Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate

Assays that can determine the response of tumor cells to cancer therapeutics could greatly aid the selection of drug regimens for individual patients. However, the utility of current functional assays is limited, and predictive genetic biomarkers are available for only a small fraction of cancer therapies. We found that the single-cell mass accumulation rate (MAR), profiled over many hours with a suspended microchannel resonator, accurately defined the drug sensitivity or resistance of glioblastoma and B-cell acute lymphocytic leukemia cells. MAR revealed heterogeneity in drug sensitivity not only between different tumors, but also within individual tumors and tumor-derived cell lines. MAR measurement predicted drug response using samples as small as 25 μl of peripheral blood while maintaining cell viability and compatibility with downstream characterization. MAR measurement is a promising approach for directly assaying single-cell therapeutic responses and for identifying cellular subpopulations with phenotypic resistance in heterogeneous tumors.United States. National Institutes of Health (R01 CA170592)United States. National Institutes of Health (R33 CA191143)National Cancer Institute (U.S.) (U54 CA143874)United States. National Institutes of Health (NIH/NIGMS T32 GM008334

Supply chain network strategy for consumer medical device introduction

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016. In conjunction with the Leaders for Global Operations Program at MIT.Cataloged from PDF version of thesis.Includes bibliographical references (pages 97-99).This thesis presents an optimization framework to model the trade-offs in strategic supply chain decision-making for a new product introduction in a real-world setting. The focus of the thesis is on a consumer medical device that Johnson & Johnson's Calibra business will launch in the future. As with any new product introduction, the launch exposes the J&J business to risk and uncertainty. We develop a mixed-integer optimization model to guide the optimal design of a global consumer medical device supply chain network comprising component suppliers, assembly facilities, sterilizers, and distribution centers. The model evaluates strategic decisions over a seven-year time horizon related to the location and capacities of various supply chain facilities and partners, transportation costs, and strategic inventory required to satisfy global demand. We developed a stochastic optimization extension of the model to protect the supply chain decision maker from demand uncertainty. Comparison of the output of the model assuming deterministic demand to a managerial heuristic resulted in total supply chain network cost reductions of 19% - 27%, amounting to hundreds of millions in present-value dollars. The stochastic optimization solution reduces infeasibility related to either not meeting the demand or transportation lead time constraints. The two models presented in this thesis enable J&J supply chain decision makers to gauge the additional costs and benefits of different network design concepts, develop a network strategy that can adapt to uncertain demand, and create a strong strategic foundation for future tactical and operational decisions.by Samer Haidar.M.B.A.S.M. in Engineering System