Developing Efficient Models to Define Economic and Low Risk High Value Manufacture of Cell Based Products

开发有效的模型来定义细胞产品的经济且低风险的高价值制造

基本信息

  • 批准号:
    EP/R031649/1
  • 负责人:
  • 金额:
    $ 51.76万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2018
  • 资助国家:
    英国
  • 起止时间:
    2018 至 无数据
  • 项目状态:
    已结题

项目摘要

New treatments for disease are increasingly made from biological materials rather than the chemicals in conventional drugs. The most advanced of these treatments use humans cells as treatments for serious and incurable diseases. Recent successes include dramatic long term remission of previously untreatable blood cancers. However, the manufacturing of such products is highly complex compared to a conventional drug. Products have to be 'grown', sometime over weeks or months, rather than quickly synthesised in a chemical reactor. This creates many challenges for manufacture. In particular there are many opportunities for the manufacturing environment to move outside an acceptable range. This can mean the product grows into the wrong number or type of cells. Further, cells talk to each other through the release of small signalling agents and deplete things from the environment around them. This creates a complex system that evolves over time, and a potentially very expensive system to handle for a manufacturer. Many current manufacturing processes for these new treatments are exceedingly inefficient and high risk due to a poor understanding of these issues.We propose to create mathematical models of these relationships so that manufacturers can create their products at lower cost and with lower risk of process failure. We propose to use different types of modelling for optimum efficiency. The first will efficiently screen for things that affect the cells being grown. The second is a specific type of modelling that describes a system in terms of the rate of change of its components and is therefore good for modelling systems that evolve with time. These models will help us understand how to control manufacture for maximum efficiency and acceptable risk.Avoiding process failure is very important because some of these products could be dangerous if the wrong cells are produced. Furthermore if a patient's own cells are being grown to treat them there is no replacement product available if manufacture fails. If we succeed it will help the UK see more ground breaking therapies at market as well as supporting a high value manufacturing industry contributing to UK economic growth.
新的疾病治疗方法越来越多地用生物材料而不是传统药物中的化学物质制成。这些治疗方法中最先进的人使用人类细胞作为严重和无法治愈的疾病的治疗方法。最近的成功包括长期缓解以前无法治疗的血液癌。但是,与常规药物相比,这种产品的生产非常复杂。必须在数周或几个月的时间内“生长”产品,而不是在化学反应器中迅速合成。这给制造带来了许多挑战。特别是,制造环境有很多机会搬出可接受的范围。这可能意味着产品成长为错误的单元格数或类型。此外,细胞通过释放小信号剂并从周围环境中耗尽事物,相互交谈。这创建了一个随着时间的流逝而发展的复杂系统,并且可能要处理制造商的可能非常昂贵的系统。由于对这些问题的了解不足,这些新处理的许多当前的制造过程非常低效和高风险。我们建议创建这些关系的数学模型,以便制造商可以以较低的成本和较低的流程故障风险来创建其产品。我们建议使用不同类型的建模来达到最佳效率。第一个将有效筛选影响正在生长的细胞的事物。第二个是一种特定的建模类型,它根据其组件的变化速率来描述系统,因此非常适合随时间发展的建模系统。这些模型将有助于我们了解如何控制生产以提高效率和可接受的风险。避免过程失败非常重要,因为如果产生错误的细胞,其中一些产品可能很危险。此外,如果正在生长患者的细胞来治疗它们,则如果制造失败,则没有可用的替代产品。如果我们成功,它将有助于英国看到市场上更多的破碎疗法,并支持高价值的制造业,从而促进英国经济增长。

项目成果

期刊论文数量(9)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Understanding cell culture dynamics: a tool for defining protocol parameters for improved processes and efficient manufacturing using human embryonic stem cells.
  • DOI:
    10.1080/21655979.2021.1902696
  • 发表时间:
    2021-12
  • 期刊:
  • 影响因子:
    4.9
  • 作者:
    Kusena JWT;Shariatzadeh M;Thomas RJ;Wilson SL
  • 通讯作者:
    Wilson SL
Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing.
The importance of cell culture parameter standardization: an assessment of the robustness of the 2102Ep reference cell line.
  • DOI:
    10.1080/21655979.2020.1870074
  • 发表时间:
    2021-12
  • 期刊:
  • 影响因子:
    4.9
  • 作者:
    Kusena JWT;Shariatzadeh M;Studd AJ;James JR;Thomas RJ;Wilson SL
  • 通讯作者:
    Wilson SL
Application of a simple unstructured kinetic and cost of goods models to support T-cell therapy manufacture.
应用简单的非结构化动力学和商品成本模型来支持 T 细胞疗法的生产。
  • DOI:
    10.1002/btpr.3205
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    2.9
  • 作者:
    Shariatzadeh M
  • 通讯作者:
    Shariatzadeh M
Modeling the selective growth advantage of genetically variant human pluripotent stem cells to identify opportunities for manufacturing process control
  • DOI:
    10.1016/j.jcyt.2024.01.010
  • 发表时间:
    2024-04-03
  • 期刊:
  • 影响因子:
    4.5
  • 作者:
    Beltran-Rendon,Catherine;Price,Christopher J.;Thomas,Robert J.
  • 通讯作者:
    Thomas,Robert J.
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Robert Thomas其他文献

Lifestyle during and after cancer treatment.
癌症治疗期间和治疗后的生活方式。
Comparison of Interlesionally and Systemically Administered Radiolabelled Monoclonal Antibodies in Implanted Tumours
植入性肿瘤病灶间和全身施用放射性标记单克隆抗体的比较
  • DOI:
  • 发表时间:
    1999
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Robert Thomas;P. Carnochan;S. Eccles;M. Brada
  • 通讯作者:
    M. Brada
Suspended Gallium Arsenide Electro-Optic Racetrack Ring Modulator
悬浮砷化镓电光跑道环形调制器
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Haoyang Li;Robert Thomas;K. Balram
  • 通讯作者:
    K. Balram
Cancer: the role of exercise in prevention and progression
癌症:运动在预防和进展中的作用
  • DOI:
    10.1108/00346650710828343
  • 发表时间:
    2007
  • 期刊:
  • 影响因子:
    1.2
  • 作者:
    Robert Thomas;N. Davies
  • 通讯作者:
    N. Davies
P006. Is it time to abandon use of fine needle aspiration cytology in the one-stop breast clinic?
  • DOI:
    10.1016/j.ejso.2015.03.042
  • 发表时间:
    2015-06-01
  • 期刊:
  • 影响因子:
  • 作者:
    Maria Samuel;Robert Thomas;Alan Askari;Mario Metry;Magdy Youssef;Sunil Amonkar;Michael Carr
  • 通讯作者:
    Michael Carr

Robert Thomas的其他文献

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{{ truncateString('Robert Thomas', 18)}}的其他基金

Collaborative Research: Elements: FaaSr: Enabling Cloud-native Event-driven Function-as-a-Service Computing Workflows in R
协作研究:要素:FaaSr:在 R 中启用云原生事件驱动的函数即服务计算工作流程
  • 批准号:
    2311124
  • 财政年份:
    2023
  • 资助金额:
    $ 51.76万
  • 项目类别:
    Standard Grant
Collaborative Research: Frameworks: DeCODER (Democratized Cyberinfrastructure for Open Discovery to Enable Research)
协作研究:框架:DeCODER(用于开放发现以支持研究的民主化网络基础设施)
  • 批准号:
    2209866
  • 财政年份:
    2022
  • 资助金额:
    $ 51.76万
  • 项目类别:
    Continuing Grant
NEON RCN: The Ecological Forecasting Initiative RCN: Using NEON-enabled near-term forecasting to synthesize our understanding of predictability across ecological systems and scales
NEON RCN:生态预测计划 RCN:使用 NEON 支持的近期预测来综合我们对跨生态系统和规模的可预测性的理解
  • 批准号:
    1926388
  • 财政年份:
    2020
  • 资助金额:
    $ 51.76万
  • 项目类别:
    Standard Grant
Green silica nano particle manufacturing
绿色二氧化硅纳米颗粒制造
  • 批准号:
    EP/L015404/1
  • 财政年份:
    2014
  • 资助金额:
    $ 51.76万
  • 项目类别:
    Research Grant
Engineering Biological Science - Processes and Systems for Haematopoietic Stem Cell Based Therapy Manufacture
工程生物科学 - 造血干细胞疗法制造的流程和系统
  • 批准号:
    EP/K00705X/1
  • 财政年份:
    2013
  • 资助金额:
    $ 51.76万
  • 项目类别:
    Fellowship
SGER: Creating an Archive for the August 14, 2003 Northeast Blackout Data
SGER:为 2003 年 8 月 14 日东北停电数据创建档案
  • 批准号:
    0540051
  • 财政年份:
    2005
  • 资助金额:
    $ 51.76万
  • 项目类别:
    Standard Grant
A Proposal to Cooperate with the Australian Centre for Energy and Environmental Markets (CEEM) to Develop and Test a New Voltage Value Approach to Controlling Voltage in an Electr
与澳大利亚能源与环境市场中心 (CEEM) 合作开发和测试控制电力电压的新电压值方法的提案
  • 批准号:
    0533130
  • 财政年份:
    2005
  • 资助金额:
    $ 51.76万
  • 项目类别:
    Standard Grant
Designing an Asynchronous Data-Flow Processor for Data-Intensive Static-Trace Computing for Large-Scale Power System Simulations
设计用于大规模电力系统仿真的数据密集型静态跟踪计算的异步数据流处理器
  • 批准号:
    0532744
  • 财政年份:
    2005
  • 资助金额:
    $ 51.76万
  • 项目类别:
    Continuing Grant
SGER: The Engineering Econometrics of Market Power in Electric Power Systems
SGER:电力系统市场力的工程计量经济学
  • 批准号:
    0333022
  • 财政年份:
    2004
  • 资助金额:
    $ 51.76万
  • 项目类别:
    Standard Grant
Industry/University Cooperative Research Center for Power Systems Engineering (PSERC)
电力系统工程产学合作研究中心(PSERC)
  • 批准号:
    0118300
  • 财政年份:
    2001
  • 资助金额:
    $ 51.76万
  • 项目类别:
    Continuing Grant

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为删失协变量制定稳健有效的策略,以改进神经退行性疾病的临床试验设计
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Developing Efficient and Effective Algorithms for Complete Analysis of a Class of Vacation Queueing Models
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