Abstract
The risk of adverse drug reactions increases in a polypharmacology setting. High-throughput drug screening with transcriptomics applied to human cells has shown that drugs have effects on several molecular pathways, and these affected pathways may be predictive proxy for adverse drug reactions. Depending on the way that different drugs may contribute to adverse drug reactions, different options may exist in the clinical setting. Here, we formulate a network framework to integrate the relationships between drugs, biological functions, and adverse drug reactions based on the high-throughput drug perturbation data from the Library of Integrated Network-Based Cellular Signatures (LINCS) project. We present network-based parameters that indicate whether a given reaction may be related to the effect of a single drug or to the combination of several drugs, as well as the relative risk of adverse drug reaction manifestation given a certain drug combination.
Highlights
For a drug to be successful, it needs to strike a balance between its therapeutic and toxic effects [1]
Adverse drug reactions (ADRs), broadly defined as harmful or unpleasant reactions resulting from therapeutic interventions, may have negative health and economic consequences [2]
The risk of ADRs increases in the context of polypharmacy, the simultaneous use of multiple different drugs by the same patient in order to treat one or more medical conditions
Summary
For a drug to be successful, it needs to strike a balance between its therapeutic and toxic effects [1]. Drug-induced gene expression high-throughput screening (GE-HTS) has generated large datasets containing profiles of the effects of drugs on gene expression in different cellular systems These datasets can be used to identify the effects of drugs on biological processes involving sets of functionally related genes, such as those annotated in databases of controlled vocabularies such as Gene Ontology (GO) [4] and/or cell signaling, metabolic, and gene regulatory pathway databases. One of the largest publicly available GE-HTS efforts is that of the original Connectivity Map (CMap) [5] and its continuation as part of the Library of Integrated Network-Based Cellular Signals (LINCS) [6,7] Through such methods, it is possible to identify multiple targets on which a drug acts (which is known as polypharmacology [8], as opposed to the previously defined polypharmacy)
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