A distribution-based approach for determining lot sizes in the filling of human-induced pluripotent stem cells.
Regen Ther 2019;
12:94-101. [PMID:
31890772 PMCID:
PMC6933470 DOI:
10.1016/j.reth.2019.04.012]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 03/14/2019] [Accepted: 04/18/2019] [Indexed: 01/19/2023] Open
Abstract
Introduction
Toward the commercial production of human-induced pluripotent stem (hiPS) cells, the process design and operation have to be standardized. Considering the change in cell quality during filling of the hiPS cells into containers, lot sizing during filling also needs to be standardized.
Methods
We present a distribution-based approach that can be used for determining the lot sizes in the filling of hiPS cells by considering change in cell quality during filling. The approach describes the “survival capability” of the cells as a continuous probability distribution, and expresses the change in quality during filling by “trimming” the distribution.
Results
A lognormal distribution was assumed as the survival capability distribution of the cells that were to be filled. The distributions after different filling times were calculated, which were compared with the distribution of the initial filling time regarding the yield of the cells and the similarity. These conditions served as strong quality constraints in determining an economically optimal lot size.
Conclusions
The presented conceptual approach would be effective in determining the lot size considering the change in cell quality during filling. For actual application, measuring the distribution information on the survival capability of hiPS cells is encouraged.
A novel lot sizing approach was developed for filling hiPS cells.
Survival capability of hiPS cells was assumed to follow a lognormal distribution.
The distributions of survival capability were calculated to determine the lot size.
Monte Carlo simulation was useful in comparing distributions.
A case study demonstrated the effectiveness of the approach.
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