Aim: Cell viability assays are critical for cell-based products. Here, we demonstrate a combined experimental and computational approach to identify fit-for-purpose cell assays that can predict changes in cell proliferation, a critical biological response in cell expansion. Materials & methods: Jurkat cells were systematically injured using heat (45 ± 1°C). Cell viability was measured at 0 h and 24 h after treatment using assays for membrane integrity, metabolic function and apoptosis. Proliferation kinetics for longer term cultures were modeled using the Gompertz distribution to establish predictive models between cell viability results and proliferation. Results & conclusion: We demonstrate an approach for ranking these assays as predictors of cell proliferation and for setting cell viability specifications when a particular proliferation response is required.

Plain language summary

In recent years, there has been a surge in the amount of cellular therapy products which have been engineered to treat patients with severe diseases. These cellular products use living cells to treat the disease, and the quality of these cell products is critical for ensuring product safety and effectiveness. Throughout the process of engineering and manufacturing these cell products, many cells can die or be in the process of dying, and the amount of dead cells in the product can impact product yield and quality. In any given cell product at any given time during the manufacturing process, cells are exposed to stresses, and these stresses can injure the cells through several mechanisms, leading to a range of cell death events that can follow different timelines. There are many existing assays which evaluate the health of the cells, known as cell viability assays, and these assays can be based on many different cell features that indicate a cell has been injured (i.e., cell membrane permeability, changes in cell metabolism, molecular markers for cell death). These cell viability assays provide different insights into the state of cell health/injury based on what cell features are being evaluated and the timing at which the viability measurements are taken, and some viability assays may be more appropriate than others for specific applications. Therefore, a method is needed to appropriately select cell viability assays that are designed to evaluate injuries to cells that occur in specific bioprocess. In this series of studies, we used a range of analytical methods to study the number of living and dead cells in a series of cell populations that we treated to induce damage to the cells, reducing their ability to grow. We then used mathematical models to determine the relationship between cell viability measurements and cell growth over time, and used the results to determine the sensitivity of the viability assays to changes in cell growth. We used a specific cell line in this example, but this technique can be applied to any cell line or cell sample population and different types of injuries can be applied to the cells. This approach can be used by manufacturers of cell-based products and therapies to identify cell viability assays that are meaningful for monitoring the production of cells and characterizing product quality.

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Papers of special note have been highlighted as: • of interest; •• of considerable interest

References

1. Mueller H, Kassack MU, Wiese M. Comparison of the usefulness of the MTT, ATP, and calcein assays to predict the potency of cytotoxic agents in various human cancer cell lines. J. Biomol. Screen 9(6), 506–515 (2004).
Medline CASGoogle Scholar
2. Petty RD, Sutherland LA, Hunter EM, Cree IA. Comparison of Mtt and Atp-Based Assays for the Measurement of Viable Cell Number. J. Biolum. Chemilum. 10(1), 29–34 (1995).
Medline CASGoogle Scholar
3. Weisenthal LM, Dill PL, Kurnick NB, Lippman ME. Comparison of Dye Exclusion Assays with a Clonogenic-Assay in the Determination of Drug-Induced Cyto-Toxicity. Cancer Res. 43(1), 258–264 (1983).
Medline CASGoogle Scholar
4. Adan A, Kiraz Y, Baran Y. Cell Proliferation and Cytotoxicity Assays. Curr. Pharm. Biotechnol. 17(14), 1213–1221 (2016).
Medline CASGoogle Scholar
5. International Organization for Standardization. ISO 20391-1:2018 Biotechnology – Cell counting – Part 1: general guidance on cell counting methods. ISO/TC 276 Biotechnology/WG3 5–9 (2018).
Google Scholar
6. Lin-Gibson S, Sarkar S, Ito Y. Defining quality attributes to enable measurement assurance for cell therapy products. Cytotherapy 18(10), 1241–1244 (2016). •• Proposes a generalized framework for establishing critical quality attributes of cell therapy products. It highlights the need to establish well-designed, fit-for-purpose measurements as well as the use of appropriate statistical methods.
MedlineGoogle Scholar
7. Bauer SR. Stem Cell-based Products in Medicine: FDA Regulatory Considerations. In: Handbook of Stem Cells. Lanza R (Ed.). Elsevier, MA, USA, 805–814 (2004).
Google Scholar
8. Hoogendoorn KH, Crommelin DJA, Jiskoot W. Formulation of Cell-Based Medicinal Products: A Question of Life or Death? J. Pharm. Sci. 110(5), 1885–1894 (2021).
Medline CASGoogle Scholar
9. Chan LL-Y, Kuksin D, Laverty DJ, Saldi S, Qiu J. Morphological observation and analysis using automated image cytometry for the comparison of trypan blue and fluorescence-based viability detection method. Cytotech. 461–473 doi: 10.1007/s10616-014-9704-5(67) (2015). • Compares two commonly used cell viability methods and demonstrates the impact of exposure to the chemicals on the resulting measurement. It emphasizes the concept of identifying an appropriate assay type which will reflect the sample's properties without properties of the dye confounding the measurement.
MedlineGoogle Scholar
10. Cadena-Herrera D, Esparza-De Lara JE, Ramírez-Ibañez ND et al. Validation of three viable-cell counting methods: manual, semi-automated, and automated. Biotechnol. Rep. 7, 9–16 (2015).
MedlineGoogle Scholar
11. Cummings BS, Schnellmann RG. Measurement of Cell Death in Mammalian Cells. Curr. Protocols Pharmacol. 25(1), 1–30 (2004).
Google Scholar
12. Mascotti K, Mccullough J, Burger SR. HPC viability measurement: trypan blue versus acridine orange and propidium iodide. Transfusion 40, 693–696 (2000).
Medline CASGoogle Scholar
13. Chong EA, Levine BL, Grupp SA et al. CAR T cell viability release testing and clinical outcomes: is there a lower limit? Blood 134(21), 1873–1875 (2019). •• Evaluates efficacy of the cell therapy product tisagenlecleucel, comparing viability measurements to patient outcome and finding no correlation between those administered product >80% viability and those administered product of lower than 80% viability. This evidence was used to argue for a lower (70%) viability release criterion and also calls for a closer study of release criteria for CAR-T products, driven by patient response.
MedlineGoogle Scholar
14. Pierce L, Sarkar S, Chan LL-Y, Lin B, Qiu J. Outcomes from a cell viability workshop: fit-for-purpose considerations for cell viability measurements for cellular therapeutic products. Cell and Gene Therapy Insights 7, 551–569 (2021). •• This review of a cell viability workshop reports the results of a survey of cell therapy industry needs for viability measurements and highlights the concept of fit-for-purpose measurement needs. It demonstrates the concept of identifying and selecting a fit-for-purpose method through evaluating measurement method considerations, biological considerations, and sample considerations.
Google Scholar
15. Milleron RS, Bratton SB. Heat Shock Induces Apoptosis Independently of Any Known Initiator Caspase-activating Complex. J. Biol. Chem. 281(25), 16991–17000 (2006).
Medline CASGoogle Scholar
16. Velichko AK, Markova EN, Petrova NV, Razin SV, Kantidze OL. Mechanisms of heat shock response in mammals. Cell. Mol. Life Sci. 70(22), 4229–4241 (2013).
Medline CASGoogle Scholar
17. Richter K, Haslbeck M, Buchner J. The Heat Shock Response: life on the Verge of Death. Molecular Cell 40(2), 253–266 (2010).
Medline CASGoogle Scholar
18. Tran SEF, Meinander A, Holmström TH et al. Heat stress downregulates FLIP and sensitizes cells to Fas receptor-mediated apoptosis. Cell Death & Differentiation 10(10), 1137–1147 (2003).
CASGoogle Scholar
19. International Organization for Standardization. ISO 20391-2 2019: Biotechnology – Cell counting – Part 2: Experimental design and statistical analysis to quantify counting method performance. ISO/TC 276 Biotechnology/WG3 8–22 (2019).
Google Scholar
20. Vaghi C, Rodallec A, Fanciullino R et al. Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors. PLOS Computational Biology 16(2), e1007178 (2020). • Explains the modeling of proliferation kinetics using the Gompertz model and reduced Gompertz model and explains the key differences in parameters for each. It explains how the Gompertz model provides a superior fit to populations data as compared to the logistic and exponential models.
Medline CASGoogle Scholar
21. Winsor CP. The Gompertz curve as a growth curve. Proc. Natl Acad. Sci. USA 18, 1–8 (1932).
Medline CASGoogle Scholar
22. Chatterjee T, Chatterjee BK, Majumdar D, Chakrabarti P. Antibacterial effect of silver nanoparticles and the modeling of bacterial growth kinetics using a modified Gompertz model. Bba-Gen Subjects 1850(2), 299–306 (2015).
Medline CASGoogle Scholar
23. Horowitz J, Normand MD, Corradini MG, Peleg M. Probabilistic Model of Microbial Cell Growth, Division, and Mortality. Appl. Environ. Microbiol. 76(1), 230–242 (2010).
Medline CASGoogle Scholar
24. Van Engeland M, Nieland LJ, Ramaekers FC, Schutte B, Reutelingsperger CP. Annexin V-affinity assay: a review on an apoptosis detection system based on phosphatidylserine exposure. Cytometry 31(1), 1–9 (1998).
Medline CASGoogle Scholar
25. Mandrekar J. Receiver operating characteristic curve in diagnostic test assessment. J. Thoracic Oncol. 5(9), 1315–1316 (2010). • Explains the utility of using a receiver operating characteristic curve in biomarker analysis applications.
Google Scholar
26. Riss T, Niles A, Moravec R, Karassina N, Vidugiriene J. Cytotoxicity assays: in vitro methods to measure dead cells. In: Assay Guidance Manual [Internet]. MD, USA, 1–16 (2019).
Google Scholar
27. Green D, Evan G. A matter of life and death. Cancer Cell 1(1), 19–30 (2002).
Medline CASGoogle Scholar
28. Guo M, Hay B. Cell proliferation and apoptosis. Curr. Opin. Cell Biol. 11(6), 745–752 (1999).
Medline CASGoogle Scholar
29. Bersenev A, Kili S. Management of ‘out of specification’ commercial autologous CAR-T cell products. Cell and Gene Therapy Insights 4(11), 1051–1058 (2018). • This paper gives several recent examples of cell therapy products which fell out of specification, highlights the idea that CAR-T products have inherent manufacturing variabilities, and stresses that these variabilities may not necessarily be reflective of product outcome. It calls for the development of an alternate approach to setting CAR-T manufacturing specifications.
Google Scholar
30. Özkaya A, Geyik C. From viability to cell death: claims with insufficient evidence in high-impact cell culture studies. PLOS ONE 17(2), e0250754 1–11 (2022).
Medline CASGoogle Scholar
31. Komatsu H, Qi M, Gonzalez N et al. A Multiparametric Assessment of Human Islets Predicts Transplant Outcomes in Diabetic Mice. Cell Transplant. 30, 9636897211052291 (2021). • This paper gives an example of the value of using multiparameter approaches to determining a biological functional outcome from bioassay measurements.
Google Scholar
32. Altman N, Krzywinski M. Predicting with confidence and tolerance. Nat. Methods 15(11), 843–845 (2018).
Medline CASGoogle Scholar

Vol. 19, No. 1

Supplemental Materials

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History

Received 14 April 2023

Accepted 6 December 2023

Published online 22 January 2024

Published in print January 2024

Information

Keywords

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/suppl/10.2217/rme-2023-0154

Author contributions

L Pierce and H Anderson are co-lead authors on this paper. SR Bauer and Su Sarkar are co-principal investigators on this paper. Su Sarkar and SR Bauer contributed to the preliminary concept and design. Su Sarkar, Sw Sarkar, H Anderson and L Pierce contributed to manuscript writing. H Anderson and L Pierce acquired the data and contributed to data analysis. SR Bauer, Sw Sarkar and S Sarkar made substantial contributions to the concepts and to data interpretation. Sw Sarkar performed computational modeling and statistical analyses.

Acknowledgments

The authors thank J Elliott (NIST), S Lund (NIST), N Lin (NIST), S Lin-Gibson (NIST), M Moos (FDA) and N Bhattarai (FDA) for critical reading and comments of the manuscript in draft. The authors acknowledge C Simon (NIST) and N Lin (NIST) for insightful discussion during research. The authors also thank K Sung (FDA) and S Sawyer (FDA) for their support in the preparation of the manuscript draft. Current affiliation for Steven Bauer is Wake Forest Institute for Regenerative Medicine, Winston-Salem, NC. Current affiliation for Swarnavo Sarkar is Georgetown Lombardi Comprehensive Cancer Center, Washington, DC.

NIST disclaimer

Certain commercial equipment, instruments or materials are identified in this paper to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose. Official contribution of the National Institute of Standards and Technology; not subject to copyright in the United States.

Financial disclosure

Funding for this project included Center for Biological Evaluation and Research (CBER) FDA 21st Century Research support and CBER Office of Therapeutic Products operating funds research support. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, stock ownership or options and expert testimony.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations.

Data sharing statement

The code for computational modeling and analysis is available at https://github.com/sarkar-s/ProVia.git. Data sets are publicly available at https://datapub.nist.gov/od/id/mds2-2524.

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Experimental and computational approach to establish fit-for-purpose cell viability assays

Abstract

Plain language summary

Graphical abstract

References