Mapping the effector response space of antibody combinations

抗体是免疫反应的关键和核心调节因素。他们特别多才多艺。 治疗药物，因为它们既能与高亲和力的靶结合，又能引导 免疫系统。事实上，抗体包括广泛的已获批准的治疗方法。 疾病指征，其中许多已知在很大程度上依赖于效应细胞(免疫) 回应。Ig G同型抗体与效应细胞上的Fcγ受体相互作用并诱导效应器 通过多种细胞类型(如巨噬细胞、单核细胞)和通过多个 包括吞噬和杀死患病细胞的过程。许多可能的设计 参数-恒定区组成、Fcγ受体、细胞群和抗原结合 组合-精确地理解、测量和操纵效应器 实现一个难以实现的目标。我们提议的工作是围绕两个IGG的假设 组合出现时可以引起不同的反应，与 对其中任何一种的反应都是自己的。使用抗体-FcγR相互作用的计算模型，我们将 找出这种紧急行为的预测案例。这些组合将进行测试，以确定其 在体外结合和效应反应，然后在两种模型中进行抗体靶向细胞杀伤。 最后，我们将使用效应器调节的计算模型来映射人类和 小鼠免疫球蛋白根据它们的效应器反应而相关。总的来说，这些努力将提供 设计更有效的抗体以达到靶向杀伤细胞的关键信息 并对现有的治疗性抗体如何发挥作用提供了更清晰的观点。

Antibodies are crucial, central regulators of the immune response. They are particularly versatile therapeutic agents due to their ability to both bind to a target with high affinity and direct the immune system. Indeed, antibodies comprise a broad range of approved therapies across disease indications, many of which are known to rely in large part on effector cell (immune) response. Antibodies of the IgG isotype interact with FcγRs on effector cells and elicit effector function through multiple cell types (e.g., macrophages, monocytes) and through multiple processes, including phagocytosis and killing of diseased cells. The many possible design parameters—constant region composition, FcγRs, cell populations, and antigen binding in combination—have made precisely understanding, measuring, and manipulating effector function an elusive goal. Our proposed work is centered around the hypothesis that two IgGs can elicit distinct responses when present in combination from what would be suggested by the response to either on its own. Using a computational model of antibody-FcγR interaction, we will identify predicted cases of this emergent behavior. These combinations will be tested for their binding and effector response in vitro and then in two models of antibody-targeted cell killing. Finally, we will use the computational model of effector regulation to map how human and mouse IgGs are related according to their effector response. In total, these efforts will provide critical information for designing more effective antibodies with the goal of targeted cell killing and provide a clearer view of how existing therapeutic antibodies function.