Enhancement of ADC selectivity by inverse targeting: Mechanistic studies and optimization

通过反向靶向增强 ADC 选择性:机理研究和优化

基本信息

项目摘要

Cancer is a major cause of morbidity and mortality in the US, with 1.8 million cases and 600 thousand cancer deaths projected for 2020. Substantial progress in cancer treatment has been made in the past two decades, largely through the development of highly targeted therapies, including development of antibody-drug conjugates (ADCs). ADCs employ monoclonal antibodies with specificity for tumor-associated antigens to increase the efficiency and selectivity of the delivery of anti-cancer toxins (i.e., payloads) to cancer cells. Although this approach has proven to be successful, with 9 anti-cancer ADCs approved for use in the US (brentuximab vedotin, trastuzumab emtansine, gemtuzumab ozogamicin, inotuzumab ozogamicin, polatuzumab vedotin, enfortumab vedotin, belantamab mafodotin, trastuzumab deruxtecan, and sacituzumab govitecan), ADC therapies are often associated with substantial off-target toxicity, narrow therapeutic windows, and high failure rates in clinical testing. This project introduces a new pharmacokinetic strategy to increase the tumor-selectivity of antibody- directed delivery of anti-cancer drugs. In our approach, payload-binding antibody fragments, termed payload- binding selectivity enhancers (PBSE), are co-administered with ADCs to decrease the exposure of healthy tissues to payload agents, thereby reducing the development of off-target toxicity, increasing the tolerable dose of ADCs, and increasing ADC efficacy. The strategy is based on the recognition that off-site ADC toxicity is primarily attributed to the released (“free”) payload molecule, and also on the hypothesis that PBSE may be employed to prevent cellular entry of free payload molecules in non-targeted cells (by preventing diffusion across plasma membranes) without altering entry of ADCs into targeted cells (which proceeds via receptor mediated endocytosis). Work in this project will focus on the development and evaluation of a novel series of PBSE that have been shown to decrease the cytotoxicity of free SN38 and Dxd. These agents are camptothecin derivatives that are employed as payloads for sacituzumab govitecan and trastuzumab deruxtecan, two recently approved ADC molecules that have shown some efficacy, but substantial toxicity, in clinical investigations. Mechanistic studies proposed in Aim #1 and Aim #2 examine relationships between PBSE molecular attributes (e.g., affinity, molecular modality [i.e., IgG, Fab, scFv, sdAb], selectivity for unconjugated payload, molecular charge, etc.) and PBSE utility in enhancing the pharmacokinetic and pharmacodynamic selectivity of ADC therapy. These findings will be integrated through the use of mechanistic mathematical modeling to assist in the selection of an optimal agent and dosing regimen for evaluation of efficacy and toxicity in Aim #3. The novel agents developed in this work may be suitable for immediate translation toward optimization of sacituzumab govitecan and trastuzumab deruxtecan therapy of refractory and resistant breast cancer.
癌症是美国发病和死亡的主要原因,有 180 万例癌症和 60 万癌症患者 预计 2020 年死亡人数。过去二十年,癌症治疗取得了实质性进展, 主要通过开发高度针对性的疗法,包括开发抗体药物偶联物 (ADC) 使用对肿瘤相关抗原具有特异性的单克隆抗体来增加 向癌细胞递送抗癌毒素(即有效负载)的效率和选择性。 该方法已被证明是成功的,9 种抗癌 ADC 已获准在美国使用(brentuximab vedotin、 曲妥珠单抗 emtansine、吉妥珠单抗 ozogamicin、inotuzumab ozogamicin、polatuzumab vedotin、enfortumab vedotin、belantamab mafodotin、曲妥珠单抗 deruxtecan 和 sacituzumab govitecan),ADC 疗法通常 与显着的脱靶毒性、狭窄的治疗窗和临床上的高失败率相关 该项目引入了一种新的药代动力学策略,以提高抗体的肿瘤选择性。 在我们的方法中,有效负载结合抗体片段,称为有效负载- 结合选择性增强剂 (PBSE) 与 ADC 共同施用,以减少健康人的暴露 组织到有效负载药剂,减少脱靶毒性的发展,从而增加耐受剂量 ADC 的毒性,以及提高 ADC 功效 该策略基于对场外 ADC 毒性的认识。 主要归因于释放的(“游离”)有效负载分子,并且还基于 PBSE 可能是 用于防止非目标细胞中游离有效负载分子进入细胞(通过防止扩散 质膜)而不改变 ADC 进入靶细胞(通过受体介导进行) 该项目的工作重点是开发和评估一系列新颖的 PBSE, 已被证明可以降低游离 SN38 和 Dxd 的细胞毒性。这些药物是喜树碱衍生物。 用作 sacituzumab govitecan 和 trastuzumab deruxtecan 的有效负载,这两种药物最近获得批准 ADC 分子在临床研究中显示出一定的功效,但具有显着的毒性。 目标 #1 和目标 #2 中提出的研究检查 PBSE 分子属性之间的关系(例如亲和力、 分子形态[即 IgG、Fab、scFv、sdAb]、未缀合有效负载的选择性、分子电荷等)和 PBSE 在增强 ADC 治疗的药代动力学和药效学选择性方面的作用。 将通过使用机械数学模型进行整合,以协助选择最佳的 用于评估目标#3中的疗效和毒性的药物和给药方案。 这项工作可能适合立即转化为 sacituzumab govitecan 和 trastuzumab 的优化 deruxtecan 治疗难治性和耐药性乳腺癌。

项目成果

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Joseph P Balthasar其他文献

Joseph P Balthasar的其他文献

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{{ truncateString('Joseph P Balthasar', 18)}}的其他基金

Pharmacokinetic / Pharmacodynamic Optimization of ADC Therapy for Acute Myeloid Leukemia
急性髓系白血病 ADC 治疗的药代动力学/药效学优化
  • 批准号:
    10561230
  • 财政年份:
    2023
  • 资助金额:
    $ 36.33万
  • 项目类别:
Enhancement of ADC selectivity by inverse targeting: Mechanistic studies and optimization
通过反向靶向增强 ADC 选择性:机理研究和优化
  • 批准号:
    10415220
  • 财政年份:
    2021
  • 资助金额:
    $ 36.33万
  • 项目类别:
Enhancement of ADC selectivity by inverse targeting: Mechanistic studies and optimization
通过反向靶向增强 ADC 选择性:机理研究和优化
  • 批准号:
    10623301
  • 财政年份:
    2021
  • 资助金额:
    $ 36.33万
  • 项目类别:
Pharmacokinetic strategies to increase monoclonal antibody uptake, distribution, and efficacy for treatment of solid tumors
增加单克隆抗体摄取、分布和治疗实体瘤疗效的药代动力学策略
  • 批准号:
    10623152
  • 财政年份:
    2020
  • 资助金额:
    $ 36.33万
  • 项目类别:
Pharmacokinetic strategies to increase monoclonal antibody uptake, distribution, and efficacy for treatment of solid tumors
增加单克隆抗体摄取、分布和治疗实体瘤疗效的药代动力学策略
  • 批准号:
    10164739
  • 财政年份:
    2020
  • 资助金额:
    $ 36.33万
  • 项目类别:
Pharmacokinetic strategies to increase monoclonal antibody uptake, distribution, and efficacy for treatment of solid tumors
增加单克隆抗体摄取、分布和治疗实体瘤疗效的药代动力学策略
  • 批准号:
    10397091
  • 财政年份:
    2020
  • 资助金额:
    $ 36.33万
  • 项目类别:
Catch and Release Immunotoxins: CAR-Bombs for Cancer
捕获并释放免疫毒素:治疗癌症的 CAR 炸弹
  • 批准号:
    10062878
  • 财政年份:
    2016
  • 资助金额:
    $ 36.33万
  • 项目类别:
Pharmacokinetic strategies to optimize IP chemotherapy
优化IP化疗的药代动力学策略
  • 批准号:
    7144306
  • 财政年份:
    2006
  • 资助金额:
    $ 36.33万
  • 项目类别:
Pharmacokinetic strategies to optimize IP chemotherapy
优化IP化疗的药代动力学策略
  • 批准号:
    7646274
  • 财政年份:
    2006
  • 资助金额:
    $ 36.33万
  • 项目类别:
Pharmacokinetic strategies to optimize IP chemotherapy
优化IP化疗的药代动力学策略
  • 批准号:
    7286074
  • 财政年份:
    2006
  • 资助金额:
    $ 36.33万
  • 项目类别:

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