A simulation platform to predict dose and therapeutic window of immunocytokines

项目总结/摘要 免疫细胞因子（IC）是工程化细胞因子与抗体缀合的融合蛋白。这些新颖的分子 是下一代基于精氨酸的免疫疗法，具有在多种疾病中的潜在应用， 如类风湿性关节炎（RA）、牛皮癣和癌症。IC被设计为选择性地靶向患病组织或特定的 具有最小全身免疫活化的免疫细胞，其通常导致重组体中的剂量限制性毒性， 细胞因子疗法然而，设计具有高靶向性的分子，预测其药代动力学， 并确定实现高功效但低毒性的剂量，即治疗窗。 我们建议开发一个IC筛查的模拟平台，该平台将通过计算预测剂量和治疗效果。 开发中的新型IC的窗口。该平台将实施定量系统药理学（QSP）模型 其在机制上描述了IC与靶细胞和脱靶细胞的结合，并将细胞因子受体占有率与 细胞活化和扩张动力学。该模型将预测体内药代动力学（PK）和药效学 (PD)输入剂量和拟定IC的给药方案。模拟将报告读数，如细胞计数和 可溶性细胞因子水平是临床上可观察到的功效和毒性的生物标志物。该模型将是通用的 足以模拟促炎和抗炎IC模块化设计将允许我们添加新的细胞类型， 细胞因子/受体，以充分模拟炎症和调节臂之间的串扰。 免疫反应 在这个快速通道建议的第一阶段，我们将证明开发一个单一机制的技术可行性。 QSP模型结构，捕获药物剂量依赖性的膨胀和收缩的四个独特的IC分子。通过 拟合每个分子的临床前和临床数据，我们将建立一个强大的人类剂量转化策略， 预测.在第二阶段，平台模型将与Applied BioMath的 Assess™基于浏览器的界面。通过此设置，用户可以交互式地探索IC设计空间并使用仿真 了解不同剂量、给药间隔、靶亲和力、细胞因子效力和药物半衰期对临床 PK/PD。我们希望这种互动工具将促进多学科药物管理系统内的有效沟通。 开发团队，并帮助他们合理地确定最佳的分子特征和剂量策略， IC。该平台还将允许虚拟患者队列模拟，以指导临床试验的选择标准。 目前还没有有效的工具来筛选IC空间中的候选分子。我们提出的计算平台 预测新型IC的最佳剂量和治疗窗口将加速冗长而昂贵的先导候选药物的开发， 选择过程，从而降低IC开发成本，促进临床试验设计，减少后期流失， 更快地将新药推向市场，使患者的医疗保健受益。

Project Summary/Abstract Immunocytokines (ICs) are fusion proteins of an engineered cytokine conjugated to an antibody. These novel molecules are the next generation of cytokine-based immunotherapies with potential applications in a diverse range of diseases such as rheumatoid arthritis (RA), psoriasis, and cancer. ICs are designed to selectively target diseased tissue or specific immune cells with minimal systemic immune activation that typically leads to dose-limiting toxicity in recombinant cytokine therapy. However, it is challenging to design a molecule with high target specificity, predict its pharmacokinetics and identify doses that achieve high efficacy but low toxicity, i.e. the therapeutic window. We are proposing to develop a simulation platform for IC screening that will computationally predict dose and therapeutic window of novel ICs under development. The platform will implement a quantitative systems pharmacology (QSP) model that mechanistically describes the binding of an IC to target and off-target cells and links cytokine receptor occupancy to cellular activation and expansion dynamics. The model will predict in vivo pharmacokinetics (PK) and pharmacodynamics (PD) for an input dose and dosing regimen of a proposed IC. Simulations will report readouts such as cell counts and soluble cytokine levels that are clinically observable biomarkers of efficacy and toxicity. The model will be general enough to simulate pro- and anti-inflammatory ICs. A modular design will allow us to add new cell types and cytokines/receptors as needed to adequately model the crosstalk between the inflammatory and regulatory arms of the immune response. In Phase I of this Fast Track proposal, we will demonstrate the technical feasibility of developing a single mechanistic QSP model structure that captures drug dose- dependent expansion and contraction of four unique IC molecules. By fitting preclinical and clinical data for each molecule, we will establish a robust translational strategy for human dose prediction. In Phase II the platform model will be integrated with and made accessible through Applied BioMath’s Assess™ browser-based interface. With this setup, users can interactively explore the IC design space and use simulations to understand the impact of varying dose, dosing interval, target affinities, cytokine potency and drug half-life on clinical PK/PD. We expect that this interactive tool will foster effective communication within multidisciplinary drug development teams, and help them rationally identify optimal molecular characteristics and dosing strategies for novel ICs. The platform will also allow virtual patient cohort simulations to guide selection criteria for clinical trials. There are currently no effective tools to screen candidate molecules in the IC space. Our proposed computational platform to predict the optimal dose and therapeutic window of novel ICs will accelerate the lengthy and expensive lead candidate selection process, and thus lower the cost of IC development, facilitate clinical trial design, reduce late stage attrition and bring new drugs to the market faster to benefit patient healthcare.