Drugs combinations are commonly used to treat multi-component diseases, severe bacterial infections, and many types of cancer. However, the actions of individual drugs are often coupled through their effects on complex intracellular networks. As a result, it is generally impossible to infer the net effect of a multi-drug combination directly from the effects of individual drugs. In the first part of this talk, I will discuss our recent work that explores how drug interactions accumulate as the number of drugs, N, in a combination increases. To answer this question, we develop a statistical model that associates drug interactions with correlations between random variables, allowing us to exploit methods from statistical physics to measure the contributions of all K-body interactions (K<=N) to a given N-drug effect. Using this framework, we then experimentally show that the bacterial responses to drug pairs are sufficient to predict the effects of larger drug combinations in both gram negative bacteria (E. coli) and gram positive (S. aureus) bacteria. Remarkably, the quantitative relationship governing the accumulation of pairwise drug interactions appears to be independent of microscopic details such as cell type and drug biochemistry. In the second part of the talk, I will discuss an adaptation of this approach to study multi-drug resistance, a growing public health threat.
Wednesday, April 17, 2013
Free and open to the public