Nair Adithya, Loveday Kate A, Kenyon Charlotte, Qu Jixin, Kis Zoltán
Department of Biological and Chemical Engineering, University of Sheffield, Sheffield, UK.
Department of Chemical Engineering, Imperial College London, London, UK.
Methods Mol Biol. 2024;2786:339-364. doi: 10.1007/978-1-0716-3770-8_16.
Quality by digital design (QbDD) utilizes data-driven, mechanistic, or hybrid models to define and optimize a manufacturing design space. It improves upon the QbD approach used extensively in the pharmaceutical industry. The computational models developed in this approach identify and quantify the relationship between the product's critical quality attributes (CQAs) and the critical process parameters (CPPs) of unit operations within the manufacturing process. This chapter discusses the QbDD approach in developing and optimizing unit operations such as in vitro transcription, tangential flow filtration, affinity chromatography, and lipid nanoparticle (LNP) formulation in mRNA vaccine manufacturing. QbDD can be an efficient framework for developing a production process for a disease-agnostic product that requires extensive experimental and model-based process-product interaction characterization during the early process development phase.
数字化设计质量(QbDD)利用数据驱动、机理或混合模型来定义和优化制造设计空间。它改进了制药行业广泛使用的质量源于设计(QbD)方法。这种方法中开发的计算模型识别并量化了产品关键质量属性(CQAs)与制造过程中单元操作的关键工艺参数(CPPs)之间的关系。本章讨论了QbDD方法在mRNA疫苗生产中开发和优化单元操作(如体外转录、切向流过滤、亲和色谱和脂质纳米颗粒(LNP)配方)方面的应用。对于在早期工艺开发阶段需要广泛的实验和基于模型的工艺-产品相互作用表征的通用疾病产品,QbDD可以成为开发生产工艺的有效框架。