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

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.