Logical process designs for stem cell manufacturing: computational support tools for improved cost-effectiveness

In this editorial, Catia Bandeiras et al discuss computational models for designing stem cell manufacturing processes.

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Sep 07, 2017
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Catia Bandeiras1,3, Stan N. Finkelstein1,2, Frederico C. Ferreira3,4, Joaquim M. S. Cabral3,4

1MIT institute for Data, Systems and Society, Massachusetts Institute of Technology, Massachusetts Ave., Cambridge, MA 02139, USA

2Division of Clinical Informatics, Harvard Medical School, 1330 Beacon Street, Brookline, MA 02446, USA

3Institute for Bioengineering and Biosciences, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal

4The Discoveries Centre for Regenerative and Precision Medicine, Lisbon Campus, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal

Stem cells potential and reality - where do we stand?

Stem cell-based therapies may be a breakthrough for several unmet medical needs. Their efficacy has already been proven for graft vs host disease, osteoarthritis, acute myocardial infarction and diabetic retinopathy, and clinical trials on several other prospective indications on the field of neurological diseases, diabetes and autoimmune diseases are also being explored (1,2,3).

The global market for cell based-therapies currently generates annual profits of more than US$1 billion, with an estimated revenue of US$20 billion in 2025 (1,3). In particular, stem cells have regenerative and immunomodulatory potential to address a diverse number of unmet medical needs. Over 4300 clinical trials related to stem cells as an intervention have been reported until now, with the majority of the trials being related with adult stem cells, such as the hematopoietic (1359 trials) and mesenchymal (654).

Pluripotent stem cells, such as induced pluripotent (8 trials) and embryonic stem cells (24 trials) are in an earlier stage of development (clinicaltrials.gov, accessed 11/08/2017); despite the large interest, regulatory approval for these therapies has been difficult. However, currently, there are five approved products in specific countries and three reimbursed products, with the price of one course of therapy rising up to dozens or even hundreds of thousands of dollars (2).

Widespread adoption of stem cell therapies – process design challenges?

The widespread application of stem cell-based therapies would benefit from reducing reimbursement price, while maintaining product profitability. Moreover, the set reimbursement price must cover the research and development and clinical trials costs, but also the manufacturing costs of such therapies, which are still extremely high when compared with conventional pharma or biotherapeutic products.

These large costs are due to largely manual product handling and manipulation (3), product and process variability, impractical scaling-up of production (4), use of xenogeneic materials, high culture media costs (5) and high costs of quality control (6). Commonly used small scale planar expansion platforms, with cells cultivated in 2D surfaces, such as T-flasks, are not enough to meet market demands and ensuring maintenance of the therapeutic potential of the product. Apart from difficult scaling-up, they do not allow control and monitoring of culture parameters, lead to development of concentration gradients and require a lot of incubation space and manual operation (6).

Therefore, other manufacturing methods need to be adopted in order to provide more cost competitive therapies (3) and with higher possibilities of being lucrative upon the thresholds for reimbursement that the payers from several countries impose on the therapies (7).

Manufacturing processes – are current designs cost efficient?

Current process design is guided by the envisioned demand and compliance with regulatory requirements. However, design of manufacturing processes streamlining for cost efficiency, while preparing a new therapy for approval and reimbursement, is often neglected. After initial regulatory approval, further process manufacturing changes are usually administrative and their validation is cost prohibitive.

In order to have a more thorough and less time consuming risk assessment of changes in process design, computational modeling of bioprocessing and bioeconomics is of great value to consider the impact of those changes on the process costs and quality of the final products, given biological and technological parameters informed by past experiments.

Computational decision support tools can contribute to faster, safer and less expensive production of therapies, namely through design of logical processes and optimization of several manufacturing parameters to achieve the lowest cost of goods (CoG) for a given demand of doses and lots of the therapy. They can also provide recommendations on which unit operations have higher impact on the process costs and need to be further optimized. These tools can also allow manufacturers to select, for a given demand of therapeutic doses, the production configuration that ensures manufacture profitability within and maximum reimbursement price thresholds accessible to relevant payers.

Logical designs are need – what is the role of computational modeling?

The area of computational modeling for stem cell manufacturing is a recent one and there are a few academic contributions in the field, either using commercial flowsheeting software, like Superpro Designer (8) or on custom-made code (4,5,9,10). The published models are focused on either the simulation of bioprocessing of allogeneic mesenchymal stem cell (MSC) based therapies (4,9,10) or the simulation of manufacturing of induced pluripotent stem cell derived differentiated cells, such as cardiomyocytes (8) and neurons (5). In terms of manufacturing challenges, the limitations of 2D culture systems were addressed in terms of CoG of expansion and the inability to meet high dose demands for doses containing high numbers of cells.

The process strategies for overcoming these limitations include the automation for processing of large multi stack systems as opposed to regular T-flasks and the use of stirred tank bioreactors with microcarriers to increase expansion area. Process modeling allows evaluation of the cost effectiveness of stem cell production in suspension over planar technologies, as well as the selection of the best combinations between upstream and downstream technologies (9,10). While most of these studies focused on deterministic parameters and employed sensitivity analysis to determine the impact of key model parameters on final costs, stochasticity was also considered with appropriate statistical distributions through the Monte Carlo method (5,10).

These tools can be employed for a multitude of manufacturing problems and ultimately propose technological and pricing changes in materials employed in cell manufacturing. Ongoing research in our group is related to the evaluation of the impact of different culture medium formulations on the growth rates of cells across multiple passages and technologies to allow a more biologically based evaluation of the impact of process changes on the economics of the process (11,12).

Further actions – comprehensive computational models?

Modeling frameworks that are able to integrate stem cell manufacturing bioeconomics and reimbursement are interesting to answer and adapt the reimbursement and cost structure of stem cell therapies. Previous works mentioned the need to reduce CoG in manufacturing of allogeneic MSC for those to become commercially viable (9). We have recently discussed together biological and economic effects on using xenogeneic culture media vs xeno-free media supporting a logical decision, beyond safety considerations, to move towards xeno-free media.

Additional ongoing studies discuss effects of required cell dosage and cell source considering clinical trial data and stem cell biological behavior (11,12). Previous studies considered the use of planar vs 3D suspension cultures (11,12); this discussion is readdressed to quantitatively highlight the key process innovations need to ensure profitability of scaled-up stem cell manufacture processes. These are critical examples illustrating the potential use of computing modeling results to process design decision making. Computational modeling can be particularly useful to assess the payers benefit when comparing conventional and regenerative therapeutic approaches.


Computational models for bioeconomics of stem cell therapy manufacturing and reimbursement are still in their early stages and have been proven to be useful to evaluate the impact of technological changes in bioprocessing in the economics of manufacturing of different types of stem cells, to evaluate the profitability of manufactured therapies against a specific reimbursement threshold. More contributions in the field would be welcome, particularly the ones connecting manufacturing and cost effectiveness models including stochastic processes for a more complete and risk based approach to stem cell therapies project management.


The authors acknowledge funding from (i) Portuguese Foundation for Science and Technology (FCT), Portugal State Budget and (ii) Lisbon’s Regional Operational Program 2014-2020 (PORL2020), European structural and investment funds – European commission. Namely, the authors thanks PORL2020 and FCT funding of iBB-Institute for Bioengineering and Biosciences (FCT reference: UID/BIO/04565/2013 and POL2020 reference 007317, including iBB grant iBB/2015/18), and the Research and Development Project Grant from the Joint Activities Program “PRECISE”, reference 016394.

The authors also thank FCT for funding of the PhD grant PD/BD/105868/2014 and MIT Portugal Program, as well to the European Commission by funding of Discoveries Centre for Regenerative and Precision Medicine, Program Teaming Health H2020-WIDESPREAD-2014, reference 664558.


  1. Coopman K, Medcalf N. From production to patient: challenges and approaches for delivering cell therapies. In: StemBook [Internet]. Cambridge (MA): Harvard Stem Cell Institute; 2014.
  2. Foley L, Whitaker M. Concise review: cell therapies: the route to widespread adoption. Stem Cells Transl Med. 1:5, 438-447 (2012)
  3. Heathman TRJ, Nienow AW, McCall MJ, Coopman K, Kara B, Hewitt CJ. The translation of cell-based therapies: clinical landscape and manufacturing challenges. Regen Med. 10:1, 49-64 (2015)
  4. Simaria A, Hassan S, Varadaraju H, Rowley J, Warren K, Vanek P et al. Allogeneic cell therapy bioprocess economics and optimization: single-use cell expansion technologies. Biotechnol. Bioeng. 111, 69-83 (2014)
  5. Jenkins MJ, Bilsland J, Allsopp TE, Ho SV, Farid SS. Patient-specific hiPSC bioprocessing for drug screening: Bioprocess economics and optimization. Biochem. Eng. J. 108, 84-97 (2016)
  6. dos Santos F, Andrade PZ, da Silva CL, Cabral JMS. Scaling-up ex vivo expansion of mesenchymal stem/stromal cells for cellular therapies. In: Mesenchymal Stem Cell Therapy, New York (NY): Humana Press (2013)
  7. Mount NM, Ward SJ, Kefalas P, Hyllner J. Cell-based therapy technology classifications and translational challenges. Phil. Trans. R. Soc. B 370, 20150017 (2015)
  8. Darkins CL, Mandenius C-F. Design of large-scale manufacturing of induced pluripotent stem cell derived cardiomyocytes. Chem Eng Res Deg. 92, 1142-1152 (2014)
  9. Hassan S, Simaria A, Varadaraju H, Gupta S, Warren K, Farid SS. Allogeneic cell therapy bioprocess economics and optimization: downstream processing decisions. Regen Med. 10:5, 591-609 (2015)
  10. Hassan S, Huang H, Warren K, Mahdavi B, Smith D, Jong S et al. Process change evaluation framework for allogeneic cell therapies: impact on drug development and commercialization. Regen Med. 11:3, 287-305 (2016)
  11. Bandeiras C, Cabral JMS, Finkelstein SN, Ferreira FC. A computational bioprocess and bioeconomics study on mesenchymal stem cells manufacturing. Scale-up and Manufacturing of Cell Based Therapies V, January 2017, San Diego, USA.
  12. Bandeiras C, Cabral JMS, Finkelstein SN, Ferreira FC. A bioprocess and bioeconomics model of manufacturing of mesenchymal stem cells. ISSCR 2017 Annual Meeting, June 2017, Boston, USA.
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Go to the profile of James L. Sherley, M.D., Ph.D.

Ultimately, the capability of computational methods for optimizing the manufacture of adult tissue stem cells and reducing production costs can only be as good as the assumptions upon which the computational models are built and the quality of the data used to develop and evaluate the fidelity of the computational models to actual biological and technical factors that govern the expansion of adult tissue stem cells in culture.  If these crucial foundations are erroneous, all the rest of the analyses will be similarly erroneous, producing misleading impressions, and wasting significant effort and resources.

Such is the state of affairs with much of current adult tissue stem cell manufacturing.  Two crucial foundations for success are either ignored or not recognized.  The first error is treating the production of adult tissue stem cells with conventions established for homogeneous cells like fermented bacteria, CHO cells, or Ab-producing hybridoma cell lines.  This is a gross error, as cultures based on adult tissue stem cells are inherently heterogeneous, containing cells in different states of lineage-related differentiation.  The desired tissue stem cells are invariably only a minute fraction of these populations at the start of their culture; and because of tissue stem cells'  inherent asymmetric self-renewal state, they are diluted to even smaller fractions during current expansion processes. 

The second error flows from the first.  It is treating the total nucleated cell count  or a larger fraction of it,  erroneously, as the stem cell-specific count, which in all previous and current processes is never the case.  This situation exists because biomarkers that are found on adult tissue stem cells, and used to quantify them for manufacturing procedures, are also expressed by more abundant committed progenitors that are produced by tissue stem cells.

Until the fields of stem cell biology and stem cell medicine grasp, understand, acknowledge, and design manufacturing processes based on these principles of asymmetric stem cell kinetics, little progress will be made to reduce this critical  barrier to success in stem cell medicine...no matter what new sophisticated optimization approaches are imagined or applied.




Lee, H.-S., Crane, G. G., Merok, J. R., Tunstead, J. R., Hatch, N. L., Panchalingam, K., Powers, M. J., Griffith, L. G., and Sherley, J. L. (2003) "Clonal Expansion of Adult Rat Hepatic Stem Cell Lines by Suppression of Asymmetric Cell Kinetics (SACK)", Biotech. & Bioeng. 83, 760-771.

Paré, J.-F. and Sherley, J. L. (2006) “Biological Principles for Ex Vivo Adult Stem Cell Expansion,” in Current Topics in Developmental Biology, ed. G. Schatten, Elsevier, Inc. (San Diego), Vol. 73, pp. 141-171.

Taghizadeh, R. R. and Sherley, J. L. (2009) “Expanding the Therapeutic Potential of Umbilical Cord Blood Hematopoietic Stem Cells,” in Perinatal Stem Cells, eds. C. L. Cetrulo, K. J. Cetrulo, and C. L. Cetrulo, Jr., Wiley-Blackwell (Hoboken) pp. 21-40.

Huh, Y. H., King, J., Cohen, J. and Sherley, J. L. (2011) “SACK-Expanded Hair Follicle Stem Cells Display Asymmetric Nuclear Lgr5 Expression with Non-Random Sister Chromatid Segregation,” Sci. Rep. 1, 175; DOI: 10.1038/srep00176.

Paré, J.-F., and Sherley, J. L. (2011) “Culture Environment-Induced Pluripotency of  SACK-Expanded Tissue Stem Cells,” J. Biomed. and Biotechnol. vol. 2011, Article ID 312457, 12 pp., 2011. doi:10.1155/2011/312457.

Paré, J.-F., and Sherley, J. L. (2013) “Ex vivo Expansion of Human Pancreatic Distributed Stem Cells by Suppression of Asymmetric Cell Kinetics (SACK),” J. Stem Cell Res. & Therapy 3: 149. doi:10.4172/2157-7633.1000149.

Sherley, J. L. (2014) “Accelerating Progress in Regenerative Medicine by Advancing Distributed Stem Cell-Based Normal Human Cell Biomanufacturing,” Pharm. Anal. Acta 5: 286. doi: 10.4172/2153-2435.1000286

Panchalingam, K., Noh, M., Huh, Y. H., and Sherley, J. L. (2016) “Distributed Stem Cell Kinetotoxicity: A New Concept to Account for the Human Carcinogenicity of Non-genotoxic Toxicants,” in Human Stem Cell Toxicology, Issues in Toxicology No. 29,  ed., J. L. Sherley, Royal Society of Chemistry (London).

James L. Sherley, M.D., Ph.D.


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