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Computer Model Devised to Predict Drug Side Effects

Pills on table

(e-Magine Art/Flickr)

2 November 2015. A systems biology lab developed a prototype computer model that can test for potential side effects of drugs from an individual’s blood sample. The team led by bioengineering professor Bernhard Palsson at University of California in San Diego published its proof-of-concept results last week in the journal Cell Systems.

Palsson and first author Aarash Bordbar are founders of the San Diego start-up enterprise Sinopia Biosciences, developing a computational platform for modeling of biological processes involving blood cells. The journal paper reports as well that Palsson and co-author Neema Jamshidi hold a patent on large-scale data-driven kinetic modeling as applied to individuals.

The team from Palsson’s systems biology lab at UC-San Diego are seeking better ways of predicting a person’s reactions to drugs, which can be highly personalized as a result of genetic and metabolic factors. A tool for predicting reactions to drugs can help personalize the choice of medications, avoid adverse effects for individuals, and better identify candidates for clinical drug trials.

Because of the complexity of predicting a drug’s side effects, the UC-San Diego team needed to devise a kinetic model that addresses the dynamics of multiple interacting variables. The researchers constructed the model combining data from whole-genome sequencing and metabolism of red blood cells that determines the ability of blood to exchange oxygen and carbon dioxide to and from the lungs and tissues in the body. The team chose red blood cells for the prototype, because of their relative simplicity and availability in blood samples, as well as the rich platform offered by the cells to find indicators of drug side effects.

The researchers drew blood samples of about 8 milliliters (0.27 fluid ounces) from 24 volunteers, then built individualized predictive models based on their genomic profiles and red blood cell metabolism. The individual kinetic models use Mass Action Stoichiometric Simulation, a kinetic modeling approach developed by Palsson and Jamshidi, based on network models. They then tested the models with a simulated dose of the anti-viral hepatitis C drug ribavirin, which in 8 to 10 percent of cases induces anemia, or decrease in the number of red blood cells.

“A goal of our predictive model is to pinpoint specific regions in the red blood cell that might increase susceptibility to this side effect,” says Brodbar in a university statement, “and predict what will potentially happen to any particular patient on this drug over time.” With their model, tests of the simulated ribavirin dose identified 2 of the 24 volunteers with a predisposition to developing anemia from the drug.

For next steps, the UC-San Diego team plans to expand the number of individuals providing blood samples to hundreds as well as develop predictive models to cover more complex platelet cells. They eventually want to design a liver cell model, since a majority of drugs are metabolized in the liver, which also is where most side effects originate.

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