Modelling cellular processes underpinning recombinant monoclonal antibody production by mammalian cells

这一建议涉及“生物加工”。生物加工总体描述了能够生产新生物药物的制造过程的范围。你可能熟悉重组DNA技术生产的第一批生物药物之一-一种称为胰岛素的小蛋白质。胰岛素现在非常成功地用于治疗一种越来越常见的代谢疾病，糖尿病。在使用胰岛素之前，糖尿病患者的寿命很短，而且伴有严重的并发症。该项目的目标是通过基因工程培养的哺乳动物细胞生产其他高价值的治疗蛋白，特别是单克隆抗体。在体内，存在于血液中的天然抗体在我们的免疫系统中起着重要作用：它们针对致病微生物和异物进行清除。重组单克隆抗体几乎与天然抗体相同，专门设计用于靶向病变细胞。与青霉素和扑热息痛等传统的小分子药物不同，单克隆抗体生物药物是大型、复杂且相对脆弱的蛋白质，必须由培养的活哺乳动物细胞产生，经基因工程改造以产生重组蛋白质产品。它们被证明是非常成功的治疗严重疾病，如类风湿性关节炎和一系列癌症。预计在未来5到10年内，所有开发中的药物中有50%将是生物制药，其中很大一部分是由哺乳动物细胞培养产生的重组蛋白。自从25年前由基因工程哺乳动物细胞培养产生的第一批重组蛋白质药物被许可作为治疗药物以来，我们已经学会了大幅提高生物制药生产工艺（生物工艺）的生产率。然而，它们仍然是复杂和昂贵的，并且工业必须进行耗时的筛选过程以找到制造足够量的重组蛋白的工程细胞。迄今为止，工业生物过程的产量主要是通过逐渐改善培养物中生产细胞的生长来增加的，而不是通过工程化每个细胞来更有效地生产产品。这很重要，因为如果我们知道如何适当地指导或编程细胞工厂，我们就可以大大提高生产过程的生产率，并减少生产细胞培养所需的时间。但这不是一个简单的问题。细胞利用并协调其复杂机制的多样性，以将例如其细胞核中的重组单克隆抗体基因转化为可以分泌出细胞的完全折叠的蛋白质。我们如何才能充分理解这条细胞“生产线”，以便我们能够合理地实施策略，以提高从重组基因到蛋白质产品的通量？在这个项目中，我们将实施一个新的，多学科的技术方法组合来回答这个问题;数学建模，基因表达，分子细胞生物学，蛋白质分析和细胞培养。我们相信这是至关重要的-一个综合的数学生物科学方法可以大大增加生物测量的信息内容和实用性，使我们能够从系统控制的角度来理解细胞过程。该项目将首次提供对细胞工厂的定量了解，从而合理地构建策略，以提高治疗性单克隆抗体生产系统的生产率。如果没有这些知识，细胞培养工程将在很大程度上仍然基于试验和错误。

This proposal is concerned with 'bioprocessing'. Bioprocessing collectively describes the range of manufacturing processes that enable the production of new biological medicines. You may be familiar with the one of the first biological medicines produced by recombinant DNA technology - a small protein called insulin. Insulin is now used very successfully to treat an increasingly common metabolic disease, diabetes. Before insulin, diabetics suffered a short life fraught with serious medical complications. This project is targeted at the production of other high-value therapeutic proteins by genetically engineered mammalian cells in culture, specifically monoclonal antibodies. In the body, natural antibodies present in the blood play an important role in our immune system: They target disease-causing microbes and foreign substances for removal. Recombinant monoclonal antibodies, being almost identical to natural antibodies, are specifically designed to target diseased cells. Unlike traditional small-molecule medicines such as penicillin and paracetamol, monoclonal antibody biopharmaceuticals are large, complex and relatively fragile proteins which have to be produced by living mammalian cells in culture, genetically engineered to produce the recombinant protein product. They are proving to be highly successful treatments for serious diseases such as rheumatoid arthritis and a range of cancers. It is anticipated that within the next five to ten years up to fifty percent of all drugs in development will be biopharmaceuticals; a very substantial proportion recombinant proteins produced by mammalian cells in culture. Since the first recombinant protein medicines produced by genetically engineered mammalian cells in culture were licensed as therapeutics over 25 years ago, we have learnt to substantially increase the productivity of biopharmaceutical manufacturing processes (bioprocesses). However, they are still complicated and expensive, and industry has to undertake time-consuming screening processes to find engineered cells making adequate amounts of recombinant protein. To date, the output of industrial bioprocesses has predominantly been increased by gradually improving the growth of producer cells in culture, and not by engineering each cell to make the product more efficiently. This is important, because if we knew how to instruct or programme the cell factory appropriately, we could substantially improve the productivity of manufacturing processes and decrease the time it takes to generate a productive cell culture. However this is not a simple problem. The cell utilises and coordinates a diverse range of its complex machinery to turn, for example, recombinant monoclonal antibody genes in its nucleus into a fully folded protein which can be secreted out of the cell. How can we understand this cellular 'production line' well enough so that we can rationally implement strategies to improve flux from recombinant genes to protein product? In this project we will implement a novel, multidisciplinary combination of technical approaches to answer this question; mathematical modelling, gene expression, molecular cell biology, protein analysis and cell culture. We believe this is crucial - an integrated mathematical bioscience approach can massively increase the information content and utility of biological measurements and enable us to understand cellular processes from a systems control perspective. This project will, for the first time, provide a quantitative understanding of the cell factory on which to rationally build strategies to increase the productivity of therapeutic monoclonal antibody production systems. Without this knowledge, cell culture engineering will largely remain based on trial and error.