New model to predict side effects of drugs

A new model that uses a patient's blood samples can predict what side effects they might experience from a drug, scientists have found.
The proof of concept study is aimed at determining how different individuals will respond to a drug treatment and could help assess whether a drug is suitable for a particular patient based on measurements taken from the patient's blood.
"We're not just interested in predicting the efficacy of a drug, but its side effects as well," said Bernhard Palsson, the Galetti Professor of Bioengineering at the Jacobs School of Engineering at University of California - San Diego.

"Side effects are very personalised. Two different people can take the same drug, but one person might experience side effects while the other doesn't," said Palsson.
"There needs to be a good way to obtain data about a drug's side effects before exposing a lot of people to the drug. This predictive model could be used to figure out what these side effects are ahead of time," said UC San Diego alumnus Aarash Bordbar, who did this research while a PhD student in Palsson's Systems Biology Research Group.
Researchers said that the predictive model would be extremely useful for pharmaceutical companies during the drug development stage.

The model predicts how variations in different people's genes impact how they metabolise a drug. Researchers used data from different people's genotypes and metabolism to build personalised models that simulate how a drug will affect a particular set of cells in the body.

"This is a unique approach to obtain personalised, predictive and mechanistic descriptions of people's physiology based on their genetic and metabolic makeup," said Palsson.
In the study, researchers focused on modelling drug side effects on red blood cells. Palsson and his team were interested in red blood cells because they are the simplest human cells and are readily available from blood samples.

Also, the red blood cell provides a simple platform for researchers to find health markers that are related to a drug's side effects.
The study was based on genomic and metabolomics data obtained from blood samples of 24 individuals. Researchers used these data to build a personalised, predictive model for each individual.
Researchers then used these predictive models to understand - at the metabolic level - why some individuals experienced side effects to ribavirin, a drug used to treat hepatitis C, while other individuals did not.
A side effect of ribavirin is that it causes anemia - a condition characterised by a decrease in red blood cell levels - in approximately 8 to 10 per cent of patients.

"A goal of our predictive model is to pinpoint specific regions in the red blood cell that might increase susceptibility to this side effect and predict what will potentially happen to any particular patient on this drug over time," said Bordbar.
The study was published in the journal Cell Systems.