Computational tool predicts how drugs work
have on a cell's molecular components, will prove significant to
drug developers, who can design compounds that will act on desired
targets, eliciting therapeutic responses free of unwanted side
effects.
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 algorithm aims to improve on this situation, resulting from a combination of computational and experimental methods. Using a reverse-engineering approach to decipher the multitude of regulatory networks operating among genes in a simple organism, the researchers went about testing 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 CMLD studies to inhibit growth in the test organism (baker's yeast) as well as in human small lung carcinoma cells.
Their 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.
The computational tool could have applications in screening data analysis, hit-to-lead development, lead optimisation, and predictive toxicology.
The research, carried out by a team of biomedical engineers and chemists at Boston University, appears in the March 4 issue of Nature Biotechnology.
