A Light sheet fluorescence microscopy and machine learning-based approach to investigate drug and biomarker distribution in whole organs and tumors

Abstract

AbstractTissue clearing and Light sheet fluorescence microscopy (LSFM) provide spatial information at a subcellular resolution in intact organs and tumors which is a significant advance over tools that limit imaging to a few representative tissue sections. The spatial distribution of drugs, targets, and biomarkers can help inform relationships between exposure at the site of action, efficacy, and safety during drug discovery. We demonstrate the use of LSFM to investigate distribution of an oncolytic virus (OV) and vasculature in xenograft tumors, as well as brain Aβ pathology in an Alzheimer’s disease (AD) mouse model. Machine learning-based image analysis tools developed to segment vasculature in tumors showed that random forest and deep learning methods provided superior segmentation accuracy vs intensity-based thresholding. Sub-cellular resolution enabled detection of punctate and diffuse intracellular OV distribution profiles. LSFM investigation in the brain in a TgCRND8 AD mouse model at 6.5 months of age enabled evaluation of Aβ plaque density in different brain regions. The utility of LSFM data to support quantitative systems pharmacology (QSP) and physiology-based pharmacokinetics (PBPK) modeling to inform drug development are also discussed. In summary, we showcase how LSFM can expand our understanding of macromolecular drug and biomarker distribution to advance drug discovery and development.