Modelling cellular processes underpinning recombinant monoclonal antibody production by mammalian cells

模拟支持哺乳动物细胞产生重组单克隆抗体的细胞过程

• 批准号: BB/E00590X/1
• 负责人: David James
• 金额: $ 66.8万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FE00590X%2F1
• 关键词: Modelling; cellular; processes; underpinning; recombinant

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.

这项提案涉及“生物加工”。生物加工统称为能够生产新生物药物的制造工艺的范围。你可能对重组DNA技术生产的第一批生物药物之一--一种名为胰岛素的小蛋白质--很熟悉。胰岛素现在被非常成功地用于治疗一种日益常见的代谢性疾病--糖尿病。在使用胰岛素之前，糖尿病患者的生命短暂，充满了严重的医疗并发症。该项目的目标是在培养中通过基因工程哺乳动物细胞生产其他高价值的治疗性蛋白，特别是单克隆抗体。在人体内，血液中存在的天然抗体在我们的免疫系统中发挥着重要作用：它们针对致病微生物和异物进行清除。重组单抗与天然抗体几乎完全相同，是专门针对疾病细胞设计的。与青霉素和扑热息痛等传统小分子药物不同，单抗生物制药是大而复杂的相对脆弱的蛋白质，必须由活的哺乳动物细胞在培养中产生，然后通过基因工程产生重组蛋白质产品。事实证明，它们对风湿性关节炎和一系列癌症等严重疾病的治疗非常成功。预计在未来五到十年内，所有开发中的药物中将有高达50%是生物制药；这是哺乳动物细胞在培养中产生的相当大比例的重组蛋白。自从25年前由基因工程哺乳动物细胞在培养中生产的第一批重组蛋白药物获得治疗许可以来，我们已经学会了大幅提高生物制药制造过程(生物过程)的生产率。然而，它们仍然复杂和昂贵，工业必须进行耗时的筛选过程，以找到制造足够数量的重组蛋白的工程细胞。到目前为止，工业生物过程的产量主要是通过逐步改善生产细胞在培养中的生长来增加的，而不是通过对每个细胞进行工程来提高产品的效率。这一点很重要，因为如果我们知道如何适当地指导或编程细胞工厂，我们可以大幅提高制造过程的生产率，并减少产生生产性细胞培养所需的时间。然而，这并不是一个简单的问题。例如，细胞利用并协调其各种复杂的机制，将其细胞核中的重组单抗基因转化为可分泌出细胞的完全折叠的蛋白质。我们如何才能足够好地了解这条细胞‘生产线’，以便我们能够合理地实施策略，以改善从重组基因到蛋白质产品的流量？在这个项目中，我们将实施一种新颖的、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。我们认为这是至关重要的--综合的数学生物科学方法可以极大地增加生物测量的信息含量和效用，并使我们能够从系统控制的角度了解细胞过程。该项目将首次提供对细胞工厂的定量了解，在此基础上合理地建立策略，以提高治疗性单抗生产系统的生产率。如果没有这方面的知识，细胞培养工程将在很大程度上停留在试验和错误的基础上。

项目成果

An empirical modeling platform to evaluate the relative control discrete CHO cell synthetic processes exert over recombinant monoclonal antibody production process titer.

一个经验建模平台，用于评估相对控制离散 CHO 细胞合成过程对重组单克隆抗体生产过程滴度的影响。

• DOI: 10.1002/bit.23146
• 发表时间: 2011
• 期刊: Biotechnology and bioengineering
• 影响因子: 3.8
• 作者: McLeod J

Predicting the Expression of Recombinant Monoclonal Antibodies in Chinese Hamster Ovary Cells Based on Sequence Features of the CDR3 Domain

• DOI: 10.1002/btpr.1839
• 发表时间: 2014-01-01
• 期刊: BIOTECHNOLOGY PROGRESS
• 影响因子: 2.9
• 作者: Pybus, Leon P.;James, David C.;Field, Ray
• 通讯作者: Field, Ray