Clinical translation pathway for identifying patient-specific drugs based on predictive simulation outcomes with ex vivo validation.

e19582 Background: The unique signature of a patient’s tumor implies that a one-size-fits-all treatment approach is suboptimal, thereby underscoring the need to rationally design therapies employing N=1 segmentation. Reuse of drugs with clinical data allows rapid translational advancement. To identify agents effective in a specific patient, we (1) employed predictive modeling using patient genomic profiling to create a simulation avatar, (2) simulated four molecularly targeted drugs with clinical data and (3) validated predictions ex-vivo in patient-derived cell lines. Methods: Clinical patient samples were analyzed for chromosomal alterations using whole genome array Comparative Genomic Hybridization (aCGH) by GenPath Diagnostics. This data were used to create a simulation avatar using Cellworks validated predictive technology, a comprehensive representation of plasma cell myeloma (PCM) physiology incorporating signaling and metabolic networks. We simulated AZD2014 (mTOR), vismodegib (Hedgehog), dasatini...