A simulation platform to predict dose and therapeutic window of immunocytokines

预测免疫细胞因子剂量和治疗窗的模拟平台

基本信息

批准号：
10698708
负责人：
Raibatak Das
金额：
$ 30万
依托单位：
APPLIED BIOMATH, LLC
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10698708
关键词：
Acceleration Address Affinity Anti-CEA Antibody Anti-Inflammatory Agents Antibodies Area Autoimmune Diseases Avidity Benchmarking Binding Biological Assay Biological Markers Biological Products CD8B1 gene Calibration Cell Count Cells Characteristics Chimeric Proteins Clinic Clinical Clinical Data Clinical Trials Clinical Trials Design Communication Cytokine Receptors Data Development Disease Disparate Dose Dose Limiting Drug Kinetics Equilibrium Eragrostis Evaluation Flow Cytometry Fostering Half-Life Healthcare Human Immune Immune Targeting Immune response Immunotherapy In Vitro Inflammatory Inflammatory Response Interleukin-10 Interleukin-12 Interleukin-15 Interleukin-2 Interleukin-4 Kinetics Lead Link Malignant Neoplasms Marketing Measurable Measurement Mediating Modeling Molecular Natural Killer Cells Oncology Online Systems Parameter Estimation Patients Pharmaceutical Preparations Pharmacodynamics Pharmacology Phase Population Process Protein Engineering Psoriasis Recombinant Cytokines Regimen Regulatory T-Lymphocyte Reporting Rheumatoid Arthritis Risk Running Scanning Selection Criteria Serum Small Business Innovation Research Grant Specificity Structure System Testing Therapeutic Time Tissues Toxic effect antibody conjugate arm cancer therapy candidate selection cell growth cell type clinical application clinical efficacy clinical predictors cohort commercialization computational platform cost cytokine cytokine therapy design drug development drug disposition effector T cell graphical user interface high risk immune activation in silico in vivo interactive tool lead candidate models and simulation multidisciplinary next generation novel novel strategies novel therapeutics pharmacokinetics and pharmacodynamics pre-clinical predicting response predictive modeling receptor screening simulation systemic toxicity tool translational approach virtual patient web app

项目摘要

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.

项目概要/摘要免疫细胞因子 (IC) 是与抗体结合的工程细胞因子的融合蛋白。是下一代基于细胞因子的免疫疗法，在多种疾病中具有潜在的应用，例如类风湿性关节炎 (RA)、牛皮癣和癌症等 IC 旨在选择性地针对患病组织或特定部位。免疫细胞具有最小的全身免疫激活，通常会导致重组中的剂量限制性毒性然而，设计具有高靶点特异性的分子并预测其药代动力学是具有挑战性的。并确定实现高效低毒的剂量，即治疗窗。我们提议开发一个用于 IC 筛选的模拟平台，该平台将通过计算预测剂量和治疗效果该平台将实施定量系统药理学（QSP）模型。从机制上描述了 IC 与靶细胞和非靶细胞的结合，并将细胞因子受体占用与该模型将预测体内药代动力学 (PK) 和药效学。 (PD) 用于建议 IC 的输入剂量和给药方案模拟将报告细胞计数和给药方案等读数。可溶性细胞因子水平是临床上可观察到的功效和毒性生物标志物。该模型将是通用的。足以模拟促炎和抗炎 IC。模块化设计将使我们能够添加新的细胞类型和根据需要充分模拟炎症和调节臂之间的串扰免疫反应。在此快速通道提案的第一阶段，我们将演示开发单一机制的技术可行性 QSP 模型结构可捕获四种独特 IC 分子的药物剂量依赖性扩张和收缩。拟合每个分子的临床前和临床数据，我们将为人体剂量建立稳健的转化策略预测在第二阶段，平台模型将与 Applied BioMath 集成并通过其访问。 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、靶标亲和力、细胞因子效力和药物半衰期对临床的影响我们期望这种互动工具将促进多学科药物内部的有效沟通。开发团队，并帮助他们合理确定新型药物的最佳分子特征和给药策略该平台还将允许虚拟患者队列模拟来指导临床试验的选择标准。目前没有有效的工具来筛选 IC 空间中的候选分子。预测昂贵新型 IC 的最佳剂量和治疗窗口将加速长期领先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，减少后期损耗和将新药更快地推向市场，以造福患者的医疗保健。