Accurate preclinical predictions of the clinical efficacy of experimental cancer drugs are highly desired but often haphazard. Such predictions might be improved by incorporating elements of the tumor microenvironment in preclinical models by providing a more physiological setting. In generating improved xenograft models, it is generally accepted that the use of primary tumors from patients are preferable to clonal tumor cell lines. Here we describe an interdisciplinary platform to study drug response in multiple myeloma, an incurable cancer of the bone marrow. This platform uses microfluidic technology to minimize the number of cells per experiment, while incorporating three-dimensional extracellular matrix and mesenchymal cells derived from the tumor microenvironment. We used sequential imaging and a novel digital imaging analysis algorithm to quantify changes in cell viability. Computational models were used to convert experimental data into dose-exposure-response "surfaces," which offered predictive utility. Using this platform, we predicted chemosensitivity to bortezomib and melphalan, two clinical multiple myeloma treatments, in three multiple myeloma cell lines and seven patient-derived primary multiple myeloma cell populations. We also demonstrated how this system could be used to investigate environment-mediated drug resistance and drug combinations that target it. This interdisciplinary preclinical assay is capable of generating quantitative data that can be used in computational models of clinical response, demonstrating its utility as a tool to contribute to personalized oncology.