Final Report Summary - GS2DS (Systematic Analysis of Gene Synergies to Discover Drug Synergies)
Drug synergy allows a therapeutic effect to be achieved with lower doses of component drugs. Drug synergy can result when drugs target the products of genes that act in parallel pathways (‘specific synergy’). Such cases of drug synergy should tend to correspond to synergistic genetic interaction between the corresponding target genes. Alternatively, ‘promiscuous synergy’ can arise when one drug non-specifically increases the effects of many other drugs, for example, by increased bioavailability.
In the research project funded by FP7 Marie Curie IRG Grant (268440), to assess the relative abundance of these drug synergy types, we examined 200 pairs of antifungal drugs in S. cerevisiae. We found 38 antifungal synergies, 37 of which were novel. While 14 cases of drug synergy corresponded to genetic interaction, 92% of the synergies we discovered involved only six frequently synergistic drugs. Although promiscuity of four drugs can be explained under the bioavailability model, the promiscuity of Tacrolimus and Pentamidine was completely unexpected. While many drug synergies correspond to genetic interactions, the majority of drug synergies appear to result from non-specific promiscuous synergy. These results and interpretations were published as an article in Molecular Systems Biology November 2011 and they represent Work Packages 1a, 1b, 2a, 3a and 3b in the proposed research plan.
In the least period of the project, we optimized the correspondence between genetic interactions and a probabilistic drug target set to predict synergistic drug pairs (Work Package 2b. As part of work package 3b, in an effort to predict drug synergies, we assembled physicochemical descriptors of drugs and computationally analyzed their relationship with drug synergy. Finally, we systematically identified 93 suppressive drug interactions in yeast and analyzed possible mechanistic explanations for these interactions. These observations further our understanding of drug interactions.
The major socio-economic aspect of this project is finding methods to predict synergistic drug pairs. In the course of the research activity, real synergistic drug pairs are found. These methods and examples may have a large impact on the understanding and controlling drug interactions, which can have positive or negative effects on human health.
In the research project funded by FP7 Marie Curie IRG Grant (268440), to assess the relative abundance of these drug synergy types, we examined 200 pairs of antifungal drugs in S. cerevisiae. We found 38 antifungal synergies, 37 of which were novel. While 14 cases of drug synergy corresponded to genetic interaction, 92% of the synergies we discovered involved only six frequently synergistic drugs. Although promiscuity of four drugs can be explained under the bioavailability model, the promiscuity of Tacrolimus and Pentamidine was completely unexpected. While many drug synergies correspond to genetic interactions, the majority of drug synergies appear to result from non-specific promiscuous synergy. These results and interpretations were published as an article in Molecular Systems Biology November 2011 and they represent Work Packages 1a, 1b, 2a, 3a and 3b in the proposed research plan.
In the least period of the project, we optimized the correspondence between genetic interactions and a probabilistic drug target set to predict synergistic drug pairs (Work Package 2b. As part of work package 3b, in an effort to predict drug synergies, we assembled physicochemical descriptors of drugs and computationally analyzed their relationship with drug synergy. Finally, we systematically identified 93 suppressive drug interactions in yeast and analyzed possible mechanistic explanations for these interactions. These observations further our understanding of drug interactions.
The major socio-economic aspect of this project is finding methods to predict synergistic drug pairs. In the course of the research activity, real synergistic drug pairs are found. These methods and examples may have a large impact on the understanding and controlling drug interactions, which can have positive or negative effects on human health.