[Editors] MIT model could predict cells' response to drugs

[Patti Richards]

MIT News Office
Massachusetts Institute of Technology
Room 11-400
77 Massachusetts Avenue
Cambridge, MA  02139-4307
Phone: 617-253-2700
http://web.mit.edu/newsoffice/www

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MIT model could predict cells' response to drugs
Work could lead to targeted therapies
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For Immediate Release
FRIDAY, JULY 27, 2007
Contact: Patti Richards
Phone: 617-253-8923
Email: prichards at mit.edu

CAMBRIDGE, MA (7/27/2007) - MIT researchers have developed a model 
that could predict how cells will respond to targeted drug therapies. 
Models based on this approach could help doctors make better 
treatment choices for individual patients, who often respond 
differently to the same drug, and could help drug developers identify 
the ideal compounds on which to focus their research.

In addition, the model could help test the effectiveness of drugs for 
a wide range of diseases, including various kinds of cancer, 
arthritis and immune system disorders,  according to Douglas 
Lauffenburger, MIT professor of biological engineering and head of 
the department. Lauffenburger is senior author of a paper on the new 
model that will appear in the Aug. 2 issue of Nature.

The model is based on similarities in the signaling pathways cells 
use to process information. Those pathways translate cells' 
environmental stimuli, such as hormones, drugs or other molecules, 
into action.

"Cells undertake behavioral functions-proliferation, differentiation, 
death-in response to stimuli in their environment," said 
Lauffenburger. "The signaling pathways are the biomolecular circuits 
that process that information from the environment and regulate the 
mechanisms that execute the behavorial functions."

The pathways work via a series of signals in which proteins, known as 
kinases, activate other cell machinery to achieve a specific result, 
e.g., expression of certain genes, or actions of cytoskeletal 
proteins. While the same stimuli can produce diverse responses in 
different types of cells, the researchers believe they can use the 
same core pathways to achieve various end results.

Lauffenburger compared a cell's strategy to playing a piano: Just as 
there are 88 keys that can be played in a vast number of combinations 
to produce different melodies, cells can use their multiple pathways 
together in many different combinations to produce different 
behaviors.

One of the key questions that Lauffenburger's group tackled was 
understanding the way in which cells interpret the signals they 
receive and how they arrive at the correct result.
The researchers approached the problem quantitatively, measuring 
activity levels in five major signaling pathways after colon 
epithelial cells were exposed to a variety of environmental stimuli. 
The behavioral outcome-cell death, inflammatory cytokine production, 
etc., was also measured.

Using that data, they constructed a model correlating outcomes with 
the combined levels of activity in the multiple pathways. The model 
was then used to correctly predict what would happen to two other 
types of epithelial cells when exposed to the same stimuli.

"Cells appear to be adding up information across multiple pathways in 
a common manner, even though the outcome of the calculations is 
different because the pathway activities are different," said 
Lauffenburger.

The researchers also tested the model on a type of blood cell, but in 
this case, it failed to accurately predict behavioral outcomes. The 
fact that a model developed with colon epithelial cells only worked 
for other types of epithelial cells is not surprising because 
different tissue types process information in different ways, 
Lauffenburger said.

To develop safe and effective drugs, researchers need to be able to 
understand how a drug works in the context of a network governing 
cell functions, not just its effect on an individual molecule. 
Lauffenburger envisions that drug companies could use this kind of 
model to test the effects of drugs that inhibit some step in a 
particular pathway.

The lead authors on the paper are former MIT doctoral students 
Kathryn Miller-Jensen and Kevin Janes. Joan Brugge, a faculty member 
at Harvard Medical School, is also an author.

The research was funded by the National Institute of General Medical 
Sciences Cell Decision Processes Center, the University of California 
at Santa Barbara-CalTech-MIT Institute for Collaborative 
Biotechnologies and the MIT Biotechnology Process Engineering Center.