Friday, February 05, 2010

Predicting and explaining drug-drug interactions

I am generally interested in chemogenomic studies and drug interaction studies as a complement to what we work on in the Krogan lab (genetic interactions). Much like in genetic interaction screening, where the fitness of double mutant strains is compared with that of the individual single mutants, chemogenomics tries to identify drug-gene interactions while drug-drug interaction screening attempts to find cases where the combined effect of two compounds on fitness is different from the expected from the combination of the single independent effects.

I read two recent papers that I found interesting regarding drug-drug interactions. One was by Bollenbach and colleagues from the Kishony lab (published in Cell) and the other was by Jansen and colleagues (published in MSB). In the first, the authors present an explanation for a previously observed drug-drug interaction. It had been previously shown that the combination of DNA and protein synthesis inhibitors results in lower reduction of fitness than expected by a neutral combination model (termed antagonist interaction). The authors show in this paper that, in the presence of DNA synthesis inhibitors, ribosomal genes are not optimally expressed. This imbalance between ribosomal production and cell growth is detrimental to the cell and can be, at least in part, corrected by protein synthesis inhibitors, explaining why these can suppress the effects of the DNA synthesis inhibitors.

Although it is a relatively simple idea (once described), I think it shows how complex these drug-drug interactions can be and to some extent also how these can provide information about a cell.

In the second paper I mentioned, Jansen and colleagues try to develop an approach to predict drug-drug interactions based on chemogenomic data. There are many obvious reasons why this would be very useful and I find this line of research extremely interesting. What I was surprised with was the simplicity of the approach and the disappointing benchmarks.

The end-result from a chemogenomic screen is a vector of drug-gene interaction scores that tell us how the combination of a drug with each mutant (normally KO strains) affect growth when compared to neutral expectation from the combined effect of the individual perturbations. It had been previously shown that drugs that have a similar drug-gene vectors tend to have similar mechanisms of action (Parsons et al. 2006 Cell). What Jansen and colleagues now claim is that the similarity of drug-gene vectors are predictive not only of similar mode of action but also of drug-drug interactions. Specifically, they try to show that drugs with similar profiles are more likely to be synergistic, such that the combined effect of both drugs is expected to be more detrimental to the cell  that the expected neutral combination.

Although the authors show experimental validation of their predictions with an accuracy of 56% they also benchmark their predictions using drug pairs  previously known to be synergistic. This benchmark is somewhat disappointing since they only see a significant enrichment of these true-positive pairs for a narrow range of cut-offs and with 2 out of 3 ways of calculating drug-profile similarity. I wish the authors had comment on this difference between the relatively poor performance based on benchmark and the very high accuracy observed in their experimental tests. They also show that these predicted synergistic pairs are well conserved from S. cerevisiae to C. albicans which is contradictory to a previous Nature Biotech paper that I mentioned in previous post.

Are drug-synergies this easy to predict and so well conserved across species? I am personally not convinced based on the data from this paper alone so I am holding off for further validation by other groups or additional larger datasets/benchmarks.