A platform to identify in vivo targets of covalent cancer drugs in 3D tissues

摘要 共价抑制剂代表了人类历史上一些最成功的药物，包括阿司匹林和 青霉素。最近，靶向共价药物已经成为实现以下目标的引人注目的方法 肿瘤学的主要目标已经被证明对于更经典的可逆小分子来说是难以捉摸的，包括 例如，致癌激酶(BTK、EGFR、FGFR、JAK3)的选择性失活，最值得注意的是， 抑制曾经被认为不能下药的KRAS蛋白。我们现在正处于兴趣的复苏之中 在共价药物方面，因为它们被证明有能力与历史上 被认为是无法下药的。然而，尽管它们被证明是成功的，而且具有内在的效力优势，但 由于担心潜在的不可逆转的脱靶问题，人们普遍不愿开发共价药物 跨不同器官系统的毒性。因此，对目标上和目标外的全面了解 体内对于共价药物来说是至关重要的。目前，还不可能确定药物在整个动物体内的结合。 在哺乳动物中具有细胞和分子分辨率。 建立在称为CATCH(清除辅助组织点击)的组织成像领域的最新突破基础上 化学)，我们建议开发一个通用的平台，用于体内成像药物与靶相互作用 通过综合应用高分辨率全身成像和 化学蛋白质组学(如基于活性的蛋白质组分析，或ABPP)通过相同的共价探针。 通过这种方式，药物靶向的活哺乳动物中的每一个细胞(包括靶点上和靶外)都可以被原位揭示。 并注册到定义的蛋白质图上，以筛选和识别体内的药物靶点。数据流 由该平台生成的可以快速将丰富的药物亲和力知识与治疗指数联系起来， 因此，加快了将化学活性转化为癌症治疗的进程。 我们的团队在化学蛋白质组学和组织成像方面拥有成熟和互补的专业知识，以 确保项目的成功实施。在这个IMAT R33应用程序中，我们计划进一步开发 捕获以描述共价激酶抑制剂的体内靶点。首先，我们将使CATCH适应3D身体组织 (目标1)。接下来，我们将把CATCH扩展到一系列共价BTK(Bruton‘s Tyroine Kinase)抑制剂(目标2)。 最后，我们将描述BTK抑制剂在小鼠心血管中的剂量依赖的体内细胞靶点 系统(目标3)。我们预计这些研究将建立活体捕捉方法来识别靶标 共价BTK抑制剂，以更好地了解其疗效和毒性。更广泛地说，已建立的平台 可广泛应用于任何共价抗癌药物，以无偏见地进行体内靶点识别。输油管道， 分析和高分辨率药物靶标数据将迅速传播，供公众查阅和探索， 对共价抗癌药物的发现和改进产生直接、直接和深远的影响。

Abstract Covalent inhibitors represent some of the most successful drugs in human history, including aspirin and penicillin. Recently, targeted covalent drugs have taken center stage as a compelling approach for achieving major goals in oncology that have proven elusive for more classical reversible small molecules, including, for instance, the selective inactivation of oncogenic kinases (BTK, EGFR, FGFR, JAK3) and, most notably, the inhibition of the once-deemed undruggable KRAS protein. We are now in the midst of a resurgence of interest in covalent drugs for their demonstrated capability to engage cancer targets that have been historically considered undruggable. However, despite their proven success and inherent advantages of potency, there has been a general reluctance to develop covalent drugs due to the concern of potential irreversible off-target toxicity across different organ systems. Hence, a comprehensive understanding of both on and off-targets in vivo is critical for covalent drugs. Currently, it is impossible to determine drug binding across a whole animal with cellular and molecular resolution in mammals. Building upon a recent breakthrough in tissue imaging termed CATCH (Clearing-Assisted Tissue click Chemistry), we propose to develop a general platform for in vivo imaging of drug-target interactions with unprecedented spatial precision by integrated applications of high-resolution whole-body imaging and chemoproteomics (such as Activity-Based Proteomic Profiling, or ABPP) through the same covalent probes. This way, every cell in a living mammal targeted by the drug (both on- and off-target) can be revealed in situ and registered onto a defined protein map to screen and identify in vivo drug targets. The data stream generated by this platform could rapidly link the rich knowledge of drug affinity to the therapeutic index, therefore accelerating the translation of chemical activities into cancer therapies. Our team has well-established and complementary expertise in chemoproteomics and tissue imaging to ensure the successful execution of the project. In this IMAT R33 application, we plan to further develop CATCH to profile in vivo targets of covalent kinase inhibitors. First, we will adapt CATCH to 3D somatic tissues (Aim 1). Next, we will expand CATCH to an array of covalent BTK (Bruton’s tyrosine kinase) inhibitors (Aim 2). Finally, we will profile dose-dependent in vivo cellular targets of BTK inhibitors in the mouse cardiovascular system (Aim 3). We anticipate that these studies will establish in vivo CATCH methods for identifying targets of covalent BTK inhibitors to better understand their efficacy and toxicity. More generally, the established platform can be broadly applied to any covalent cancer drug for unbiased in vivo target identification. The pipeline, analytics, and high-resolution drug target data will be rapidly disseminated for public access and exploration, releasing an immediate, direct, and profound impact on covalent cancer drug discovery and refinement.