Sagmeister Patrick, Wechselberger Patrick, Herwig Christoph
Vienna University of Technology, Institute of Chemical Engineering, Research Area Biochemical Engineering, Vienna, Austria.
PDA J Pharm Sci Technol. 2012 Nov-Dec;66(6):526-41. doi: 10.5731/pdajpst.2012.00889.
Recent initiatives summarized under the term quality by design (QbD) urge for science and risk-based pharmaceutical bioprocess development strategies. One of the most accepted concepts communicated by the regulatory authorities is the concept of design space-a multidimensional combination of critical process parameter (CPP) ranges where the quality acceptance criteria (critical quality attributes, CQAs) are fulfilled. Current design space development along QbD principles focuses on the investigation of statistical CPP/CQA interactions, while the biological mechanistic of this interaction is hardly considered. Furthermore, the plethora of available online and offline data gathered within design space development is typically not used for the demonstration of process understanding. Here we present a methodology about how typical recorded process data can be processed and used to gather mechanistic process knowledge within upstream design space development, without the need for further experiments or additional analytical procedures. Data derived from online and offline measurements (off gas quantification, air flows, substrate flows, biomass dry cell weight measurements) were processed into scale-independent information in the form of specific rates and yield coefficients (data processing). Subsequently, the obtained information was regressed with the investigated process parameters aiming at the investigation of mechanistic interactions (information processing). The power of the presented approach was demonstrated on a multivariate study involving two process parameters (induction phase temperature and induction phase feeding strategy) aiming at the production of recombinant product in an Escherichia coli K12 strain. The knowledge successfully extracted indicated a time dependency of the metabolic load posed on the system, a possible down regulation of the promoter at reduced temperatures, and reduced cell lysis at higher specific feeding regimes. The presented data and information processing methodology for mechanistic process knowledge extraction is fully complementary to the task of design space development for QbD submissions and can serve as the basis of mechanistic modeling.
Manufacturing of pharmaceuticals intended for human use is under tight control of government authorities. To further improve product quality and allow more manufacturing flexibility, government agencies started to encourage manufactures to investigate and understand their manufacturing processes scientifically. This should lead to quality by design (QbD), hence a manufacturing that is so well understood that final product quality can be guaranteed by the manufacturing process itself.
近期以质量源于设计（QbD）这一术语概括的倡议，敦促采用基于科学和风险的药物生物工艺开发策略。监管机构传达的最被认可的概念之一是设计空间概念——关键工艺参数（CPP）范围的多维组合，在此范围内质量验收标准（关键质量属性，CQA）得以满足。当前遵循QbD原则的设计空间开发侧重于统计CPP/CQA相互作用的研究，而这种相互作用的生物学机制却很少被考虑。此外，在设计空间开发过程中收集的大量在线和离线数据通常未用于展示工艺理解。在此，我们提出一种方法，说明如何处理典型的记录工艺数据，并用于在上游设计空间开发中获取工艺机制知识，而无需进一步实验或额外分析程序。从在线和离线测量（尾气定量、空气流量、底物流量、生物量干细胞重量测量）获得的数据被处理成特定速率和产率系数形式的与规模无关的信息（数据处理）。随后，将获得的信息与所研究的工艺参数进行回归，旨在研究机制相互作用（信息处理）。在一项涉及两个工艺参数（诱导期温度和诱导期补料策略）的多变量研究中展示了所提出方法的效力，该研究旨在利用大肠杆菌K12菌株生产重组产品。成功提取的知识表明系统所承受的代谢负荷存在时间依赖性、低温下启动子可能下调以及较高特定补料方式下细胞裂解减少。所提出的用于提取工艺机制知识的数据和信息处理方法与用于QbD申报的设计空间开发任务完全互补，可作为机制建模的基础。
用于人类的药品制造受到政府当局的严格监管。为进一步提高产品质量并允许更大的制造灵活性，政府机构开始鼓励制造商科学地研究和理解其制造工艺。这应能实现质量源于设计（QbD），即一种对制造工艺理解透彻，以至于最终产品质量可由制造工艺本身保证的制造方式。
