2019-07-22 –, PH 103N
Pharmacometics is commonly used to optimize drug doses and pre-screen drugs before clinical trials. In this workshop, users familiar with Julia will learn about pharmacometrics and how to perform the model simulations, while pharmacometricians will learn how to use Julia to build the models they know from their field. The focus will be on simulating bioequivalance studies with Bioequivalence.jl, performing nonlinear mixed-effects modeling (NLME) simulation and estimation with Pumas.jl, and non-compartmental analysis (NCA) with the PumasNCA submodule.
Phamacometrics is the practice of using mathematical models to predict the effect of drugs on a patient’s internal biology. This field has become a standard part of pharmaceutical research, with major pharmaceutical companies routinely utilizing these methodologies to optimize dosing schedules and analyze clinical trial data for efficacy and toxicity before performing expensive clinical trials. These models are nonlinear mixed effects models where the nonlinearity is given by a system of differential equations. Because the process is limited by the speed and flexibility of the differential equation solvers, there has been increasing interest in using Julia for this practice.
In this workshop we will walk both Julia users and pharmaceutical practitioners through the process of pharmacometric modeling in Julia. Pharmaceutical practitioners will be paired with Julia users to work on guided exercises to learn both the pharmacometric modeling workflows and their implementation in Julia. Workshop participants will learn to make use of Pumas.jl and Bioequivalence.jl to perform clinical trial simulations, and analyze the results using the Pumas Non-Compartmental Analysis (NCA) functionality. Users will learn how to implement Pk/Pd models with complex dosing schedules and incorporating population models and estimate population parameters from data. Advanced users can explore the function-based interface of Pumas.jl to define delay and stochastic differential equation models, and optimize the runtime of their simulations using the full functionality of DifferentialEquations.jl. The participants will leave with a clear understanding of how to use the Julia package ecosystem to efficiently handle these difficult pharmacometric models, and will have a new perspective for understanding the differential equation solver advances being discussed at JuliaCon.
Chris' research and software combines AI with differential equation models of human organs to give patients accurate and personalized drug doses: reducing pain and complications for patients while reducing treatment costs for hospitals.
Chris Rackauckas is an applied mathematics instructor at the Massachusetts Institute of Technology and a senior research analyst at the University of Maryland, School of Pharmacy in the Center for Translational Medicine. Chris's recent work is focused on bringing personalized medicine to standard medical practice through the proliferation of mathematical software. His work on developing the DifferentialEquations.jl solver suite along with over a hundred other Julia packages, not only earned him the inaugural Julia Community Prize and front page features in tech community sites, it is also the foundation of the PuMaS.jl package for Pharmaceutical Modeling and Simulation, set to release in March 2019. Chris’ work with PuMaS makes it possible to predict the optimal medication dosage for individuals, reducing the costs and potential complications associated with treatments. The software is currently being tested in the administration of treatment for neonatal abstinence syndrome (NAS), an opioid withdrawal disorder in newborn babies. NAS requires medically administered morphine doses every four hours to prevent the infants from experiencing withdrawal symptoms. PuMaS is being used to predict personalized safe dosage regimens by incorporating realistic biological models (quantitative systems pharmacology) and deep learning into the traditional nonlinear mixed effects (NLME) modeling framework. This software and its methodology are also being tested in clinical trials at Johns Hopkins University for its ability to predict an individual's drug response to vancomycin and automatically prescribe optimal doses directly from a patient's health records.
Chris started this work while completing his Masters and Ph.D. at the University of California, Irvine where he was awarded the Mathematical and Computational Biology institutional fellowship, the Graduate Dean's Fellowship, the National Science Foundation's Graduate Research Fellowship, the Ford Predoctural Fellowship, the NIH T32 Predoctural Training Grant, and the Data Science Initiative Summer Fellowship. His research with his advisor, Dr. Qing Nie, focused on the methods for simulating stochastic biological models and detailing how the randomness inherent in biological organisms can be controlled using stochastic analysis. Chris bridged the gap between theory and practice by having a "wet lab bench" in Dr. Thomas Schilling’s lab, where these methodologies were tested on zebrafish. Fluorescence Light Microscopy (FLIM) measurements of retinoic acid in the zebrafish hindbrain showed that the predicted control proteins could attenuate inherent biological randomness. The result was a verified mathematical theory for controlling the randomness in biological signaling. Chris received the Kovalevsky Outstanding Ph.D. Thesis Award from the Department of Mathematics upon graduation and was showcased in an interview "Interdisciplinary Case Study: How Mathematicians and Biologists Found Order in Cellular Noise" in iScience.
As an undergraduate at Oberlin College, Chris was awarded the NSF S-STEM scholarship and the Margaret C. Etter Student Lecturer Award by the American Crystallographic Association, an award usually given for PhD dissertations, for his work on 3+1 dimensional incommensurate crystal structure identification of H-acid. This award was given for Service Crystallography for its potential impact on industrial dye manufacturing.
