BRIC DOCTORATE PROGRAMME: Development of a computational tool for predicting the impact of bioprocess conditions on protein glycosylation

大多数获得许可的单抗在其重链上都含有一个一致的N-连接的糖基化位点。结合在这个部位的低聚糖通过缩短单抗的血清半衰期或直接影响它们在体内触发的机制，极大地影响了单抗作为治疗药物的疗效。据广泛报道，细胞培养条件，如碳源类型和利用率、溶氧浓度、氨浓度、介质pH和培养方式等都会影响蛋白质的糖基化，从而对最终产品质量有很大影响(1-4)。最近，美国FDA和欧洲药品管理局建议在生物制药生产中实施按设计质量(QBD)范式。它的实施需要使用给定产品的所有可用知识来设计、优化和控制制造过程。目标是确保在制造过程的每个阶段都将质量融入到产品中。提出详细的数学模型可以在QBD范围内的生物制药生产过程的设计、控制和优化中发挥关键作用。据我们所知，目前还没有数学模型将糖基化方面的产品质量与细胞培养条件联系起来。具有这种能力的模型将对生物过程设计和控制、培养基配方和遗传工程策略的假设检验有用。我们建议开发一种新的数学工具，将生物过程条件与蛋白质糖基化联系起来。该工具将包括四个组成部分：营养物质进入细胞的运输，核糖体(NSD)的胞浆合成，NSD从胞浆到高尔基体的运输，以及最后，它们添加到目标蛋白质上。缺碳直接影响NSD的胞内可用，没有NSD，高尔基体中的糖代谢就不能持续。其他研究表明，高氨浓度(4)和极端的pH(5)会导致较差的糖谱。因此，工艺条件直接影响NSD的合成，进而影响糖基化过程和最终产品质量。我们计划用使用稳定和瞬时转染体进行的细胞培养的实验数据来验证这个基于模型的工具。即使制造细胞系被稳定地转染和克隆，瞬时转基因对于快速提供用于临床评估的材料也是特别有意义的。因此，至关重要的是，所提供的材料与最终生产单元生产线所生产的材料相似。该项目将围绕以下目标组织：a)实验分析残留葡萄糖和谷氨酰胺浓度、氨积累和培养pH对NSD利用率和产生的糖形式变化的影响。B)比较稳定和瞬时转染型工业CHO细胞产生的糖型。C)利用目标1中产生的实验数据开发和验证瞬时和稳定转染体的动力学数学模型(我们已经开始开发葡萄糖可获得性影响的建模工具)。D)缩小瞬时和稳定转染体的血糖谱之间差距的工艺和基因工程策略的计算设计和实验评估。[1]Hayter等，Biotechnol Bioeng 1992，39：327。[2]Wong等，Biotechnol Bioeng 2005，89：164。[3]Trummer等，Biotechnol Bioeng 2006，94：1045。[4]Gawlitzek等，Biotechnol Bioeng 2000，68：637。[5]鲍里斯，《自然·生物技术》1993，11：720。

Most licensed monoclonal antibodies (mAbs) contain a consensus N-linked glycosylation site on their heavy chains. The oligosaccharides attached to this site greatly influence the efficacy of mAbs as therapeutics either by reducing their serum half-life or by directly affecting the mechanisms they trigger in vivo. It has been widely reported that cell culture conditions, such as carbon source type and availability, dissolved oxygen concentration, ammonia concentration, medium pH and culture mode, affect protein glycosylation, thus having great impact on end product quality (1-4). Recently, the US FDA and the European Medicines Agency have proposed the implementation of the Quality by Design (QbD) paradigm to the manufacture of biopharmaceuticals. Its implementation requires the use of all available knowledge of a given product for the design, optimization and control of the manufacturing process. The goal is to ensure that quality is built into the product at every stage of the manufacturing process. It is proposed that detailed mathematical models may play a critical role in the design, control and optimization of biopharmaceutical manufacturing processes under the QbD scope. To our knowledge, there are currently no mathematical models that relate product quality in terms of glycosylation with cell culture conditions. A model with this capability would be useful for bioprocess design and control, culture media formulation and hypothesis testing for genetic engineering strategies. We propose the development of a novel mathematical tool that links bioprocess conditions to protein glycosylation. The tool will encompass four components: transport of nutrients into the cell, cytosolic synthesis of nucleotide sugar donors (NSDs), transport of NSDs from the cytosol to the Golgi apparatus, and, finally, their addition onto the protein of interest. Lack of carbon availability directly affects the intracellular availability of NSDs, without which the glycan metabolism in the Golgi cannot continue. Other studies have shown that high ammonia concentrations (4) and extremes of pH (5) lead to poor glycoprofiles. Process conditions therefore directly affect the synthesis of NSDs, which, in turn, affects the glycosylation process and the final product quality. We plan to validate this model-based tool with experimental data from cell cultures conducted using stable and transient transfectants. Even though manufacturing cell lines are stably transfected and clonal, transient transfections are of particular interest for the rapid provision of material for clinical evaluation. It is therefore of paramount importance that the material provided is similar to what would be produced from the final production cell lines. The project will be organised around the following objectives: a) Experimental analysis of the effect of residual glucose and glutamine concentration, ammonia accumulation and culture pH on the availability of NSDs and the variation of glycoforms produced. b) Comparison of glycoforms produced by stable and transiently transfected industrial CHO cell lines. c) Development and validation of kinetic mathematical model for the transient and stable transfectants using the experimental data generated in objective 1 (We have already begun the development of the modelling tool for the effects of glucose availability). d) Computational design and experimental evaluation of process and genetic engineering strategies for narrowing the gap between the glycomic profiles of transient and stable transfectants. [1] Hayter et al., Biotechnol Bioeng 1992, 39:327. [2] Wong et al., Biotechnol Bioeng 2005, 89:164. [3] Trummer et al., Biotechnol Bioeng 2006, 94:1045. [4] Gawlitzek et al., Biotechnol Bioeng 2000, 68:637. [5] Borys, Nature Biotechnol 1993, 11:720.