Research ArticleResearch

CPV of the Future: AI-Powered Continued Process Verification for Bioreactor Processes

Andrej Ondracka, Arnau Gasset, Xavier García-Ortega, David Hubmayr, Joeri van Wijngaarden, José Luis Montesinos-Seguí, Francisco Valero and Toni Manzano
PDA Journal of Pharmaceutical Science and Technology May 2023, 77 (3) 146-165; DOI: https://doi.org/10.5731/pdajpst.2021.012665

References

  1. 1.↵
    1. Smith C. L.
    Advanced Process Control: Beyond Single Loop Control; John Wiley & Sons, 2011.
  2. 2.↵
    1. Huang J.,
    2. O'Connor T.,
    3. Ahmed K.,
    4. Chatterjee S.,
    5. Garvin C.,
    6. Ghosh K.,
    7. Ierapetritou M.,
    8. Jeffers M.,
    9. Pla D. L.,
    10. Lee S. L.,
    11. Lovett D.,
    12. Lyngberg O.,
    13. Mack J.,
    14. McManus E.,
    15. Romero‐Torres S.,
    16. Undey C.,
    17. Venkatasubramanian V.,
    18. Warman M.
    AIChE PD2MAdvanced Process Control Workshop‐Moving APCForward in the Pharmaceutical Industry. J.Adv. Manuf. Process. 2021, 3 (1), e10071.
    OpenUrl
  3. 3.↵
    1. Mora A.,
    2. Zhang S. S.,
    3. Carson G.,
    4. Nabiswa B.,
    5. Hossler P.,
    6. Yoon S.
    Sustaining an Efficient and Effective CHO Cell Line Development Platform by Incorporation of 24-Deep Well Plate Screening and Multivariate Analysis. Biotechnol. Prog. 2018, 34 (1), 175–186. https://doi.org/10.1002/btpr.2584.
    OpenUrl
  4. 4.↵
    1. Masood A.,
    2. Hashmi A.
    AI Use Cases in the Industry. In: Cognitive Computing Recipes; Apress: Berkeley, CA, 2019., pp 383–396.
  5. 5.↵
    FDA. Process validation: general principles and practices. Guidance for Industry, 2011.
  6. 6.↵
    International Conference for Harmonisation, Quality Guideline Q12: Technical and Regulatory Considerations for Pharmaceutical Product Lifecycle Management. ICH: Geneva, 2017.
  7. 7.↵
    1. Peña D. A.,
    2. Gasser B.,
    3. Zanghellini J.,
    4. Steiger M. G.,
    5. Mattanovich D.
    Metabolic Engineering of Pichia pastoris. Metab. Eng. 2018, 50, 2–15.
    OpenUrl
  8. 8.↵
    1. Juturu V.,
    2. Wu J. C.
    Heterologous Protein Expression in Pichia pastoris: Latest Research Progress and Applications. ChemBioChem 2018, 19 (1), 7–21.
    OpenUrlCrossRef
  9. 9.↵
    1. Looser V.,
    2. Bruhlmann B.,
    3. Bumbak F.,
    4. Stenger C.,
    5. Costa M.,
    6. Camattari A.,
    7. Fotiadis D.,
    8. Kovar K.
    Cultivation Strategies to Enhance Productivity of Pichia pastoris: A Review. Biotechnol. Adv. 2015, 33 (6 Part 2), 1177–1193.
    OpenUrlCrossRefPubMed
  10. 10.↵
    1. Vogl T.,
    2. Glieder A.
    Regulation of Pichia pastoris Promoters and Its Consequences for Protein Production. New Biotechnol. 2013, 30 (4), 385–404.
    OpenUrl
  11. 11.↵
    1. Cos O.,
    2. Ramón R.,
    3. Montesinos J. L.,
    4. Valero F.
    Operational Strategies, Monitoring and Control of Heterologous Protein Production in the Methylotrophic Yeast Pichia pastoris under Different Promoters: A Review. Microb. Cell Fact. 2006, 5 (1), 17.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Ortega X.,
    2. Cámara E.,
    3. Ferrer P.,
    4. Albiol J.,
    5. Montesinos-Seguí J. L.,
    6. Valero F.
    Rational Development of Bioprocess Engineering Strategies for Recombinant Protein Production in Pichia pastoris (Komagataella phaffi) Using the Methanol-Free GAP Promoter. Where Do We Stand? New Biotechnol. 2019, 53, 24–34.
    OpenUrl
  13. 13.↵
    1. Zhang A.-L.,
    2. Luo J.-X.,
    3. Zhang T.-Y.,
    4. Pan Y.-W.,
    5. Tan Y.-H.,
    6. Fu C.-Y.,
    7. Tu F.-Z.
    Recent Advances on the GAP Promoter Derived Expression System of Pichia pastoris. Mol. Biol. Rep. 2009, 36 (6), 1611–1619.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Garcia-Ortega X.,
    2. Ferrer P.,
    3. Montesinos J. L.,
    4. Valero F.
    Fed-Batch Operational Strategies for Recombinant Fab Production with Pichia pastoris Using the Constitutive GAP Promoter. Biochem. Eng. J. 2013, 79, 172–181.
    OpenUrl
  15. 15.↵
    1. Çalık P.,
    2. Ata Ö.,
    3. Güneş H.,
    4. Massahi A.,
    5. Boy E.,
    6. Keskin A.,
    7. Öztürk S.,
    8. Zerze G. H.,
    9. Özdamar T. H.
    Recombinant Protein Production in Pichia pastoris under Glyceraldehyde-3-Phosphate Dehydrogenase Promoter: from Carbon Source Metabolism to Bioreactor Operation Parameters. Biochem. Eng. J. 2015, 95, 20–36.
    OpenUrl
  16. 16.↵
    1. Huang M.,
    2. Bao J.,
    3. Nielsen J.
    Biopharmaceutical Protein Production by Saccharomyces cerevisiae: Current State and Future Prospects. Pharm. Bioprocess. 2014, 2, 167–182.
    OpenUrl
  17. 17.↵
    1. Baumann K.,
    2. Maurer M.,
    3. Dragosits M.,
    4. Cos O.,
    5. Ferrer P.,
    6. Mattanovich D.
    Hypoxic Fed-Batch Cultivation of Pichia pastoris Increases Specific and Volumetric Productivity of Recombinant Proteins. Biotechnol. Bioeng. 2008, 100 (1), 177–183.
    OpenUrlCrossRefPubMed
  18. 18.↵
    1. Baumann K.,
    2. Carnicer M.,
    3. Dragosits M.,
    4. Graf A. B.,
    5. Stadlmann J.,
    6. Jouhten P.,
    7. Maaheimo H.,
    8. Gasser B.,
    9. Albiol J.,
    10. Mattanovich D.,
    11. Ferrer P.
    A Multi-Level Study of Recombinant Pichia pastoris in Different Oxygen Conditions. BMC Syst. Biol. 2010, 4, 141.
    OpenUrlCrossRefPubMed
  19. 19.↵
    1. Garcia-Ortega X.,
    2. Valero F.,
    3. Montesinos-Seguí J. L.
    Physiological State as Transferable Operating Criterion to Improve Recombinant Protein Production in Pichia pastoris through Oxygen Limitation. J. Chem. Technol. Biotechnol. 2017, 92 (10), 2573–2582.
    OpenUrl
  20. 20.↵
    1. Simutis R.,
    2. Lübbert A.
    Hybrid Approach to State Estimation for Bioprocess Control. Bioengineering 2017, 4 (1), 21.
    OpenUrl
  21. 21.↵
    1. Bayer B.,
    2. Striedner G.,
    3. Duerkop M.
    Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization. Biotechnol. J. 2020, 15 (9), e2000121.
    OpenUrl
  22. 22.↵
    1. Brunner V.,
    2. Siegl M.,
    3. Geier D.,
    4. Becker T.
    Biomass Soft Sensor for a Pichia pastoris Fed‐Batch Process Based on Phase Detection and Hybrid Modeling. Biotechnol. Bioeng. 2020, 117 (9), 2749–2759.
    OpenUrl
  23. 23.↵
    1. Solle D.,
    2. Hitzmann B.,
    3. Herwig C.,
    4. Pereira Remelhe M.,
    5. Ulonska S.,
    6. Wuerth L.,
    7. Prata A.,
    8. Steckenreiter T.
    Between the Poles of Data-Driven and Mechanistic Modeling for Process Operation. Chem. Ing. Tech. 2017, 89 (5), 542–561.
    OpenUrl
  24. 24.↵
    1. Hong M. S.,
    2. Velez‐Suberbie M. L.,
    3. Maloney A. J.,
    4. Biedermann A.,
    5. Love K. R.,
    6. Love J. C.,
    7. Mukhopadhyay T. K.,
    8. Braatz R. D.
    Macroscopic Modeling of Bioreactors for Recombinant Protein Producing Pichia pastoris in Defined Medium. Biotechnol. Bioeng. 2021, 118 (3), 1199–1212.
    OpenUrl
  25. 25.↵
    1. Barrigon J. M.,
    2. Valero F.,
    3. Montesinos J. L.
    A Macrokinetic Model-Based Comparative Meta-Analysis of Recombinant Protein Production by Pichia pastoris under AOX1 Promoter. Biotechnol. Bioeng. 2015, 112 (6), 1132–1145.
    OpenUrl
  26. 26.↵
    Council of Europe. European Pharmacopoeia (Ph. Eur.), 9th edition, Council of Europe: Strasbourg, France, 2017.
  27. 27.↵
    1. Breiman L.
    Random Forests. Mach. Learn. 2001, 45 (1), 5–32.
    OpenUrlCrossRefPubMedWeb of Science
  28. 28.↵
    1. Beiroti A.,
    2. Hosseini S. N.,
    3. Aghasadeghi M. R.,
    4. Norouzian D.
    Comparative Study of μ ‐Stat Methanol Feeding Control in Fed‐Batch Fermentation of Pichia pastoris Producing HBsAg: An Open‐Loop Control versus Recurrent Artificial Neural Network‐Based Feedback Control. J. Chem. Technol. Biotechnol. 2019, 94 (12), 3924–3931.
    OpenUrl
  29. 29.↵
    1. Hosseini S. N.,
    2. Javidanbardan A.,
    3. Khatami M.
    Accurate and Cost‐Effective Prediction of HBsAg Titer in Industrial Scale Fermentation Process of Recombinant Pichia pastoris by Using Neural Network Based Soft Sensor. Biotechnol. Appl. Biochem. 2019, 66 (4), 681–689.
    OpenUrl
  30. 30.↵
    1. Wang X.,
    2. Guo T.,
    3. Hao W.,
    4. Guo Q.
    Predicting Model Based on LS-SVM for Inulinase Concentration during Pichia pastoris' Fermentation Process. 2019 Chinese Control Conference (CCC); Guangzhou, China; July 27–30, 2019; pp 1531–1536.
  31. 31.↵
    1. Cintron R.
    Human Factors Analysis and Classification System Interrater Reliability for Biopharmaceutical Manufacturing Investigations. Ph.D. dissertation, Walden University, 2015.
  32. 32.↵
    1. Liu F. T.,
    2. Ting K. M.,
    3. Zhou Z.-H.
    Isolation-Based Anomaly Detection. ACM Trans. Knowl. Discov. Data 2012, 6 (1), 1–39.
    OpenUrlCrossRef
  33. 33.↵
    1. Breunig M. M.,
    2. Kriegel H.-P.,
    3. Ng R. T.,
    4. Sander J.
    LOF: Identifying Density-Based Local Outliers. SIGMOD Record 2000, 29 (2), 93–104.
    OpenUrlCrossRefWeb of Science
  34. 34.↵
    1. Schölkopf B.,
    2. Platt J. C.,
    3. Shawe-Taylor J.,
    4. Smola A. J.,
    5. Williamson R. C.
    Estimating the Support of a High-Dimensional Distribution. Neural Comput. 2001, 13 (7), 1443–1471.
    OpenUrlCrossRefPubMedWeb of Science
  35. 35.↵
    1. Lee J.,
    2. Davari H.,
    3. Singh J.,
    4. Pandhare V.
    Industrial Artificial Intelligence for Industry 4.0-Based Manufacturing Systems. Manuf. Lett. 2018, 18, 20–23.
    OpenUrl
  36. 36.↵
    1. Dwivedi Y. K.,
    2. Hughes L.,
    3. Ismagilova E.,
    4. Aarts G.,
    5. Coombs C.,
    6. Crick T.,
    7. Duan Y.,
    8. Dwivedi R.,
    9. Edwards J.,
    10. Eirug A.,
    11. Galanos V.,
    12. Ilavarasan P. V.,
    13. Janssen M.,
    14. Jones P.,
    15. Kar A. K.,
    16. Kizgin H.,
    17. Kronemann B.,
    18. Lal B.,
    19. Lucini B.,
    20. Medaglia R.,
    21. Le Meunier-FitzHugh K.,
    22. Le Meunier-FitzHugh L. C.,
    23. Misra S.,
    24. Mogaji E.,
    25. Sharma S. K.,
    26. Singh J. B.,
    27. Raghavan V.,
    28. Raman R.,
    29. Rana N. P.,
    30. Samothrakis S.,
    31. Spencer J.,
    32. Tamilmani K.,
    33. Tubadji A.,
    34. Walton P.,
    35. Williams M. D.
    Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy. Int. J. Inf. Manage. 2021, 57, 101994.
    OpenUrl
  37. 37.↵
    1. Sánchez A.,
    2. Ferrer P.,
    3. Serrano A.,
    4. Pernas M. A.,
    5. Valero F.,
    6. Rúa M. L.,
    7. Casas C.,
    8. Solà C.
    Characterization of the Lipase and Esterase Multiple Forms in an Enzyme Preparation from a Candida rugosa Pilot-Plant Scale Fed-Batch Fermentation. Enzyme Microb. Technol. 1999, 25 (3–5), 214–223.
    OpenUrl
  38. 38.↵
    1. Ugo K. A..,
    2. Vivian Amara A.,
    3. Kenechuwku U.,
    4. Cn I.
    Microbial Lipases: A Prospect for Biotechnological Industrial Catalysis for Green Products: A Review. Ferment. Technol. 2017, 6 (2), 1000144.
    OpenUrl
  39. 39.↵
    1. Nieto‐Taype M. A.,
    2. Garrigós‐Martínez J.,
    3. Sánchez‐Farrando M.,
    4. Valero F.,
    5. Garcia‐Ortega X.,
    6. Montesinos‐Seguí J. L.
    Rationale‐Based Selection of Optimal Operating Strategies and Gene Dosage Impact on Recombinant Protein Production in Komagataella phaffii (Pichia pastoris). Microb. Biotechnol. 2020, 13 (2), 315–327.
    OpenUrl
  40. 40.↵
    1. Cámara E.,
    2. Albiol J.,
    3. Ferrer P.
    Droplet Digital PCR-Aided Screening and Characterization of Pichia pastoris Multiple Gene Copy Strains. Biotechnol. Bioeng. 2016, 113 (7), 1542–1551.
    OpenUrlCrossRef
  41. 41.↵
    1. Garcia-Ortega X.,
    2. Adelantado N.,
    3. Ferrer P.,
    4. Montesinos J. L.,
    5. Valero F.
    A Step Forward to Improve Recombinant Protein Production in Pichia pastoris: From Specific Growth Rate Effect on Protein Secretion to Carbon-Starving Conditions as Advanced Strategy. Process Biochem. 2016, 51 (6), 681–691.
    OpenUrl
  42. 42.↵
    1. Cos O.,
    2. Serrano A.,
    3. Montesinos J. L.,
    4. Ferrer P.,
    5. Cregg J. M.,
    6. Valero F.
    Combined Effect of the Methanol Utilization (Mut) Phenotype and Gene Dosage on Recombinant Protein Production in Pichia pastoris Fed-Batch Cultures. J. Biotechnol. 2005, 116 (4), 321–335.
    OpenUrlCrossRefPubMedWeb of Science
  43. 43.↵
    1. Carnicer M.,
    2. Baumann K.,
    3. Töplitz I.,
    4. Sánchez-Ferrando F.,
    5. Mattanovich D.,
    6. Ferrer P.,
    7. Albiol J.
    Macromolecular and Elemental Composition Analysis and Extracellular Metabolite Balances of Pichia pastoris Growing at Different Oxygen Levels. Microb. Cell Fact. 2009, 8 (1), 65.
    OpenUrlCrossRefPubMed
  44. 44.↵
    1. Garrigós-Martínez J.,
    2. Nieto-Taype M. A.,
    3. Gasset-Franch A.,
    4. Montesinos-Seguí J. L.,
    5. Garcia-Ortega X.,
    6. Valero F.
    Specific Growth Rate Governs AOX1 Gene Expression, Affecting the Production Kinetics of Pichia pastoris (Komagataella phaffii) PAOX1-Driven Recombinant Producer Strains with Different Target Gene Dosage. Microb. Cell Fact. 2019, 18 (1), 187.
    OpenUrl
  45. 45.↵
    1. Jordà J.,
    2. de Jesus S. S.,
    3. Peltier S.,
    4. Ferrer P.,
    5. Albiol J.
    Metabolic Flux Analysis of Recombinant Pichia pastoris Growing on Different Glycerol/Methanol Mixtures by Iterative Fitting of NMR-Derived 13C-Labelling Data from Proteinogenic Amino Acids. New Biotechnol. 2014, 31 (1), 120–132.
    OpenUrl
  46. 46.↵
    1. Ponte X.,
    2. Montesinos-Seguí J. L.,
    3. Valero F.
    Bioprocess Efficiency in Rhizopus oryzae Lipase Production by Pichia pastoris under the Control of PAOX1is Oxygen Tension Dependent. Process Biochem. 2016, 51 (12), 1954–1963.
    OpenUrl
  47. 47.↵
    1. Ponte X.,
    2. Barrigón J. M.,
    3. Maurer M.,
    4. Mattanovich D.,
    5. Valero F.,
    6. Montesinos-Seguí J. L.
    Towards Optimal Substrate Feeding for Heterologous Protein Production in Pichia pastoris (Komagataella Spp) Fed-Batch Processes under PAOX1 Control: A Modeling Aided Approach. J. Chem. Technol. Biotechnol. 2018, 93 (11), 3208–3218.
    OpenUrl
  48. 48.↵
    1. Gasset A.,
    2. Garcia-Ortega X.,
    3. Garrigós-Martínez J.,
    4. Valero F.,
    5. Montesinos-Seguí J. L.
    Innovative Bioprocess Strategies Combining Physiological Control and Strain Engineering of Pichia pastoris to Improve Recombinant Protein Production. Front. Bioeng. Biotechnol. 2022, 10, 818434.
    OpenUrl
  49. 49.↵
    1. Manzano T.,
    2. Fernàndez C.,
    3. Ruiz T.,
    4. Richard H.
    Artificial Intelligence Algorithm Qualification: A Quality by Design Approach to Apply Artificial Intelligence in Pharma. PDA J. Pharm. Sci. Technol. 2021, 75 (1), 100–118.
    OpenUrlAbstract/FREE Full Text
  50. 50.↵
    1. Hahn G. J.
    Industry 4.0: A Supply Chain Innovation Perspective. Int. J. Prod. Res. 2020, 58 (5), 1425–1441.
    OpenUrl
Back to top

In This Issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools
CPV of the Future: AI-Powered Continued Process Verification for Bioreactor Processes
Andrej Ondracka, Arnau Gasset, Xavier García-Ortega, David Hubmayr, Joeri van Wijngaarden, José Luis Montesinos-Seguí, Francisco Valero, Toni Manzano
PDA Journal of Pharmaceutical Science and Technology May 2023, 77 (3) 146-165; DOI: 10.5731/pdajpst.2021.012665
Twitter logo Facebook logo Mendeley logo

Jump to section

Keywords