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Trends in Process Analytical Technology: Present State in Bioprocessing

  • Marco Jenzsch
  • Christian Bell
  • Stefan Buziol
  • Felix Kepert
  • Harald Wegele
  • Christian Hakemeyer
Chapter
Part of the Advances in Biochemical Engineering/Biotechnology book series (ABE, volume 165)

Abstract

Process analytical technology (PAT), the regulatory initiative for incorporating quality in pharmaceutical manufacturing, is an area of intense research and interest. If PAT is effectively applied to bioprocesses, this can increase process understanding and control, and mitigate the risk from substandard drug products to both manufacturer and patient. To optimize the benefits of PAT, the entire PAT framework must be considered and each elements of PAT must be carefully selected, including sensor and analytical technology, data analysis techniques, control strategies and algorithms, and process optimization routines. This chapter discusses the current state of PAT in the biopharmaceutical industry, including several case studies demonstrating the degree of maturity of various PAT tools.

Graphical Abstract

Hierarchy of QbD components

Keywords

Bioprocess monitoring and control PAT QbD 

Symbols and Abbreviations

ANN

Artificial neural network

API

Active pharmaceutical ingredient

CIP

Cleaning in place

CPP

Critical process parameter

CQA

Critical quality attribute

EMA

European Medicine Agency

FDA

U.S. Food and Drug Administration

HPLC

High-performance liquid chromatography

ICH

International Council for Harmonisation

mAb

Monoclonal antibody

MIR

Mid-infrared

MOC

Material of construction

MPC

Model predictive control

MSPC

Multivariate statistical process control

MVDA

Multivariate data analysis

NIR

Near infrared

OUR

Oxygen uptake rate

PCV

Packed cell volume

PHC

Personalized healthcare

QbD

Quality by Design

QC

Quality control

ROI

Return on investment

RQ

Respiratory quotient

RVR

Relevance vector regression

SVR

Support vector regression

VCD

Viable cell density

Notes

Acknowledgements

Part of the explorations presented here was a collaborative work together with 4Tune Engineering Ltd., and YourEncore. We gratefully acknowledge this support.

References

  1. 1.
    Food and Drug Administration (2004) Guidance for industry guidance for industry PAT – a framework for innovative pharmaceutical development, manufacturing and quality assurance. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Biologics Evaluation and Research, RockvilleGoogle Scholar
  2. 2.
    Croughan MS, Konstantinov KB, Cooney C (2015) The future of industrial bioprocessing: batch or continuous? Biotechnol Bioeng 112(4):648–6651CrossRefPubMedGoogle Scholar
  3. 3.
    Jungbauer A, Peng J (2011) Continuous bioprocessing: an interview with Konstantin Konstantinov from Genzyme. Biotechnol J 6(12):1431–1434CrossRefGoogle Scholar
  4. 4.
    Glassey J, Gernaey KV, Clemens C, Schulz TW, Oliveira R, Striedner G, Mandenius CF (2011) Process analytical technology (PAT) for biopharmaceuticals. Biotechnol J 6:369–377CrossRefPubMedGoogle Scholar
  5. 5.
    Jose EJ, Folque F, Menezes JC, Werz S, Strauss U, Hakemeyer C (2011) Predicting Mab product yields from cultivation media components using near-infrared and 2D-fluorescence spectroscopies. Biotechnol Prog 27:1339–1346CrossRefPubMedGoogle Scholar
  6. 6.
    Kirdar AO, Chen G, Weidner J, Rathore AS (2010) Application of near-infrared (NIR) spectroscopy for screening of raw materials used in the cell culture medium for the production of a recombinant therapeutic protein. Biotechnol Prog 26(2):527–531PubMedGoogle Scholar
  7. 7.
    Lee HW, Christie A, Liu JJ, Yoon S (2012) Estimation of raw material performance in mammalian cell culture using near infrared spectra combined with chemometrics approaches. Biotechnol Prog 28(3):824–832CrossRefPubMedGoogle Scholar
  8. 8.
    Hakemeyer C, Strauss U, Werz S, Folque F, Menezes JC (2013) Near-infrared and two-dimensional fluorescence spectroscopy monitoring of monoclonal antibody fermentation media quality: aged media decreases cell growth. Biotechnol J 8(7):835–846CrossRefPubMedGoogle Scholar
  9. 9.
    Prajapati P, Solanki R, Modi V, Basuri T (2016) A brief review on NIR spectroscopy and its pharmaceutical applications. IJPCA 3(3):117–123Google Scholar
  10. 10.
    Trunfio N, Lee H, Starkey J, Agarabi C, Liu J, Yoon S (2017) Characterization of mammalian cell culture raw materials by combining spectroscopy and chemometrics. Biotechnol Prog. doi: 10.1002/btpr.2480 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Calvalhal AV, Saucedo VM (2012) Process analytical technology advances and applications in recombinant protein cell culture processes. In: Undey C, Low D, Menezes JC, Koch M (eds) PAT applied in biopharmaceutical process development and manufacturing. CRC Press, Boca Raton, pp 93–126Google Scholar
  12. 12.
    Cervera AE, Petersen N, Lantz AE, Larsen A, Gernaey KV (2009) Application of near-infrared spectroscopy for monitoring and control of cell culture and fermentation. Biotechnol Prog 25:1561–1581PubMedGoogle Scholar
  13. 13.
    Sellick CA, Hansen R, Jarvis RM, Maqsood AR, Stephens GM, Dickson AJ, Goodacre R (2010) Rapid monitoring of recombinant antibody production by mammalian cell cultures using Fourier transform infrared spectroscopy and chemometrics. Biotechnol Bioeng 106:432–442PubMedGoogle Scholar
  14. 14.
    Saucedo V, Milligan M, Lewin-Koh N, Coleman D, Wolk B, Larson T, Arroyo A (2009) Practical issues implementing an in-situ NIR for real time monitoring of cell culture bioreactors. In: ACS annual conference, Washington, DCGoogle Scholar
  15. 15.
    Clavaud M, Roggo Y, Von Daeniken R, Liebler A, Schwabe JO (2012) Chemometrics and in-line near infrared spectroscopic monitoring of a biopharmaceutical Chinese hamster ovary cell culture: prediction of multiple cultivation variables. Talanta 90:12–21CrossRefGoogle Scholar
  16. 16.
    Hakemeyer C, Strauss U, Werz S, Jose GD, Folque F, Menezes JC (2012) At-line NIR spectroscopy as effective PAT monitoring technique in Mab cultivations during process development and manufacturing. Talanta 90:12–21CrossRefPubMedGoogle Scholar
  17. 17.
    Henriques JG, Buziol S, Stocker E, Voogd A, Menezes JC (2009) Monitoring mammalian cell cultivations for monoclonal antibody production using near-infrared spectroscopy. Adv Biochem Eng Biotechnol 116:73–97PubMedGoogle Scholar
  18. 18.
    Abu-Absi NR, Kenty BM, Cuellar ME, Borys MC, Sakhamuri S, Strachan DJ, Li ZJ (2011) Real time monitoring of multiple parameters in mammalian cell culture bioreactors using an in-line Raman spectroscopy probe. Biotechnol Bioeng 108:1215–1221CrossRefPubMedGoogle Scholar
  19. 19.
    Ashton L, Hogwood CEM, Tait AS, Kuligowski J, Smales CM, Bracewell DG, Dickson AJ, Goodacre R (2015) UV resonance Raman spectroscopy: a process analytical tool for host cell DNA and RNA dynamics in mammalian cell lines. J Chem Technol Biotechnol 90(2):237–243CrossRefGoogle Scholar
  20. 20.
    Ashton L, Xu Y, Brewster VL, Cowcher DP, Sellick CA, Dickson AJ, Stephens GM, Goodacre R (2013) The challenge of applying Raman spectroscopy to monitor recombinant antibody production. Analyst 138(22):6977–6985CrossRefPubMedGoogle Scholar
  21. 21.
    Berry BN, Dobrowsky TM, Timson RC, Kshirsagar R, Ryll T, Wiltberger K (2015) Quick generation of Raman spectroscopy based in-process glucose control to influence biopharmaceutical protein product quality during mammalian cell culture. Biotechnol Prog. doi: 10.1002/btpr.2205 CrossRefPubMedGoogle Scholar
  22. 22.
    Sun L, Hsiung C, Pederson CG, Zou P, Smith V, von Gunten M, O’Brien NA (2016) Pharmaceutical raw material identification using miniature near-infrared (MicroNIR) spectroscopy and supervised pattern recognition using support vector machine. Appl Spectrosc 70(5):816–825CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Carvell J, Graham L, Downey B (2013) Insights into monitoring changes in the viable cell density and cell physiology using scanning, multi-frequency dielectric spectroscopy. 23rd ESACT meeting: better cells for better health. BMC Proc 7(6):4CrossRefGoogle Scholar
  24. 24.
    Downey BJ, Graham LJ, Breit JF, Glutting NK (2014) A novel approach for using dielectric spectroscopy to predict viable cell volume (VCV) in early process development. Biotechnol Prog 30(2):479–487CrossRefPubMedGoogle Scholar
  25. 25.
    Druzinec D, Weiss K, Elseberg C, Salzig D, Kraume M, Pörtner R, Czermak P (2014) Process analytical technology (PAT) in insect and mammalian cell culture processes: dielectric spectroscopy and focused beam reflectance measurement (FBRM). Methods Mol Biol 1104:313–341CrossRefPubMedGoogle Scholar
  26. 26.
    Justice C, Brix A, Freimark D, Kraume M, Pfromm P, Eichenmueller B, Czermak P (2011) Process control in cell culture technology using dielectric spectroscopy. Biotechnol Adv 29(4):391–401CrossRefPubMedGoogle Scholar
  27. 27.
    Cannizzaro C, Gügerli R, Marison I, Stockar UV (2003) On-line biomass monitoring of CHO perfusion culture with scanning dielectric spectroscopy. Biotechnol Bioeng 84(5):597–610CrossRefPubMedGoogle Scholar
  28. 28.
    Hantelmann K, Kollecker M, Hull D, Hitzmann B, Scheper T (2006) Two-dimensional fluorescence spectroscopy: a novel approach for controlling fed-batch cultivations. J Biotechnol 121:410–417CrossRefPubMedGoogle Scholar
  29. 29.
    Schwab K, Hesse F (2013) 2D fluorescence spectroscopy for real-time aggregation monitoring in upstream processing. 23rd ESACT meeting: better cells for better health. BMC Proc 7(6):94CrossRefGoogle Scholar
  30. 30.
    Alvarez A, Simutis R (2004) Application of Kalman filter algorithm in GMC control strategy for fed-batch cultivation process. Inf Technol Ir Valdymas 1:7–12Google Scholar
  31. 31.
    de Assisa AJ, Filho RM (2000) Soft sensors development for on-line bioreactor state estimation. Comput Chem Eng 24:1099–1103CrossRefGoogle Scholar
  32. 32.
    Jenzsch M, Simutis R, Eisbrenner G, Stückrath I, Lübbert A (2006) Estimation of biomass concentrations in fermentation processes for recombinant protein production. Bioprocess Biosyst Eng 29:19–27CrossRefPubMedGoogle Scholar
  33. 33.
    Luttmann R, Bracewell DG, Cornelissen G (2012) Soft sensors in bioprocessing: a status report and recommendations. Biotechnol J 7(8):1040–1048CrossRefPubMedGoogle Scholar
  34. 34.
    Montague G, Morris J (1994) Neural-network contributions in biotechnology. Trends Biotechnol 12:312–324CrossRefPubMedGoogle Scholar
  35. 35.
    Sundström H, Enfors SO (2008) Software sensors for fermentation processes. Bioprocess Biosyst Eng 31:145–152CrossRefPubMedGoogle Scholar
  36. 36.
    Clementschitsch F, Bayer K (2006) Improvements of bioprocess monitoring: development of novel concepts. Microb Cell Factories 5:19CrossRefGoogle Scholar
  37. 37.
    Sandor M, Rudinger F, Solle D, Bienert R, Grimm C, Gross S (2013) NIR-spectroscopy for bioprocess monitoring and control. 23rd ESACT meeting: better cells for better health. BMC Proc 7(6):29CrossRefGoogle Scholar
  38. 38.
    Waarvik TL (1987) US Patent 4683207A, 28 Jul 1987Google Scholar
  39. 39.
    Barringer GE Jr (2010) US Patent 2010/0047122, 25 Feb 2010Google Scholar
  40. 40.
    Rapoport P, Wang SH, Pascoe D (2006) Implementation of online amino acid analysis for medium and feed optimization in mammalian cell culture. In: AIChE annual meeting, Paper 58c, Nov 2006, San Francisco, CAGoogle Scholar
  41. 41.
    St Amand MM, Ogunnaike BA, Robinson AS (2014) Development of at-line assay to monitor charge variants of MAbs during production. Biotechnol Prog 30(1):249–2255Google Scholar
  42. 42.
    Behrendt U, Koch S, Gooch DD, Steegmans U, Comer MJ (1994) Mass spectrometry: a tool for on-line monitoring of animal cell cultures. Cytotechnology 14:157–162CrossRefPubMedGoogle Scholar
  43. 43.
    Schmidberger T, Huber R (2013) Advanced off-gas measurement using proton transfer reaction mass spectrometry to predict cell culture. 23rd ESACT meeting: better cells for better health. BMC Proc 7(6):14CrossRefGoogle Scholar
  44. 44.
    Paalme RT, Tiisma K, Kahru A, Vanatalu K, Vilu R (1990) Glucose-limited fed-batch cultivation of Escherichia coli with computer-controlled fixed growth. Biotechnol Bioeng 35:312–319CrossRefPubMedGoogle Scholar
  45. 45.
    Zupke C, Brady LJ, Slade PG, Clark P, Caspary RG, Livingston B, Taylor L, Bigham K, Morris AE, Bailey RW (2015) Real-time product attribute control to manufacture antibodies with defined N-linked glycan levels. Biotechnol Prog 31(5):1433–1441CrossRefPubMedGoogle Scholar
  46. 46.
    Aehle M, Bork K, Schaepe S, Kuprijanov A, Horstkorte R, Simutis R, Lübbert A (2012) Increasing batch-to-batch reproducibility of CHO-cell cultures using a model predictive control approach. Cytotechnology 64:623–634CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Aehle M, Schaepe S, Kuprijanov A, Simutis R, Lübbert A (2011) Simple and efficient control of CHO cell cultures. J Biotechnol 153:56–61CrossRefPubMedGoogle Scholar
  48. 48.
    Craven S, Whelan J, Glennon B (2014) Glucose concentration control of a fed-batch mammalian cell bioprocess using a nonlinear model predictive controller. J Process Control 24:344–357CrossRefGoogle Scholar
  49. 49.
    Gnoth S, Kuprijanov A, Simutis R, Lubbert A (2010) Simple adaptive pH control in bioreactors using gain-scheduling methods. Appl Microbiol Biotechnol 85(4):955–964CrossRefPubMedGoogle Scholar
  50. 50.
    Jenzsch M, Gnoth S, Kleinschmidt M, Simutis R, Lübbert A (2007) Improving the batch-to-batch reproducibility of microbial cultures during recombinant protein production by regulation of the total carbon dioxide production. J Biotechnol 128:858–867CrossRefPubMedGoogle Scholar
  51. 51.
    Kuprijanov A, Schaepe S, Aehle M, Simutis R, Lübbert A (2012) Improving cultivation processes for recombinant protein production. Bioprocess Biosyst Eng 35(3):333–340CrossRefPubMedGoogle Scholar
  52. 52.
    Schalk R, Geoerg D, Staubach J, Raedle M, Methner FJ, Beuermann T (2017) Evaluation of a newly developed mid-infrared sensor for real-time monitoring of yeast fermentations. J Biosci Bioeng 123(5):651–657CrossRefPubMedGoogle Scholar
  53. 53.
    Schuler MM, Marison IW (2012) Real-time monitoring and control of microbial bioprocesses with focus on the specific growth rate: current state and perspectives. Appl Microbiol Biotechnol 94(6):1469–1482CrossRefPubMedGoogle Scholar
  54. 54.
    Fahrner RL, Lester PM, Blank GS, Reifsnyder DH (1998) Real-time control of purified product collection during chromatography of recombinant human insulin-like growth factor-I using an on-line assay. J Chromatogr A 827(1):37–43CrossRefPubMedGoogle Scholar
  55. 55.
    Fahrner RL, Blank GS (1999) Real-time control of antibody loading during protein A affinity chromatography using an on-line assay. J Chromatogr A 849(1):191–196CrossRefPubMedGoogle Scholar
  56. 56.
    Rathore AS, Yu M, Yeboah S, Sharma A (2008) Case study and application of process analytical technology (PAT) towards bioprocessing: use of on-line high-performance liquid chromatography (HPLC) for making real-time pooling decisions for process chromatography. Biotechnol Bioeng 100(2):306–316CrossRefPubMedGoogle Scholar
  57. 57.
    Brower KP, Ryakala VK, Bird R, Godawat R, Riske FJ, Konstantinov K, Warikoo V, Gamble J (2014) Single-step affinity purification of enzyme biotherapeutics: a platform methodology for accelerated process development. Biotechnol Prog 30(3):708–717CrossRefPubMedGoogle Scholar
  58. 58.
    Barackman J, Prado I, Karunatilake C, Furuya K (2004) Evaluation of on-line high-performance size-exclusion chromatography, differential refractometry, and multi-angle laser light scattering analysis for the monitoring of the oligomeric state of human immunodeficiency virus vaccine protein antigen. J Chromatogr A 1043(1):57–64CrossRefPubMedGoogle Scholar
  59. 59.
    Watson DS, Kerchner KR, Gant SS, Pedersen JW, Hamburger JB, Ortigosa AD, Potgieter TI (2015) At-line process analytical technology (PAT) for more efficient scale up of biopharmaceutical microfiltration unit operations. Biotechnol Prog. doi: 10.1002/btpr.2193 CrossRefPubMedGoogle Scholar
  60. 60.
    Rathore AS, Mittal S, Lute S, Brorson K (2012) Chemometrics applications in biotechnology processes: predicting column integrity and impurity clearance during reuse of chromatography resin. Biotechnol Prog 28(5):1308–1314CrossRefPubMedGoogle Scholar
  61. 61.
    Bork C, Holdridge S, Walter M, Fallon E, Pohlscheidt M (2014) Online integrity monitoring in the protein A step of mAb production processes – increasing reliability and process robustness. Biotechnol Prog 30(2):383–390CrossRefPubMedGoogle Scholar
  62. 62.
    Crone C (2013) Cleaning validation: a timely solution for improving quality and containing cost. Pharm Eng 33(6):52–58Google Scholar
  63. 63.
    Jawadekar M (2012) A novel tool for cleaning validation. In: Light induced fluorescence technology, Contract Pharma, 30 May 2012Google Scholar
  64. 64.
    Lyndgaard CB, Rasmussen MA, Engelsen SB, Thaysen D, van den Berg F (2014) Moving from recipe-driven to measurement-based cleaning procedures: monitoring the cleaning-in-place process of whey filtration units by ultraviolet spectroscopy and chemometrics. J Food Eng 126:82–88CrossRefGoogle Scholar
  65. 65.
    Schaepe S, Kuprijanov A, Sieblist C, Jenzsch M, Simutis R, Lübbert A (2014) Current advances in tools improving bioreactor performance. Curr Biotechnol 3(4):133–144Google Scholar
  66. 66.
    Schaepe S, Jenzsch M, Kuprijanov A, Simutis R, Lübbert A (2013) Batch-to-batch reproducibility of fermentation processes by robust operational design and control. Pharm Bioprocess 1(3):297–307CrossRefGoogle Scholar
  67. 67.
    Wang J, Yu T, Jin C (2006) On-line estimation of biomass in fermentation process using support vector machine. Chin J Chem Eng 14(3):383–388CrossRefGoogle Scholar
  68. 68.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297Google Scholar
  69. 69.
    Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244Google Scholar
  70. 70.
    Ji J, Wang HQ, Chen K, Liu Y, Zhang N, Yan JJ (2012) Recursive weighted kernel regression for semi-supervised soft-sensing modeling of fed-batch processes. J Taiwan Inst Chem Eng 43(1):67–76CrossRefGoogle Scholar
  71. 71.
    Tetko IV, Livingstone DJ, Luik AI (1995) Neural network studies. 1. Comparison of overfitting and overtraining. J Chem Inf Comput Sci 35(5):826–833CrossRefGoogle Scholar
  72. 72.
    Desai K, Badhe Y, Tambe SS, Kulkarni BD (2006) Soft-sensor development for fed-batch bioreactors using support vector regression. Biochem Eng J 27(3):225–239CrossRefGoogle Scholar
  73. 73.
    Aehle M, Simutis R, Lübbert A (2010) Comparison of viable cell concentration estimation methods for a mammalian cell cultivation process. Cytotechnology 62(5):413–422CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Liu GH, Zhou DW, Xu HX, Mei CL (2010) Model optimization of SVM for a fermentation soft sensor. Expert Syst Appl 37(4):2708–2713CrossRefGoogle Scholar
  75. 75.
    Pohlscheidt M, Charaniya S, Bork C, Jenzsch M, Nötzel T, Lübbert A (2013) Bioprocess and fermentation monitoring. In: Flickinger MC (ed) The encyclopedia of industrial biotechnology: bioprocess, bioseparation and cell technology1st edn. Wiley, New York, pp 1471–1491Google Scholar
  76. 76.
    Chang Y, Bork C, Thömmes J (2005) Transition analysis of process chromatography data for real-time monitoring of column quality and performance. In: International forum for process analytical technology, Arlington, VAGoogle Scholar
  77. 77.
    Larson T, Davis J, Lam H, Cacia J (2003) Use of process data to assess chromatographic performance in production scale protein purification columns. Biotechnol Prog 19:485–492CrossRefPubMedGoogle Scholar
  78. 78.
    Miller MJ (2012) Rapid micro methods and EMA’s post approval change management protocol. Eur Pharm Rev 17(2):65–67Google Scholar
  79. 79.
    Riley B (2011) A regulators view of rapid microbiology methods. Eur Pharm Rev 16(5):59–61Google Scholar
  80. 80.
    Parveen S, Kaur S, David SAW, Kenney JL, McCormick WM, Gupta RK (2011) Evaluation of growth based rapid microbiological methods for sterility testing of vaccines and other biological products. Vaccine 29:8012–8023CrossRefPubMedGoogle Scholar
  81. 81.
    Denoya C, Reyes J, Ganatra M, Eshete D (2011) Rapid sterility testing using ATP bioluminescence based Pallchek™ rapid microbiology system. In: Moldenhauer O (ed) Rapid sterility testing. PDA and DHI Publishing, Bethesda, pp 433–461Google Scholar
  82. 82.
    Miller MJ, Lindsay H, Valverde-Ventura R, O’onner MJ (2009) Evaluation of the BioVigilant IMD-A, a novel optical spectroscopy technology for the continuous and real-time environmental monitoring of viable and nonviable particles. Part I. Review of the technology and comparative studies with conventional methods. PDA J Pharm Sci Technol 63(3):245–258PubMedGoogle Scholar
  83. 83.
    EMA (2012) Questions and answers on post approval change management protocols. Committee for Medicinal Products for Human Use (CHMP), European Medicines Agency, EMA/CHMP/CVMP/QWP/586330/2010Google Scholar
  84. 84.
    Food and Drug Administration (2012) Amendments to sterility test requirements for biological products final rule. 21 CFR Parts 600, 610, and 680 [Docket No. FDA–2011–N–0080] 77(86):26162–26175Google Scholar
  85. 85.
    Gray JC, Morandell D, Gapp G, Le Goff N, Neuhaus G, Staerk A (2011) Identification of microorganisms after milliflex rapid detection - a possibility to identify nonsterile findings in the milliflex rapid sterility test. PDA J Pharm Sci Technol 65(1):42–54PubMedGoogle Scholar
  86. 86.
    Gray JC, Staerk A, Berchtold M, Hecker W, Neuhaus G, Wirth A (2010) Growth-promoting properties of different solid nutrient media evaluated with stressed and unstressed micro-organisms: prestudy for the validation of a rapid sterility test. PDA J Pharm Sci Technol 64(3):249–263PubMedGoogle Scholar
  87. 87.
    Kamat MS, Lodder RA, DeLuca PP (1989) Near infra-red spectroscopic determination of residual moisture in lyophilized sucrose through intact glass vials. Pharm Res 6(11):961–965CrossRefPubMedGoogle Scholar
  88. 88.
    Findlay WP, Peck GR, Morris KR (2005) Determination of fluidized bed granulation end point using near-infrared spectroscopy and phenomenological analysis. J Pharm Sci 94:604–612CrossRefPubMedGoogle Scholar
  89. 89.
    Rantanen J, Antikainen O, Mannermaa JP, Yliruusi J (2000) Use of the near-infrared reflectance method for measurement of moisture content during granulation. Pharm Dev Technol 5:209–217CrossRefPubMedGoogle Scholar
  90. 90.
    Zhou X, Hines P, Borer MW (1998) Moisture determination in hygroscopic drug substances by near infrared spectroscopy. J Pharm Biomed Anal 17(2):219–225CrossRefPubMedGoogle Scholar
  91. 91.
    Berntsson O, Zackrisson G, Ostling G (1997) Determination of moisture in hard gelatin capsules using near-infrared spectroscopy: applications to at-line process control of pharmaceutics. J Pharm Biomed Anal 15:895–900CrossRefPubMedGoogle Scholar
  92. 92.
    Buice RGJ, Gold TB, Lodder RA, Digenis GA (1995) Determination of moisture in intact gelatin capsules by near-infrared spectrophotometry. Pharm Res 12:161–163CrossRefPubMedGoogle Scholar
  93. 93.
    Broad NW, Jee RD, Moffat AC, Eaves MJ, Mann WC, Dziki W (2000) Non-invasive determination of ethanol, propylene glycol and water in a multi-component pharmaceutical oral liquid by direct measurement through amber plastic bottles using Fourier transform near-infrared spectroscopy. Analyst 125(11):2054–2058CrossRefPubMedGoogle Scholar
  94. 94.
    Roggo Y, Chalus P, Maurer L, Lema-Martinez C, Edmond A, Jent N (2007) A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J Pharm Biomed Anal 44:683–700CrossRefPubMedGoogle Scholar
  95. 95.
    Lin TP, Hsu CC (2002) Determination of residual moisture in freeze-dried protein pharmaceuticals using a rapid and noninvasive method: near infrared spectroscopy. PDA J Pharm Sci Technol 56:196–205PubMedGoogle Scholar
  96. 96.
    Cogdill RP, Anderson CA, Delgado M, Chisholm R, Bolton R, Herkert T, Afnan AM, Drennen JK (2005) Process analytical technology case study: part I. Feasibility studies for quantitative near-infrared method development. AAPS PharmSciTech 6(2):262–272CrossRefGoogle Scholar
  97. 97.
    Cogdill RP, Anderson CA, Delgado M, Chisholm R, Bolton R, Herkert T, Afnan AM, Drennen JK (2005) Process analytical technology case study: part II. Development and validation of quantitative near-infrared calibrations in support of a process analytical technology application for real-time release. AAPS PharmSciTech 6(2):273–283CrossRefGoogle Scholar
  98. 98.
    Shanley A (2012) The pulse of pharmaceutical manufacturing. Pharm Manufac, 4 Apr 2012Google Scholar
  99. 99.
    González-Martínez JM, Folch-Fortuny A, Llaneras F, Tortajada M, Picó J, Ferrer A (2014) Metabolic flux understanding of Pichia pastoris grown on heterogenous culture media. Chemom Intel Lab Syst 134:89–99CrossRefGoogle Scholar
  100. 100.
    Then-Kania A (2011) Postapproval CMC changes in the United States with a focus on biopharmaceuticals – current status and an outlook in the pharmaceutical development. Master thesis, University of BonnGoogle Scholar
  101. 101.
    Moore CVM (2013) Multivariate tools for modern pharmaceutical control – FDA perspective. In: IFPAC annual meeting, 22 Jan 2013Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marco Jenzsch
    • 1
  • Christian Bell
    • 2
  • Stefan Buziol
    • 3
  • Felix Kepert
    • 4
  • Harald Wegele
    • 4
  • Christian Hakemeyer
    • 5
  1. 1.Roche Pharma Technical Operations – Biologics ManufacturingPenzbergGermany
  2. 2.Roche Pharma Technical Operations – Biologics Analytical Development EuropeBaselSwitzerland
  3. 3.Roche Pharma Technical Operations – Bioprocess Development EuropePenzbergGermany
  4. 4.Roche Pharma Technical Operations – Biologics Analytical Development EuropePenzbergGermany
  5. 5.Roche Pharma Technical Operations – Biologics Global Manufacturing Science and TechnologyMannheimGermany

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