Predicting new molecular targets for known drugs - PubMed (original) (raw)

. 2009 Nov 12;462(7270):175-81.

doi: 10.1038/nature08506. Epub 2009 Nov 1.

Vincent Setola, John J Irwin, Christian Laggner, Atheir I Abbas, Sandra J Hufeisen, Niels H Jensen, Michael B Kuijer, Roberto C Matos, Thuy B Tran, Ryan Whaley, Richard A Glennon, Jérôme Hert, Kelan L H Thomas, Douglas D Edwards, Brian K Shoichet, Bryan L Roth

Affiliations

Predicting new molecular targets for known drugs

Michael J Keiser et al. Nature. 2009.

Abstract

Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug-target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty 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, five of which were potent (<100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.

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Conflict of interest statement

The authors declare competing financial interests: details accompany the full-text HTML version of the paper at www.nature.com/nature.

Figures

Figure 1

Figure 1. Drug-target networks, before and after predicting off-targets

(A) Known drug-target network. Each drug (gold) is linked to its known protein targets (cyan) by a gray edge. Each edge denotes a _K_i of 1 μM or better for that drug to its target. (B) Predicted drug-target network. Drugs and proteins are linked as per the known drug-target network in (A), but with the addition of red edges representing SEA off-target predictions with E-values ≤ 10-10.

Figure 2

Figure 2. Testing new off-target activities

(A-F) Radioligand competition binding assays: (A) Doralese at D4, (B) Sedalande and Dimetholizine at α1D, (C) Fabahistin at 5-HT5A, (D) Motilium at α1A, (E) Prozac at β1, and (F) Vadilex at the serotonin transporter. (G-H) Investigating 5-HT2A as the target of DMT-induced hallucination: (G) 5-HT2A-mediated Ca2+ response was measured after treating HEK 293 cells stably expressing the human 5-HT2A receptor with DMT or 5-HT. DMT's EC50 was found to be 118±29 nM (vs. 5-HT's 6.6±0.4 nM baseline, n = 3), with an Emax of 23±0.4% (n = 3), confirming that DMT is a potent partial agonist at 5-HT2A receptors. (H) DMT elicited head twitch behavior only in 5-HT2A wild-type mice, confirming that it is a hallucinogenic 5-HT2A agonist. **, p < .01.

Figure 3

Figure 3. Discovered off-targets network

Bipartite network where drugs (gold) are linked by gray edges to their known targets (violet) and by red arrows to their discovered off-targets (cyan). Gray edges denote binding at 1 μM or better, where these affinities are known. Node sizes increase with number of incident edges. Target abbreviations: 5-HT_x_, serotonin receptor type x; 5-HTT, serotonin transporter; β1+, β1 adrenergic agonist; β1-, β1 adrenergic antagonist; β3+, β3 adrenergic agonist; σ1, σ1-receptor; CA, carbonic anhydrase; DAT, dopamine transporter; HIV1RT, HIV-1 reverse transcriptase; hERG, human Ether-a-go-go Related Gene channel; K+, Potassium channel; NET, norepinephrine transporter; NMDA, _N_-methyl-_D_-aspartate receptor; VMAT2, vesicular monoamine transporter 2.

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