Structural Identifiability and Indistinguishability Analysis as Tools for Quantitative and Systems Pharmacology to Support the 3Rs

结构可识别性和不可区分性分析作为定量和系统药理学工具支持 3R

• 批准号: NC/K001205/1
• 负责人: Michael Chappell
• 金额: $ 31.27万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NC%2FK001205%2F1
• 关键词: Structural; Identifiability; Indistinguishability; Analysis; Tools

Animals are extensively used in toxicology testing to assess safety and risk of the process examined on humans and the environment. It is estimated that over 1 million animals are used across Europe each year in toxicology tests with around one fifth of all animals used in the UK involved in such tests. The mainstream use of valid, robust mathematical models that can be used with greater fidelity and confidence than those currently used in practice to accurately simulate the outcomes of such tests would therefore clearly have huge impacts on the number of animals required in the future, supporting the replacement and reduction of the use of animals in toxicology testing experiments and providing step changes in animal welfare. This proposal aims to overcome certain shortcomings that exist in the modelling of toxicological processes by introducing and applying established and novel systems modelling tools and techniques namely structural identifiability and indistinguishability analyses. These tools have proven ability to improve the fidelity of models in terms of confidence in parameter estimation and optimising experiment design. These techniques will be applied to exemplar problems in toxicology that will provide a more robust generic analytical framework to improve parameter estimation, sensitivity, robustness, reliability and validity of these models. In turn this step change in modelling will enhance experiment design, improve risk and safety assessment and thus help support the replacement, reduction and refinement of animal use. This research will be supported by industry (AstraZeneca) and will be applied to models for the improvement of both human and animal safety. This work will provide new tools and seed the emergence of a new field, namely Quantitative and Systems Toxicology.

动物被广泛用于毒理学测试，以评估该过程对人类和环境的安全性和风险。据估计，欧洲每年有超过100万只动物被用于毒理学测试，其中约五分之一的英国动物参与了此类测试。因此，主流使用有效、健壮的数学模型，可以比目前用于实践的模型更真实和更自信地用于准确模拟此类测试的结果，显然将对未来所需的动物数量产生巨大影响，支持在毒理学测试实验中取代和减少使用动物，并提供动物福利的阶段性变化。这项建议旨在通过引入和应用既有和新颖的系统建模工具和技术，即结构可辨识性和不可区分性分析，克服毒理学过程建模方面存在的某些缺点。这些工具已被证明有能力在参数估计和优化实验设计的置信度方面提高模型的保真度。这些技术将被应用于毒理学中的样本问题，这将提供一个更强大的通用分析框架，以改进这些模型的参数估计、灵敏度、稳健性、可靠性和有效性。反过来，模型中的这一步变化将改进实验设计，改进风险和安全评估，从而有助于支持动物使用的替换、减少和改进。这项研究将得到工业(阿斯利康)的支持，并将应用于改善人类和动物安全的模型。这项工作将提供新的工具，并孕育一个新领域的出现，即定量和系统毒理学。