Patricia F. Fitzpatrick Dimond Ph.D. Technical Editor of Clinical OMICs President of BioInsight Communications

Big Data and next-generation technologies find common targets, shared side effects, and new drug combinations.

Disease heterogeneity, particularly among cancers such as hematologic malignancies, has greatly complicated the search for therapeutic targets and for drugs that can be used against them. Scientists have noted that with regard to blood cancers, for example, one approach is to reduce the heterogeneity into relatively homogenous groupings based on cellular pathways rather than on single genes. As in the case of single gene mutations, the disruption of specific pathways could then be exploited therapeutically.

In 2011, the NIH’s QSP (Quantitative and Systems Pharmacology) Workshop issued a report that defined QSP as an approach to translational medicine. “That combines computational and experimental methods to elucidate validate and apply new pharmacological concepts to the development and use of small molecule and biologic drugs.”

Representatives from academia and industry concluded that an urgent need exists to “reinvigorate academic pharmacology as a core discipline of translational medicine. The need for new approaches to drug development is clear.” While they noted that the rate of progress in basic biomedical research is high, QSP “will require innovative science and new organizational structures: simply scaling up existing ideas and methods will not work.”

And they noted, the growing understanding of cellular and tissue-level networks suggests that the therapeutic and toxic effects of drugs can best be understood at a systems level. “Biochemical networks targeted by drugs are qualitatively similar but quantitatively different in different tissues, genetic backgrounds, development stages, and disease states, and the operation of these networks is profoundly impacted by patient lifestyle and history. The effect of a drug on a network—positive or negative—can therefore only be fully understood in terms of multifactorial and quantitative differences.”

But if finding novel single new drugs is tough, identifying novel combinations that work, potentially at multiple points in a pathway, has proven yet more challenging.

Novel Tools Make New Combinations Possible

Scientists say, however, that availability of big databases and increasing knowledge of disease pathways, unique biological assays, and computational and modeling tools provide new opportunities for the rational development of combination therapies for conditions including cancer, cardiovascular disease, and metabolic and infectious diseases.

Investigators have recently demonstrated they can identify previously unknown side effects, drug interactions, and potentially therapeutic combinations by using multiple tools. And, they say, the potential for finding new uses for old drugs using sophisticated tools has dramatically increased.

One approach is to identify new uses for old drugs or unique drug combinations by predicting drug targets based on similarities in their side effects. The rapidly increasing amount of publicly available knowledge in biology and chemistry enables them, scientists working at EMBL said, to revisit many “open problems” by the systematic integration and analysis of heterogeneous novel data. The integration of relevant data not only allows analyses at the network level, but also provides a more global view on drug-target relations.

Pointing out that drug targets have thus far been predicted on the basis of molecular or cellular features—for example, by exploiting similarity in chemical structure or in activity across cell lines—EMBL scientists reported in 2008 that side effect similarities among drugs can be used to infer whether two drugs share a target. Among 746 marketed drugs, the investigators identified a network of 1,018 side effect-driven drug-drug relationships, 261 of which were formed by chemically dissimilar drugs used for different therapeutic indications.

Nine of these were tested and confirmed, and validated in cell assays, documenting the feasibility of using phenotypic information to infer molecular interactions and hinting at new uses of marketed drugs.

In 2009, investigators working at the department of pharmaceutical chemistry, University of California, San Francisco, compared 3,665 FDA-approved and investigational drugs against hundreds of targets, defining each target by its ligands. In their paper entitled “Predicting new molecular targets for known drugs,” the investigators used a “chemical similarity” approach, which they say is a systematic and comprehensive approach to suggest both side effects and new indications for many drugs.

Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations, thirty of which were tested experimentally, including the antagonism of the beta(1) receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H(4) receptor by the enzyme inhibitor Rescriptor.

Overall, 23 new drug-target associations were confirmed; the physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse.

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.

Patricia Fitzpatrick Dimond, Ph.D. ([email protected]), is technical editor at Genetic Engineering & Biotechnology News.

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