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.
癌症是美国发病率和死亡率的主要原因,有180万例和60万癌症 预计2020年的死亡。在过去的二十年中,癌症治疗取得了长足进展, 在很大程度上通过开发高度靶向疗法,包括开发抗体 - 药物缀合物 (ADC)。 ADCS雇员单克隆抗体具有特异性的肿瘤相关抗原,以增加 抗癌毒素(即有效载荷)递送到癌细胞的效率和选择性。虽然这个 事实证明,方法是成功的,在美国使用了9种抗癌ADC(Brentuximab vedotin, 曲妥珠单抗emtansine,gemtuzumab ozogamicin,inotuzumab ozogamicin,polatuzumab vedotin,enfortumab Vedotin,Belantamab Mafodotin,Trastuzumab Deruxtecan和Sacituzumab Govitecan),ADC疗法通常是 与大量脱靶毒性,狭窄的治疗窗口和临床高失败率有关 测试。该项目引入了一种新的药代动力学策略,以增加抗体 - 抗癌药的定向输送。在我们的方法中,有效载荷结合抗体片段,称为有效载荷 - 结合选择性增强子(PBSE)与ADC共同管理以减少健康的暴露 组织到有效载荷剂,从而减少了脱靶毒性的发展,增加了可耐受的剂量 ADC的ADC效率提高。该策略是基于认识到异地ADC毒性的认识 首先归因于已发布的(“免费”)有效载荷分子,也归因于PBSE可能是 用于防止细胞进入非靶向细胞中的免费有效载荷分子(通过防止扩散 质膜)不改变ADC进入靶细胞(通过受体介导的进行靶向细胞 内吞作用)。该项目的工作将重点介绍一系列新型PBSE的开发和评估 已显示可降低游离SN38和DXD的细胞毒性。这些代理是camptothecin衍生物 用作sacituzumab govitecan和trastuzumab deruxtecan的有效载荷,两名最近批准 在临床研究中,ADC分子显示出一些有效性但具有实质性毒性的分子。机理 AIM#1和AIM 2在PBSE分子属性之间提出的研究(例如,亲和力, 分子模态[即IgG,Fab,SCFV,SDAB],无偶联有效载荷的选择性,分子电荷等)和 PBSE实用程序可增强ADC治疗的药代动力学和药效学选择性。这些发现 将通过使用机械数学模型来集成 代理和给药方案,用于评估目标3中的效率和毒性。新颖的代理商在此发展 工作可能适合立即翻译以优化sacituzumab govitecan和trastuzumab 难治性和抗性乳腺癌的Deruxtecan治疗。

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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