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Identifying Effective Multi-drug Combinations Made Easy

by Hannah Punitha on  January 12, 2009 at 6:57 PM Research News   - G J E 4
 Identifying Effective Multi-drug Combinations Made Easy
Search algorithms used in digital communications can help make the cumbersome procedure of identifying effective multi-drug combinations easy, say researchers.
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Led by Dr. Giovanni Paternostro, a scientist at the Burnham Institute for Medical Research, a research team used the stack sequential algorithm developed for digital communications to search for optimal drug combinations.

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Writing about their work in PLoS Computational Biology, the researchers said that the algorithm could integrate information from different sources, including biological measurements and model simulations, which differed it from the classic systems biology approach by having search algorithms rather than explicit quantitative models as the central element.

The researchers further said that the variability of biological systems was the fundamental motivation for the strategy.

Combination therapies have demonstrated efficacy in treating complex diseases such as cancer and hypertension, but it is difficult to identify safe and effective combination treatment regimens using only trial and error, said Dr. Paternostro.

As personalized medicine moves from the present emphasis on diagnosis and prognosis to therapy, the problem of searching for optimal drug combinations uniquely suited to the genetic and molecular profile of each patient will need to be solved. This research is a first step in that direction, the researcher added.

Current methodology for identifying effective combination therapies involves exhaustive testing. However, the exponential expansion of possibilities precludes exploring large combinations using this approach.

Many chemotherapy regimens include six drugs from a pool of 100. A study including all combinations at three different doses would have to digest 8.9 x 1011 possibilities. The problem requires a new approach rather than more efficient screening technology.

The researchers, during the study, tested a small subset of the possible drug combinations identified using the algorithms in two biological model systems.

One system studied improvement in the physiological decline associated with aging in Drosophila melanogaster fruit flies, while the other tested for selective killing of cancer cells.

In both cases, effective drug combinations were identified by combining the algorithm with biological tests.

Our work was greatly helped by collaborators with expertise in medicine, engineering and physics from Burnham, University of California, San Diego and Michigan State University, said Dr Paternostro.

We especially benefited from suggestions from Dr. Andrew Viterbi, inventor of the Viterbi algorithm so widely used in digital communications, who pointed to parallels between this biological problem and signal decoding, the researcher added.

The study was funded by the Ellison Medical Foundation, National Institutes of Health and the National Science Foundation.

Source: ANI
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