2019-07-22, 13:30–17:00, 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.