AI enhanced lifetime-based mesoscopic in vivo imaging of tissue molecular heterogeneity

摘要 药物-靶点结合的定量被认为是药物开发中最关键的参数 管道，因为它是治疗行动的核心。但是，这些参数只能通过侵入性评估 生物化学和免疫组织化学（IHC）方法。在此，我们建议整合和 优化多模态光学成像平台，其可以提供直接纵向（多个时间点） 与药物相关的跨相同组织体积的药物-靶标接合分布的测量 递送功效参数，包括肿瘤脉管系统和药物反应的指标，如代谢。 成像平台将在活体动物的人乳腺肿瘤和患者源性异种移植物中进行验证 接受HER 2-曲妥珠单抗治疗。此外，由于MFMT是依赖于图像的间接成像技术， 在复杂的计算任务上，我们将进一步开拓深度学习方法的使用， 精确、无参数和用户友好的2D和3D MFMT图像形成。

Abstract Quantification of drug-target engagement is recognized as the most crucial parameter in the drug development pipeline as it is central to therapeutic action. Though, such parameter can only be assessed via invasive biochemical and immunohistochemical (IHC) approaches in ex vivo tissues. Herein, we propose to integrate and optimize a multimodal optical imaging platform that can provide direct longitudinal (multiple time points) measurements of the drug-target engagement distribution across the same tissue volume in correlation with drug delivery efficacy parameters, including, tumor vasculature, and indicators of drug response, such as metabolism. The imaging platform will be validated in human breast tumor and patient derived xenografts in live animals subjected to HER2-trastuzumab therapy. Additionally, as MFMT is an indirect image formation technique relying on complex computational tasks, we will further pioneer the use of Deep Learning methodologies for fast, accurate, parameter-free and user friendly 2D and 3D MFMT image formation.