A Screening Pattern Recognition Method Finds ... - ACS Publications

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A Screening Pattern Recognition Method Finds New and Divergent Targets for Drugs and Natural Products Anne Mai Wassermann,*,† Eugen Lounkine,† Laszlo Urban,† Steven Whitebread,† Shanni Chen,† Kevin Hughes,† Hongqiu Guo,† Elena Kutlina,† Alexander Fekete,† Martin Klumpp,‡ and Meir Glick† †

Novartis Institutes for Biomedical Research Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States Novartis Institutes for Biomedical Research Inc., Novartis Campus, 4056 Basel, Switzerland

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S Supporting Information *

ABSTRACT: Computational target prediction methods using chemical descriptors have been applied exhaustively in drug discovery to elucidate the mechanisms-of-action (MOAs) of small molecules. To predict truly novel and unexpected small molecule−target interactions, compounds must be compared by means other than their chemical structure alone. Here we investigated predictions made by a method, HTS fingerprints (HTSFPs), that matches patterns of activities in experimental screens. Over 1,400 drugs and 1,300 natural products (NPs) were screened in more than 200 diverse assays, creating encodable activity patterns. The comparison of these activity patterns to an MOA-annotated reference panel led to the prediction of 5,281 and 2,798 previously unknown targets for the NP and drug sets, respectively. Intriguingly, there was limited overlap among the targets predicted; the drugs were more biased toward membrane receptors and the NPs toward soluble enzymes, consistent with the idea that they represent unexplored pharmacologies. Importantly, HTSFPs inferred targets that were beyond the prediction capabilities of standard chemical descriptors, especially for NPs but also for the more explored drug set. Of 65 drug−target predictions that we tested in vitro, 48 (73.8%) were confirmed with AC50 values ranging from 38 nM to 29 μM. Among these interactions was the inhibition of cyclooxygenases 1 and 2 by the HIV protease inhibitor Tipranavir. These newly discovered targets that are phylogenetically and phylochemically distant to the primary target provide an explanation for spontaneous bleeding events observed for patients treated with this drug, a physiological effect that was previously difficult to reconcile with the drug’s known MOA.

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agents.3 Although this knowledge-driven approach and human expertise are valuable assets in drug discovery,7 they introduce a strong bias and prevent us from testing fresh hypotheses and exploring unchartered territories in chemical and biological space. This is also reflected by the slow progress made in the identification of novel drug targets from the human genome.8 Over the past decade, computational prediction methods have helped to alleviate human bias in the generation of target hypotheses and explore small molecule MOAs more systematically.9−16 One caveat is that most of these methods revert to chemical structure as molecular descriptors and are therefore also limited in their prediction capabilities. The ligand space that can be predicted for a target is largely defined by the chemistry that we have already explored. As a result, predictions based on chemical classifiers remain challenging for complex molecules such as NPs that are structurally distinct from the synthetic molecule space covered by bioactivity-annotated databases.17 However, 36% of the first-in-class small molecule NMEs approved between 1999 and 2008 originated from natural substances.3 For this compound class, computational

he advent of molecular biology and the release of the sequenced human genome led to the rise of rational molecular target-based screening as a new paradigm in drug discovery.1 However, decades later, it is increasingly being recognized that target-based approaches failed to meet expectations.2 In many cases, pursued targets were devalidated in late stages of drug discovery or clinical trials, i.e., they were found to not be relevant for the pathogenesis of a disease and/ or their modulation did not lead to the desired clinical effects. A recent analysis3 of first-in-class small molecule drugs approved by the FDA between 1999 and 2008 showed that only 17 new molecular entities (NMEs) had been identified by target-based approaches, whereas 28 had been discovered by phenotypic screening, strengthening the importance of physiologically more relevant assays for successful drug discovery. Accordingly, phenotypic screening is experiencing a renaissance in the pharmaceutical industry.4,5 Active compounds discovered in a phenotypic screen may interact with a variety of targets or molecular pathways, and the deconvolution of their mechanism-of-action (MOA) is often a challenging and tedious process.6 Given both time and resource constraints, present screening and MOA elucidation efforts are primarily guided by prior knowledge about a compound (class), e.g., nucleoside analogues are tested in the pursuit of anticancer © 2014 American Chemical Society

Received: January 6, 2014 Accepted: May 6, 2014 Published: May 6, 2014 1622

dx.doi.org/10.1021/cb5001839 | ACS Chem. Biol. 2014, 9, 1622−1631

ACS Chemical Biology

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Figure 1. Target prediction with HTS fingerprints. A schematic depiction of the HTSFP-TID approach is shown for the anticholinergic drug Dicyclomine.

predictions using biological descriptors9,10,13 are more promising. By design, these methods that use data from biological experiments to find similarities in the MOAs of small molecules are ignorant of chemical structure and enable target predictions for previously unchartered regions of chemical space. Biological descriptors developed and used at Novartis are HTS fingerprints18 (HTSFPs), which capture the biological effects of small molecules across more than 200 biochemical and cellular assays (please refer to Petrone et al.18 for a detailed description of these fingerprints). Here, we introduce and evaluate HTSFP-based target identification (HTSFP-TID) validated by in vitro experiments and clinical phenotypes that, in their scale, are unprecedented for biological descriptors.

the HTSFP (of the 524 targets in our benchmark system (see Methods) only 95 targets were represented by biochemical assays, and 44 were associated with cellular assays in the HTSFP). Applying a p-value threshold of 10−20, 6,474 target predictions were made for 865 NPs and 4,790 target predictions for 599 drugs. To investigate the reliability of these predictions, we used public, commercial, and in-house structure−activity databases and retrospectively confirmed 934 (14.4%) and 1,393 (29.1%) of the predicted interactions for NPs and drugs, respectively, with a potency (Ki, Kd, EC50, IC50, or AC50) of at least 5 μM (results for other p-value thresholds are reported in Supplementary Table 1). Only 4% of the targets predicted for NPs (259 of 6,474) and 12.5% of the targets predicted for drugs (599 of 4,790) were rejected on the basis of reported negative measurements. Hence, of the predicted target−ligand interactions for which experimental data could be retrieved, 78.3% and 69.9% were confirmed for NPs and drugs, respectively (Supplementary Table 1). Some of the targets for which we made predictions were represented by assays in our assay panel. To make sure that our good prediction performance was not biased by compound−target pairs for which primary assay results were already available, we excluded these predictions (accounting for 24.7% and 33.8% of the predictions made for drugs and natural products, respectively) and recalculated precision values. However, also after removal of potentially “easy-to-infer” compound−target pairs, precisions achieved for NPs and drugs remained stable with 77.7% and 69.3% of all interactions for which experimental measurements could be found being correct. It should be noted that, although the entire HTSFP consists of 234 assays, all drugs and NPs have only been tested in a subset of the full assay panel because the 234 assays were run at different time points in Novartis over the past decade, and each time a differently composed compound collection was screened. Therefore, HTSFPs of compounds are generally incomplete, and differing subsets of the full assay deck were used for the comparison of individual NPs and drugs to the reference panel. We required that all of the natural products and drugs for which we predicted targets had been tested in at least 50 assays; still, assay subset sizes differed substantially for compounds in our benchmark sets (Supplementary Figure 1).

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RESULTS AND DISCUSSION We apply HTSFP-TID to make predictions for 1,357 natural products (NPs) and 1,416 experimental small molecules and marketed drugs (hereafter generally referred to as drugs). Our large-scale target prediction enables us to detect differences in the protein classes predicted for the two data sets, reveal target classes that so far have been underrepresented in target elucidation efforts, and devise strategies for a more effective targeting of the druggable genome.19 Our results show that even for highly investigated compounds such as marketed drugs, HTSFP-TID provides fresh hypotheses that were previously not pursued because they were not obvious based on the chemical structure of a molecule or against human intuition. HTSFP-TID for Natural Products and Drugs. We predicted targets using HTSFP-TID for 1,357 molecules from the Novartis NP library and 1,416 drugs from ChEMBL20 and DrugBank.21 For each molecule, HTSFP similarities, i.e., screening pattern similarities to biologically annotated compounds in the Novartis screening collection, were calculated and human protein targets that were enriched among compounds with similar bioactivity profiles were inferred. For each target, a p-value as an indicator of the likelihood that its enrichment occurred by chance was calculated (see Methods and Figure 1). Importantly, all target annotations of biologically similar compounds are considered, and the method is able to predict targets that are not part of the assay panel encoded in 1623



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Figure 2. Natural product targets predicted by HTSFP-TID. (a−c) For three natural products, targets predicted with HTSFP-TID are shown (VEGFR2, vascular endothelial growth factor receptor 2; EHMT2, Histone-lysine N-methyltransferase, H3 lysine-9 specific 3). The three predictions were verified by public bioactivity data and are annotated with their potency and source databank. The prediction for Gliotoxin shown in panel c was made with the biochemical HTSFP, whereas the other two predictions were made with the full HTSFP.

undrugged targets (Figure 3a). This highlights the opportunity offered by biological descriptors to expand our target

The median number of assays available for a query compound was 154 for NPs and 168 for drugs, respectively. Interestingly, we made the observation that there was no clear correlation between the number of assays a compound had been tested in and the precision of our predictions (Supplementary Figure 2). Furthermore, we created HTSFP versions that consisted of either cell-based or cell-free assays only and used them for target prediction. Results obtained for these fingerprints are reported in the Supporting Information (see Supplementary Results, Supplementary Table 2, and Supplementary Figure 3). To find out whether compound−target interactions predicted by HTSFP-TID were beyond the prediction capability of 2D chemical similarity, we repeated our experiments using structural fingerprints (ECFP422) instead of HTSFPs. In line with previous observations that biological and chemical similarity are often orthogonal,18 only a fraction of the target predictions made on the basis of biological similarity were also found when using chemical descriptors (10.1% for NPs and 19.9% for drugs). Precision was slightly higher for chemical descriptors with 80.6% and 71.6% of all experimentally tested predictions found in bioactivity databases being active. Whereas biological descriptors generated more predictions for NPs than for drugs, the opposite was found for chemical descriptors: 6,367 predictions were made for drugs but only 4,000 for NPs. These results reflect that for a marketed drug its chemical neighborhood is better explored than for an NP and more structurally similar compounds exist in bioactivity databases that enable target prediction. Turning this finding on its head, we can assume that for chemical singletons target prediction using HTSFPs is more promising than using chemical descriptors. Figure 2 reports target predictions for three natural products that were verified by public bioactivity data and were not made with our chemical descriptor-based approach. Target Spaces of Natural Products and Drugs. To learn more about the target space reached by HTSFP-TID (using the full fingerprint), we classified predicted targets using the recently published Drug-Gene Interaction Database (DGIdb; see Supporting Information).23 Targets were considered as drugged if a drug interaction could be retrieved in DGIdb, as druggable if they were listed in DGIdb but no drug interaction was found, and as currently undruggable if they were not part of DGIdb. Of course, the classification into druggable and undruggable is based on our current pharmacological knowledge and may be subject to change in the future. Importantly, both categories can be summarized under one umbrella as currently undrugged. Interestingly, about 39% and 23% of predictions for NPs and drugs, respectively, were made for

Figure 3. Target spaces. (a) For the NP and drug benchmark sets, frequencies of drugged, druggable, and undruggable targets among predictions made by biological (HTSFP) similarity are displayed. (b) For drugs, NPs, and synthetic small molecules that have NP-like bioactivity profiles, relative frequencies of different target classes among predictions made by HTSFP-TID are shown. Black diamonds report target class frequencies among known annotations for the three compound sets. Target classes follow a protein hierarchy developed at Novartis.

hypotheses to previously less explored target space and steer drug discovery projects to the investigation of new biology. Next, to further dissect the target space predicted by HTSFPTID, we evaluated prediction performance for individual target classes. The first important observation that we made was that, for all target classes, true target−ligand pairs were significantly enriched among the predictions in comparison to their frequency of occurrence in the database (one-sided Fisher’s exact test: p < 0.01; Supplementary Table 3). For example, 444 predicted GPCR-drug interactions could be confirmed with a potency ≤5 μM, whereas only 247 interactions that were 1624



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Table 1. Novel Target Predictions Confirmed by in Vitro Experiment

The column “unique” indicates whether the prediction was made by HTSFPs only (“Y”es) or was also obtained when ECFP4 was used as molecular descriptor (“N”o). bThe result was obtained from an antagonist or inhibition assay. cThe result was obtained from an agonist assay.

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predicted for this target class could be rejected at a 5 μM threshold. This is a 6.5-fold enrichment of active GPCR-ligand pairs over their occurrence in our databases, where 3,233 active and 29,588 inactive GPCR−drug pairs were found (p = 10−275, one-sided Fisher’s exact test). The reliability of our predictions for all investigated target classes encouraged us to use our HTSFP-TID results to analyze the different pharmacologies represented by NPs and drugs. For NPs, enzymes were the predominantly predicted target class (69.3%), with 35.3% of all predictions being made for kinases (Figure 3b). These numbers agreed surprisingly well with the known target annotations for these compounds (60.6% are reported for enzymes, 33.6% for kinases; Supplementary Table 4). For drugs, 43.6% of all predictions comprised kinases, making them also the most frequently predicted target class for this data set. However, this percentage was more than 2-fold higher than the relative frequency with which kinases occurred in the known target set (20.1%), suggesting that many drug-kinase interactions remain to be discovered (Supplementary Table 4). For drugs, serine/ threonine and tyrosine kinases were predicted with very similar frequencies, whereas for NPs more predictions were made for tyrosine kinases (Supplementary Figure 4). Proteases were predicted with a frequency of 14.9% for NPs but were much less represented (4.9% of all predictions) for drugs (Figure 3b). Predicted targets were mainly aspartate, serine, and cysteine proteases with very few predictions for metalloproteases (Supplementary Figure 4). Only 3.9% of all predictions for NPs targeted G protein-coupled receptors (GPCRs) (Figure 3b) that represented 8.9% of the known target interactions for this molecule set (Supplementary Table 4). By contrast, for the drug set, GPCRs occurred in 22.6% of all suggested target− ligand pairs, in agreement with the higher frequency of GPCRs among the reported interactions for this target class (29.9%) (Figure 3b, Supplementary Table 4). Notably, 135 molecules from the drug set originated from natural sources. For this drug subset, target class distributions of predictions were similar to the NP data set, with a predominant occurrence of enzymes, in particular kinases, and very few predictions made for GPCRs (Supplementary Table 5). The different target spaces covered by NPs and drugs support the use of NPs as probes in chemical biology as they might be able to modulate and explore a biological system very differently than many synthesized compounds. On the other hand, HTS fingerprints provide us with the means to retrieve also synthetic low molecular weight compounds that have bioactivity profiles similar to those of NPs. To investigate whether we would be able to design a focused set of small molecules from the screening collection that addresses the same target space as NPs, we determined the compound with the most similar screening pattern to each NP in our study under the additional constraints that this nearest neighbor was of synthetic origin and compliant with the Lipinski rule of five.24 As anticipated, a subsequent application of HTSFP-TID to this focused set suggested a target space similar to the space predicted for NPs (Figure 3b), showing that drug-like screening subsets can be explicitly designed for the exploration of new biological mechanisms in drug discovery projects. Finally, a comparison to target classes predicted by chemical descriptors for NPs and drugs reconfirmed that the two similarity methods covered partly distinct target spaces, with HTSFPs predicting more kinases and proteases and ECFPs predicting more GPCRs and other enzymes (Supplementary

Table 4). We further wanted to explore the targets predicted by HTSFP-TID for drugs. Therefore, we selected 10 drugs with so far little explored pharmacology for in vitro testing of HTSFPbased target predictions to find out whether we could shed further light on their biological actions. Finding On- and Off-targets for Marketed Drug and Biological Probes. The 10 drugs for in vitro experiments were mainly selected on the basis of three different criteria: (i) a drug was not very well explored in target space, i.e., only few activity annotations were available in our databases, (ii) a target predicted for a drug had the potential to explain its efficacy and/or side effects, or (iii) a predicted target was, based on target family annotations, phylogenetically distant to the onand off-targets known for a drug. Binding or functional assays were available at Novartis for prospective testing of 27 targets that were predicted to interact with these compounds and for which we could not retrieve a dose−response measurement in public or in-house databases. Overall, we investigated 65 target−ligand interactions between the 10 compounds and 27 targets (Table 1, Supplementary Table 6 and Supplementary Figure 5). Cyclooxygenase (COX) inhibition leads to reduced clot formation and increased bleeding. The human immunodeficiency virus (HIV) protease inhibitor Tipranavir has a box warning for intracranial hemorrhage that was observed for multiple patients receiving a Tipranavir/Ritonavir combination therapy (Boehringer Ingelheim Pharmaceuticals Inc. Tipranavir (APTIVUS) product information). HTSFP predicted COX-1 and COX-2 as protein targets for Tipranavir. In vitro experiments confirmed these targets with IC50 values of 5.8 μM (COX-1) and 3.8 μM (COX-2), i.e., concentrations that are much lower than the peak plasma concentrations found for Tipranavir-treated HIV-infected patients in clinical studies (cmax as large as 94 μM reported for female adults in Pharmapendium: http://www.pharmapendium.com, although the free concentration available for target interactions is assumed to be lower because Pharmapendium also reports plasma protein binding of 99.9% in vitro). Furthermore, Graff et al. have demonstrated that five patients medicated with Tipranavir showed substantial decreases in platelet aggregation,25 which is a generally applied biomarker for COX inhibition. Reproduction of these effects was achieved in vitro, and decreased thromboxane B2 (TxB2) formation was proven.25 Because COX enzymes catalyze the synthesis of thromboxane precursors within platelets, reduced TxB2 levels can directly be explained by the inhibition of this enzyme. We found it rather puzzling that, despite the many experimental data supporting nonselective COX or selective COX-2 inhibition, no study investigating this target interaction for Tipranavir could be found in the literature. Instead, the effect of Tipranavir on signaling cascades involving other proteases, i.e., enzymes from the same target class as the primary target, had been studied: the Tipranavir product information reports that “analyses of stored plasma from adult patients treated with APTIVUS [trade name of Tipranavir] capsules and pediatric patients treated with APTIVUS oral solution (which contains a vitamin E derivative) showed no effect of APTIVUS/ritonavir on vitamin Kdependent coagulation factors (Factor II and Factor VII), Factor V, or on prothrombin or activated partial thromboplastin times.” Hence, in this case, HTSFPs provided an unbiased approach to target identification for Tipranavir. Importantly, it revealed a target that was not found using chemical similarity since the drug is structurally very distinct from known COX 1626



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inhibitors. It is also important to record that Ritonavir, the coapplied antiviral agent, does not affect the COX enzymes. For the antipsoriatic retinoid analogue Acitretin COX-1 was predicted with a very low p-value of 5 μM are considered inactive. For each of the 524 proteins, numbers of active and inactive molecules are calculated in the two different compound sets (similar or dissimilar to the probe) and a 2×2 contingency table is built. Then, a one-sided Fisher’s exact test is applied to determine whether the set that is biologically similar to the probe is significantly enriched with active compounds. Proteins for which low p-values are obtained are predicted as molecular targets for the probe (Figure 1). The 5 μM cutoff for labeling compounds as active or inactive against a target for the generation of a contingency table is rather strict but chosen to extract a clear signal from the reference panel. If the bioactivity profile of a query compound is similar to the profiles of compounds that are potent against a target, the confidence that this target is modulated by the query is increased. However, in the prospective evaluation of our approach, we did not apply such a strict potency cutoff but considered all molecules with an AC50 ≤ 30 μM as active. An analogous target prediction workflow with chemical descriptors (ECFP4) and the benchmark sets used in our study are described in detail in the Supporting Information. 1629



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In Vitro Experiments. New predictions were prospectively tested in vitro using binding or functional assays available at Novartis. All compounds were tested in dose−response format using a 30 μM final top assay concentration. Concentration−response curves were calculated using in-house software. A detailed description of all assays can be found in the Supporting Information.

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ASSOCIATED CONTENT

S Supporting Information *

This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare the following competing financial interest(s): All authors of the manuscript are employees of the Novartis Institutes for BioMedical Research.

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ACKNOWLEDGMENTS A.M.W. is supported by the NIBR Education Office. The authors thank E. Gregori-Puigjané, I. Wallace, J.-W. Davies, and Y. Wang for insightful discussions; R. Smith for help with compound management; and B. Shoichet for edits to the manuscript

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