Identifying mechanism-of-action targets for drugs and probes Elisabet Gregori-Puigjanéa,1, Vincent Setolab,1, Jérôme Herta, Brenda A. Crewsc, John J. Irwina, Eugen Lounkined, Lawrence Marnettc, Bryan L. Rothb,e,2, and Brian K. Shoicheta,2 aDepartment of Pharmaceutical Chemistry, University of California, 1700 Fourth Street, San Francisco, CA 94143-2550; bDepartment of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, 4009 Genetics Medicine CB7365, Chapel Hill, NC 27599-7365; eNational Institute of Mental Health Psychoactive Drug Screening Program, Division of Chemical Biology and Medicinal Chemistry, Program in Neuroscience and Lineberger Cancer Center, 4009 Genetics Medicine CB7365, Chapel Hill, NC 27599-7365; cDepartment of Biochemistry, Vanderbilt University School of Medicine, 23rd Avenue South and Pierce Room 850 RRB, Nashville, TN 37232-0146; and dNovartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139 Edited by Stephen J. Benkovic, The Pennsylvania State University, University Park, PA, and approved May 8, 2012 (received for review March 16, 2012) Notwithstanding their key roles in therapy and as biological while, an ingenious chemical biology tool set has emerged for probes, 7% of approved drugs are purported to have no known target identification (7–9). This not only improved our under- primary target, and up to 18% lack a well-defined mechanism of standing and use of these drugs but has enabled their deployment action. Using a chemoinformatics approach, we sought to “de-or- as tools to probe biology. phanize” drugs that lack primary targets. Surprisingly, targets Most of these chemical biology approaches focus on a particu- could be easily predicted for many: Whereas these targets were lar target class or experimental strategy and do not lend them- not known to us nor to the common databases, most could be con- selves to the target identification for the diverse set of drugs firmed by literature search, leaving only 13 Food and Drug Admin- for which targets remain unknown. We therefore investigated istration—approved drugs with unknown targets; the number of the chemoinformatic similarity ensemble approach (SEA) to link- drugs without molecular targets likely is far fewer than reported. ing drugs to targets, followed by experimental testing in direct The number of worldwide drugs without reasonable molecular binding assays. Whereas SEA and other chemoinformatics ap- – targets similarly dropped, from 352 (25%) to 44 (4%). Nevertheless, proaches (10 12) have liabilities (to which we shall return), they BIOPHYSICS AND there remained at least seven drugs for which reasonable mechan- can comprehensively and systematically interrogate all targets for COMPUTATIONAL BIOLOGY ism-of-action targets were unknown but could be predicted, in- which ligands are known. Previously, we used SEA to discover cluding the antitussives clemastine, cloperastine, and nepinalone; off-targets and mechanism-of-action targets for over 30 drugs the antiemetic benzquinamide; the muscle relaxant cyclobenzapr- against over 40 targets (10, 13). In this strategy, a likely target for ine; the analgesic nefopam; and the immunomodulator lobenzarit. a drug is identified when the known ligands for a target resemble For each, predicted targets were confirmed experimentally, with the “bait” drug much more closely than would be expected at affinities within their physiological concentration ranges. Turning random; over 2,500 molecular targets have enough ligands to this question on its head, we next asked which drugs were specific be interrogated by this method. Extending this work, we screened enough to act as chemical probes. Over 100 drugs met the standard almost 1,000 target “orphan” drugs, finding for many high-like- criteria for probes, and 40 did so by more stringent criteria. A che- lihood predictions of sensible targets. Surprisingly, it often hap- mical information approach to drug-target association can guide pened that these predicted targets were already reported in the therapeutic development and reveal applications to probe biology, literature, even though they were unknown to the drug-target a focus of much current interest. databases; only rarely did the target orphan status of a drug sur- vive close scrutiny. Still, several did, and for these we tried to pre- chemical tools ∣ drug target identification ∣ polypharmacology dict and experimentally test sensible primary targets. Also, with so many drugs well associated with targets, we thought to turn this ecent studies suggest that for many approved drugs, a primary approach on its head and identify those drugs for which targets Rtarget is unknown. In an influential review, Drews reported and specificities were well-enough known to act as biological that 7% of approved drugs lack a defined molecular target (1), probes, a subject of much current interest in the community. We and in their seminal paper on modern drug development, Over- considered the application of this approach to investigating ington et al. were only able to assign mechanism-of-action protein mechanism-of-action targets for new molecules emerging from targets to about 82% of Food and Drug Administration (FDA) phenotypic programs in drug discovery, and for the relatively approved drugs (2). Given the critical role small molecule drugs rapid identification of drugs and drug candidates that may be play in medicine, and their potential to serve as tools in biology, used as biological probes. the lack of a commonly accepted primary target for so many mo- Results lecules seemed almost provocative. Almost all drug discovery now begins with activity of molecules Finding Primary Targets for Drugs. Our initial goal was to find on a molecular target, and it is hard to imagine how the primary molecular targets for drugs for which the primary target was target of such molecules would be unknown. However, at least half of drugs date from the premolecular era, when their action Author contributions: B.K.S. designed research; E.G.-P., V.S., J.H., and B.A.C. performed was explored against whole tissues, rarely on isolated proteins, research; E.G.-P., J.H., J.J.I., E.L., L.M., B.L.R., and B.K.S. analyzed data; and E.G.-P. and and target identities were only inferred from tissue-based re- B.K.S. wrote the paper. sponses. Even today, 37% of first-in-class drugs derive from phe- The authors declare no conflict of interest.. notypic screens (3), and the targets for some new drugs remain This article is a PNAS Direct Submission. unknown. Recent examples include the emergent polypharmacol- Freely available online through the PNAS open access option. ogy of imatinib and olanzipine, where the multiple targets not 1E.G.-P. and V.S. contributed equally to this work. only explain side effects but also therapeutic efficacy (4). Thus, 2To whom correspondence may be addressed. E-mail: [email protected] or much effort has been spent on target discovery, including large- [email protected]. scale experimental screening of approved therapeutics against This article contains supporting information online at www.pnas.org/lookup/suppl/ G-protein coupled receptors (GPCRs) (5) and kinases (6). Mean- doi:10.1073/pnas.1204524109/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1204524109 PNAS Early Edition ∣ 1of6 Downloaded by guest on October 2, 2021 unknown. We began with the 1,382 approved drugs from the 2.13% 0.64% DrugBank database. From the 127 drugs with an unknown pri- 0.96% mary target, 44 were discarded as topically applied, acting via a nonprotein target (e.g., chelators) or as diagnostics, while 5 were discarded for having a molecular weight greater than 1,000 dal- tons. This yielded 78 approved drugs, 5.6% of those in DrugBank, a figure consistent with the literature (1). To discover molecular targets for these 78 drugs, we looked for chemical similarities to ligand sets for over 2,500 targets, using SEA (10, 14). SEA describes each target by its known ligands, as represented by topological fingerprints (here extended connec- tivity fingerprints [ECFP] ECFP_4 (15)). The query molecule is compared to the set of known ligands for a target by summing all pair-wise similarities. This raw score is normalized for the size 96.31% bias by comparing it to a score expected at random for similar set sizes, using the basic local alignment search tool (BLAST) algorithms (16). The final scores, as in BLAST, are expectation values (E-values); the smaller this E-value, the more significant the drug-target association. For 10 of the 78 drugs, the SEA-predicted target exactly matched one of the chemical libraries we interrogated. For 60 Target in Drug Bank v3 No protein target −6 of the remaining 68 drugs, E-values ranged from 9.84 × 10 Target in literature / patent Unknown target to 1.85 × 10−251. We selected those predictions that were related to the drug’s indication. For 19 drugs, the predicted targets were Fig. 1. Distributions of the DrugBank v3 approved drugs by to whether they confirmed by literature (Table S1). A mechanistically sensible tar- have a database-assigned target (blue), a literature one (green), act by a non- get could be found manually in the literature for another 31 drugs protein target (red), or if their molecular target is unknown (purple). for which it could not be predicted using SEA, leaving only 19 drugs (1.51%) for which a reasonable therapeutic target could simply lacking an indication (4,323). For the remaining 1,259 — not be identified. compounds, 420 were found in DrugBank 336 as approved Before submission of this paper, DrugBank was updated to drugs. Thus, our final set consisted of 839 worldwide-approved version 3 (17), resulting in a more comprehensive list of targets. drugs, from which 208 had a protein target annotation in the Many of the drugs that previously had no target had one anno- NPC, 70 in ChEMBL, and 5 in MDDR.