Bitterness prediction in-silico: A step towards better drugs

G Model IJP 16548 No. of Pages 4 International Journal of Pharmaceutics xxx (2016) xxx–xxx Contents lists available at ScienceDirect International Journal of Pharmaceutics journal homepage: www.elsevier.com/locate/ijpharm Bitterness prediction in-silico: A step towards better drugs Malkeet Singh Bahiaa,b,1 , Ido Nissima,b,1, Masha Y. Niva,b,* a Institute of Biochemistry, Food Science and Nutrition, The Robert H. Smith Faculty of Agriculture, Food, and Environment, The Hebrew University of Jerusalem, Rehovot, 76100, Israel b The Fritz Haber Center for Molecular Dynamics, The Hebrew University of Jerusalem, 91904, Israel A R T I C L E I N F O Article history: Received 13 February 2017 Received in revised form 23 March 2017 Accepted 27 March 2017 Available online xxx Keywords: Bitterness GPCRs Taste receptors Promiscuity in-silico Prediction Pediatric drugs Tas2Rs A B S T R A C T Bitter taste is innately aversive and thought to protect against consuming poisons. Bitter taste receptors (Tas2Rs) are G-protein coupled receptors, expressed both orally and extra-orally and proposed as novel targets for several indications, including asthma. Many clinical drugs elicit bitter taste, suggesting the possibility of drugs re-purposing. On the other hand, the bitter taste of medicine presents a major compliance problem for pediatric drugs. Thus, efficient tools for predicting, measuring and masking bitterness of active pharmaceutical ingredients (APIs) are required by the pharmaceutical industry. Here we highlight the BitterDB database of bitter compounds and survey the main computational approaches to prediction of bitter taste based on compound's chemical structure. Current in silico bitterness prediction methods provide encouraging results, can be constantly improved using growing experimental data, and present a reliable and efficient addition to the APIs development toolbox. © 2017 Elsevier B.V. All rights reserved. 1. Introduction Bitter taste is one of the basic taste modalities, the role of which is typically linked to guarding against consumption of poisons (Chandrashekar et al., 2006). However, not all bitter compounds are toxic: many dietary phytonutrients commonly found in fruits and vegetables (Drewnowski and Gomez-Carneros, 2000), as well as many clinical drugs (Mennella et al., 2013) elicit bitter taste sensation. The bitterness of drug molecules presents a major problem of compliance for children (Mennella and Beauchamp, 2008). Sensory tasting of drug candidates by humans is not a trivial matter, since it requires ethical approval achievable only after a thorough toxicological study. Thus, efficient prediction of compounds’ bitterness in the initial stages of drug discovery is of great interest. Several computational (in-silico) studies predicted bitter taste of compounds based on their chemical structure, as described below and summarized in Fig. 1. The cost-effectiveness and the possibility to improve the prediction quality based on the growing body of experimental data suggest that in-silico bitterness * Corresponding author at: Institute of Biochemistry, Food Science and Nutrition, The Robert H. Smith Faculty of Agriculture, Food, and Environment, The Hebrew University of Jerusalem, Rehovot, 76100, Israel. E-mail address: masha.niv@mail.huji.ac.il (M.Y. Niv). 1 These authors contributed equally to this work. prediction could become a practical step in the process of developing pediatric drugs. 2. Bitter compounds: databases and properties We have recently established the BitterDB (Wiener et al., 2012), a freely accessible repository of compounds that were reported to be bitter for humans or to activate bitter taste receptors (Tas2Rs) in cell-based functional assays. The bitter-tasting compounds are recognized by a subfamily of G-protein coupled receptors, the Tas2R (Chandrashekar et al., 2006). This family is comprised of 25 receptor subtypes in humans, activated by varying number of partially activated ligands (Meyerhof et al., 2010). Most of the information on the bitter compounds was extracted from the Merck Index (O’Neil, 2006), Fenaroli’s handbook of flavor ingredients (Burdock and Fenaroli, 2005) and research articles retrieved from PubMed. Currently, the BitterDB comprises close to 700 bitter compounds, 120 of which were experimentally assigned to their cognate Tas2Rs. BitterDB can be searched using words, molecular identifiers (such as SMILES or CAS registry number), chemical properties, or the associated bitter taste receptors. Additionally, chemical structures may be submitted as queries to find identical or similar compounds within the BitterDB. Notably, bitter compounds, even those that activate the same bitter taste receptor subtype, may vary dramatically in their chemical structures and physicochemical properties (Di Pizio and Niv, http://dx.doi.org/10.1016/j.ijpharm.2017.03.076 0378-5173/© 2017 Elsevier B.V. All rights reserved. Please cite this article in press as: M.S. Bahia, et al., Bitterness prediction in-silico: A step towards better drugs, Int J Pharmaceut (2017), http:// dx.doi.org/10.1016/j.ijpharm.2017.03.076 G Model IJP 16548 No. of Pages 4 2 M.S. Bahia et al. / International Journal of Pharmaceutics xxx (2016) xxx–xxx There are several methods for predicting bitterness of compounds (see Fig. 1), as summarized below. are applicable when biological data is available for a focused chemical series. Such models were established for the prediction of bitterness of several analogues of sesquiterpene lactones and for some classes of peptides (Ley, 2008). Other well-established techniques use ‘ligand-based pharmacophore’ (LBP) models which are three-dimensional representations of structural features conserved among known actives, and ‘shape-based screening’ which allows to filter compounds based upon shape and electrostatics similarity to the query molecule. Roland and coworkers (Roland et al., 2013) described the structural requirements of flavonoids for the activation of two bitter receptors – Tas2R14 and 39. Using a test set of 73 (for Tas2R14) and 77 (for Tas2R39) compounds, the LBP models predicted activation of these receptors by flavonoids. 68% sensitivity (the ratio between predicted true positives and the total true positives, also known as true positives rate) and 65% specificity (the ratio between predicted true negatives and the total true negatives, also known as true negatives rate) was achieved for Tas2R14 and 85% sensitivity, 78% specificity for Tas2R39. Additionally, favorable and unfavorable ligand molecular features for receptor activation were highlighted. Levit and co-workers screened the BitterDB compounds which were not yet assigned to a particular taste receptor, and the DrugBank dataset of clinical drugs (Wishart et al., 2006) for prospective prediction of Tas2R14 activators using LBP and shapebased models. Subsequently, 9 out of 11 BitterDB predicted compounds and 11 out of 23 DrugBank predicted compounds were experimentally confirmed as Tas2R14 activators (Levit et al., 2014). 3.1. Ligand-based methods 3.2. Structure-based methods Identification of previously unknown bitter compounds can rely on similarity to known bitterants, an approach well established in drug discovery and widely used when the structure of the target protein is not available (Sliwoski et al., 2014). For example, Quantitative Structure-Activity-Relation (QSAR) models With the constant rise of structural information of the target proteins, including GPCRs (Di Pizio et al., 2016; Yarnitzky et al., 2010), structure-based methods which predict novel compounds that favorably fit into the binding site of the target (Irwin and Shoichet, 2016; Kitchen et al., 2004) become more and more 2015; Levit et al., 2014). For example, though hydrophobicity was suggested to describe the bitterness of peptides (Ney's Q rule (Ney, 1971)), it was not predictive of bitterness of soy (Cho et al., 2004) or cheese (Toelstede and Hofmann, 2008) peptides. Indeed, the value of logP (octanol-water partition) of bitter compounds varies rather widely, i.e. 6.292 for streptomycin or 4.836 for MgBr2 vs. 6.484 for adlupulone from beer or 6.417 for Polysorbate 60 (data obtained via the BitterDB http://bitterdb.agri.huji.ac.il/dbbitter.php#compoundBrowse). An early attempt to classify bitter compounds (Rodgers et al., 2005) used a large clustering analysis to build a hierarchical phylogenetic-like tree based on the maximal common substructures of over 800 proprietary bitter molecules. The final tree contained 93 nodes with at least 8 members each, suggesting there are at least 93 sub-structures that may be linked to bitterness. Some bitter compounds are Tas2R-selective, meaning that they activate a single Tas2R subtype. Others activate multiple Tas2R, and can be termed Tas2R-promiscuous. Overall, we found that the Tas2R-promiscous compounds tend to be smaller, more globular and more hydrophobic than Tas2R-selective compounds. A linear regression tool built using these features correctly classified an external set of bitter molecules into Tas2R-selective and Tas2Rpromiscuous bitterants (Di Pizio and Niv, 2015). 3. Predicting bitter compounds Fig. 1. Iterative usage of experimental data towards improvement of computational predictive tools. Please cite this article in press as: M.S. Bahia, et al., Bitterness prediction in-silico: A step towards better drugs, Int J Pharmaceut (2017), http:// dx.doi.org/10.1016/j.ijpharm.2017.03.076 G Model IJP 16548 No. of Pages 4 M.S. Bahia et al. / International Journal of Pharmaceutics xxx (2016) xxx–xxx applicable. The ‘structure-based pharmacophore’ (SBP) approach translates the receptor’s binding site into a three-dimensional representation of structural features (Wolber and Langer, 2005). Such techniques were shown to provide differentiation between known activators and known non-activators of Tas2R39 (Roland et al., 2015). The best model displayed 63% sensitivity (specificity was not reported) when used on a test set of 176 molecules containing 92 actives and 84 non-actives of Tas2R39. Docking of ligands to proteins are methods that explore possible binding poses of the ligands in the (usually pre-defined) binding site, and return the orientation with the best score (a simplified estimate of the most favorable free energy of binding). Docking to homology models has worked well for some GPCRs, as assessed by success in prospective prediction of new X-ray structures of ligand-receptor complexes via community wide GPCRdock experiments (Kufareva et al., 2014), and from the ability to predict novel ligands, i.e. (Carlsson et al., 2011). Docking methods were used to elucidate details of bitterant-Tas2R interactions and to rationalize activities (Levit et al., 2012; Di&Pizio and Niv, 2014; Karaman et al., 2016) and for discovering novel bitter ligands (unpublished data). In some cases, atomistic molecular dynamics simulations were integrated with ligand docking towards a more accurate investigation of binding modes of bitter compounds (Marchiori et al., 2013; Sandal et al., 2015). While ligand- and structure-based methods successfully discovered novel ligands of particular bitter taste receptors, they are less suited towards the general classification of compounds as either bitter or non-bitter: a compound that does not activate a particular Tas2R subtype could still activate one or several of the remaining 24 Tas2Rs. A labor-intensive approach to overcome this problem is to develop ligand-based models for all subgroups of active ligands, and supplement those with structure-based predictors for each one of the 25 human bitter taste receptors. Another, more practical alternative, is to develop general bitter/ non-bitter classifiers using modern machine learning (ML) techniques. 3.3. Machine learning techniques ML is a large field of research in computer science, dedicated to developing algorithms that learn from data in supervised or unsupervised manner. In practical terms, a training set of data is provided as input for an ML algorithm (such as artificial neural networks, Naïve Bayes classification, Support Vector Machine (SVM), random forest, etc.). The algorithm iteratively learns how to classify molecules as either positive or negative within the training set, and then validated on test set(s). Model performance may vary wildly, and critically depends on the training set provided. Rodgers et al. used a dataset of 649 bitter and 13530 randomly selected (and assumed non-bitter) molecules to develop a Naïve Bayes classifier based on circular fingerprints (MOLPRINT 2D) and information-gain feature selection (Rodgers et al., 2006). The selected classifier had 70% sensitivity and 93% specificity in a 5-fold cross-validation. Bitterness predictor BitterX (Huang et al., 2016) combined ligand-based descriptors with a SVM approach. The true positives set was built from BitterDB and additional manually curated compounds (500 compounds in total); the true negatives set was comprised mainly of compounds not described as ‘bitter’ in the literature. 500 representative structures obtained by a clustering procedure were chosen in order to balance the bitter and the nonbitter sets. The predictor was trained and applied to an in-house dataset of 220 compounds. Among compounds predicted as bitter, five compounds (four of which appear in the BitterDB) were experimentally tested in cell-based assays and confirmed to activate at least one Tas2R. 3 Our lab is currently extending the BitterDB to include a set of compounds that may serve as a reliable non-bitter set. Using this set as true negatives and the BitterDB set as true positives, we have developed an ‘AdaBoost-based’ ML classifier that is applicable to predict both bitterness and non-bitterness of compounds (DaganWiener et al., submitted and ongoing work). 4. Summary and outlook The encouraging results obtained so far, combined with further growth of experimental data, can provide reliable and inexpensive computational tools for developing active pharmaceutical ingredients (APIs). The ligand-based methods are most suitable when activity data is available for a chemical series of compounds; structure-based methods are most suitable when binding site of the structural model of the receptor was confirmed by sitedirected mutagenesis (Levit et al., 2012), and machine learning techniques are applicable when bitterness at large (not related to particular receptor or subfamily of ligands) is investigated. Importantly, allowing the training and prediction datasets to be publicly available will enable benchmarking of different ML methods and constant improvement of predictors. It is therefore important to maintain and update public databases, such as BitterDB. It is likely that computational prediction of bitterness will become a useful addition to the pharmaceutical researcher toolbox, alongside with additional techniques, such as animalbased and e-tongue models (Clapham et al., 2012). Acknowledgements The authors express their gratitude to the Planning and Budgeting Committee (PBC) of the Council for Higher Education in Israel for providing postdoctoral fellowship to Dr. Malkeet Singh Bahia in the Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel. Funding by the ISF494/16 and ISF-NSFC2463/16 grants to MYN, and membership in COST actions CM1027 (GLISTEN) and CA15135 (Mu. Ta.Lig), are gratefully acknowledged. 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