Translation of antibody-dependent cell cytotoxicity (ADCC) from in vitro to in vivo

抗体依赖的细胞毒性(ADCC)是单抗的主要效应功能，可使免疫系统靶向杀伤肿瘤细胞等细胞。这三种最畅销的抗癌药物利用ADCC，但它也被用于其他领域，用于降低自身免疫性疾病患者的B细胞水平，并用于治疗刚刚接受骨髓移植的Epstein-Barr病毒患者。在文献中已经努力对这一过程进行建模，其中已使用类似Michaelis-Menten的模型和随机模型来模拟体外试验中的ADCC效力。霍夫曼等人最新的机械论模型。针对不同的免疫：靶细胞比率，将抗体水平与ADCC效力联系起来。然而，这些模型未能捕捉到这一过程的多尺度复杂性，据我们所知，尽管这种效应功能在单抗治疗中是多么常见，但文献中既没有多尺度模型也没有体内ADCC模型。准确描述ADCC和其他抗体效应器功能的全部复杂性的机制模型的开发将为未来的抗体设计提供信息，并开始能够从体外到体内数据进行外推。该项目的目标包括开发ADCC的微观尺度(受体水平)模型，然后将其转化为宏观尺度(细胞水平)模型。这些模型及其分析将被用来提供ADCC的体内模型。还将考虑其他类似的抗体效应器功能和免疫细胞，并将使用可用的数据对模型进行验证。该项目的最终目标是使用开发的模型及其见解来为临床试验的各个方面提供信息，例如患者对基于ADCC的疗法的敏感性、治疗效果所需的最低抗体剂量浓度以及将患者数据纳入模型以预测患者体内的ADCC效力，并将其与临床结果联系起来。研究方法的新方面包括对受体水平过程的详细建模，如微观模型中的结合亲和力，通过结合微观和宏观模型来制定和计算多尺度ADCC的技术，将ADCC从体外转化为体内，以及建立用于ADCC过程的基于试剂的模型(ABM)PDE模型，并从数学上考虑是否或何时可以将ABM简化为PDE。该项目将与葛兰素史克合作，涵盖数学肿瘤学、数学免疫学和计算生物学领域。因此，该项目属于EPSRC数学生物学、数学科学和物理科学研究领域。

Antibody-dependent cellular cytotoxicity (ADCC) is a primary effector function of monoclonal antibodies (mAbs), which enables the targetted killing of cells such as tumour cells by the immune system. The three best selling cancer drugs utilize ADCC but it has also been utilized in other areas to reduce B-cell levels in patients with auto-immune diseases and as therapy for Epstein-Barr virus patients who have just received a bone-marrow transplant. There have been efforts to model this process in the literature where Michaelis-Menten like models and stochastic models have been used to simulate ADCC potency within in vitro assays. The most recent mechanistic model by Hoffman et al. linked antibody levels to ADCC potency for different immune:target cell ratios. However, these models fail to capture the multi-scale complexity of this process as a whole and to the best of our knowledge, there does not exist either a multi scale model or in vivo model for ADCC in the literature despite how common this effector function is in mAb therapies. The development of mechanistic models that accurately describe the full complexity of ADCC and other antibody effector functions will inform future antibody design and begin to enable extrapolation of in vitro to in vivo data. The aims for this project include the development of a micro-scale (receptor level) model for ADCC and then to translate this to a macro-scale (cell level) model. These models and their analysis will then be used to inform an in vivo model of ADCC. Other similar antibody effector functions and immune cells will also be considered and models will be validated with data where available. An ultimate objective for this project is to use the models developed and their insights to inform aspects of clinical trials such as patient susceptibility to ADCC-based therapeutics, minimum antibody-dose concentration needed for a therapeutic effect and the incorporation of patient data into models to predict ADCC potency within the patient, linking this to clinical outcome. Novel aspects of the research methodology include detailed modelling of receptor-level processes such as binding avidity in the micro-scale model, techniques to formulate and compute a multi-scale of ADCC by combining the micro and macro scale models, translating ADCC from in vitro to in vivo and the formulation of agent-based model (ABM) PDE models for ADCC processes with mathematical consideration of if or when the ABM can be reduced to the PDE. This project will be in collaboration with GlaxoSmithKline and will cover areas in mathematical oncology, mathematical immunology and computational biology. As such, this project falls within the EPSRC mathematical biology, mathematical sciences and physical sciences research areas.