University of California, Irvine
Informed decision-making across various phases of drug development is challenging as it has to utilize an enormous amount of information produced during the development process. Models of drug, disease and trials are particularly useful in summarizing essential information in a succinct and efficient manner, allowing the integration of knowledge from different studies and external sources. Simulations combined with appropriate assumptions, such models can explore the potential outcomes of yet-to-be-conducted studies, enabling optimization of the study design to increase the probability of success and de-risk investment. Pharmacokinetic and Pharmacodynamic (PK/PD) models are one such class of models used by pharmaceutical professionals to guide drug development and clinical therapeutic decisions. These models allow forecasting drug concentrations in the body and the response to the drug at various dose levels. Further, these models require flexible and high performance solving of differential equations to simulate patient data. However, the development of open-source software tools for this discipline lag due to the requisite performance and domain-expertise. In this talk we will introduce PKPDSimulator.jl, a Julia package for simulation of PK/PD models. This package includes the addition of functions for implementing compartmental PK/PD models and schedules of dosing and other discrete events. We will demonstrate how models of clinical trials are developed to simulate clinical measurements and responses to drug administration. Unique features which make new ground in the field, such as the ability to incorporate stochastic differential equations, delay differential equations, and discrete stochastic models will be emphasized.
Chris Rackauckas is a 4th year Ph.D. Candidate in Mathematics at the University of California, Irvine. He is the principal author of many Julia packages, including the JuliaDiffEq packages (DifferentialEquations.jl) and ParallelDataTransfer.jl, and has contributed to numerous other packages related to scientific computing. Chris is also actively engaged in the Julia community as the author of the StochasticLifestyle blog and the tutorial “A Deep Introduction to Julia for Data Science and Scientific Computing”.
