Researchers May Soon Predict Drug Side Effects on Different Patients


Researchers seek to determine which side effects will be experienced by different patient groups.

Researchers seek to determine which side effects will be experienced by different patient groups.

One of the most difficult hurdles for patients on a specialty drug regimen to overcome are the debilitating side effects that accompany some treatments.

To address this issue, investigators from the University of California at San Diego constructed a proof of concept predictive model that may be able to forecast side effects different patients are likely to experience on a specific therapeutic regimen.

The researchers seek to determine how different patients will respond to treatment in order to evaluate whether a medication is appropriate for an individual based on the results of a blood test.

"We're not just interested in predicting the efficacy of a drug, but its side effects as well," said Bernhard Palsson, Galetti professor of Bioengineering at the Jacobs School of Engineering at UC San Diego. "Side effects are very personalized. Two different people can take the same drug, but one person might experience side effects while the other doesn't."

The study, published in the journal Cell Systems, may eventually bolster the efforts of pharmaceutical companies during the drug development stage to allow for predictive drug screenings prior to the start of clinical trials. This would help evaluate which patient groups are more or less likely to experience side effects.

"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,” said researcher Aarash Bordbar. “This predictive model could be used to figure out what these side effects are ahead of time.”

Specifically, the model projects how genetic variations in different patients could potentially influence how a drug is metabolized.

The study utilized data from different patient genotypes and metabolism to construct personalized models that simulate how a treatment impacts a particular set of cells in the body.

"This is a unique approach to obtain personalized, predictive and mechanistic descriptions of people's physiology based on their genetic and metabolic makeup," Palsson noted.

Investigators modeled side effects on red blood cells, which offers a simple platform to determine health markers associated with the side effects from a specific drug.

Researchers evaluated genomic and metabolomics data collected from the blood samples of 24 patients to establish a personalized predictive model for each patient. This predictive model was subsequently used to evaluate at the metabolic level why some patients with hepatitis C experienced side effects from ribavirin while other patients did not.

Anemia affects approximately 8 to 10% of patients on a ribavirin regimen, the study noted.

"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," Bordbar said.

Investigators said they will next work to develop predictive models for platelet cells, which are more complex than red blood cells. The long-term goal is to eventually put together a liver cell model, due to the fact that the liver is where most drugs are metabolized and where many side effects manifest.

"This study is a step forward in demonstrating that patients could be precisely treated based on their genetic makeup," Palsson said.

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