How do the world’s biggest pharma companies develop new treatments for killing cancerous cells?
The answer involves Julia and PumasQSP.
One of the world’s ten largest pharmaceutical firms used MATLAB, C++, and SimBiology for pharmacometric modeling and simulation. But they wanted more speed. Could Julia and PumasQSP deliver?
Using Julia, they created an in vitro model of a Multispecific Immune Cell Engager, a molecule that creates a physical synapse between immune and target cells to help increase the ability to kill cancerous cells. This model incorporates 356 ordinary differential equations.
The goal is to model binding and shedding at the receptor level, and cell activation and cell killing at the cellular level.
What was the result?
Julia + PumasQSP provided more accurate modeling and simulation 37x faster than MATLAB.
“We loved to see that speedup,” explained the team’s principal scientist. Furthermore, “We really love how well everything works together! … All these packages and their communication!”
Additional benefits include:
Fast differential equation solvers, all written in the same language
One-line functions for complex quantitative systems pharmacology (QSP) algorithms such as Global Sensitivity Analysis and Virtual Population
PumasQSP cloud computing capabilities using JuliaHub
