Drug-induced liver injury (DILI) is a major reason for the dropout of candidate compounds from drug testing and the withdrawal of pharmaceuticals from clinical use.Among the various mechanisms of liver injury, the accumulation of bile acids (BAs) within hepatocytes is thought to be a primary mechanism for the development of DILI.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 analyzing drug-induced changes in disease-specific patterns of gene expression, a new algorithm called De MAND identifies the genes involved in implementing a drug's effects.The method could help predict undesirable off-target interactions, suggest ways of regulating a drug's activity, and identify novel therapeutic uses for FDA-approved drugs, three critical challenges in drug development." By analyzing drug-induced changes in disease-specific patterns of gene expression, a new algorithm called De MAND identifies the genes involved in implementing a drug's effects.Specifically, for every pair of genes representing an interaction, the algorithm computes the level of dysregulation introduced between them following exposure to the drug.
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.
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 (Mo A) 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.
Analysis of cellular perturbation profiles identified established Mo A proteins for 70% of the tested compounds and elucidated novel proteins that were experimentally validated.
Finally, unknown-Mo A 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.