CONTACT: JENNIFER BROWN
Iowa City IA 52242
(319) 335-9917; fax(319) 384-4638
Release: Nov. 4, 2002
UI researchers use computational methods to predict drug side effects
programs that simulate biological events in virtual reality are playing an
increasingly important role in many areas of pharmaceutical research. Adrian
Elcock, D.Phil., University of Iowa assistant professor of biochemistry, is
studying whether purely computational approaches can be used to identify unintended
drug interactions that might cause side effects.
In general, a drug works by binding to a specific receptor molecule in the
body to produce a therapeutic effect. If the same drug cross-reacts with a
different receptor molecule, a side effect can occur that can range from a
mildly unpleasant physical effect to a life-threatening reaction. Because
side effects often are not discovered until late in the drug development process,
side effects that limit or even prohibit a drugs use can represent a
huge waste of time and money.
"Basically every drug has side effects," Elcock said. "We
are trying to develop computational methods that would allow us to predict
side effects at the outset of the drug development process before a drug is
even in clinical tests."
Elcock and Bill Rockey, an M.D./Ph.D. student in the UI Medical Scientist
Training Program, tested the ability of a common computational method known
as a drug-docking program to successfully predict drug-receptor side interactions.
The UI researchers used a series of docking simulations to compare the programs
computational results, which predict the likelihood that a drug will bind
to a particular receptor, with experimental results that measure real receptor-drug
interactions. The researchers found that, given certain constraints, the docking
program could successfully predict which receptor molecules would interact
with a particular drug and which would not. The study, which was funded by
the UI, was published in a recent issue of Proteins: Structure, Function and
The UI study examined a series of clinically important drugs known as kinase
inhibitors, including Gleevec, a recently approved treatment for acute myeloid
leukemia. Kinases are a large family of proteins, which control many important
"The whole point of this study was to compare the computational results
from our approach with real experimental data for these drugs," Elcock
said. "We were able to predict with a good degree of confidence which
kinases would be inhibited by these drugs and which ones wouldnt."
Importantly, the method did not produce any false negatives, situations
where the docking program predicts a weak receptor-drug interaction, but the
real experiment shows a strong interaction.
"If we had missed any strong interaction between a drug and a receptor,
that would be a very bad sign for the usefulness of this method because a
side effect could be caused by just a single interaction," Elcock explained.
The method did produce several false positives. That is, it predicted strong
interactions that were not found in the real experiments. However, getting
false positives should not be a major problem.
"This method is not a replacement for the actual experimental testing,"
Elcock said. "But it may be a very good guide. So long as this method
allows us to throw out a lot of the chaff, which we were able to do, having
a few false positives left over to be eliminated by real experiments is not
really a problem."
Many interesting drug targets, including kinases, occur in large protein
families. Within these protein families, different family members often have
similar docking sites and drugs bind to those sites in similar ways. The docking
program uses what is known about the structure of a drug bound to one receptor
as a model for how that drug will interact with similar receptors.
Thus, an essential prerequisite of this approach is the availability of
at least one x-ray crystal structure of a drug bound to a real receptor protein.
A crystal structure is obtained by firing X-rays at a crystallized protein.
This produces a diffraction pattern and analysis of that pattern reveals the
three-dimensional shape of the protein.
The receptor-drug structure identifies a correct binding orientation for
the drug and the location of the docking site -- the region on the receptor
protein where the drug binds. These are critical pieces of information for
the docking program.
The shape of a drug-bound receptor also provides a template to construct
models of similar receptors. This approach, known as homology modeling, allows
researchers to build models of receptor proteins with known protein sequences
even when no x-ray crystal structure is available for those proteins. The
success of the docking program demonstrated by the UI study suggests that
homology modeling may be broadly useful in screening many proteins for potential
With the initial study proving the feasibility of using computational methods
to predict unintended receptor-drug interactions, Elcock hopes to extend the
research on two fronts. Working with known drugs for which there are crystal
structures of the drug bound to a receptor, the UI team will screen those
drugs against all other receptors to identify potential side effects.
The researchers also will use the docking program to investigate normal
metabolic pathways. Substances called metabolites are formed naturally during
metabolic processes. Determining which proteins interact with metabolites
may help researchers understand metabolic pathways and also identify important
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