Andrew M. Stein
I'm a Director of Pharmacometrics at Novartis in Cambridge, MA. I did postdoctoral research at the Institute for Math and its Applications, a PhD in Applied Mathematics at University of Michigan, and a Bachelors and Masters in Mechanical Engineering at MIT.
Inspired by theoretical physicists who use mathematical models of the physical world to uncover insights that guide engineers, my goal is to use mechanistic and statistical models of preclinical and clinical data to uncover new insights that guide drug development. My expertise is in oncology and immunology, with experience in leukemia [1,2], solid tumors [3,4,5,6], chimeric antigen receptor T-cells [7,8], and target mediated drug disposition of biologics [9,10,11,12].
For building understanding, it is useful to imagine that we understand biological systems well enough to write down equations to describe them. However, for informing decisions, it is critical to acknowledge that we often do not understand biology well enough to write down equations that make accurate predictions. To support model-informed decision-making in the face of uncertainty, I have co-developed an Uncertainty Pedigree Table (discussed in this presentation) and adapted the FDA's Context of Use Table .