New Tool to Aid Drug Development

A new mathematical algorithm can predict the precise effects a given compound will have on a cell's molecular components, aiding the efforts of researchers to design compounds that will act only on desired gene and protein targets and elicit responses free of unwanted side effects.

Until now, there have been few ways to predict the effects of optimal drug design. The molecular targets of many drug targets are unknown and are often difficult to tease out from among the thousands of gene products found in a typical organism. This is why the drug-designing process is neither precise nor efficient .

A team of biochemical engineers and chemists at Boston University (MA, USA) has tried to make the process more efficient and precise. The team used a combination of computational and experimental methods to build and verify their tool, first using a reverse-engineering approach to decipher the multitude of regulatory networks operating among genes in a simple organism, then testing the ability of the resulting network models to predict gene and pathway targets for a variety of drug treatments. They used this tool to predict the molecular targets of a potential new anticancer compound (PTSB) shown in studies to inhibit growth in the test organism (baker's yeast) as well as in human small lung carcinoma cells.

Their algorithm predicted that PTSB acted on thioredoxin and thioredoxin reductase, findings that not only validated the tool's capability but also paved the way to investigations of a potentially new class of therapeutic compounds. The research team was led by Tim Gardner, an assistant professor in the College of Engineering's department of biomedical engineering, and James Collins, professor in biomedical engineering. The team's findings were reported in the March 4, 2005, issue of Nature Biotechnology.




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