A Systems Approach
Recent approaches to finding novel drug combinations have used a systems approach to identifying known drugs that work well together. In the October 9th issue of Science Translational Medicine, researchers at the Icahn School of Medicine at Mt. Sinai found that by adding a second drug to the diabetes drug rosiglitazone, adverse cardiac drug effects in diabetic patients dropped greatly. They made the discovery though analyzing data from the FDA Adverse Event Reporting System (FDAERS) to discover whether second drugs could lower the rate of heart attacks.
In addition, investigators compared their results with Mt. Sinai’s electronic health records system. Compared with many other commonly used second drugs, "we found that the drug emanative, often given along with rosiglitazone to get better control of blood glucose, also very substantially reduced the heart attack rate in rosiglitazone users," said Ravi Iyengar, Ph.D., Dorothy H. and Lewis Rosenstiel professor in the department of pharmacology and systems therapeutics and director of the Systems Biology Center at the Icahn School of Medicine at Mount Sinai. The investigator, using the same findings, could also predict how these beneficial drug interactions might work in diabetic mice, finding that the heart attack rate declined.
Dr. Iyengar said that the beneficial effects of rosiglitazone and emanative are not unique: “We found nearly 19,000 other drug combinations in the FDA database, where the second drug appears to reduce a wide range of side effects of the first drug. Other beneficial effects were demonstrated when lisinopril was added to a statin, where the rate of statin-associated rhabdomylosis, a kind of muscle tissue wasting, declined.”
Dr. Iyengar and his colleagues had previously worked on developing methods to “allow for development of an integrated research methodology to identify general principles of the drug discovery process”. Noting that “the global relationship between drugs that are approved for therapeutic use and the human genome is not known” they used graph-theory methods to analyze FDA approved drugs and their known molecular targets. Specifically, the investigators connected the list of active ingredients extracted from one database to those known human protein targets included in the DrugBank database to construct a bipartite network.
“We computed network statistics and conducted Gene Ontology analysis on the drug targets and drug categories,” they said. “We find that drug to drug-target relationship in the bipartite network is scale-free. Several classes of proteins in the human genome appear to be better targets for drugs since they appear to be selectively enriched as drug targets for the currently FDA-approved drugs.”
Last year, Russ Altman, M.D., Ph.D., a bioengineer at Stanford University in California, reported in Science Translational Medicine that he and his colleagues had developed an algorithm to sort through the massive amount of “adverse events” data reported to the FDA annually. The algorithm uncovered thousands of previously unknown side effects caused by taking drugs in combination.
“It’s a step in the direction of a complete catalogue of drug–drug interactions,” says Dr. Altman. The team compiled a database of 1,332 drugs and possible side effects that were not listed on the labels for those drugs. The algorithm yielded an average of 329 previously unknown adverse events for each drug, way beyond the 69 or so found on most drug labels.
Most recently, co-author Nicholas Tatonetti, Ph.D., of Columbia University, also writing in Science Translational Medicine, said that next-generation technologies applied to drug discovery may “usher in” a new paradigm in drug discovery, providing a “more nuanced” approach to drug design involving the use of multiple drugs in combination to target interacting or complementary pathways.
In a paper entitled “High-Throughput Methods for Combinatorial Drug Discovery,” by Tatonetti and colleagues X. Sun and S. Vilar; the authors noted that these technologies that allow the measurement of millions of cellular data points simultaneously, enabling a new view of medicine as a system of interacting genes and pathways rather than the result of an individual protein or gene. These methods include high-throughput biological measurements (genetics, transcripts, and chemogenetic interactions) and the computational methods they necessitate to further combinatorial drug design. In particular, they highlighted state-of-the-art analytical methods including network analysis, integrative informatics, and dynamic molecular modeling, all successfully used in combinatorial drug discovery.