1 Validation procedure Testset 1 batch Error of Prediction (SEP) 0.54 Quantitative Analysis Model Viability Concentration Range Algorithm PCs Viability [%] 0 – 100 PLS 5 Validation procedure Testset K05 Error of Prediction (SEP) 3 • scatter pattern from shape change • correlation to cell lyses • More metabolites in medium viable dead Spectroscopy in Cell Culture Selectivity Sensitivity Costs Robustness Raman UV / VIS MIR NIR 49 Literature • • • • • • • • • • ASTM International. E2629 - 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, 73–97 (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), 636–45 (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, 28–38 (2013). Bienert R and Grimm C, Zellfabriken unter ständiger Beobachtung – Ein NIR-Spektroskopischer Einblick in industrielle Bioprozesse, Nachrichten aus der Chemie 61, 1046-1050 (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 near-infrared spectroscopy, Journal of Biotechnology, in press (DOI: 10.1016/j.jbiotec.2014.09.004) Hoehse M, Alves-Rausch J, Prediger A, Roch P, Grimm C, Review: Near-Infrared Spectroscopy in Upstream Bioprocesses, Pharmaceutical Bioprocessing, submitted 50
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