Christian C. Felder

Xiong Liu, Craig E. Thomas, Christian C. Felder

Pharmaceutical companies are relying more often on external sources of innovation to boost their discovery research productivity. However, more in-depth knowledge about how external innovation may translate to successful product launches is still required in order to better understand how to best leverage the innovation ecosystem. We analyzed the pre-approval publication histories for FDA-approved new molecular entities (NMEs) and new biologic entities (NBEs) launched by 13 top research pharma companies during the last decade (2006-2016). We found that academic institutions contributed the majority of pre-approval publications and that publication subject matter is closely aligned with the strengths of the respective innovator. We found this to also be true for candidate drugs terminated in Phase 3, but the volume of literature on these molecules is substantially less than for approved drugs. This may suggest that approved drugs are often associated with a more robust dataset provided by a large number of institutes. Collectively, the results of our analysis support the hypothesis that a collaborative research innovation environment spanning across academia, industry and government is highly conducive to successful drug approvals.

Robert A. Bridges, Chris Felder, Chelsey Hoff

Scientists and engineers rely on accurate mathematical models to quantify the objects of their studies, which are often high-dimensional. Unfortunately, high-dimensional models are inherently difficult, i.e. when observations are sparse or expensive to determine. One way to address this problem is to approximate the original model with fewer input dimensions. Our project goal was to recover a function f that takes n inputs and returns one output, where n is potentially large. For any given n-tuple, we assume that we can observe a sample of the gradient and output of the function but it is computationally expensive to do so. This project was inspired by an approach known as Active Subspaces, which works by linearly projecting to a linear subspace where the function changes most on average. Our research gives mathematical developments informing a novel algorithm for this problem. Our approach, Active Manifolds, increases accuracy by seeking nonlinear analogues that approximate the function. The benefits of our approach are eliminated unprincipled parameter, choices, guaranteed accessible visualization, and improved estimation accuracy.