Quantitative analysis of pharmacological mechanism by intravital imaging

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This proposal is from an inter-disciplinary team with expertise in imaging, systems biology and clinical research (Weissleder, Sorger, Mitchison and Danuser). It responds to RFA-CA-11-005, and proposes to create and validate a platform for quantitative cancer pharmacology in mice using live imaging at the single-cell level. The immediate goal is to determine how individual cancer cells in tumors respond to single and dual-agent therapy in vivo by measuring both the uptake and distribution of drugs in tumor cells (pharmaco-kinetics, PK), as well as the multiple downstream responses that play out over different time-scales (pharmaco-dynamics, PD). Our focus will be on the use of navitoclax (ABT263; an investigational antagonist of Bcl-2 and Bcl-Xl) and gemcitabine (a well established, S-phase-specific cytotoxic drug), both individually and in combination. Aim 1 will develop and validate live-cell biosensors for measuring pre-drug tumor states and drug responses in mouse xenograft tumors by sub-cellular resolution intravital imaging. Aim 2 will develop injectable imaging agents based on novel bioorthogonal chemistries that will be used to label drugs fluorescently or with 18F. These agents will then be validated using the biosensors from aim 1. Aim 3 will focus on the development of computational methods for extracting quantitative data from intravital images, and on the creation of data- driven, multi-scale (PK/PD; single cell kinetics and mechanistic/molecular) mathematical models that elucidate therapeutically relevant differences between mono and combination therapy. Overall, our goal is to gain an understanding of drug action that is: a) quantitative (in the treatment of data that may vary in time and space); b) probabilistic (accounting for cell-to-cell and tumor-to-tumor variability); c) mechanistic at the molecular level; d) post-genomic (analyzing diverse cell lines and patient samples with known genetic differences); e) integrative (assuming that determinants of drug response are multi-factorial); f) mathematically sophisticated (with respect to mass-action, lumped parameter and stochastic modeling); and g) medically relevant (by analyzing a combination therapy currently under clinical investigation and by developing translatable measurement methods). Success of the project will result in an integrated platform for single-cell cancer pharmacology. This platform will not only serve to overcome prevailing impediments to the translation of preclinical results into a clinical setting, but will provide the knowledge base for rational and predictive combination therapy. Likewise, it will improve our ability to develop effective drugs capable of inhibiting new and existing targets, and could also be more broadly used in the development of other emerging anti-cancer drugs.