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All investors require some understanding of the potential risk and reward of a given venture before they’re willing to invest, and the less they know about it, the less capital will be available. This is especially true with biomedical assets in which the risks and rewards are equally outsized and hard to evaluate. Therefore, a prerequisite for addressing the so-called “Valley of Death” in early-stage biomedical R&D funding is better risk analytics. In this talk, Prof. Lo will describe his latest research on estimating historical probabilities of success for clinical trials and then applying machine-learning techniques to predict future success rates across a variety of drug/indication pairs. With more accurate assessments of these success probabilities, capital can be deployed more efficiently and at lower cost, thereby bringing greater amounts of capital into the biopharma industry at a time when capital is needed most.

 

2019 MIT Increased Productivity in the Biopharmaceutical Industry Conference

“Mice are not little people” – a refrain becoming louder as the strengths and weaknesses of animal models of human disease become more apparent. At the same time, three emerging approaches are headed toward integration: powerful systems biology analysis of cell-cell and intracellular signaling networks in patient-derived samples; 3D tissue engineered models of human organ systems, often made from stem cells; and micro-fluidic and meso-fluidic devices that enable living systems to be sustained, perturbed and analyzed for weeks in culture. This talk will highlight the integration of these rapidly moving fields to understand difficult clinical problems, with an emphasis on translating academic discoveries into practical use. Technical challenges in modeling complex diseases with “organs on chips” approaches include the need for relatively large tissue masses and organ-organ cross talk to capture systemic effects, as well as new ways of thinking about scaling to capture multiple different functionalities from drug clearance to cytokine signaling crosstalk. Examples in gynecology, metabolic diseases and other chronic inflammatory conditions will be highlighted.