The tool is of major significance as one of the major challenges in drug discovery is to distinguish the molecular targets of a bioactive compound from the hundreds to thousands of additional gene products that respond indirectly to changes in the activity of the targets.
Although drug development is an active field of research, there have been few ways to predict optimal drug design. The molecular targets of many drug candidates are unknown and are often difficult to tease out from among the thousands of gene products found in a typical organism. This "blindness" in the welter of potential cellular targets means that the process of designing therapeutic drugs is neither precise nor efficient.
The mathematical algorithm predicts the precise effects a given compound will have on a cell's molecular components or chemical processes.
The research team, from Boston University, used a combination of computational and experimental methods to build and verify their tool. They first used a reverse-engineering approach to decipher the regulatory networks operating among genes in a set of 515 whole-genome yeast expression profiles resulting from a variety of treatments (compounds, knockouts and induced expression).
They then tested the ability of the resulting network models to predict gene and pathway targets for a variety of drug treatments. Finally, they used the 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.
The algorithm predicted, and subsequent experiments verified, that PTSB acted on thioredoxin and thioredoxin reductase, findings that not only validate the tool's capability but could also pave the way to investigations of a potentially new class of therapeutic compounds.