Drug Synergy and Mechanism of Action
Even when a drug has been shown to be effective for the treatment of human disease, the particular molecular mechanism through which it produces its therapeutic effect is in most cases a mystery. Moreover, once a drug enters the body, it becomes part of a complex system and can interact with other molecules in unpredictable ways, and can have different effects in different cell types. In many cases it is these off-target effects that limit a drug’s usefulness in a clinical setting.
This presents a key challenge to drug discovery and development, as knowing a drug’s mechanism of action could make it possible to better optimize its effectiveness, identify combination therapies in a more rational manner, and better predict the likelihood of toxic effects.
Our laboratory has demonstrated that the network context that systems biology provides can offer ways to address this challenge. Just as genome-wide models of molecular interactions within cells provide a mechanistic context for understanding the emergence of cellular phenotypes, they also make it possible to study exactly how therapeutic interventions change the regulatory interaction networks these models describe. In this way, we can gain highly informative clues into how they produce their therapeutic effects.
LINCS (Library of Integrated Network-Based Cellular Signatures)
Our laboratory is a member of the LINCS Program, a multi-institutional, collaborative project initiated by the National Institutes of Health whose goal is to identify and categorize molecular signatures that occur when cells are exposed to agents that perturb their normal function. We currently have two grants, the first focusing on technology development, and a second on the design of new computational tools.
By using high-throughput experimentation to perturb cells with pairs of agents in a very systematic fashion, our goal is to generate and categorize the molecular signatures that result in a way that will enable us to better understand the mechanisms of action (MoA) of many drugs. Using a variety of computational algorithms we also aim to predict — without the time and expense of actually testing every possible combination in the laboratory — which combinations of small molecules are most likely to have synergistic effects in live cells. Our hypothesis is that using combinations of compounds that target distinct, disease-maintaining modules in diseased cells hold more likelihood of effectiveness than compound combinations that target the same pathway.
DeMAND: An algorithm for predicting drug mechanism of action
In 2015, our laboratory published Detecting Mechanism of Action by Network Dysregulation (DeMAND), which built on other algorithms developed in our laboratory for the modeling of regulatory networks. DeMAND identifies proteins involved in a drug’s mechanism of action on a genome-wide basis. After developing a model of the particular cellular context before treatment, we then subject it to treatment and assess its global dysregulation of their molecular interactions following compound perturbation. Specifically, for every pair of genes representing an interaction, the algorithm computes the level of dysregulation introduced between them following exposure to the drug. It then identifies proteins participating in the most significant number of dysregulated interactions, hypothesizing that these are the ones most likely to play a key role in the drug’s mechanism of action.
Analysis of cellular perturbation profiles identified established MoA proteins for 70% of the tested compounds and elucidated novel proteins that were experimentally validated. Finally, unknown-MoA compound analysis revealed altretamine, an anticancer drug, as an inhibitor of glutathione peroxidase 4 lipid repair activity, which was experimentally confirmed, thus revealing unexpected similarity to the activity of sulfasalazine. This suggests that regulatory network analysis can provide valuable mechanistic insight into the elucidation of small-molecule MoA and compound similarity.
Selected related publications
Woo JH, Shimoni Y, Yang WS, Subramaniam P, Iyer A, Nicoletti P, Rodríguez Martínez M, López G, Mattioli M, Realubit R, Karan C, Stockwell BR, Bansal M, Califano A. Elucidating compound mechanism of action by network perturbation analysis. Cell. 2015 Jul 16;162(2):441-51.
Bansal M, Yang J, Karan C, Menden MP, Costello JC, Tang H, Xiao G, Li Y, Allen J, Zhong R, Chen B, Kim M, Wang T, Heiser LM, Realubit R, Mattioli M, Alvarez MJ, Shen Y; NCI-DREAM Community, Gallahan D, Singer D, Saez-Rodriguez J, Xie Y, Stolovitzky G, Califano A; NCI-DREAM Community. A community computational challenge to predict the activity of pairs of compounds. Nat Biotechnol. 2014 Dec;32(12):1213-22.