Phytomedicine 18 (2011) 119–133 Contents lists available at ScienceDirect Phytomedicine journal homepage: www.elsevier.de/phymed Predicting cyclooxygenase inhibition by three-dimensional pharmacophoric profiling. Part II: Identification of enzyme inhibitors from Prasaplai, a Thai traditional medicine Birgit Waltenberger a, Daniela Schuster b,c, Sompol Paramapojn d, Wandee Gritsanapan d, Gerhard Wolber b,c, Judith M. Rollinger a, Hermann Stuppner a,∗ a Institute of Pharmacy, Pharmacognosy, University of Innsbruck, 6020 Innsbruck, Austria b Institute of Pharmacy, Pharmaceutical Chemistry, University of Innsbruck, 6020 Innsbruck, Austria c Inte:Ligand GmbH, 1070 Vienna, Austria d Department of Pharmacognosy, Faculty of Pharmacy, Mahidol University, Bangkok 10400, Thailand article info abstract Keywords: Prasaplai is a medicinal plant mixture that is used in Thailand to treat primary dysmenorrhea, which is Prasaplai characterized by painful uterine contractility caused by a significant increase of prostaglandin release. Traditional medicine of Thailand Cyclooxygenase (COX) represents a key enzyme in the formation of prostaglandins. Former studies Natural products revealed that extracts of Prasaplai inhibit COX-1 and COX-2. In this study, a comprehensive literature Cyclooxygenase survey for known constituents of Prasaplai was performed. A multiconformational 3D database was cre- Pharmacophore Virtual screening ated comprising 683 molecules. Virtual parallel screening using six validated pharmacophore models for COX inhibitors was performed resulting in a hit list of 166 compounds. 46 Prasaplai components with already determined COX activity were used for the external validation of this set of COX pharmacophore models. 57% of these components were classified correctly by the pharmacophore models. These find- ings confirm that the virtual approach provides a helpful tool (i) to unravel which molecular compounds might be responsible for the COX-inhibitory activity of Prasaplai and (ii) for the fast identification of novel COX inhibitors. © 2010 Elsevier GmbH. All rights reserved. Introduction restricted by “forbidden” areas, so-called exclusion volumes, and shapes, of which the latter are usually derived from highly active Virtual screening techniques are very common and widespread ligands. One pharmacophore model usually represents one certain in medicinal chemistry (Ekins et al. 2007b,a; Kirchmair et al. 2008). binding mode to a receptor or an enzyme. If a compound fulfils the The general goal of applying such methods is to filter large com- requirements of a pharmacophore model, it is more likely to show pound databases in silico in order to focus experimental efforts biological activity than compounds that do not fit into the model. on those candidates which are most promising for showing the Originally, pharmacophore-based virtual screening has been desired pharmacological effect. Today, the pharmacophore concept developed to find bioactive synthetic compounds. More recently, is one of the most widely established methods for virtual screening this approach has also shown to be valuable in the field of natural (Langer et al. 2006; Leach et al. 2010). By definition, a pharma- products for the identification of bioactive constituents (Rollinger cophore is the ensemble of steric and electronic features that is et al. 2006, 2008). In earlier studies single pharmacophore mod- necessary to ensure the optimal supramolecular interactions with els were used for the virtual screening of natural product (NP) a specific biological target and to trigger or block its biological databases (Rollinger et al. 2004, 2005). Technological evolution response (Wermuth et al. 1998). Common pharmacophoric fea- enabled upscaling of the virtual screening protocols using parallel tures include hydrogen bond donors and acceptors, hydrophobic screening (PS) techniques (Rollinger 2009; Rollinger et al. 2009). interactions, aromatic ring systems, positively or negatively ioniz- In pharmacophore-based PS, single compounds or small databases able functions, and data on their location in the three-dimensional are virtually screened against a series of pharmacophore mod- (3D) space. Moreover, pharmacophore models can be sterically els, aiming at the prediction of pharmacological activity profiles of these molecules (Kirchmair et al. 2008; Rollinger 2009). Herein we present a further application scenario of PS, i.e. the search for ∗ structurally diverse natural compounds with a defined molecular Corresponding author. Tel.: +43 512 507 5300; fax: +43 512 507 2939. E-mail address: [email protected] (H. Stuppner). mode of action. 0944-7113/$ – see front matter © 2010 Elsevier GmbH. All rights reserved. doi:10.1016/j.phymed.2010.08.002 120 B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133 Traditional medicine often uses plant mixtures which contain publicly available via the Protein Data Bank (PDB) (Berman et al. hundreds of compounds from different biosynthetic origin and dif- 2003). Possible chemical interactions between the ligand(s) and ferent chemical scaffolds. In this study, we selected Prasaplai, a the macromolecule are analyzed, and pharmacophore features Thai traditional medicine, as a sample for the application of PS are placed where interactions are observed. For the ligand-based because (i) it is a complex mixture of NPs, (ii) it is used in traditional approach, only information on known biological activity of ligands medicine to treat inflammatory processes (List of Herbal Medicinal is required. An algorithm defines common chemical features of a set Products A.D. 2006), and (iii) its anti-inflammatory activity has of bioactive molecules (Schuster and Wolber 2010). For this study, already been confirmed. The hexane extract (25 ␮gml−1) inhibited both approaches were applied. All generated models were theo- both cyclooxygenase (COX)-1 and COX-2 up to 64.43 and 84.50%, retically evaluated if they found clinically used COX inhibitors and respectively (Nualkaew et al. 2005) suggesting that Prasaplai acts excluded inactive compounds from the hit list. The best six mod- at least partially via the inhibition of COX enzymes. els were used for further experiments. A more detailed description Prasaplai is composed of twelve ingredients: ten crude plant of the pharmacophore model generation and validation and the drugs (the roots of Acorus calamus L., the bulbs of Allium sativum L., PS procedure is provided in part I of this study (Schuster et al. the pericarps of Citrus hystrix DC., the rhizomes of Curcuma zedoaria 2010). Roscoe, the bulbs of Eleutherine americana Merr, the seeds of Nigella sativa L., the fruits of Piper chaba Hunt, the fruits of Piper nigrum L., NPs database generation the rhizomes of Zingiber cassumunar Roxb., and the rhizomes of Zingiber officinale Roscoe) and two pure compounds (sodium chlo- An extensive literature survey was performed in order to col- ride and camphor). The main component of Prasaplai is Zingiber lect compounds of the different plants contained in the Prasaplai cassumunar rhizome; it makes up to 50% (w/w) of the mixture. Cam- mixture. These compounds were stored as 3D structure models in phor makes up to 0.6% (w/w) while the other components are equal a database, the so-called Prasaplai database. When stereochem- in weight. Prasaplai is widely used by Thai traditional doctors for istry was not completely specified, all possible stereoisomers were relieving primary dysmenorrhea and adjusting the cycle of men- built and stored. Since it is not clear, which 3D conformations the struation (List of Herbal Medicinal Products A.D. 2006; Nualkaew molecules would adopt in the interaction with the target protein, et al. 2004). structures were handled as collections of low-energy 3D conform- The correlation between gynecological disorders and the release ers. of inflammatory mediators was reviewed recently (Hayes and Rock 2002; Connolly 2003). Primary dysmenorrhea is characterized by Parallel virtual screening painful uterine contractility caused by a significant increase of prostaglandin release compared with normal menstruation. Since The structures in the Prasaplai database were virtually screened COX-1 and COX-2 represent key enzymes in the formation of against the pharmacophore model set. A compound was considered prostaglandins, inhibitors of COX are effective therapeutics and the to be a hit only if all functions of at least one pharmacophore model treatment of first choice. were mapped. COX-1 and COX-2 are ideal model targets for a case study since X-ray crystal structures with bound inhibitors, a large number of known active ligands, and datasets for theoretical model valida- Results and discussion tion are available. In our study, a set of five structure-based models and one ligand-based pharmacophore model for COX inhibitors Generation and validation of COX inhibitors pharmacophore were applied to the constituents of Prasaplai in order to (i) unravel models which compounds of Prasaplai might be responsible for the COX- inhibitory activity and (ii) to validate our pharmacophore models Several PDB complexes were used as templates for exhaustive using published knowledge about constituents of this herbal rem- pharmacophore model generation. Suitable validation processes edy. were applied to the models to select diverse ones with high enrichment factors and high restrictivity. This approach led to a final collection of five structure-based pharmacophore models of Materials and methods COX enzymes, which were built based upon atomic coordinates