UNS:Intergrating novel nutrient feeding strategies with computational glycosylation models to improve production of complex biotherapeutics from mammalian factories

重组促红细胞生成素（rEPO）等生物制药通过使接受者能够治疗慢性肾衰竭或其他疾病，已经改变了美国和世界各地数百万患者的生活。不幸的是，提供这些药物的费用往往令人望而却步，限制了有需要的患者获得和负担得起生物治疗。该项目将通过使用新型培养基添加剂来改变生物制造，从而解决成本和效率方面的挑战。通过改变生物制药的特性，使这些产品具有更长的循环寿命，使患者能够以更长的间隔服用更低的剂量，rEPO和其他药物的质量将得到提高。同样，该项目将通过结合新型廉价营养物质来降低生产成本，从而提高生产细胞生产高质量药物的能力。同时，将实施先进的计算模型，以确定最佳的培养基配方，以产生高质量的生物制药。此外，从高中到研究生阶段的学生将接受教育并从事重要的生物加工技术，包括哺乳动物细胞培养，媒体设计和制药制造。为了实现这些目标，将实施实验和计算系统生物技术方法，其中培养基将被设计为优化生物治疗药物的糖基化谱，如在中国仓鼠卵巢（CHO）细胞中产生的重组红细胞生成素（rEPO）。CHO细胞已成为产生糖蛋白生物治疗药物的主要细胞工厂。糖蛋白治疗剂的寡糖（或聚糖）成分的结构和性质对产品的质量、功效和价值极为重要。生物合成中，聚糖的结构由两个因素决定：糖基化酶的水平和核苷酸糖底物的可用性。该项目将整合实验和计算方法来操纵营养成分，以提高这些关键核苷酸糖底物的水平，并改善糖基化。我们将研究一系列新的糖类似物，以增加核苷酸糖库，提高rEPO和其他生物制品的质量。这些新型的糖类似物生产简单，成本低廉，在特定的碳基上含有化学修饰，有助于穿过细胞膜，有效地进入核苷酸糖合成途径。为了阐明这些和其他营养物质的影响，这些培养基成分将被纳入目前仅基于糖基化酶转移酶活性的n -链糖基化计算模型。该模型将被扩展到预测培养基中营养物或补充剂对核苷酸糖生物合成的最终聚糖结构的影响。当前糖信息学套件的这种扩展将使用户能够为糖蛋白生物治疗药物中存在的理想n -聚糖谱设计最佳培养基组成。通过包括营养物质对代谢的影响，并将其与最终的聚糖结构联系起来，该建模工具将具有显著的多功能性和能力，可以快速且经济有效地提高生物治疗产品的质量。因此，在计算算法的帮助下，新的营养物质将被纳入哺乳动物细胞培养的生物处理培养基配方中，以增加所需的复杂高质量生物治疗药物的产量和产量，并减少耗时和昂贵的实验研究的需要。这种方法可能对许多生物过程和生物产品产生广泛的影响。该奖项由CBET生物技术与生化工程项目颁发，由材料研究部生物材料项目共同资助。

1512265Betenbaugh, Michael J. Biopharmaceuticals such as recombinant erythropoietin (rEPO) have transformed the lives of millions of patients in the US and around the world by enabling the recipients to address chronic renal failure or other illnesses. Unfortunately, the costs of providing these drugs are often prohibitive, limiting the availability and affordability of biotherapeutic treatments for patients that need them. This project will address both cost and efficacy challenges by transforming biomanufacturing with novel media additives. The quality of rEPO and other drugs will be enhanced by altering the properties of biopharmaceuticals in ways that endow these products with longer circulatory lifetimes, allowing patients to take lower doses at longer intervals. Likewise, this project will lower the costs of manufacturing by incorporating novel inexpensive nutrients that improve the capacity of producer cells to generate high quality drugs. In tandem, advanced computational models will be implemented in order to determine the optimal media formulations for generating high quality biopharmaceuticals. In addition, students from the high school to the post-graduate level will be educated and engaged in important bioprocessing techniques including mammalian cell culture, media design, and pharmaceutical manufacturing. In order to achieve these goals, an experimental and computational systems biotechnology approach will be implemented in which the media will be designed to optimize the glycosylation profile of biotherapeutics such as recombinant erythropoietin (rEPO) produced in Chinese hamster ovary (CHO) cells. CHO cells have emerged as a major cell factory for generating glycoprotein biotherapeutics. The structure and nature of the oligosaccharide (or glycan) component of a glycoprotein therapeutic is extremely important to the quality, efficacy, and value of the products. Biosynthetically, glycan structure is dictated by two factors: levels of glycosylation enyzmes and availability of nucleotide sugar substrates. This project will integrate experimental and computational methods to manipulate nutrient components to enhance the levels of these critical nucleotide sugar substrates and improve glycosylation. A series of novel sugar analogs will be investigated for their capacity to increase the nucleotide sugar pool and improve quality of rEPO and other biological products. These novel sugar analogs, which are simple and inexpensive to produce, contain chemical modifications on specific carbon groups that facilitate crossing the cell membrane for efficient channeling into pathways for nucleotide sugar synthesis. In order to elucidate the impact of these and other nutrients, these media components will be incorporated into a computational model of N-linked glycosylation that currently is based only on glycosylation enzyme transferase activity. The model will be extended to predict the influence on final glycan structures of nucleotide sugar biosynthesis from nutrients or supplements in the media. Such an expansion of the current glycoinformatics suite will enable users to design optimal media compositions for a desirable N-glycan profile present on glycoprotein biotherapeutics. By including the effect of nutrients on metabolism and linking that to the final glycan structure, this modeling tool will have significant versatility and power for rapidly and cost-effectively improving biotherapeutic product quality. As a result, novel nutrients will be incorporated into the bioprocessing media formulation of mammalian cell cultures with the assistance of comptutational algorithms in order to increase production and yield of desirable complex high quality biotherapeutics and reduce the need for time consuming and expensive experimental investigation. This approach may have a broad impact across a number of bioprocesses and biological products.This award by the Biotechnology and Biochemical Engineering Program of CBET is co-funded by the Biomaterials Program of the Division of Materials Research.