Thomas R J, Hourd P C, Williams D J
Healthcare Engineering Group, Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK.
J Biotechnol. 2008 Sep 10;136(3-4):148-55. doi: 10.1016/j.jbiotec.2008.06.009. Epub 2008 Jul 11.
The translation of experimental cell-based therapies to volume produced commercially successful clinical products requires the development of capable, economic, scaleable (and therefore frequently necessarily automated) manufacturing processes. Application of proven quality engineering techniques will be required to interrogate, optimise, and control in vitro cell culture processes to regulatory and clinically acceptable specifications. We have used a Six Sigma inspired quality engineering approach to design and conduct a factorial screening experiment to investigate the expansion process of a population of primary bone marrow-derived human mesenchymal stem cells on a scaleable automated cell culture platform. Key cell culture process inputs (seeding density, serum concentration, media quantity and incubation time) and important cell culture process responses (cell number and the expression of alkaline phosphatase, STRO-1, CD105 and CD71) were identified as experimental variables. The results rank the culture factors and significant culture factor interactions by the magnitude of their effect on each of the process responses. This level of information is not available from conventional single factor cell culture studies but is essential to efficiently identify sources of variation and foci for further process optimisation. Systematic quality engineering approaches such as those described here will be essential for the design of regulated cell therapy manufacturing processes because of their focus on identifying the sources of and the control of variation, an issue that is at the core of current Good Manufacturing Practice.
将基于细胞的实验性疗法转化为批量生产且商业上成功的临床产品，需要开发高效、经济、可扩展（因此通常必然是自动化的）制造工艺。需要应用经过验证的质量工程技术，以将体外细胞培养工艺探究、优化并控制到符合监管和临床要求的规格。我们采用了受六西格玛启发的质量工程方法，来设计并开展一项析因筛选实验，以研究在可扩展的自动化细胞培养平台上，原代人骨髓间充质干细胞群体的扩增过程。关键细胞培养工艺输入（接种密度、血清浓度、培养基量和孵育时间）以及重要的细胞培养工艺响应（细胞数量以及碱性磷酸酶、STRO-1、CD105和CD71的表达）被确定为实验变量。结果根据各培养因素及其相互作用对每个工艺响应的影响程度，对培养因素及其显著的相互作用进行了排序。这种信息水平是传统的单因素细胞培养研究所无法提供的，但对于有效识别变异来源和进一步工艺优化的重点至关重要。由于关注于识别变异来源和进行变异控制，像本文所述的系统性质量工程方法对于受监管的细胞治疗制造工艺设计至关重要，而变异问题是当前良好生产规范的核心。
