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I will share two examples of mathematics used in the biopharma industry.

1) How much better could patient responses be if we used the same drugs but changed the dosing regimen? With a mathematical model for the disease-therapy dynamics, and a quantitative therapeutic goal, we can apply optimal control to predict the regimen that will perform the best according to the goal. We can also quantify how much better the outcomes are predicted to be in comparison with standard of care regimens. I will explain the method using some examples, and will discuss some of the challenges of applying optimal control in the biopharma industry.

2) Immunotherapies don't work for many cancer patients; but when they do work, they can work extremely well. So it is important to figure out as early as possible if a patient will be a "responder" or a "non-responder": we don't want to switch their therapy if they will end up responding, and we don't want to waste their time on a therapy they won't respond to. I will present a simple mathematical model of tumor dynamics I used to fit the patient tumor size data. I combined the fitted model parameters with a machine learning technique to create an early predictor of response/non-response. I will also show how I validated the predictor on data sets for different cancer types and immunotherapies.

Faculty host: Mariel Vazquez Please contact Professor Vazquez if you would like to meet with the speaker or join the dinner after the talk (mariel@math.ucdavis.edu)