Predicting Cyclooxygenase Inhibition by Three-Dimensional Pharmacophoric Proﬁling

Phytomedicine 18 (2011) 119–133

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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.

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 published in PDB entries representing protein/ligand complexes General experimental procedures (Table 1). Since this structure-based model set was not able to recognize actives of diverse chemical structures, a ligand-based Molecular modeling studies were performed on an Intel Pen- pharmacophore model was developed that was able to identify tium Core 2 Duo 6400 equipped with 1 GB RAM running Linux other scaffolds as well. This model was generated by aligning the Fedora Core 6. PS calculations were carried out on an Intel Cen- bioactive conformations of (S)-flurbiprofen and SC-558 (Schuster trino Core 2 Duo T7500 with 2 GB RAM running Windows XP. For et al. 2010). pharmacophore model generation and validation and PS experiments the software programs LigandScout 1.03 (Inte:Ligand GmbH, Generation and PS of the Prasaplai database Vienna, Austria, 2006), Catalyst 4.11 (Accelrys Software Inc., San Diego, USA, 2005), and Discovery Studio 2.1 (Accelrys Software Inc., A comprehensive literature survey for known components of San Diego, USA, 2007) were used. Prasaplai was performed. 3D structures of these compounds were collected resulting in a molecular library containing a total num- Pharmacophore modeling ber of 683 NPs. The Prasaplai database was subjected to a PS using the five structure-based and the ligand-based COX phar- Pharmacophore models may be obtained either via the macophore models. This process resulted in a virtual hit list structure-based or via the ligand-based approach. Structure-based containing 166 potential COX inhibitors. Fig. 1 shows the num- pharmacophore model generation uses 3D structural informa- bers of known components of the different plant ingredients of tion on the target protein, which is usually obtained from X-ray Prasaplai and the numbers of virtual hits (VH) retrieved from the crystallography. Protein structures in complex with ligands are PS. B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133 121

Table 1 COX Inhibitor Pharmacophore Models used for PS of Prasaplai Components.

3D charta Name 1cqe-1 1pge-2-s 2ayl-1 PDB entry 1cqe (Picot et al. 1994) 1pge (Loll et al. 1996) 2ayl (Gupta et al. 2006) Complex COX-1/ﬂurbiprofen COX-1/iodosuprofen COX-1/ﬂurbiprofen

3D charta Name 4cox-2 6cox-1-s Ligand-based model PDB entry 4cox (Kurumbail et al. 1996) 6cox (Kurumbail et al. 1996)– Complex COX-2/indometacin COX-2/S-558 –

a 3D chart of pharmacophore model with underlying COX ligand(s). Exclusion volumes are omitted for better transparency. Instead, the surface of the binding pocket is depicted to show the steric constraints of the model.

Compound evaluation procedure For the validation of the pharmacophore models there was not differentiated between COX-1 and COX-2 inhibition since the phar- The obtained VH were critically analyzed according to their macophore models are not selective for one isoform. According COX-inhibitory activity that is already evident from literature. For to their inhibition values for COX-1 and/or COX-2, the respective 25 VH literature data about their COX-inhibitory activity were compounds were grouped into three categories: compounds with available (Table 2). These compounds are ingredients from five IC50 values below 25.0 ␮M, between 25.0 and 150.0 ␮M, and above of the ten plants Prasaplai is composed of, i.e. Acorus calamus, 150.0 ␮M were considered as highly active, moderately active, and Nigella sativa, Piper nigrum, Zingiber cassumunar and Zingiber offic- inactive, respectively. Highly or moderately active VH as well as inale. Consequently, the pharmacophore model set was validated inactive non-VH were assumed to be predicted correctly by the using compounds of these five plants. All known constituents (VH pharmacophore model set; inactive VH as well as highly or mod- as well as non-VH, i.e. structures that were not recognized by any erately active non-VH were assumed to be predicted incorrectly of the six pharmacophore models) of these plants were evalu- (Fig. 2). The correctness of the virtual prediction was determined for ated on available COX-inhibitory activity. Only those structures each of these five plants (Table 4). Fig. 3 shows the general workflow were considered for the validation process and are described in performed in this study. detail in this study for which published data about the inhibition of COX enzymes are available. The relevant non-VH are shown in Table 3. Zingiber cassumunar Five VH of Zingiber cassumunar have already been evaluated on their COX-2 inhibitory activity (Table 2). Four compounds showed IC50 values on COX-2 of 2.71–20.68 ␮M. Only one compound was inactive. Therefore, 80% of the VH were predicted correctly. For five non-VH published data about their ability to inhibit COX were available (Table 3). Limonene, (E)-4-(3,4- dimethoxyphenyl)but-3-en-1-ol and vanillin showed to be inactive on COX-1 and/or COX-2. There are many publications available describing the suppressive effect of curcumin on the expression of COX-2 leading to a decreased enzyme activity (Surh and Kundu 2007). However, the information regarding direct inhibition of COX is inconsistent, showing a range of IC50 from 15.9 to over 100 ␮M. Based on these data curcumin was considered as moderately active. Except for curcumin, all known actives were found by the pharmacophore models. These compounds are even highly active COX inhibitors. The inactives were not recognized, thus Fig. 1. Number of VH obtained from PS (grey columns) vs. number of known com- predicted correctly, except for one compound, i.e. (E)-4-(3,4- ponents of the plants Prasaplai is composed of (white columns). dimethoxyphenyl)but-3-en-1-yl acetate. 122 B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133 ), ) Henry et Henry et Henry et Henry et M) ( M) ( M) ( M) ( ␮ ␮ ␮ ␮ Momin et al. Momin et al. Henry et al. 2002 M) ( M) ( Jager et al. 2008 M) ( ␮ ␮ ␮ M( ␮ ) ) ) ) ) ) =12 50 96% (359 52.69% (510 64.39% (480 No inhibition (390 al. 2002 2003 2003 No inhibition (438 No inhibition (413 al. 2002 al. 2002 al. 2002 IC ∼ ), ) No inhibition (393 ) Henry et Henry et Henry et M) ( M) ( M) ( ␮ ␮ ␮ Momin et al. Momin et al. Henry et al. 2002 Henry et al. 2002 M) ( Jager et al. 2008 M) ( M) ( M) ( ␮ ␮ ␮ ␮ M( ␮ ) ) ) ) ) =52 50 93% (359 11% (393 No inhibition (390 al. 2002 3.32% (510 2003 46.15% (480 2003 No inhibition (438 al. 2002 No inhibition (413 al. 2002 ∼ IC ∼ Nigella , Zingiber , sativa Acorus calamus Acorus calamus Acorus calamus Nigella sativa Nigella sativa Nigella sativa officinale Nigella sativa -Asarone -Linolenic acid CompoundPalmitic acid Asaraldehyde Prasaplai plant origin COX-1 inhibitory activity␣ Myristic acid COX-2 inhibitory activityPentadecanoic acid Structure ␣ Palmitoleic acid Table 2 VH with published COX-inhibitory activities. B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133 123 ), ) ), ), ) ) M) Henry et Henry et ␮ ) M) ( M) ( ␮ ␮ ), 68.41% Henry et al. 2002 Huss et al. 2002 Su et al. 2002b Jager et al. 2008 Su et al. 2002b M) ( M( ␮ M( M( M( Momin et al. 2003 ␮ ␮ ␮ ␮ ) M) ( ␮ = 0.6 = 1.9 = 0.7 = 129 50 50 50 50 94% (357 Henry et al. 2002 No inhibition (352 al. 2002; Su et al. 2002b No inhibition (295 al. 2002 IC IC ∼ Little or no activity (354 ( (354 IC IC ), ), ) ), Henry et Henry et ) Su et al. M) ( M) ( M( ␮ ␮ Momin et al. ␮ Henry et al. 2002 Su et al. 2002b Henry et al. 2002 M) ( Jager et al. 2008 M) ( ␮ M( = 85.3 M) ( ␮ ␮ M( ␮ 50 ␮ ) ) ), IC =85 = 52.2 a 50 50 87% (357 2003 45.32% (354 ∼ IC 25% (354 No inhibition (352 al. 2002; Su et al. 2002b No inhibition (295 al. 2002 nd 2002b IC Piper , Nigella sativa Nigella sativa Nigella sativa Nigella sativa Nigella sativa nigrum Linoleic acid Oleic acid Stearic acid Erucic acid Eugenol 124 B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133 ) ) ) ) ) ) M) Henry et ␮ M) ( ␮ Yano et al. 2006 ) Han et al. 2005 Han et al. 2005 Han et al. 2005 Han et al. 2005 M) ( Han et al. 2005 M( M( ␮ M( M( ␮ ␮ ␮ ␮ M( ␮ ) >50 = 14.97 = 2.71 = 20.68 = 3.64 50 50 50 50 50 Henry et al. 2002 ( al. 2002 ) 42.64% (100 )) Little or no activity (632 No inhibition (693 Yano et al. 2006 Henry et al. 2002 Henry et al. 2002 M) ( ␮ M) ( M) ( ␮ ␮ nd IC 29% (632 12% (693 27.23% (100 ndnd IC IC nd IC nd IC Zingiber cassumunar Piper nigrum Piper nigrum Piper nigrum Zingiber cassumunar Zingiber cassumunar Zingiber cassumunar Zingiber cassumunar ) -dimethoxy- ,4 Continued )-Trans-3-(4-hydroxy-3- methoxy-phenyl)-4-[(E)-3,4- dimethoxy-styryl]cyclo-hex-1- ene 3-en-1-yl acetate 1,3-diene 4-[(E)-3 but-1,3-diene styryl]cyclohex-1-ene ± CompoundNonanoic acid Octanoic acid Methyleugenol Prasaplai plant origin(E)-4-(3,4-Dimethoxy-phenyl)but- COX-1 inhibitory activity4-(2,4,5-Trimethoxy-phenyl)but- COX-2 inhibitory activityTrans-3-(3,4-dimethoxy-phenyl)- Structure 4-(3,4-Dimethoxy-phenyl) ( Table 2 ( B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133 125 Tjendraputra et al. Tjendraputra et al. Tjendraputra et al. Tjendraputra et al. Tjendraputra et al. M( M( M( M( M( ␮ ␮ ␮ ␮ ␮ ) ) ) ) ) = 2.1 = 12.5 = 24.5 = 10.0 = 7.2 50 50 50 50 50 2001 2001 2001 2001 2001 ndndnd IC nd IC nd IC IC IC Zingiber officinale Zingiber officinale Zingiber officinale Zingiber officinale Zingiber officinale nd, not determined. a [6]-Shogaol [8]-Gingerdiol [6]-Paradol [8]-Gingerol [8]-Shogaol 126 B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133 ) ) ) ) ) Marsik et al. 2005 Marsik et al. 2005 Marsik et al. 2005 Marsik et al. 2005 M( M( M( M( ␮ ␮ ␮ ␮ ) = 0.3 = 0.1 = 0.9 = 1.0 formation only in a 2 50 50 50 50 nd Wagner et al. 1986 M( ␮ Peana et al. 2006 M) ( )IC ␮ )IC )IC )IC Gerhaeuser et al. Marsik et al. 2005 Marsik et al. 2005 Marsik et al. 2005 Marsik et al. 2005 M( M( M( M( M( ␮ ␮ ␮ ␮ ␮ ) > 100 = 2.6 = 0.6 > 100 = 0.2 50 50 50 50 50 Significant reduction of COX-2 expression and PGE IC IC IC IC IC 33.4% inhibition of PG biosynthesis at 37 2003b the highest concentration (1000 , , , Nigella sativa Nigella sativa , , Zingiber officinale Zingiber officinale , , Zingiber cassumunar Acorus calamus Piper nigrum Acorus calamus Piper nigrum Nigella sativa Nigella sativa Nigella sativa Nigella sativa Piper nigrum CompoundLinalool Prasaplai plant originLimonene Thymoquinone COX-1 inhibitory activityThymohydroquinone COX-2 inhibitory activityDithymoquinone Structure Thymol Piperine Table 3 Non-VH with published COX-inhibitory activities. B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133 127 ) ) ) ) Sakuma et al. 1997 Sakuma et al. 1997 Jayaprakasam et al. Han et al. 2005 Su et al. 2002a ) M( M( M) ( ␮ ␮ M( M( ␮ ␮ ␮ ) 0.25 0.25 ) ∼ ∼ >50 >50 50 50 2007 54% (454 Wagner et al. 1986 M) ( ␮ Muller-Jakic et al. 1994 ) ) M) ( )IC ␮ Jayaprakasam et al. Dewhirst 1980 Stohr et al. 1999 Su et al. 2002a M( M( M) ( M( ␮ ␮ ␮ ␮ ) = 225 > 100 >50 50 50 50 Reduction of arachidonic acid metabolites by 50% at Reduction of arachidonic acid metabolites by 50% at IC 15% (454 2007 IC ndIC IC 31% inhibition of COX (224 No inhibition of PG biosynthesis (37 Piper nigrum Piper nigrum Piper nigrum Piper nigrum Piper nigrum Zingiber cassumunar Zingiber cassumunar Piper nigrum phenyl)but-3-en-1-ol Nonanal Trans-2-nonenal Safrole Spathulenol Pellitorine (E)-4-(3,4-Dimethoxy- Vanillin Ledol 128 B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133 ) ) ), ), Gafner et al. 2004 Fiebich et al. 2003 Zhang et al. 2004 Carcache-Blanco et Carcache-Blanco et Handler et al. 2007 M( M( M( M( M( M) ( ␮ ␮ ␮ ␮ ␮ ␮ ) ) = 15.9 > 100 > 241 > 233 = 3.70 50 50 50 50 50 nd 11% (241 IC IC IC IC al. 2006 al. 2006 ), ), ) Zhang et ) M) ( M) ␮ ␮ Gafner et al. 2004 Gerhaeuser et al. Dewhirst 1980 Gerhaeuser et al. Handler et al. 2007 M( M( M( M( ␮ M( ␮ ␮ ␮ ␮ ) ) ) = 18.8 =50 > 100 > 500 > 100 50 50 50 50 50 Gerhaeuser et al. 2003a No inhibition (100 ( IC ndIC IC IC No inhibition (241 al. 2004 IC 2003b 2003b IC Zingiber cassumunar Zingiber cassumunar Zingiber officinale Zingiber officinale Zingiber officinale Zingiber officinale ) Continued -Hydroxystigmast-4-en- 3-one nd, not determined. -Sitosterol ␤ a CompoundVanillic acid Prasaplai plant originCurcumin COX-1 inhibitory activity␤ COX-2 inhibitory activity6 Structure 1,8-Cineole Ascorbic acid Table 3 ( B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133 129

Fig. 2. Decision tree for validation of pharmacophore model set.

Zingiber ofﬁcinale 2 below 25.0 ␮M. Due to its high inhibitory activity especially on Six VH of Zingiber ofﬁcinale have already been tested for their COX-2, ␣-linolenic acid was also considered to be highly active. COX-inhibitory activity (Table 2). [6]-Shogaol, [8]-gingerdiol, [6]- For six non-VH data on the inhibition of COX-1 and/or COX- paradol, [8]-gingerol, and [8]-shogaol showed IC50 values for COX- 2 were found (Table 3). According to biological tests, linalool,

Table 4 Determination of correctness of virtual prediction. 130 B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133

Table 4 (Continued )

a Threshold: active, IC50 ≤ 150.0 ␮M; inactive, IC50 > 150.0 ␮M. bGrey, correct prediction, active VH, inactive non-VH; hatched, false prediction, inactive VH, active non-VH. cCorrectness of prediction referring to one plant. Number of correctly predicted structures/total number of structures × 100. Example Acorus calamus: three inactive VH, two inactive non-VH; two out of ﬁve structures predicted correctly; 40% correct prediction.

limonene, ␤-sitosterol, 6␤-hydroxystigmast-4-en-3-one, and 1,8- erucic acid showed little or no inhibitory activity on COX-1 and cineole were classified as inactive. Ascorbic acid proofed to be COX-2 and thus were considered as inactive. The available lit- inactive on COX-1 and active on COX-2. According to a third pub- erature data for oleic acid were inconsistent. Therefore, it was lication ascorbic acid induces the formation and the release of considered as moderately active. Eugenol has been tested to be COX-catalyzed arachidonic acid metabolites via the activation of moderately active on COX-2. phospholipase A2 (Steinhour et al., 2008). Based on these incon- The two highly active VH ␣-linolenic acid and linoleic acid sistent literate data ascorbic acid was considered as moderately belong to the class of oligo-unsaturated fatty acids. Although active. this substance class was not part of either model generation Except for ascorbic acid, all known actives of Zingiber offici- molecules or model refinement data sets, it was correctly iden- nale were predicted correctly (they are even highly active), and tified by the pharmacophore models. This proofs that the model all known inactives were not recognized by the pharmacophore set can successfully perform the task of scaffold hopping. The models. This rate of 92% correct prediction is notably high. other virtually predicted fatty acids have been determined to be inactive or have only weak inhibitory activity. Obviously, the phar- Nigella sativa macophore models could not differentiate between these active For ten VH of Nigella sativa data on their ability to inhibit and inactive compounds due to their high similarity. Basically, COX were available (Table 2). According to their high inhibition the substance class comprising active compounds was identi- described in literature, especially of COX-2, ␣-linolenic acid and fied. linoleic acid were considered as highly active. Palmitoleic acid, For six non-VH literature data about their COX-inhibitory activ- myristic acid, pentadecanoic acid, palmitic acid, stearic acid, and ity were found (Table 3). Limonene and linalool have already been B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133 131

Acorus calamus For three VH of Acorus calamus literature data about their COX-inhibitory activity were available (Table 2). Palmitic acid, ␣- asarone, and asaraldehyde were considered as inactive due to no or weak inhibition of COX-1 as well as COX-2. Only two non-VH have already been determined on their COX-inhibitory activity: linalool and limonene did not show signiﬁcant inhibitory activity on COX (Table 3).

Allium sativum, Curcuma zedoaria 58 structures from Allium sativum were virtually screened using the pharmacophore model set. This approach resulted in one virtual hit (S-allylmercaptocysteine). Literature survey for COX inhibition data for this substance did not retrieve any information. The virtual screening of the 104 structures from Curcuma zedoaria resulted in a hit list comprising ﬁve structures. Also for these ﬁve compounds no literature data about their COX-inhibitory activity were available. COX-inhibitory effects of plant extracts have been reported (Sendl et al. 1992; Ali et al. 1993; Ali 1995; Tohda et al. 2006). However, since there was no information available about the correctness of the prediction of those VH, compounds of these plants were not used for the validation of the pharmacophore models.

Citrus hystrix, Eleutherine americana, Piper chaba The PS of the 37 structures from Citrus hystrix, the 14 from Fig. 3. General workﬂow of the virtual PS approach performed in this study. Eleutherine americana and the 18 from Piper chaba with the six pharmacophore models resulted in hit lists comprising four, eleven, and twelve structures, respectively. Since literature data on COX inhi- discussed above. The IC values of thymoquinone, thymohydro- 50 bition for any of those hits were not available, compounds of these quinone and thymol were determined to be 0.2–2.6 ␮M for COX-1 plants were not included in the validation process. and 0.1–1.0 ␮M for COX-2. Also dithymoquinone showed an IC50 value for COX-2 of <1.0 ␮M. Only its IC value for COX-1 was deter- 50 Camphor, sodium chloride, artefacts mined to be >100 ␮M. The fact that these highly active compounds were not recognized by the COX pharmacophore model set may be Camphor and sodium chloride are the two pure compounds due to different reasons. Our hypothesis is that the structure-based in the Prasaplai mixture. Therefore camphor was also added to models recognize mainly fatty acids, and since these hydroquinone the molecule library that was virtually screened with the phar- derivatives belong to a totally different structure class, they were macophore models, as well as the three artefacts that originate not found. For the ligand-based pharmacophore model an aromatic during storage of Prasaplai. These fatty acid esters arise from the ring is mandatory which thymoquinone and dithymoquinone do interaction of compounds of Nigella sativa and Zingiber cassumunar not comprise. Thymohydroquinone and thymol are too small to (Nualkaew et al. 2004). Camphor was not recognized by the phar- ﬁt into the model which results in the missing of one hydropho- macophore models, the three artefacts were found. However, their bic feature when mapping the structures into the pharmacophore COX-inhibitory activity has not been determined yet. model.

Summary of pharmacophore models validation Piper nigrum COX inhibition data were found for four VH of Piper nigrum Basically, the compounds with known activity on COX were (Table 2). Eugenol and methyleugenol showed to be moderately often predicted correctly by the pharmacophore models (Fig. 4, active. Nonanoic acid and octanoic acid were considered to be inac- VH; Fig. 5, non-VH). In the case of Acorus calamus, 40% of the com- tive. For nine non-VH, COX-inhibitory data were available in the pounds with known activity on COX were predicted correctly, i.e. literature (Table 3). Linalool, limonene, safrole, spathulenol, pellitorine, and ledol were described to show no or only weak COX inhibition. Thus they were considered as inactive. Piperine showed to be moderately active. Nonanal and trans-2-nonenal were considered as highly active. In summary, three of those nine non-VH showed inhibitory activity on COX enzymes. We suggest that the structure-based pharmacophore models are very selective and thus do not recognize these substance classes. In the case of the ligand-based pharmacophore model piperine misses one hydrophobic feature. The problem of nonanal and trans-2-nonenal is that they do not feature an aromatic ring. If the aromatic ring in the ligand-based pharmacophore model was exchanged by a hydrophobic feature, these two structures were identiﬁed as hits. However, without the aromatic ring the model would be very unselective and PS of the Fig. 4. Numbers of VH included in pharmacophore models validation. Threshold:

Prasaplai database would retrieve a hit list comprising many false highly active, IC50 < 25.0 ␮M (dark grey); moderately active, IC50 = 25.0–150.0 ␮M positive VH. (light grey); inactive, IC50 > 150.0 ␮M (white). 132 B. Waltenberger et al. / Phytomedicine 18 (2011) 119–133

to another binding mode which is not covered by the pharmacophore model set. However, by the application of the Prasaplai components to PS with a set of COX pharmacophore models, molecular compounds were identified that are at least in part responsible for the COX- inhibitory activity of the Thai mixture. Furthermore, the results of the pharmacophore models validation with Prasaplai components confirm that – in comparison to a random screening approach – the virtual approach is able to increase the chance to find actives.

Conclusion

Fig. 5. Numbers of non-VH included in pharmacophore models valida- Computational approaches are effective strategies in drug dis-

tion. Threshold: highly active, IC50 < 25.0 ␮M (dark grey); moderately active, covery. They have the potential to decrease cost and time of ␮ ␮ IC50 = 25.0–150.0 M (light grey); inactive, IC50 > 150.0 M (white). drug development. In this study, the applicability and efficiency of pharmacophore-based PS was shown when searching for bioactive two inactives out of five compounds with experimentally deter- NPs. In this application scenario, active compounds were success- mined COX-inhibitory activity. For the compounds of Nigella sativa, fully identified from a structurally diverse mixture. In total, 57% of Piper nigrum, and Zingiber cassumunar with known activity on COX- the compounds in the validation set were predicted correctly by 38, 62, and 80% correct prediction was obtained, respectively. For the COX pharmacophore models. Thus, the pharmacophore-based the compounds of Zingiber officinale the highest rate of correct pre- virtual PS revealed as a powerful tool to identify COX inhibitors diction was achieved: eleven out of twelve components, i.e. 92%. from a complex mixture of NPs. We suggest that this approach can Only one active non-VH, ascorbic acid, was not recognized by the be applied to several kinds of plants and plant mixtures and is not pharmacophore model set, thus predicted incorrectly. limited to COX enzymes but can also be used for other targets. In total, from the 25 VH with known activity on COX, eleven were reported to be highly active, three moderately active, and eleven Acknowledgment inactive. This gives a rate of correct prediction of 56%. Further, out of the 21 non-VH already determined for their COX activity, six This work was supported by the National Research Network – are highly active, three are moderately active and twelve non-VH project “DNTI” S10703-B03 granted by the Austrian Science Fund are inactive, resulting in 57% of correct prediction. Thus, also the (FWF), by a Young Talents Grant (Nachwuchsförderung) by the Uni- combination of these VH and non-VH provides a total rate of correct versity of Innsbruck to D.S., and by the Thailand Research Fund (The prediction of 57%, i.e. 26 out of 46 compounds. Royal Golden Jubilee Ph.D. Program, grant PHD/0202/2547).

Discussion References

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