Drugs combinations are commonly employed in the treatment of
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 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 effects of three-drug and four-drug
combinations are dominated by interactions between pairs of drugs in gram
negative (/E. coli/) and gram positive (/S. aureus/) bacteria as well as in
multiple types of human cancer cells. Even more surprising, we find that
the quantitative relationship governing the accumulation of pairwise drug
interactions appears to be independent of microscopic details such as cell
type and drug biochemistry. I will conclude by briefly introducing our
ongoing efforts--both theoretical and experimental--to understand and
control the emergence of drug resistance in dynamic multi-drug
environments. Interested students are encouraged to contact me
(kbwood@umich.edu