Scientists developing model that predicts drug side effects in different patients


Scientists developing model that predicts drug side effects in different patients

Researchers developing a model that predicts how different people may react to the same drug have produced an example that models red blood cells from data extracted from individuals' blood samples.

The researchers created a kinetic model of each person's red blood cells to predict how they might react to a drug.

The team, from the University of California-San Diego (UCSD), describes the model and how they tested it in a paper published in the journal Cell Systems.

Senior author Bernhard Palsson, a professor of bioengineering, says:

"We're not just interested in predicting the efficacy of a drug, but its side effects as well."

A drug can produce different reactions in different people - some may experience side effects while others do not.

The intention behind the model is to find an effective way to predict a drug's side effects before exposing a lot of people to it. Such a tool would be invaluable for drug developers - they could carry out predictive screening ahead of clinical trials, for example.

The model is based on the fact that different people have different variations of the same gene and these variations affect how their bodies metabolize or process a drug.

The model is designed to use two sources of personal data - information about a person's genetic type (their genotype) and information about their individual metabolism - to simulate how a particular type of cell in the body might react to a drug.

Prof. Palsson explains:

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

A kinetic model of a person's red blood cells

For the study, the team chose to model a simple type of human cell that is readily available from blood samples - red blood cells.

Using genetic and metabolic data derived from the blood samples of 24 healthy individuals, the team created "whole-cell kinetic models" of the red blood cells for each person.

A kinetic model is one that simulates the chemical reactions of a system - in this case, a cell - and takes into account variables such as the rates of those reactions and the changing levels of the products of the reactions (in this case, metabolites).

The researchers made several useful discoveries about how to typify individuals at the red blood cell level. For example, when looking at metabolites as indicators of individuality (i.e. the genotype) - they found that "personalized kinetic rate constants, rather than metabolite levels, better represent the genotype."

They also used the models to identify individuals at risk of anemia induced by ribavirin - a drug used to treat hepatitis C - and how genetic variation may protect against this side effect. Ribavirin-induced anemia occurs in 8-10% of patients - it causes a drop in their red blood cells.

The team wants to use the model to identify specific regions in the red blood cell that are responsible for this side effect. They say it may also be able to predict how individual patient's reactions change over time.

The researchers say they now need to test their idea by building models for much larger groups, hundreds rather than dozens, of people. Nevertheless, Prof. Palsson concludes:

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

He and his colleagues plan to extend their approach to platelet cells - they are more complex than red blood cells. Eventually, they want to model cells of the liver, because that is the organ where most drugs are broken down and where most side effects arise.

Earlier this year, Medical-Diag.com learned how experts who analyzed a multi-gene test for predicting antidepressant response and use of health care resources in patients with depression found it was more effective than tests based on the individual genes. The multi-gene test, called GeneSight, assesses DNA variations that affect efficacy, metabolism and adverse effects of many psychiatric drugs.

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