Deciphering the Mechanisms of Statin–Ezetimibe Drug Combinations Using Boolean Logical Modeling and Transcriptomic Data

Drug Combinations offer increased therapeutic efficacy and reduced toxicity compared with single agents. Understanding a drug combination's mechanisms of action (MoA) can provide important insights into therapeutic efficacy. The MoA of many FDA-approved drugs, however, often remains unclear. To decipher the underlying molecular mechanisms of drugs used alone and in combination, we investigated the combination of a statin (atorvastatin or simvastatin) plus ezetimibe using drug-treated RNA-seq transcriptome data from the human hepatocyte-like SOAT2-only-HepG2 cells and from liver biopsies of non-obese normolipidemic patients with uncomplicated cholesterol gallstone disease in the Stockholm Study. We proposed a novel Boolean logical modeling framework to simulate the MoA of a drug combination using fourteen two-variable Boolean models. Thereafter, a pattern matching approach was applied to associate drug-induced differentially expressed genes with the idealized differential expression templates derived from Boolean models. We found 1560 and 565 genes differentially expressed in at least one treatment condition in SOAT2-only-HepG2 cells and liver biopsies, respectively. Our analysis revealed both expected and novel combinatorial modes of the statins and ezetimibe. We mapped the downstream genes of each combinatorial mode to the human protein-protein interactome and obtained underlying pathways, which are important for understanding the therapeutic effects of the drug combinations. Functional enrichment and disease-association analyses of the downstream genes also provide critical insights into the additional therapeutic actions of the drugs. Our study demonstrates that drug-induced transcriptomes, integrated with the human interactome, are informative in deciphering the MoA of drug combinations using Boolean logical modeling.