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 Modeler's Hippocratic Oath, below.
- I will remember that I didn’t make the world, and it doesn’t satisfy my equations.
- Though I will use models to boldly predict study outcomes, I will not be overly impressed by mathematics.
- I will never sacrifice reality for elegance without explaining why I have done so.
- I will never give the people who use my model false comfort about its accuracy. Instead, I will make explicit its assumptions and oversights.
- I understand that my work may guide decisions, sometimes without me being there to offer input and provide caveats.
Contact: andy (dot) stein (at) gmail (dot) com