LBPO.BCS02 · 生物信息与计算 · Late-Breaking
定量系统药理学(QSP)助力AMP01的研究与转化,AMP01是一种新型次世代抗PD1双特异性药物,可放大并将内源性IL15重定向至PD1高表达T细胞,从而最大化疗效和治疗指数
Quantitative Systems Pharmacology (QSP) enables research and translation of AMP01, a novel next generation anti PD1 bispecific that amplifies and redirects endogenous IL15 to PD1 high expressing T Cells, maximizing efficacy and therapeutic index
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摘要 Abstract
中文摘要
背景:对经临床验证的细胞因子进行安全有效的给药令人振奋,但仍难以实现。主要挑战在于治疗指数(TI),即在提供充分的细胞因子肿瘤暴露的同时最小化其他部位的暴露,以避免全身性免疫激活。此前解决这一问题的尝试,包括工程化的IL15融合蛋白(FP)和PD1 x IL15双特异性融合蛋白,受限于毒性和抗药抗体的产生。AMP01是一种抗PD1 x 抗IL15双特异性生物制剂,是应对这一挑战的新颖解决方案。它被设计用于捕获并将内源性IL15重定向至PD1高表达T细胞,从而提供PD1抑制并将IL15靶向递送至肿瘤和肿瘤微环境。通过其设计,AMP01有望成为一款真正改进的次世代PD1项目,同时具备检查点抑制活性和靶向免疫激动活性。
方法:我们利用AMP01的体外、小鼠和非人灵长类数据,以及此前发表的抗PD1、IL15-FP和IL15 x PD1-FP药代动力学(PK)和药效动力学(PD)数据,开发了一个QSP模型。该模型还纳入了复杂的生物学和药物作用机制,例如IL15的合成与清除;IL15可溶性和膜结合受体结合;IL15暴露的改变;IL15介导的NK和T细胞动力学;PD1 PK和PD1抑制;以及动态的靶点介导药物处置。该模型描述了血浆中的PK和观察到的细胞动力学,并模拟了对照治疗药物和AMP01在肿瘤及其他外周组织中的效应。
结果:该模型 1) 通过预测最优的AMP01结合特性来指导AMP01设计和候选选择,从而在选择性靶向PD1高表达、IL15受体表达免疫细胞的同时选择性地维持肿瘤内高PD1覆盖,以最大化TI和疗效;2) 阐明了模型参数(如可溶性IL15和IL15受体浓度;膜结合受体表达、合成和清除速率;免疫细胞数量和动力学)的不确定性和变异性如何影响AMP01的靶点结合、细胞因子活性介导、非线性PK以及针对模拟虚拟患者变异性的给药;3) 支持计算机模拟的对照差异化,以更好地理解使用血液、肿瘤及其他外周室中模拟细胞动力学的安全有效给药和变异性。该模型仍在持续开发中,将用于提供首次人体剂量预测,以影响IND、监管决策和1期试验设计。
结论:该模型加速了AMP01的设计和选择,AMP01是一种潜在同类最佳的次世代多功能抗PD1疗法,并为IL15介导免疫细胞动力学的复杂性提供了洞见,以更好地理解针对模拟虚拟患者变异性的安全性和有效性。
查看英文原文 English abstract
Background: Safe and efficacious dosing of clinically validated cytokines is exciting but remains elusive. The primary challenge has been therapeutic index (TI), i.e. providing sufficient cytokine tumor exposure while minimizing exposure elsewhere to avoid systemic immune activation. Prior attempts to address this, including engineered IL15-fusion proteins (FP) and PD1 x IL15 bispecific-FPs, have been limited by toxicity and development of anti-drug antibodies. AMP01, an anti PD1 x anti IL15 bispecific biologic, is a novel solution to this challenge. It is designed to capture and redirect endogenous IL15 to PD1high expressing T Cells, thus providing PD1 inhibition and targeted delivery of IL15 to the tumor and tumor microenvironment. By design, AMP01 promises to be a truly improved next generation PD1 program with both checkpoint inhibitory and targeted immune agonistic activity.
Method: We developed a QSP model by leveraging AMP01 in vitro, mouse, and non-human primate data, and previously published anti PD1, IL15-FP, and IL15 x PD1-FP pharmacokinetic (PK) and pharmacodynamic (PD) data. The model also incorporates complex biology and drug MOA, e.g., IL15 synthesis and clearance; IL15 soluble and membrane bound receptor binding; alterations in IL15 exposure; IL15 mediated NK and T Cell dynamics; PD1 PK and PD1 inhibition; and dynamic target mediated drug disposition. The model described the PK and observed cell dynamics in the plasma, and simulated effects in tumor and other peripheral tissue of comparator therapeutics and AMP01.
Results: The model 1) informed AMP01 design and candidate selection by predicting optimal AMP01 binding characteristics to maximize TI and efficacy by selectively targeting PD1 high, IL15 receptor expressing immune cells while maintaining high PD1 coverage in the tumor selectively; 2) clarified how uncertainty and variability in model parameters (e.g., soluble IL15 and IL15 receptor concentration; membrane bound receptor expression, synthesis and clearance rates; immune cell numbers and dynamics) impacts AMP01 target engagement, mediation of cytokine activity, nonlinear PK, and dosing for simulated virtual patient variability; and 3) enables in silico comparator differentiation to better understand safe and efficacious dosing and variability using simulated cell dynamics in the blood, tumor, and other periphery compartments. This model continues to be developed and will be used to provide first in human dose predictions to impact IND, regulatory decisions, and phase 1 trial design.
Conclusions: The model accelerated the design and selection of AMP01, a potentially best in class next-generation multi-functional anti-PD1 therapy and provided insights into the complexity of IL15 mediated immune cell dynamics to better understand safety and efficacy for simulated virtual patient variability.
利益披露 Disclosure
J. M. Burke, None..
A. Mukhopadhyay, None..
M. Fogg, None..
S. Tom-Yew, None..
H. Pratap, None..
D. Flowers, None..
D. Marcantonio, None..
S. Minucci, None..
D. Hausman, None.
D. de Graaf,
Chugai Pharmaceuticals Independent Contractor.
S. Dixit, None.