Targeting the aberrant epidermal growth factor receptor (EGFR) signaling pathway is an attractive choice for many cancers (e.g., non-small cell lung carcinoma (NSCLC) and head and neck squamous cell carcinoma (HNSCC)). Despite the development of promising therapeutics, incomplete target engagement and acquired resistance (e.g., mutagenesis and intracellular signaling pathway rewiring) ensure that curative options still elude patients. To address limitations posed by standard drug evaluation assays (e.g., western blot, bulk plasma monitoring, immunohistochemistry), we have developed a novel dynamic, fluorescence-based platform termed intracellular paired agent imaging (iPAI). iPAI quantifies intracellular protein target engagement using two matched small-molecule, cell membrane-permeable agents: one targeted to the protein of interest and one untargeted, which accounts for non-specific therapeutic uptake. Currently, our iPAI panel includes successfully characterized tyrosine kinase inhibitors targeting the kinase binding domain of numerous proteins in the EGFR pathway, including erlotinib (EGFR). Here, we present a pharmacokinetic uptake study using our novel iPAI erlotinib reagents: a targeted erlotinib probed conjugated to silicon tetramethylrhodamine (Erl- SiTMR-T) and an untargeted reagent conjugated to tetramethylrhodaime (Erl-TMR-UT). An initial uptake study in a cell derived xenograft (CDX) model of NSCLC was performed by administering the Erl iPAI reagents systemically via tail vein injection, where drug uptake was quantified in the tumor over time. Excitingly, evidence of heterogeneous uptake was observed in the iPAI injected cohort, displaying distinct drug-uptake within a single tumor. Characterization of additional iPAI agents targeting downstream effectors (e.g., AKT, PI3K, MEK and ERK) is ongoing and will allow us to visualize complex drug-target interactions and quantify their downstream signaling partners during treatment regimens for NSCLC and other cancers. Together, we anticipate these iPAI probes will improve understanding of current limitations in personalized cancer therapy.