Decoding the Multi-Drug Response in Populations of Bacteria and Human Cancer Cells
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 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. We begin by experimentally showing that interactions between pairs of drugs can change dramatically when cells develop resistance to at least one of the drugs in a combination. To unify the wide diversity of observed resistance phenotypes, we hypothesize that drug-resistance modifies how cells interpret extracellular drug concentrations but preserves other features of the original multi-drug response. We formalize this hypothesis by decomposing the two-drug response surface into three simpler, one-dimensional "basis" functions: the single drug dose response curves for each drug alone and a single drug-drug coupling function. Surprisingly, we find that drug-resistant and drug-sensitive cells--even those with drastically different response surfaces--are characterized by basis functions that are simple re-scalings of one another, suggesting that resistance mutations act as a nonlinear transformation of drug concentration space but leave undisturbed all other features of the original two-drug growth response. We verify these scaling relationships in a wide range of organisms, including both gram-positive (S. aureus, E faecalis) and gram negative (E. coli) drug-resistant bacteria, drug resistant yeast (S. cerevisiae), and drug-resistant cancers, including melanoma, non small cell lung cancer, and breast cancer. These results identify a surprising scaling relationship between drug-sensitive and drug-resistant cells that can be exploited to provide rapid, systematic adaptation of multi-drug therapies targeting emergent drug-resistance. Finally, we show how these findings can accelerate the development of cell-selective, therapeutically potent multi-drug therapies.