News Release

Computational innovation predicts how patients will respond to drug treatments

Peer-Reviewed Publication

American Chemical Society

Not all medications heal. Designed for a broad impact, some, because of the way they interact with individual biochemistries, can actually make certain patients sicker and even lead to death. General pharmacological formulations and scattershot therapies may now prove a thing of the past, thanks to a computational innovation announced by Michael Korenberg, a professor in the department of Electrical and Computer Engineering at Queen’s University in Kingston, Ontario, Canada.

Korenberg’s work is detailed in the Feb. 21 inaugural issue of the Journal of Proteome Research, a new peer-reviewed bimonthly journal published by the American Chemical Society, the world’s largest scientific society.

Based on mathematical techniques devised by Korenberg, cancer-treatment effectiveness can be evaluated computationally for individual patients. The advance, although not yet in clinical use, brings society closer to an era of personalized medicine, in which compounds are tailored to an individual’s unique genetic makeup, biochemistry and lifestyle.

If treatments thus can be precisely targeted to a given malady, cures for an array of conditions are far more likely. Rather than a handful of pebbles tossed toward a target in the hopes that one or more might strike the center, this approach can be likened to an arrow headed directly toward a bull’s-eye.

“Now, for the first time, this work and that of other researchers raises the prospect of individualizing cancer treatment; what will succeed and what will fail,” Korenberg posits. “Doctors will be able to predict the outcome of treatment simply based on a gene-expression profile taken at the time of diagnosis. And we’re likely to be able to predict adverse drug reactions as well.”

Korenberg’s work is based on a 1999 study headed by cancer researcher, Dr. Todd R. Golub. In that study, researchers examined genetic profiles of cancer patients and were able to develop a method of more accurately classifying the cancers.

Using the Golub samples, Korenberg was able to predict, with up to nearly 80 percent accuracy, how a group of patients with acute myeloid leukemia would respond to a particular chemotherapy. His work is part of a new discipline known as pharmacogenomics, which includes the study of the pattern of expression of genes involved in the drug response of a patient. Given that many drugs only work for a bare majority of recipients, it is essential that genetic differences be taken into account when determining treatment.

Yet another difficulty is how medications sometimes induce more illness. One study, published in the Journal of the American Medical Association in 1998, estimates that serious drug-induced side effects caused over two million hospitalizations. The goal of personalized medicine would be to develop drugs for specific genetic profiles in order to minimize or eliminate altogether such adverse reactions.

Another benefit of Korenberg’s computational innovation may be expedited drug development. Twenty years ago, the introduction of any new medication involved, on average, 30 clinical trials. Now, because of heightened concern over adverse drug reactions, it is not uncommon to have about 70 trials per drug. Correlating genetic profiles with drug-interactions databases may speed that process by months.

“For every month we save during clinical trials, it means people can be helped sooner,” Korenberg says. “It also means potential cost savings of millions of dollars.”

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Michael Korenberg, P. Eng., is a professor in the Department of Electrical and Computer Engineering at Queen’s University in Kingston, Ontario, Canada.


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