Skip to main content
SpringerLink
Log in

Integrated Process and Supply Chain Design and Optimization

  • Chapter
  • First Online:
Biopharmaceutical Manufacturing

Part of the book series: Cell Engineering ((CEEN,volume 11))

  • 562 Accesses

Abstract

Advanced Therapy Medicinal Products (ATMPs) are an emerging class of therapeutic the development of which is primarily based on genetically engineering genes, cells, or tissues. Owing to their promising clinical outcomes in the treatment of rare and life-threatening diseases, ATMPs, have been gaining increasing interest from the global scientific and industrial community. The rapid growth in the demand for ATMPs is challenging manufacturers who are required to scale up production under process and demand uncertainties. In this chapter, we discuss the complexities of the global ATMP supply chain, highlighting the underlying interdependencies across raw materials, stakeholders and trade-offs with respect to supply chain costs, product availability, and environmental footprints of the manufacturing process. We present a comprehensive literature review on Process Systems Engineering tools in this space and discuss how those can assist systematic and proactive decision-making throughout stages of process development, design and optimization, and supply chain planning for ATMPs. We propose an integrated computational framework for process design and optimization and conclude with its demonstration on an industrially relevant case study, focusing on the quantification of uncertainty in process performance, sensitivity analysis, and process optimization for plasmid DNA production, a key ATMP raw material.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

AAV:

Adeno-associated Virus

ATMPs:

Advanced Therapy Medicinal Products

AV:

Adenovirus

BO:

Bayesian Optimization

C&GT:

Cell and Gene Therapies

CapEX:

Capital Expenditures

CAR:

Chimeric Antigen Receptor

CDMOs:

Contract Manufacturing and Development Organizations

cGMP:

Current Good Manufacturing Practice

CMOs:

Contract Manufacturing Organizations

COGS:

Cost of Goods Sold

COM:

Component Object Model

CPU:

Central Processing Unit

DFO:

Derivative-free Optimization

DSP:

Downstream Process

EI:

Expected Improvement

GP:

Gaussian Process

GSA:

Global Sensitivity Analysis

GWP:

Global Warming Potential

HSV:

Herpes Simplex Virus

IQR:

Interquartile Range

KPIs:

Key Performance Indicators

LCA:

Life Cycle Assessment

LCB:

Lower Confidence Bound

LHS:

Latin Hypercube Sampling

LSA:

Local Sensitivity Analysis

LV:

Lentivirus

M:

Median

MES:

Max-value Entropy Search

NLP:

Non-linear Programming

OpEx:

Operating Expenditures

pDNA:

Plasmid DNA

PI:

Probability of Improvement

PSE:

Process Systems Engineering

QA:

Quality Assurance

QC:

Quality Control

RBF:

Radial Basis Function

RS-HDMR:

Random Sampling High Dimensional Model Representation

RV:

Retrovirus

SA:

Sensitivity Analysis

TCR:

T-Cell Receptor

UCB:

Upper Confidence Bound

UF/DF:

Ultrafiltration/Diafiltration

USP:

Upstream Process

VV:

Viral Vector

References

  1. Schumacher TNM (2002) T-cell-receptor gene therapy. Nat Rev Immunol 2:512–519. https://doi.org/10.1038/nri841

    Article  CAS  PubMed  Google Scholar 

  2. Naldini L (2015) Gene therapy returns to centre stage. Nature 526:351–360. https://doi.org/10.1038/nature15818

    Article  CAS  PubMed  Google Scholar 

  3. Neves LS, Rodrigues MT, Reis RL, Gomes ME (2016) Current approaches and future perspectives on strategies for the development of personalized tissue engineering therapies. Expert Rev Precis Med Drug Dev 1:93–108. https://doi.org/10.1080/23808993.2016.1140004

    Article  Google Scholar 

  4. Hunt TD (2023) Cell & gene: state of the industry briefing

    Google Scholar 

  5. The TransMission: Current FDA Approved Gene and Cell Therapies. https://www.mirusbio.com/blog/fda-approved-gene-cell-therapies. Accessed 27 Feb 2023

  6. Approved Cellular and Gene Therapy Products | FDA. https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products/approved-cellular-and-gene-therapy-products. Accessed 28 Feb 2023

  7. The Alliance for Regenerative Medicine (2022) Regenerative medicine: the pipeline momentum builds H1

    Google Scholar 

  8. Regenerative TA for (2021) Regenerative medicine: disrupting the status quo. 1–40

    Google Scholar 

  9. Burnell S, Siciliano P Cell and gene therapy in 2040

    Google Scholar 

  10. Seoane-Vazquez E, Shukla V, Rodriguez-Monguio R (2019) Innovation and competition in advanced therapy medicinal products. EMBO Mol Med 11:1–5. https://doi.org/10.15252/emmm.201809992

    Article  CAS  Google Scholar 

  11. Bluebird Bio has serious cash flow concerns; inability to strike reimbursement deals in Europe partly to blame

    Google Scholar 

  12. Bulcha JT, Wang Y, Ma H, Tai PWL, Gao G (2021) Viral vector platforms within the gene therapy landscape. Signal Transduct Target Ther 6:1–23. https://doi.org/10.1038/s41392-021-00487-6

    Article  CAS  Google Scholar 

  13. Lukashev AN, Zamyatnin AA (2016) Viral vectors for gene therapy: current state and clinical perspectives. Biochem Mosc 81:700–708. https://doi.org/10.1134/S0006297916070063

    Article  CAS  Google Scholar 

  14. Lundstrom K (2018) Viral vectors in gene therapy. Diseases 6:42. https://doi.org/10.3390/diseases6020042

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Fisher PB, Savino R, Betapudi V, Goswami R, Subramanian G, Silayeva L, Newkirk I, Doctor D, Chawla K, Chattopadhyay S, Chandra D, Chilukuri N (2019) Gene therapy leaves a vicious cycle. Front Oncol 1:297. https://doi.org/10.3389/fonc.2019.00297

    Article  Google Scholar 

  16. CHMP (2018) Package insert – LUXTURNA (voretigene neparvovec-rzyl)

    Google Scholar 

  17. CHMP (2019) Package insert: ZONGELSMA (onasemnogene abeparvovec-xioi)

    Google Scholar 

  18. CHMP (2019) Package insert: KYMRIAH (tisagenlecleucel)

    Google Scholar 

  19. U.S. Food & Drug Administration (FDA) (2017) FDA approves CAR-T cell therapy to trat adults with certain types of large B-cell lymphoma. https://www.fda.gov/news-events/press-announcements/fda-approves-car-t-cell-therapy-treat-adults-certain-types-large-b-cell-lymphoma. Accessed 17 Mar 2021

  20. Capra E, Gennari A, Loche A, Temps C (2022) Viral-vector therapies at scale: today’s challenges and future opportunities

    Google Scholar 

  21. Lambrix E, Kent-Egan H (2022) Viral vectors: shortages and innovation. In: Biotech review of the year

    Google Scholar 

  22. Petitt S, Glover C, Madsen J, Pratik J (2018) Key considerations in gene therapy manufacturing for commercialization

    Google Scholar 

  23. Cameau E, Pedregal A, Glover C (2019) Cost modelling comparison of adherent multi-trays with suspension and fixed-bed bioreactors for the manufacturing of gene therapy products. Cell Gene Ther Insights 5:1663–1674. https://doi.org/10.18609/cgti.2019.175

    Article  Google Scholar 

  24. Valkama AJ, Oruetxebarria I, Lipponen EM, Leinonen HM, Käyhty P, Hynynen H, Turkki V, Malinen J, Miinalainen T, Heikura T, Parker NR, Ylä-Herttuala S, Lesch HP (2020) Development of large-scale downstream processing for lentiviral vectors. Mol Ther Methods Clin Dev 17:717–730. https://doi.org/10.1016/j.omtm.2020.03.025

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Merten O-W, Schweizer M, Chahal P, Kamen A (2014) Manufacturing of viral vectors: part II. Downstream processing and safety aspects. Pharm Bioprocess 2:237–251. https://doi.org/10.4155/pbp.14.15

    Article  Google Scholar 

  26. de Rooij J, DeConto J, Schaenzler G, Bauer D, Barre K, Duskin M, Kohli A, Watanabe K (2019) Upstream and downstream solutions for AAV manufacturing. Cell Gene Ther Insights 5:1017–1029. https://doi.org/10.18609/cgti.2019.110

    Article  Google Scholar 

  27. Terova O, Soltys S, Hermans P, de Rooij J, Detmers F (2018) Overcoming downstream purification challenges for viral vector manufacturing: enabling advancement of gene therapies in the clinic. Cell Gene Ther Insights 4:101–111. https://doi.org/10.18609/cgti.2018.017

    Article  Google Scholar 

  28. Qu W, Wang M, Wu Y, Xu R (2015) Scalable downstream strategies for purification of recombinant adeno-associated virus vectors in light of the properties. Curr Pharm Biotechnol 16:684–695

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Perry C, Rayat ACME (2021) Lentiviral vector bioprocessing. Viruses 13:268. https://doi.org/10.3390/v13020268

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Vellinga J, Smith JP, Lipiec A, Majhen D, Lemckert A, van Ooij M, Ives P, Yallop C, Custers J, Havenga M (2014) Challenges in manufacturing adenoviral vectors for global vaccine product deployment. Hum Gene Ther 25:318–327. https://doi.org/10.1089/hum.2014.007

    Article  CAS  PubMed  Google Scholar 

  31. Gosse M, Jones C, Jesus D, D’Costa S (2022) Regulatory & supply chain implications for plasmids as critical starting materials in the manufacture of viral vector gene therapy products. Cell Gene Ther Insights 8:279–286. https://doi.org/10.18609/cgti.2022.044

    Article  Google Scholar 

  32. Labbé RP, Vessillier S, Rafiq QA (2021) Lentiviral vectors for T cell engineering: clinical applications, bioprocessing and future perspectives. Viruses 13:1528. https://doi.org/10.3390/v13081528

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ohlson J (2020) Plasmid manufacture is the bottleneck of the genetic medicine revolution. Drug Discov Today 25:1891–1893. https://doi.org/10.1016/J.DRUDIS.2020.09.040

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Capra E, Gennari A, Loche A (2022) Viral-vector therapies at scale: today’s challenges and future opportunities. McKinsey. https://www.mckinsey.com/industries/life-sciences/our-insights/viral-vector-therapies-at-scale-todays-challengesand-future-opportunities

  35. Loche A, Mossmann W, van der Veken L, Yang G COVID-19 and cell and gene therapy: how to keep innovation on track. https://www.mckinsey.com/industries/life-sciences/our-insights/covid-19-and-cell-and-gene-therapy-how-to-keepinnovation-on-track

  36. Shah N (2004) Pharmaceutical supply chains: key issues and strategies for optimisation. Comput Chem Eng 28:929–941

    Article  CAS  Google Scholar 

  37. Kurata H, Ishino T, Ohshima Y, Yohda M CDMOs play a critical role in the biopharmaceutical ecosystem. https://doi.org/10.3389/fbioe.2022.841420

  38. Elekidis AP, Georgiadis MC (2022) Optimal contract selection for contract manufacturing organizations in the secondary pharmaceutical industry. Comput Chem Eng 165:107957. https://doi.org/10.1016/J.COMPCHEMENG.2022.107957

    Article  CAS  Google Scholar 

  39. Gene Therapy Manufacturing Fails to Meet Demand: Implications for Biopharma. L.E.K. Consulting. https://www.lek.com/insights/ei/gene-therapy-manufacturing-fails-meet-demand-implications-biopharma. Accessed 23 Aug 2022

  40. Lam C, Meinert E, Yang A, Cui Z (2021) Comparison between centralized and decentralized supply chains of autologous chimeric antigen receptor T-cell therapies: a UK case study based on discrete event simulation. Cytotherapy 23:433–451. https://doi.org/10.1016/j.jcyt.2020.08.007

    Article  CAS  PubMed  Google Scholar 

  41. Papathanasiou MM, Stamatis C, Lakelin M, Farid S, Titchener-Hooker N, Shah N (2020) Autologous CAR T-cell therapies supply chain: challenges and opportunities? Cancer Gene Ther. https://doi.org/10.1038/s41417-019-0157-z

  42. Triantafyllou N, Bernardi A, Lakelin M, Shah N, Papathanasiou MM (2022) A digital platform for the design of patient-centric supply chains. Sci Rep 12:17365. https://doi.org/10.1038/s41598-022-21290-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Harrison RP, Rafiq QA, Medcalf N (2018) Centralised versus decentralised manufacturing and the delivery of healthcare products: a United Kingdom exemplar. Cytotherapy 20:873–890. https://doi.org/10.1016/j.jcyt.2018.05.003

    Article  PubMed  Google Scholar 

  44. Harrison RP, Ruck S, Rafiq QA, Medcalf N (2018) Decentralised manufacturing of cell and gene therapy products: learning from other healthcare sectors. Biotechnol Adv 36:345–357

    Article  PubMed  Google Scholar 

  45. Sarkis M, Bernardi A, Shah N, Papathanasiou MM (2021) Emerging challenges and opportunities in pharmaceutical manufacturing and distribution. Processes 9:1–16. https://doi.org/10.3390/pr9030457

    Article  Google Scholar 

  46. Bernardi A, Papathanasiou MM, Lakelin M, Shah N (2021) Assessment of intermediate storage and distribution nodes in personalised medicine. Comput Aided Chem Eng 50:1997–2002. https://doi.org/10.1016/B978-0-323-88506-5.50309-0

    Article  CAS  Google Scholar 

  47. NHS (2022) Delivering a “net zero” national health service classification: official

    Google Scholar 

  48. HM Government (2021) Life sciences vision

    Google Scholar 

  49. Sarkis M, Bernardi A, Shah N, Papathanasiou MM (2021) Decision support tools for next-generation vaccines and advanced therapy medicinal products: present and future. Curr Opin Chem Eng 32

    Google Scholar 

  50. Pereira Chilima TD, Moncaubeig F, Farid SS (2020) Estimating capital investment and facility footprint in cell therapy facilities. Biochem Eng J 155. https://doi.org/10.1016/j.bej.2019.107439

  51. Jenkins MJ, Farid SS (2018) Cost-effective bioprocess design for the manufacture of allogeneic CAR-T cell therapies using a decisional tool with multi-attribute decision-making analysis. Biochem Eng J 137:192–204. https://doi.org/10.1016/j.bej.2018.05.014

    Article  CAS  Google Scholar 

  52. Pereira Chilima TD, Moncaubeig F, Farid SS (2018) Impact of allogeneic stem cell manufacturing decisions on cost of goods, process robustness and reimbursement. Biochem Eng J 137:132–151. https://doi.org/10.1016/J.BEJ.2018.04.017

    Article  CAS  Google Scholar 

  53. Comisel RM, Kara B, Fiesser FH, Farid SS (2021) Gene therapy process change evaluation framework: transient transfection and stable producer cell line comparison. Biochem Eng J 176:108202. https://doi.org/10.1016/J.BEJ.2021.108202

    Article  CAS  Google Scholar 

  54. Comisel RM, Kara B, Fiesser FH, Farid SS (2021) Lentiviral vector bioprocess economics for cell and gene therapy commercialization. Biochem Eng J 167:107868. https://doi.org/10.1016/j.bej.2020.107868

    Article  CAS  Google Scholar 

  55. Ferreira RG, Gordon NF, Stock R, Petrides D (2021) Adenoviral vector covid-19 vaccines: process and cost analysis. Processes 9. https://doi.org/10.3390/pr9081430

  56. Kis Z, Papathanasiou M, Calvo-Serrano R, Kontoravdi C, Shah N (2019) A model-based quantification of the impact of new manufacturing technologies on developing country vaccine supply chain performance: a Kenyan case study. J Adv Manuf Process. https://doi.org/10.1002/amp2.10025

  57. Sarkis M, Tak K, Chachuat B, Shah N, Papathanasiou MM (2022) Towards resilience in next-generation vaccines and therapeutics supply chains. In: Symposium on computer aided process engineering (ESCAPE 32)

    Google Scholar 

  58. Kis Z, Tak K, Ibrahim D, Papathanasiou MM, Chachuat B, Shah N, Kontoravdi C (2022) Pandemic-response adenoviral vector and RNA vaccine manufacturing. NPJ Vaccines 7. https://doi.org/10.1038/s41541-022-00447-3

  59. Caballero JA, Grossmann IE (2008) Rigorous flowsheet optimization using process simulators and surrogate models. Comput Aided Chem Eng 25:551–556. https://doi.org/10.1016/S1570-7946(08)80097-1

    Article  CAS  Google Scholar 

  60. Boukouvala F, Ierapetritou MG (2013) Surrogate-based optimization of expensive flowsheet modeling for continuous pharmaceutical manufacturing. J Pharm Innov 8:131–145. https://doi.org/10.1007/s12247-013-9154-1

    Article  Google Scholar 

  61. Cozad A, Sahinidis NV, Miller DC (2014) Learning surrogate models for simulation-based optimization. AICHE J 60:2211–2227. https://doi.org/10.1002/aic.14418

    Article  CAS  Google Scholar 

  62. Quirante N, Caballero JA (2016) Large scale optimization of a sour water stripping plant using surrogate models. Comput Chem Eng 92:143–162. https://doi.org/10.1016/j.compchemeng.2016.04.039

    Article  CAS  Google Scholar 

  63. Lin Z, Wang J, Nikolakis V, Ierapetritou M (2017) Process flowsheet optimization of chemicals production from biomass derived glucose solutions. Comput Chem Eng 102:258–267. https://doi.org/10.1016/j.compchemeng.2016.09.012

    Article  CAS  Google Scholar 

  64. Wang Z, Escotet-Espinoza MS, Ierapetritou M (2017) Process analysis and optimization of continuous pharmaceutical manufacturing using flowsheet models. Comput Chem Eng 107:77–91. https://doi.org/10.1016/j.compchemeng.2017.02.030

    Article  CAS  Google Scholar 

  65. Kim SH, Boukouvala F (2020) Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques. Optim Lett 14:989–1010. https://doi.org/10.1007/s11590-019-01428-7

    Article  Google Scholar 

  66. Panahi M, Khezri V, Yasari E, Skogestad S (2021) Application of surrogate models as an alternative to process simulation for implementation of the self-optimizing control procedure on large-scale process plants-a natural gas-to-liquids (gtl) case study. Ind Eng Chem Res 60:4919–4929. https://doi.org/10.1021/acs.iecr.0c05715

    Article  CAS  Google Scholar 

  67. Janus T, Engell S (2021) Iterative process design with surrogate-assisted global flowsheet optimization. Chem Ing Tech 93:2019–2028. https://doi.org/10.1002/cite.202100095

    Article  CAS  Google Scholar 

  68. Ludl PO, Heese R, Höller J, Asprion N, Bortz M (2022) Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet simulations with constraints. Front Chem Sci Eng 16:183–197. https://doi.org/10.1007/s11705-021-2073-7

    Article  Google Scholar 

  69. van de Berg D, Savage T, Petsagkourakis P, Zhang D, Shah N, del Rio-Chanona EA (2022) Data-driven optimization for process systems engineering applications. Chem Eng Sci 248:117135. https://doi.org/10.1016/J.CES.2021.117135

    Article  Google Scholar 

  70. Seider WD, Lewin D, Seader JD, Widgado RG, Gani R, Ng KM (2016) Product and process design principles: synthesis, analysis and evaluation, 4th edn. Wiley, New York

    Google Scholar 

  71. Heinzle E, Biwer AP, Cooney C (2006) Development of sustainable bioprocesses: modeling and assessment, 1st edn. Wiley, Chichester

    Book  Google Scholar 

  72. Towler G, Sinnott R (2008) Chemical engineering design: principles, practice and economics of plant and process design. Elsevier, Oxford

    Google Scholar 

  73. Jagschies G, Lindskog E, Lacki K, Galliher P (2018) Biopharmaceutical processing: development, design and implementation of manufacturing processes, 1st edn. Elsevier, Amsterdam

    Google Scholar 

  74. Hauschild MZ, Rosenbaum RK, Irving Olsen S (2018) Life cycle assessment: theory and practice, 1st edn. Springer, Cham

    Book  Google Scholar 

  75. Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181:259–270. https://doi.org/10.1016/j.cpc.2009.09.018

    Article  CAS  Google Scholar 

  76. Kucherenko S, Rodriguez-Fernandez M, Pantelides C, Shah N (2009) Monte Carlo evaluation of derivative-based global sensitivity measures. Reliab Eng Syst Saf 94:1135–1148. https://doi.org/10.1016/j.ress.2008.05.006

    Article  Google Scholar 

  77. Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) Global sensitivity analysis. The primer. Wiley, Chichester

    Google Scholar 

  78. Cucurachi S, Borgonovo E, Heijungs R (2016) A protocol for the global sensitivity analysis of impact assessment models in life cycle assessment. Risk Anal 36:357–377. https://doi.org/10.1111/risa.12443

    Article  CAS  PubMed  Google Scholar 

  79. Groen EA, Bokkers EAM, Heijungs R, de Boer IJM (2017) Methods for global sensitivity analysis in life cycle assessment. Int J Life Cycle Assess 22:1125–1137. https://doi.org/10.1007/s11367-016-1217-3

    Article  Google Scholar 

  80. Kucherenko S, Song S (2017) Different numerical estimators for main effect global sensitivity indices. Reliab Eng Syst Saf 165:222–238. https://doi.org/10.1016/j.ress.2017.04.003

    Article  Google Scholar 

  81. Sobol IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 55:271

    Article  Google Scholar 

  82. Kucherenko S (2013) SOBOLHDMR: a general-purpose modeling software. Methods Mol Biol 1073:191–224. https://doi.org/10.1007/978-1-62703-625-2_16

    Article  PubMed  Google Scholar 

  83. Brochu E, Cora V, Freitas N (2010) A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. CoRR abs/1012.2599

    Google Scholar 

  84. Shahriari B, Swersky K, Wang Z, Adams RP, de Freitas N (2016) Taking the human out of the loop: a review of Bayesian optimization. Proc IEEE 104:148–175. https://doi.org/10.1109/JPROC.2015.2494218

    Article  Google Scholar 

  85. Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. https://doi.org/10.48550/arxiv.1206.2944

  86. Rasmussen CE, Williams CKI (2005) Gaussian processes for machine learning. MIT Press, Cambridge

    Book  Google Scholar 

  87. Schulz E, Speekenbrink M, Krause A (2018) A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions. J Math Psychol 85:1–16. https://doi.org/10.1016/j.jmp.2018.03.001

    Article  Google Scholar 

  88. Kushner HJ, Yin G (1997) Stochastic approximation algorithms and applications, Applied mathematics. Springer, New York

    Book  Google Scholar 

  89. Kushner HJ (1964) A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. J Basic Eng 86:97–106. https://doi.org/10.1115/1.3653121

    Article  Google Scholar 

  90. Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13:455–492. https://doi.org/10.1023/A:1008306431147

    Article  Google Scholar 

  91. Mockus J, Tiesis V, Zilinskas A (2014) The application of Bayesian methods for seeking the extremum. In: Towards global optimization. North Holland, pp 117–129

    Google Scholar 

  92. Cox DD, John S (1992) A statistical method for global optimization. In: Proceedings 1992 IEEE international conference on systems, man, and cybernetics, vol. 2, pp 1241–1246

    Google Scholar 

  93. Wang Z, Jegelka S (2017) Max-value entropy search for efficient bayesian optimization. https://doi.org/10.48550/arxiv.1703.01968

  94. Przybylowski M, Bartido S, Borquez-Ojeda O, Sadelain M, Rivière I (2007) Production of clinical-grade plasmid DNA for human phase I clinical trials and large animal clinical studies. Vaccine 25:5013–5024. https://doi.org/10.1016/j.vaccine.2007.04.077

    Article  CAS  PubMed  Google Scholar 

  95. Urthaler J, Ascher C, Wöhrer H, Necina R (2007) Automated alkaline lysis for industrial scale cGMP production of pharmaceutical grade plasmid-DNA. J Biotechnol 128:132–149. https://doi.org/10.1016/j.jbiotec.2006.08.018

    Article  CAS  PubMed  Google Scholar 

  96. Prather KJ, Sagar S, Murphy J, Chartrain M (2003) Industrial scale production of plasmid DNA for vaccine and gene therapy: plasmid design, production, and purification. Enzym Microb Technol 33:865–883. https://doi.org/10.1016/S0141-0229(03)00205-9

    Article  CAS  Google Scholar 

  97. Carnes A, Williams J (2008) Plasmid DNA manufacturing technology. Recent Pat Biotechnol 1:151–166. https://doi.org/10.2174/187220807780809436

    Article  Google Scholar 

  98. da Gama R, Petrides D (2021) Manufacturing of pharmaceutical grade plasmid DNA (pDNA) process modeling and cost analysis for the process manufacturing industries. https://doi.org/10.13140/RG.2.2.12780.28800

  99. Sun B, Yu XH, Yin Y, Liu X, Wu Y, Chen Y, Zhang X, Jiang C, Kong W (2013) Large-scale purification of pharmaceutical-grade plasmid DNA using tangential flow filtration and multi-step chromatography. J Biosci Bioeng 116:281–286

    Article  CAS  PubMed  Google Scholar 

  100. Eon-Duval A, Burke G (2004) Purification of pharmaceutical-grade plasmid DNA by anion-exchange chromatography in an RNase-free process. J Chromatogr B Analyt Technol Biomed Life Sci 804:327–335. https://doi.org/10.1016/j.jchromb.2004.01.033

    Article  CAS  PubMed  Google Scholar 

  101. Balandat M, Karrer B, Jiang DR, Daulton S, Letham B, Wilson AG, Bakshy E (2019) BoTorch: a framework for efficient Monte-Carlo Bayesian optimization. https://doi.org/10.48550/arxiv.1910.06403

Download references

Acknowledgements

MMP and NS would like to acknowledge funding from the UK Engineering & Physical Sciences Research Council (EPSRC) for the Future Targeted Healthcare Manufacturing Hub hosted at University College London with UK university part-.

ners (Grant Reference: EP/P006485/1). Financial and in-kind support from the consortium of industrial users and sector organizations is also acknowledged. NT is thankful for the Marit Mohn Scholarship awarded by the Department of Chemical Engineering, Imperial College London.

Competing Interests

The authors declare no competing interests.

Author information

Authors and Affiliations

  1. The Sargent Centre for Process Systems Engineering, Imperial College London, London, UK

    Niki Triantafyllou, Miriam Sarkis, Nilay Shah, Cleo Kontoravdi & Maria M. Papathanasiou

  2. Department of Chemical Engineering, Imperial College London, London, UK

    Niki Triantafyllou, Miriam Sarkis, Nilay Shah, Cleo Kontoravdi & Maria M. Papathanasiou

Authors
  1. Niki Triantafyllou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miriam Sarkis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nilay Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cleo Kontoravdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maria M. Papathanasiou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maria M. Papathanasiou .

Editor information

Editors and Affiliations

  1. Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany

    Ralf Pörtner

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Triantafyllou, N., Sarkis, M., Shah, N., Kontoravdi, C., Papathanasiou, M.M. (2023). Integrated Process and Supply Chain Design and Optimization. In: Pörtner, R. (eds) Biopharmaceutical Manufacturing. Cell Engineering, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-031-45669-5_7

Download citation

Publish with us

Policies and ethics

Search

Navigation