Advertisement

Process System Engineering Methodologies Applied to Tissue Development and Regenerative Medicine

  • Ágata Paim
  • Nilo S. M. Cardozo
  • Patricia PrankeEmail author
  • Isabel C. Tessaro
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1078)

Abstract

Tissue engineering and the manufacturing of regenerative medicine products demand strict control over the production process and product quality monitoring. In this chapter, the application of process systems engineering (PSE) approaches in the production of cell-based products has been discussed. Mechanistic, empirical, continuum and discrete models are compared and their use in describing cellular phenomena is reviewed. In addition, model-based optimization strategies employed in the field of tissue engineering and regenerative medicine are discussed. An introduction to process control theory is given and the main applications of classical and advanced methods in cellular production processes are described. Finally, new nondestructive and noninvasive monitoring techniques have been reviewed, focusing on large-scale manufacturing systems for cell-based constructs and therapeutic products. The application of the PSE methodologies presented here offers a promising alternative to overcome the main challenges in manufacturing engineered tissue and regeneration products.

Keywords

PSE Tissue engineering Regenerative medicine Mathematical modeling Process control Optimization Biomaterials 

References

  1. 1.
    Bersimis S, Panaretos J, Psarakis S (2005) Multivariate statistical process control charts and the problem of interpretation: a short overview and some applications in industry. In: 7th Hellenic European conference on computer mathematics and its applications, Athens, GreeceGoogle Scholar
  2. 2.
    Bersimis S, Psarakis S, Panaretos J (2007) Multivariate statistical process control charts: an overview. Qual Reliab Eng Int 23:517–543.  https://doi.org/10.1002/qre.829 CrossRefGoogle Scholar
  3. 3.
    Boccaccio A, Uva AE, Fiorentino M et al (2016a) Geometry design optimization of functionally graded scaffolds for bone tissue engineering: a Mechanobiological approach. PLoS One 11:e0146935.  https://doi.org/10.1371/journal.pone.0146935 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Boccaccio A, Uva AE, Fiorentino M et al (2016b) A Mechanobiology-based algorithm to optimize the microstructure geometry of bone tissue scaffolds. Int J Biol Sci 12:1–17.  https://doi.org/10.7150/ijbs.13158 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Caro JJ, Möller J (2016) Advantages and disadvantages of discrete-event simulation for health economic analyses. Expert Rev Pharmacoecon Outcomes Res 16:327–329.  https://doi.org/10.1586/14737167.2016.1165608 CrossRefPubMedGoogle Scholar
  6. 6.
    Cervera-Padrell AE, Skovby T, Kiil S et al (2012) Active pharmaceutical ingredient (API) production involving continuous processes – a process system engineering (PSE)-assisted design framework. Eur J Pharm Biopharm 82:437–456.  https://doi.org/10.1016/j.ejpb.2012.07.001 CrossRefPubMedGoogle Scholar
  7. 7.
    Couet F, Mantovani D (2012) Optimization of culture conditions in a bioreactor for vascular tissue engineering using a mathematical model of vascular growth and remodeling. Cardiovasc Eng Technol 3:228–236.  https://doi.org/10.1007/s13239-012-0088-4 CrossRefGoogle Scholar
  8. 8.
    Coy RH, Evans OR, Phillips JB, Shipley RJ (2017) An integrated theoretical-experimental approach to accelerate translational tissue engineering. J Tissue Eng Regen Med 12:e53–e59.  https://doi.org/10.1002/term.2346 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Curaj A, Wu Z, Fokong S et al (2015) Noninvasive molecular ultrasound monitoring of vessel healing after intravascular surgical procedures in a preclinical setup. Arterioscler Thromb Vasc Biol 35:1366–1373.  https://doi.org/10.1161/ATVBAHA.114.304857 CrossRefPubMedGoogle Scholar
  10. 10.
    de Araújo ACB, Hori ES, Skogestad S (2007) Application of Plantwide control to the HDA process. II regulatory control. Ind Eng Chem Res 46:5159–5174.  https://doi.org/10.1021/ie061393z CrossRefGoogle Scholar
  11. 11.
    Dias MR, Guedes JM, Flanagan CL et al (2014) Optimization of scaffold design for bone tissue engineering: a computational and experimental study. Med Eng Phys 36:448–457.  https://doi.org/10.1016/j.medengphy.2014.02.010 CrossRefPubMedGoogle Scholar
  12. 12.
    do Nascimento RJA, de Macedo GR, dos Santos ES, de Oliveira JA (2017) Real time and in situ near-infrared spectroscopy (Nirs) for quantitative monitoring of biomass, glucose, ethanol and Glycerine concentrations in an alcoholic fermentation. Braz J Chem Eng 34:459–468.  https://doi.org/10.1590/0104-6632.20170342s20150347 CrossRefGoogle Scholar
  13. 13.
    Fu AS, Solorio LD, Alsberg E, Saidel GM (2017) Mathematical modelling of glycosaminoglycan production by stem cell aggregates incorporated with growth factor-releasing polymer microspheres. J Tissue Eng Regen Med 11:481–488.  https://doi.org/10.1002/term.1940 CrossRefPubMedGoogle Scholar
  14. 14.
    Georgieva P, Oliveira R, Feyo de Azevedo S (2002) Instrumentation and process control – process control. In: Roginski H, Fuquay JW, Fox PF (eds) Encyclopedia of dairy sciences. Academic, London, pp 1401–1410CrossRefGoogle Scholar
  15. 15.
    Geris L (2014) Regenerative orthopaedics: in vitro, in vivo … in silico. Int Orthop 38:1771–1778.  https://doi.org/10.1007/s00264-014-2419-6 CrossRefPubMedGoogle Scholar
  16. 16.
    Huang H, Harlé K, Movellan J, Paulus M (2016) Using optimal control to disambiguate the effect of depression on sensorimotor, motivational and goal-setting functions. PLoS One 11:e0167960.  https://doi.org/10.1371/journal.pone.0167960 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Hunsberger J, Harrysson O, Shirwaiker R et al (2015) Manufacturing road map for tissue engineering and regenerative medicine technologies. Stem Cells Transl Med 4:130–135.  https://doi.org/10.5966/sctm.2014-0254 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Ibrahim I, Oruganti SV, Pidaparti R (2017) Simulation of healing threshold in strain-induced inflammation through a discrete informatics model. IEEE J Biomed Heal Inform 22:941.  https://doi.org/10.1109/JBHI.2017.2669729 CrossRefGoogle Scholar
  19. 19.
    Je H, Kim M, Kwon H (2017) Bioluminescence assays for monitoring Chondrogenic differentiation and cartilage regeneration. Sensors 17:1306.  https://doi.org/10.3390/s17061306 CrossRefGoogle Scholar
  20. 20.
    Kachouie NN, Fieguth P, Ramunas J, Jervis E (2005) A model-based hematopoietic stem cell tracker. In: Kamel M, Campilho A (eds) Image analysis and recognition, Lecture No. Springer, Berlin/Heidelberg, pp 861–868CrossRefGoogle Scholar
  21. 21.
    Kishida M, Ford Versypt AN, Pack DW, Braatz RD (2013) Optimal control of one-dimensional cellular uptake in tissue engineering. Optimal Control Appl Methods 34:680–695.  https://doi.org/10.1002/oca.2047 CrossRefGoogle Scholar
  22. 22.
    Klosterhoff BS, Tsang M, She D et al (2017) Implantable sensors for regenerative medicine. J Biomech Eng 139:21009.  https://doi.org/10.1115/1.4035436 CrossRefGoogle Scholar
  23. 23.
    Kochaki SM (2017) Optimizing bioengineered vascular systems: a genetic algorithm approach. Utah State University, LoganGoogle Scholar
  24. 24.
    Konakovsky V, Clemens C, Müller M et al (2016) Metabolic control in mammalian fed-batch cell cultures for reduced lactic acid accumulation and improved process robustness. Bioengineering 3:5.  https://doi.org/10.3390/bioengineering3010005 CrossRefPubMedCentralGoogle Scholar
  25. 25.
    Kropp C, Massai D, Zweigerdt R (2017) Progress and challenges in large-scale expansion of human pluripotent stem cells. Process Biochem 59:244–254.  https://doi.org/10.1016/j.procbio.2016.09.032 CrossRefGoogle Scholar
  26. 26.
    Kupfer ME, Ogle BM (2015) Advanced imaging approaches for regenerative medicine: emerging technologies for monitoring stem cell fate in vitro and in vivo. Biotechnol J 10:1515–1528.  https://doi.org/10.1002/biot.201400760 CrossRefPubMedGoogle Scholar
  27. 27.
    Lei J, Levin SA, Nie Q (2014) Mathematical model of adult stem cell regeneration with cross-talk between genetic and epigenetic regulation. Proc Natl Acad Sci 111:E880–E887.  https://doi.org/10.1073/pnas.1324267111 CrossRefPubMedGoogle Scholar
  28. 28.
    Li S, Liu Y, Zhou Q et al (2014) A novel axial-stress bioreactor system combined with a substance exchanger for tissue engineering of 3D constructs. Tissue Eng Part C Methods 20:205–214.  https://doi.org/10.1089/ten.TEC.2013.0173 CrossRefPubMedGoogle Scholar
  29. 29.
    Liu Y-J, André S, Saint Cristau L et al (2017) Multivariate statistical process control (MSPC) using Raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW). Anal Chim Acta 952:9–17.  https://doi.org/10.1016/j.aca.2016.11.064 CrossRefPubMedGoogle Scholar
  30. 30.
    Lubowiecka I (2015) Mathematical modelling of implant in an operated hernia for estimation of the repair persistence. Comput Methods Biomech Biomed Engin 18:438–445.  https://doi.org/10.1080/10255842.2013.807506 CrossRefPubMedGoogle Scholar
  31. 31.
    Mattes R, Root D, Sugui M et al (2010) Monitoring viable cell density in bioreactors using near-infrared spectroscopy. Bioprocess J 8:38–41.  https://doi.org/10.12665/J84.Sugui CrossRefGoogle Scholar
  32. 32.
    Mehrian M, Guyot Y, Papantoniou I et al (2017) Maximizing neotissue growth kinetics in a perfusion bioreactor: an in silico strategy using model reduction and Bayesian optimization. Biotechnol Bioeng 115:617–629.  https://doi.org/10.1002/bit.26500 CrossRefPubMedGoogle Scholar
  33. 33.
    Mercier SM, Rouel PM, Lebrun P et al (2016) Process analytical technology tools for perfusion cell culture. Eng Life Sci 16:25–35.  https://doi.org/10.1002/elsc.201500035 CrossRefGoogle Scholar
  34. 34.
    Miller DC, Syamlal M, Mebane DS et al (2014) Carbon capture simulation initiative: a case study in multiscale modeling and new challenges. Ann Rev Chem Biomol Eng 5:301–323.  https://doi.org/10.1146/annurev-chembioeng-060713-040321 CrossRefGoogle Scholar
  35. 35.
    Namas RA, Bartels J, Hoffman R et al (2013) Combined in silico, in vivo, and in vitro studies shed insights into the acute inflammatory response in middle-aged mice. PLoS One 8:e67419.  https://doi.org/10.1371/journal.pone.0067419 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Pang K, Wan Y-W, Choi WT et al (2014) Combinatorial therapy discovery using mixed integer linear programming. Bioinformatics 30:1456–1463.  https://doi.org/10.1093/bioinformatics/btu046 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Peng T, Liu L, MacLean AL et al (2017) A mathematical model of mechanotransduction reveals how mechanical memory regulates mesenchymal stem cell fate decisions. BMC Syst Biol 11:55.  https://doi.org/10.1186/s12918-017-0429-x CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Ratkovic K (2016) Limitations in direct and indirect methods for solving optimal control problems in growth theory. Industrija 44:19–46.  https://doi.org/10.5937/industrija44-10874 CrossRefGoogle Scholar
  39. 39.
    Ribeiro FO, Gómez-Benito MJ, Folgado J et al (2015) In silico Mechano-chemical model of bone healing for the regeneration of critical defects: the effect of BMP-2. PLoS One 10:e0127722.  https://doi.org/10.1371/journal.pone.0127722 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Robertson DD, Sharma GB, Boyan BD (2016) Using mathematical modeling to design effective regenerative medicine strategies for Orthopaedics. J Am Acad Orthop Surg 24:e18–e19.  https://doi.org/10.5435/JAAOS-D-15-00621 CrossRefPubMedGoogle Scholar
  41. 41.
    Rüde U, Willcox K, McInnes LC et al (2016) Research and education in computational science and engineering, ColoradoGoogle Scholar
  42. 42.
    Runger GC, Alt FB, Montgomery DC (1996) Contributors to a multivariate statistical process control chart signal. Commun Stat Theory Methods 25:2203–2213.  https://doi.org/10.1080/03610929608831832 CrossRefGoogle Scholar
  43. 43.
    Schuerlein S, Schwarz T, Krziminski S et al (2017) A versatile modular bioreactor platform for tissue engineering. Biotechnol J 12:1600326.  https://doi.org/10.1002/biot.201600326 CrossRefGoogle Scholar
  44. 44.
    Smith D, Glen K, Thomas R (2016) Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation. Biotechnol Prog 32:215–223.  https://doi.org/10.1002/btpr.2199 CrossRefPubMedGoogle Scholar
  45. 45.
    Stepanyan V, Krishnakumar K (2012) Adaptive control with reference model modification. J Guid Control Dyn 35:1370–1374.  https://doi.org/10.2514/1.55756 CrossRefGoogle Scholar
  46. 46.
    Sun Y, Wang Q (2017) In-silico analysis on 3D biofabrication using kinetic monte carlo simulations. Adv Tissue Eng Regen Med Open Access 2(5):00045.  https://doi.org/10.15406/atroa.2017.02.00045 CrossRefGoogle Scholar
  47. 47.
    Sweeney PW, Walker-Samuel S, Shipley RJ (2018) Insights into cerebral haemodynamics and oxygenation utilising in vivo mural cell imaging and mathematical modelling. Sci Rep 8 (1):1373. https://doi.org/10.1038/s41598-017-19086-zGoogle Scholar
  48. 48.
    Turksoy K, Cinar A (2014) Adaptive control of artificial pancreas systems – a review. J Healthc Eng 5:1–22CrossRefGoogle Scholar
  49. 49.
    Venkateswarlu C (2016) Perspectives of process systems engineering. Austin Chem Eng 3:1022Google Scholar
  50. 50.
    Vieira AC, Guedes RM, Tita V (2015) Damage-induced hydrolyses modelling of biodegradable polymers for tendons and ligaments repair. J Biomech 48:3478–3485.  https://doi.org/10.1016/j.jbiomech.2015.05.025 CrossRefPubMedGoogle Scholar
  51. 51.
    Wahabzada M, Besser M, Khosravani M et al (2017) Monitoring wound healing in a 3D wound model by hyperspectral imaging and efficient clustering. PLoS One 12:e0186425.  https://doi.org/10.1371/journal.pone.0186425 CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Wu H, Read E, White M et al (2015) Real time monitoring of bioreactor mAb IgG3 cell culture process dynamics via Fourier transform infrared spectroscopy: implications for enabling cell culture process analytical technology. Front Chem Sci Eng 9:386–406.  https://doi.org/10.1007/s11705-015-1533-3 CrossRefGoogle Scholar
  53. 53.
    Yeh JS-M, Sennoga CA, McConnell E et al (2015) A targeting microbubble for ultrasound molecular imaging. PLoS One 10:e0129681.  https://doi.org/10.1371/journal.pone.0129681 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Ágata Paim
    • 1
  • Nilo S. M. Cardozo
    • 2
  • Patricia Pranke
    • 3
  • Isabel C. Tessaro
    • 4
  1. 1.Department of Chemical EngineeringUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
  2. 2.Simulation Laboratory, Department of Chemical EngineeringUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
  3. 3.Hematology and Stem Cell Laboratory, Faculty of PharmacyUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
  4. 4.Laboratory of Membrane Separation Processes, Department of Chemical EngineeringUniversidade Federal do Rio Grande do SulPorto AlegreBrazil

Personalised recommendations