Abstract
There many possible types of drug-target interactions, because there are a surprising number of ways in which drugs and their targets can associate with one another. These relationships are expressed as polypharmacology and polyspecificity. Polypharmacology is the capability of a given drug to exhibit activity with respect to multiple drug targets, which are not necessarily in the same activity class. Adverse drug reactions ('side effects') are its principal manifestation, but polypharmacology is also playing a role in the repositioning of existing drugs for new therapeutic indications. Polyspecificity, on the other hand, is the capability of a given target to exhibit activity with respect to multiple, structurally dissimilar drugs. That these concepts are closely related to one another is, surprisingly, not well known. It will be shown in this work that they are, in fact, mathematically related to one another and are in essence 'two sides of the same coin'. Hence, information on polypharmacology provides equivalent information on polyspecificity, and vice versa. Networks are playing an increasingly important role in biological research. Drug-target networks, in particular, are made up of drug nodes that are linked to specific target nodes if a given drug is active with respect to that target. Such networks provide a graphic depiction of polypharmacology and polyspecificity. However, by their very nature they can obscure information that may be useful in their interpretation and analysis. This work will show how such latent information can be used to determine bounds for the degrees of polypharmacology and polyspecificity, and how to estimate other useful features associated with the lack of completeness of most drug-target datasets.
Highlights
The study of drug-target interactions and their manifestation in polypharmacology and polyspecificity is playing a major role in the growing field of chemogenomics in particular, and in drug research in general
While data on inactive drug-target pairs does not provide information that is useful for identifying drug targets, its availability reduces the size of the category of drugs of unknown activity, which improves the bounds on the degrees of polypharmacology and polyspecificity
Summary and conclusions The study of polypharmacology is becoming increasingly important in drug research because it raises awareness of the inherent lack of specificity of drugs and xenobiotics for specific targets
Summary
The study of drug-target interactions and their manifestation in polypharmacology and polyspecificity is playing a major role in the growing field of chemogenomics in particular, and in drug research in general. While data on inactive drug-target pairs does not provide information that is useful for identifying drug targets, its availability reduces the size of the category of drugs of unknown activity, which improves the bounds on the degrees of polypharmacology and polyspecificity The details of this argument are presented in a forthcoming section and are exemplified by the expression given in Equation [22]. The issue can be overcome by utilizing the adjacency matrix of the network, which provides a faithful representation of its edge structure, and preserves the relations associated with active drug-target pairs Because of this the degrees of polypharmacology and polyspecificity can be computed directly from adjacency matrices. Grant information The author(s) declared that no grants were involved in supporting this work
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