Apoptosis‐based method for determining lot sizes in the filling of human‐induced pluripotent stem cells

Standardization in process design and operation is needed in the commercial production of human‐induced pluripotent stem (hiPS) cells. Lot sizing in the filling of hiPS cells into containers, a part of the preservation process, also needs to be standardized because of the temporal changes in cell quality during the process. Here, we present an apoptosis‐based method that can determine lot sizes in the filling of hiPS cells considering temporal changes in cell quality. Two indicators were developed for (i) the cell quality change using reactive oxygen species (ROS) measurement and (ii) the cell survival and probability of filling success, which are parts of the lot‐sizing problem. Using computational simulation, a map out of the optimal lot size was produced that minimized the expected production costs at a given cell demand and an acceptable change in cell quality. At a filling temperature of 4°C, the largest possible lot size was calculated as 6 L (corresponding to a filling time of 125 min). The results of a sensitivity analysis recommended cold filling or the addition of an antioxidant. The presented method is effective to determine the lot size considering the change in cell quality during filling. The study uniquely combines the experimental results with mathematical modeling and computational simulation techniques. The map out of the optimal lot size could guide the development of industrial filling processes of hiPS cells.

[1]  Ruth E. Chambers Topics , 1973, Seminars in Perinatology.

[2]  Renzo Akkerman,et al.  Classifying and modeling setups and cleanings in lot sizing and scheduling , 2017, Eur. J. Oper. Res..

[3]  Yeu-Shiang Huang,et al.  Determination of optimal lot size and production rate for multi-production channels with limited capacity , 2015, Int. J. Syst. Sci..

[4]  Rongbing Huang,et al.  Integrated pricing and lot-sizing decision in a two-echelon supply chain with a finite production rate , 2015 .

[5]  Hirokazu Sugiyama,et al.  Economic Model for Lot-Size Determination in Pharmaceutical Injectable Manufacturing , 2019, Journal of Pharmaceutical Innovation.

[6]  G. Bitran,et al.  Computational Complexity of the Capacitated Lot Size Problem , 1982 .

[7]  Masahiro Kino-oka,et al.  A distribution-based approach for determining lot sizes in the filling of human-induced pluripotent stem cells , 2019, Regenerative therapy.

[8]  M. Odell,et al.  The hormone-releasing intrauterine device has benefits over hysterectomy for many women. Press release. , 2009, Acta obstetricia et gynecologica Scandinavica.

[9]  Robert M. Vogel,et al.  Mitochondrial origins of fractional control in regulated cell death , 2019, Nature Communications.

[10]  Jacob Cohen,et al.  A power primer. , 1992, Psychological bulletin.

[11]  M. Kino‐oka,et al.  Kinetic analysis of cell decay during the filling process: Application to lot size determination in manufacturing systems for human induced pluripotent and mesenchymal stem cells , 2018 .

[12]  K. Machida,et al.  Characterization of in vivo tumorigenicity tests using severe immunodeficient NOD/Shi-scid IL2Rγnull mice for detection of tumorigenic cellular impurities in human cell-processed therapeutic products , 2015, Regenerative therapy.

[13]  P. Lachenbruch Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[14]  Tak Yee Aw,et al.  Reactive oxygen species, cellular redox systems, and apoptosis. , 2010, Free radical biology & medicine.

[16]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[17]  Dieter Eibl,et al.  Single-Use Technology in Biopharmaceutical Manufacture , 2011 .

[18]  M. Kino‐oka,et al.  Suppression of time-dependent decay by controlling the redox balance of human induced pluripotent stem cells suspended in a cryopreservation solution , 2020 .

[19]  Hirokazu Sugiyama,et al.  Slow freezing process design for human induced pluripotent stem cells by modeling intracontainer variation , 2020, Comput. Chem. Eng..

[20]  F. Rombouts,et al.  Modeling of the Bacterial Growth Curve , 1990, Applied and environmental microbiology.

[21]  Hemanthram Varadaraju,et al.  Downstream Technology Landscape for Large-Scale Therapeutic Cell Processing , 2013 .

[22]  A. Hubel,et al.  Algorithm‐driven optimization of cryopreservation protocols for transfusion model cell types including Jurkat cells and mesenchymal stem cells , 2017, Journal of tissue engineering and regenerative medicine.

[23]  Joana Galvao,et al.  Unexpected low‐dose toxicity of the universal solvent DMSO , 2014, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[24]  Elisabet Capón-García,et al.  Rigorous approach to scheduling of sterile drug product manufacturing , 2016, Comput. Chem. Eng..

[25]  Masahiko Hirao,et al.  Erratum to: Decision-Support Method for the Choice Between Single-Use and Multi-Use Technologies in Sterile Drug Product Manufacturing , 2017, Journal of Pharmaceutical Innovation.

[26]  F. Gòdia,et al.  A general artificial neural network for the modelization of culture kinetics of different CHO strains , 2001, Cytotechnology.