fflc^cj^^lljp^ O Sources and Chemistry of Resveratrol
Navindra P. Seeram University of California, Los Angeles Vishal V. Kulkarni and Subhash Padhye University of Pune, India
CONTENTS
Introduction 17 Sources of Resveratrol 18 Structure of Resveratrol 22 Chemical Analyses of Resveratrol 23 Synthesis of Resveratrol 24 Theoretical and SAR Studies of Resveratrol 25 Conclusion 26 References 26
INTRODUCTION Stilbenoids are phenol-based plant metabolites widely represented in nature and implicated with human health benefits against problems such as cancer, inflammation, neurodegenerative disease, and heart disease. Among stilbenes, the phytoalexin resveratrol (3,4',5-trihydroxystilbene; Figure 2.1) has attracted immense attention from biologists and chemists due to its numerous biological properties. Resveratrol is a pivotal molecule in plant biology and plays an important role as the parent molecule of oligo mers known as the viniferins [1]. It is also found in nature as closely related analogs, derivatives, and conjugates (Table 2.1) [1-80]. In addition, the inherent structural simplicity of the resveratrol molecule allows for the rational design of new chemotherapeutic agents, and hence a number of its synthetic adducts, analogs, derivatives, and conjugates have been reported (Table 2.1) [1-80].
17 18 Resveratrol in Health and Disease
Trans-resveratrol (frans-3,4',5-trihydroxystilbene)
C/s-resveratrol (c/s-3,4',5-trihydroxystilbene)
FIGURE 2.1 Chemical structures of trans- and r/.v-resveratrol (3.4'. .5-trihydrox- vstilbene).
Numerous efforts have been directed to studies of structure-activity relationships (SARs) of resveratrol and its analogs with the goal of increasing and enhancing their //; V/IY; biological potency and bioavailability. The pharmacological activity of resveratrol has also stimulated the development of numerous chemical analytical methods for its measurement in different matrices such as plant extracts, wines and other beverages, and food-derived products, as well as in biological fluids and tissues. Because of the numerous biological properties and implications in health and disease associated with resveratrol, the focus of this chapter is on its occurrence, chemical analyses, synthesis, and studies of its chemistry.
SOURCES OF RESVERATROL
The sources of resveratrol and its related natural and synthetic derivatives, conjugates, and analogs are shown in Table 2.1 [1 80]. Res\eratrol was first identified in 1940 from the white hellebore lily VcrdHiini grciiulifloniiu O. Loes [81], although its richest known natural source is the Asian medicinal plant Folygomiin ciispidatitin (Japanese "Ko-jo-kon"). The occurrence of resveratrol was popularized in 1992 when it was discovered as a constituent of red wine, and implicated in the "French paradox," an epidemio logical finding of an inverse relationship between red wine consumption and the incidence of heart disease. Resveratrol has also been implicated with Sources and Chemistry of Resveratrol 19
TABLE 2.1 Natural and Synthetic Sources of Resveratrol and its Analogs
Compound and sources Ref.
Resveratrol (3.4'.5-lrihydro.\ystilbenc)" 1-13 Red grape, grapevine, grape leaf and berr\' skin, imiseadine grape, red wine, blueberry, cranberry, bilberry, lingonberry, sparkleberry. deerberry. partridgeberry; Polygonuiv cuspidulwu (Japanese knotweed); Monis spp. (including mulberry); lily {Vcrutnmi spp.); legumes [Cassia spp.. Pterolohiwu hcxapcuilliim): peanuts (Aiachls hypogaca): Rht'uni spp. (including rhubarb); eucalyptus; spruce (Picca spp); pine [Pimis spp.); Poaceae (grasses, including Festuca. Hordcum, Poa. Stipa. and Lolium spp.); Trifo/iiiui spp.; Nothofagus spp.; Artocarpus spp.; Gneium spp.; Plcuroptcrus ciliiiwrvis: Baultinia raceiuosa: Paeoniu lacliflora: Sci/la nervosa: Tctrastignui hypoglauvwu: Rwnvx hiiccphalophonis: Yucca spp.; Sniilax spp. Dihydrores\eralrol (/n;/;.v-.3.5.4'-trihydroxybibcnzylslilbene)"' 14. 15 Dioscorca spp.; Biilhopliyllum trisic Piceatannol or astringinin (//•i:(/;.v--'^.4,.3'.5'-tetrahydroxystilbene) .3, 15-20 White tea tree (Melaleuca leucadendron): Asian legume (Cassia garretliaiia). C. margiiiata: rhubarb (Rheum spp.); Euphorbia lagascae: Polygonwn ciispidaluiir. Vitis vinijera Dihydropiccalaniiol (//7/)).v-3.4,3'.5'-tctrahydroxybibenzylstilbene)'' 17 Cassia garrettiaiui Gnetol (nY«;.s-2.6.3'.5'.-tctrahydroxystilbene) 7. 21. 22 Gnetuiu spp. (including G. monaiuni. G. africanwu. G. giiei>u)ii, G. ula) Oxyresveratrol (/r((//.s-2.3'.4.5'-tclrahydroxyslilbene) '). 23-25 Moras spp.; Madura poiiiifera: Arioawpus gonwzlanus: Schoenocaulou officinale Hydroxyres\eratrol (//•rt/»-2.3,5.4'-tetrahydroxystilbcne) 3 Polygonum cuspidatum 7'/Y/«i--3.4.5.4'-tetrahydroxystilbene'' 26 r/Y/H.v-3.3'.4',5.5'-pentahydroxystilbcne" 27. 28 Eucalyptus wcmdoo: Vouacapoua americana. V. macropetala Pinosylvin (nY;/;.v-3.5-dihydroxystilbene)'' 1. 26. 29-34 Gneium cleistostachyum: Alpinia katsumadal; Polyalthia longifolia: Polygonum nodosum: Pinus spp. (including Scottish pine. P. sylvesiris) Dihydropinosylvin (/n/«.y-3.5-dihydroxybibenzylstilbene)" 35-37 Dioscorea halalas ryY»;.v-2.4.4'-trihydroxystilbcne" 36. 37 r/Y//;.v-3.5.3'-trihydroxystilbene" 38. 39 7'/Y//?.v-3.4.5-trihydroxystilbene" 40 rjY»«-3.4.4'-trihydroxystilbone'' 40. 41 r/Y//;.v-3.4-dihydroxystilbene'' 36. 37. 41 r(Y//;.v-3.4'-dihydroxystilbeiie'' 38. 39 r(Y»;.v-3.3'-dih\droxvstilbene'' 38. 39
(conlinued) 20 Resveratrol in Health and Disease
TABLE 2.1 Continued
Compound and sources Ref.
r)Y(/).v-2.4-dihydroxystilbene'' 36. 37 ryYOT.v-4,4'-dihydroxyslilbenc" 36. 37. 40. 41 r/v/;)5-3-hydroxystilbene" 38. 39 ryv//i.s-4-hydroxystilbene (/j-hydroxystilbenc)'' 36. 37. 40 rjY//;,v-halogenatcd-3.5.4'-trihydroxystilbenes 42. 43 (lluoro-. chloro-, and iodorcsvcratrols)'' Dimethoxypinosylvin (//Y//(.v-3.5-dimcthoxystilbene)" 26 Rhapontigenin or 3-melhoxyies\'cratrol 6. 44. 45 (/ra/;.v-3.5.3'-trihydroxy-4'-melhoxystilbcne)" Rheum spp. [iiicliuling R. liuipontiiwn. R. wululatimi): Scilla nervosa Isorhapontigenin (//YH;.v-3.5.4'-trihydioxy-3'-methoxystilbcnc)'' 7. 46. 47 Giu'iiini spp.: Bekmieaiuhi chiiiensis Desoxyrhapontigenin or 4-mcthoxyresvcratrol 29. 48-50 (r/Y»7.v-3.5-dihydroxy-4'-mellioxyslilbene) Gnetuin eleisiosiiuhvwn. Rheum uitduUitum: Kneiiia iiusirosidineiisis: Rumex hueephcdophonis Pinostilbene or 3-methox\rcs\cratrol (/;Y//(.s-5.4'-dihydroxy-3-inethoxystilbene) 50 Rumex hueephidopliorus 7"/Y»;.s-3.4'-dinietlioxy-5-h\droxystilbcnc'' Knema austrosiamensis 48. 49 C'(.v-3.5.3'-trihydroxy-4'-inclhoxystilbcnc'' 51 Trimcthyircsveratrol (/yY//;.v-3.5.4'-trimethoxystilbcne)" Piero/ohiuni hexapetaUum 8. 26. 52 Gnetuclcistol D or 2-mctliox\oxyrcsveratrol 29 (//Y//;.v-2-mcthoxy-3'.4,5-trih\droxysti]benc) Gnetum eleistostaehyum Gnetuclcistol E or 3-methoxyisorhapontigenin 29 (//Y//;i-3.3'-dimcthoxy-5.4'-dihydroxystilbene) Gnetum eleistostaehyum Trans- and (7'.v-3.5.4'-trimcthoxy-3'-hydroxystilbene'' 51 Trans- and (•/.v-3,5.3'-trimethoxy-4'-hydroxystilbene" 51 Trans- and (;'/.v-3.5-diniethoxy-3'.4'-dihydroxystilbene'' 51 Trans- and (•/.v-3.5-dihydrox_\'-3'-amino-4'-methoxystilbene'' 51 Trans- and (7'.v-3.5-dimctlioxy-4'-aminostilbenc'' 51 Trans- and r/.v-3,4',5-trimcthoxy-3'-aniinostilbene'' 51 Trans- and (•/.v-3,5-dimclhoxy-4'-nitrosti!bcnc'' 51 Trans- and (•/.v-3.4',5-trimclhoxy-3'-nitrostilbcnc" 51 r/Y»7.y-5.4'-dihydroxy-3-mcthoxystilbcnc Rumex hiicephalophorus 51 Pterostilbene (/n/;;,v-3.5-dimcthoxy-4'-hydroxystilbcnc)'' 8. 51. 53. 54 Draeena eoehiinhinensis: Pleroearpus spp. (including P santalinus. P marsupium): Vitis vim'fera: Pterolohhim hexapetcilhim r/.v-3.5-dimethoxy-4'-h\droxystilbcnc'' 51 3.4.5.4'-tctramcthoxystilbcnc'' 26 3.4.5.3'-tctramcthoxystilbcnc'' 26 3.4.5.3'.4'-pcntamcthox\stilbcnc"' 26 7";Y//;.v-3.4.3'.5'-tctramcthox\slilbcnc Crotahuia nui(hirensis '^5 Sources and Chemistry of Resveratrol 21
TABLE 2.1 Continued
Compound and sources Ref.
Trails- and (:/.v-3.3'.5.5'-tetrahydroxy-4-methoxystilbene Yucca pcriculosa. 56-60 Y. schidiiicra: Cassia pudihwula T'(v//).v-4.4'-dihydroxystilbene Yucca pcriculosa 56 Trans-J-liy(lro\y-5-ine!h()xys!ilhcih' Cryplocarya iilcnhuri^cnsis 59 7"/Y//;.v-4.,V-dihydroxy-5'-methoxystilbene Dracaena loureiri 60 7'j7(/;.s-4-li\droxy-3'.5'-dimethoxystilbenc Dracaena loureiri. D. cochinchinensis 60. 61 Piccid or po!\'datin or resNeratrol-3-glucoside 2. 6. 62, 63 (»7;/;.v-3.5.4'-trihydroxystilbene-3-0-P-D-glucopyranosidc) Polygonum ctispidaUiin: Rlwuni rhaponlicuiu: Picea spp.: lentils (Lens culinaris) Rhapontin or rhaponticin 2. 6 (/n/«.v-3.3'.5-irihydroxy-4'-methoxystilbene-3-0-P-D-glucopyranosidc) Rlu'uni spp.: eucalyptus Deoxyrhapontin (/r(//;.v-3.5-dihydroxy-4'-methoxystilbcnc-3-0-(3-D-giucopyrano- 6 side) Rheum rhapoiuicum Isorhapontin (nY/;(.v-3.4'.5-trihydroxy-3'-mcthoxystilbene-3-0-p-r)-glucopyrano- 6. 62 side) Pin us sihiricu: Picea spp. Piceatannol glucoside (3.5.3',4'-tctrahydroxystilbene-4'-0-P-D-glucopyranoside) 2. 6 Rheum rhapontieum: Polygonum cuspidatum: spruce Pinostilbenosidc (r/v/;(.v-3-methoxy-5-h\droxystilbene-4'-0-P-r)-glucopyranoside) 64 Pinus koraieiisis Resveralroloside or resveratrol-4'-glucopyranosidc 2, 6. 3. 65 (rc(//;.v-3.5.4'-trihydroxystilbene-4'-0-P-D-glucopyranoside) Polrgoiuim cuspidalum: Pinus spp.; I'ifis vinijera Astringin (//Y;//.v-3,4,3'.5'-tetrahydroxystilbene-3'-0-P-D-glucopyranoside) 3. 62. 65 Picea spp.; Vilis vinijera Piceid-2"-0-ga]late and -2"-0-coumarate Pleuropierus ciliinerYis 66 Rhaponticin-2"-0-gallate and -6"-0-gallale Rhubarb {Rheum undulatum) 67 Piceatannol-6"-0-gallate Chinese rhubarb (Rhei rhizoma) 68 C/.v-rcsveratrol-3.4'-C-P-diglucoside litis vinijera (cell suspension culture) 69 Combretaslatinsa and their glycosides 70 (e.g.. combretastain A = ov/».v-2',3'-dihydroxy-3,4,4'.5-tetraniethoxystilbene) 5-Methox\ -;/Y//;.v-resveratrol-3-0-rutinoside Elephantorrhiza goelzei 71 Oxyresveratrol-2-O-P-glucopyranoside SclwenocauUm ojjicinale 25 Resveratrol-3.4'-0.0'-di-P-D-glucopyranoside Schoenocaulon officinale 25 Mulberrosides (e.g.. c/.v-oxyresveratrol diglucoside) 72. 73 Morus aiha (cell cultures). M. Ihou Gnetupendins (isorhapontigenin dimer glucosides): gnemonosides 73. 74 (res\eratrol oligomer glucosides) Gnclunt pendulum, (i. gnenioii CJaykissacin (5-(P-D-glucos\Ioxy)-3-h\droxy-/y7//;.v-stilbene-2-carbox\'lic acid) 75 Ciavlussucia h(ucata. (J. Jroiidosa
(conlinued) 22 Resveratrol in Hedlth and Disease
TABLE 2.1 Continued
Compound and sources Ref.
Rcsveralrol oligomers and oiigostiibencs (including \inil'crins) Dipterocarpaccac. 76-79 Gnelaccac. Vilaceae. Cyperaccac. and l.cguniinosac plants (including I'atica piniviflorn. \ . riissiik. \ . (>hl(»igij(>Uii: Wncria indlai: Shoreci Itievifoiia. S. hciusk'vaiui: Paconia laclUloni: Sophora niodrcrojiiciiiii. S. leachiima: dneluiu vciiosuiu: Cypenis longu.s: I puiut honiccnsis: Iris c/tirkci) Bibenzyl dcri\ati\cs (mcthoxy-hydroxy-dilndrostilbcncs including alfoliol I, SO gigantoh' Nicleiua boothi
•'Compounds obtained s\iitlieticall\.
benefits against diseases such as cancer (reviewed by Aggarwal et al. [82]). Resveratrol has been identified from a number of dietary sources including red grapes, muscadine grapes, cranberries, bilberries, blueberries, lingon- berries. sparkleberry, deerberry. partridgeberry, and peanuts. However, resveratrol is also consumed in the forms of botanical dietary supplements and herbal formulations used in traditional Chinese medicine (TCM) and Indian Ayurvedic medicine [83], where it is commonly used as an active ingredient. Other plant sources of resveratrol include Vitis spp. (including grapes, grapevines, leaves, and berry skins); Yucca spp.; Sniilax spp.; Moms spp. (including mulberry); lily {\'ercitntni spp.); legumes {Cassia spp., Ptcrolohiuni hc.xapetalluni): Rlwiini spp. (including rhubarb); eucalyptus; spruce (Picea spp); pine (Pimis spp.); Poaceae (grasses, including Festuca, Hordcimu Poa. Stipa. and Loliiini spp.); TrifoUiim spp.; Nothofagus spp.; Artocarpus spp; Gnetimi spp.; Pletiropterus ciliinervis: Baiiliinia racemosa; Paeonia lactiflora: Scilla nervosa: Tetrastignia hvpoglaucum: and Riimex hiicephalophonis (Table 2.1). In addition numerous synthetic analogs of resveratrol have been reported (Table 2.1).
STRUCTURE OF RESVERATROL
Resveratrol is an off-white powder (from MeOH) with a melting point o^ 253 to 255 C, molecular formula oi C14H12O3, and molecular weight of 228.25gmol. The essential structural skeleton of the molecule comprises o^ two aromatic rings joined by a styrene double bond (Figure 2.1). The presence of the double bond facilitates trans and cis isomeric forms of resveratrol, which correspond to E and Z diasteromers, respecti\ely (Figure 2.1). However, because nY»i.v-resveratrol is the preferred steric form and is relatively stable if protected from high pH and light [84], Sources and Chemistry of Resveratrol 23 it is the commonly studied form of resveratrol as reported by most laboratories [82]. The ultraviolet (UV) absorption maxima (/max) for t^h^ trans and ci.s isomers are 308 and 288 nm, respectively, which allows for their detection and separation by high-performance liquid chromatography (HPLC) [85]. Besides these differences in spectrophotometric properties, the two isomers can also be distinguished by the chemical shifts in their nuclear magnetic resonance (NMR) spectra [84-86]. r/Y//7.v-resveratrol is commercially avail able and on exposure to UV irradiation rapidly converts to the cis form [1-3,84-89]. 77Y//w-resveratrol. studied under different conditions, has been shown to be stable for months, except in high pH buffers, when protected from light [84]. C/,v-resveratrol, although extremely light sensitive, can also remain stable in the dark at ambient temperature in 50% EtOH for at least 35 days over the range 5.3 to 52.8 i-unol/l [84]. Apart from photochemical conversion, low pH also causes c/.v-resveratrol to isomerize to traiis- resveratrol. The free enthalpy difference between synthetic //Y//7,v-resveratrol and photochemically prepared r/.v-resveratrol was estimated to be similar to common stilbenes. with the trans isomer being more stable by about 11 to 14kJ/mol [85]. In addition, pK;, values of /ra/^v-resveratrol corresponding to the mono-, di-, and triprotonation of the system were 9.3, 10.0, and 10.6, respectively [85]. Hence, resveratrol occurs predominantly as the trans isomer and reports of the presence of the cis isomer, e.g., in certain wines, is attributed to photoisomeric conversion, enzyme action during fermentation, or release from viniferins [1-3.86 89]. r;Y//;.v-resveratrol has been shown to be the more biologically active form of resveratrol. However, as regards the struc ture of the molecule, apart from the stereochemistry of the styrene bond, the positions of the phenolic substituents on the aromatic rings also play an important role in determining its biological activity. The molecular structure of resveratrol has been examined in detail and theoretical energy calcu lations for several excited states of /ra/w-resveratrol and /ra/75-cr-viniferin have shown the importance of the /;-4'-OH group for biological activity [87,90]. Hence, natural and synthetic derivatives of resveratrol (Table 2.1) have been well examined from a SAR perspective in an effort to study resveratroFs impact on human health and disease.
CHEMICAL ANALYSES OF RESVERATROL Over the past decade several methods have been developed to detect the presence and measure levels of resveratrol and its analogs based on the use of HPLC and gas chromatography (GC) [1-3,84-89]. Much attention has been focused on method development since studying the biological properties of resveratrol requires the analyses of complex mixtures con taining \ery small amounts of stilbenes, and complete and quick 24 Resveratrol in Health and Disease extractions are required to minimize the loss from isomerization or denaturation. Generally. HPLC methods using reverse phase CI8 columns coupled with UV detection (photodiode array [PDA] or diode array detector [DAD]) can adequately distinguish between resveratrol isomers and their analogs based on their different absorbance maxima. However, the use of mass spectrometry (MS), tluorimetry, and electrochemical detectors (ECDs). which are more specific than UV detection, has considerably improved sensitivity and decreased sample size [85.86]. GC methods with or without MS detection, although not as popular as HPLC. have also been frequently employed but require trimethylsilyl derivatization of resveratrol and its analogs [85,86].
SYNTHESIS OF RESVERATROL
Although resveratrol is a naturally occurring polyphenol that has been isolated from more than 70 plant species, it is not feasible to isolate this compound in sufficient quantities required for //; vitro and //; vivo biological tests. For example, it has been reported that 1 kg of dried grape skin can provide only 92 mg of resveratrol [1-3]. Hence, in the past decade great interest has arisen concerning resveratrol synthesis because of the numerous biological properties associated with this compound. There have also been reports on the production of stilbenes from cell culture and biotrans formation studies [19,65] and from grapevine leaves that are stressed to increase the production of phytoalexins [89]. A survey of synthetic schemes reported for the production of resveratrol, although not exhaustive due to the large number of patented methods, follows. Many of the synthetic schemes described for resveratrol and its analogs rely on the Wittig or Wittig-Horner reaction. In the Wittig reaction, coupling of a benzyl anion with benzaldehdye forms a styrene double bond in 7 to 8 steps, and several attempts have been made to reduce the number of steps and increase the yield. The first reports of the synthesis of resveratrol were in 1940 by Takaoka [91], and in 1941 by Spath and Kromp [92], after Takaoka isolated resveratrol from the roots of Veratnim grandiflorum [80]. In 1940 Takaoka described the synthesis of resveratrol dimethyl and trimethyl ethers, which was carried out using Perkin condensation of /7-anisyl acetic acid sodium salt with 1,3-dimethoxybenzaldehyde in acetic anhydride [91]. The product formed never crystallized so could not be compared with the natural product. Spiith and Kromp reinvestigated the method by purifying a small sample of trimethoxystilbene carboxylic acid by sublimation and decarboxylation and isolating the trimethyl ether of resveratrol [92]. In 1997 Alonso et al. described the synthesis of resveratrol and its analogs pinosilvine and piceatannol [93]. A 3.5-dimethoxybenzyl trimethyl silyl ether was coupled with aldehydes in the presence of lithium powder Sources and Chemistry of Resveratrol 25
and a catalytic amount of naphthalene. The expected alcohol was dehydrated and demethylated to yield the hydroxylated stilbene derivatives. Orsini et al. synthesized combreastatin and resveratrol and their corre sponding glycosides via the Wittig reaction [70], In 2001 Eddarir et al. described the organometallic synthesis of resveratrol, in which resveratrol was fluorinated on the styrene double bond [42]. Guiso et al., in 2002, employed the Heck reaction affording only the natural E isomer, i.e., the tnins isomer of resveratrol in 70% yield [94]. A one-pot synthesis of 4-methoxyiodobenzene with vinyltrimethylsilane under arylation- desiiylation conditions has recently been described by Jeffery and Ferber, which by removal of excess vinyltrimethylsilane and arylation of the 4-methoxystyrene by 4-methoxyiodobenzene in a one-flask reaction yields (£')-3,4',5-trimethoxystilbene [95]. When demethylated this leads to resveratrol in 85% yield [95]. In the last few years synthetic chemistry has also branched out from classic chemistry to combinatorial chemistry. Hence, although res veratrol has been synthesized using conventional organic chemistry, recently researchers have carried out syntheses based on combinatorial methods. For example, resveratrol has been prepared by a method that involves a solid-phase cross metathesis reaction wherein a 4-vinylphenol was attached to a Merrifield resin affording a supported styreneyl ether [96,97]. This can then be coupled by a ruthenium carbene to various styrenes to yield selective (f^stilbenoids [96,97].
THEORETICAL AND SAR STUDIES OF RESVERATROL It has been well established that the interaction of biological molecules strongly depends upon the electrostatic fields generated in the process of charge transfer and is mainly determined by geometrical factors. A large number of theoretical or modeling studies have been carried out on resveratrol [87,98,99]. Del Nero and De Melo have reported a semiempirical calculation of the electronic and structural properties of rraz/.v-resveratrol, ?ra/7.s-stilbene, and diethylstilbesterol [98]. The analyses of the calculated bond lengths and chain rearrangements gave an insight of how chemical modifications of these molecules could affect the possible physiological properties of resveratrol. Semiempirical self-consistent field molecular orbital (SCFMO) calculations were used to calculate the structural and electronic properties of resveratrol and its analogs wherein the geometry of the systems was optimized and the electronic properties were calculated at the level of the AMI method [99]. Stivala et al. have used the thermodynamic parameters and the formation enthalpies (A//,) calculated by semiempirical methods to discuss the antioxidant activity of c/,s- and //Y/7;.v-resveratrol [100]. In addition, density functional theory (DFT) has also been proposed 26 Resveratrol in Health and Disease to be reliable in the study of energetics and geometrical properties of proton transfer and other ion-molecule reactions. Hence, Cao et al. have employed DFT calculations to obtain the geometry, the spin density, the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), and the single electron distribution of the 4'- and 5-radical of resveratrol [87]. It was found that resveratrol was a potent antioxidant with the 4'-0H group being more reactive than the 3- and 5-OH groups because of resonance effects. The dominant feature of the resveratrol radical was a semiquinone structure, which determined its stability. Delocalization of the unpaired electron density was mainly on the oxygen atom and its ortho and para positions. Hence, the antioxidant activity of resveratrol was found to be related to its spin density and unpaired electron distribution of the oxygen atom [87].
CONCLUSION Resveratrol is a dietary polyphenol that is reported to have numerous biological properties and implications for human health and disease. However, given its low levels in food sources including red wine, it is unlikely that desired biological endpoints will be achieved from normal dietary consumption. In addition, its bioavailability and concentration in blood and tissues may fall well below levels required for most biological activities. Hence, continued research is necessary to evaluate the synergis tic and or additive effects of resveratrol with other food and food-related constituents. In addition, future studies should focus on the uptake and urinary excretion of its conjugated forms and metabolites formed //; V7IY; by physiological changes and by enzymatic action of gut microflora. A thorough understanding of the chemistry of this molecule and its related conjugates and derivatives is important for correlation of its observed in vitro and in vivo biological properties and eventually for translation into practical benefits for human health.
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Org. Biomol. Chem., 2006. 4, 2858 - 2868, DOI: 10.1039/b606365a
3D-QSAR of historic deacetylasc inhibitors: hydroxamate analogues
Dhanshri C. Juvale, Vishal V. Kulkarni, Hemantkumar S. Deokar, Nilesh K. Wagh, Subhash B. Padhve and Vithal M. Kulkarni
The histone deacetylase enzyme has increasingly become an attractive target for developing novel anticancer drugs. Hydroxamates are a new class of anticancer agents reported to act by selective inhibition of the histone deacetylase (HDAC) enzyme. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were employed to study three-dimensional quantitative structure-activity relationships (3D-QSARs). QSAR models were derived from a training set of 40 molecules. An external test set consisting of 17 molecules was used to validate the CoMFA and CoMSIA models. All molecules were superimposed on the template structure by atom-based, multifit and the SYBYL QSAR rigid body field fit alignments. The statistical quality of the QSAR models was assessed using the parameters r'conv, r"cv and r'pred. In addition to steric and electronic fields, ClogP was also taken as descriptor to account for lipophilicity. The resulting models exhibited a good conventional rconv and cross-validated r"cv values up to 0.910 and 0.502 for CoMFA and 0.987 and 0.534 for CoMSIA. Robust cross-validation by 2 groups was perfonned 25 times to eliminate chance correlation. The CoMFA models exhibited good external predictivity as compared to that of CoMSIA models. These 3D-QSAR models are very useful for design of novel HDAC inhibitors.
Large hydrophobic Ir"^-,.,^ Ue\a\ binding and region ^^^i>^A\ ,, . VA\A '• \~ 'iy<*''Cipfiifc site
Royal Society of Chemistry 2006 IS(;A:-!(J,OI:;C | Organic & Biomoleculai Chemistry
3D-QSAR of histone deacetylase inhibitors: hydroxamate analoguesf
Dhanshri C. Juvale," Vishal V. Kulkarni,* Hemantkumar S. Deokar," Nilesh K. Wagh," Subhash B. Padhye* and Vithal M. Kulkarni*"
Received 5th May 2006, Accepted 5tli June 2006 First published as ait Advance Article on the web 27th June 2006 DOI: I0.1039/b606365a
The liisloiie deacetylase enzyme has increasingly become an attractive target for developing no\el anticancer drugs. Hydroxaniates are a new class of anticancer agents reported to act by selecti\e inhibit ion of the histone deacetylase (HDAC) enzyme. Comp;irali\c molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSl.A) were employed to study three-dimensional quantitative structure-activity relationships (.^D-QSARs). QSAR models were derived from a training set of 40 molecules. An external test .set consisting of 17 molecules was used to validate the C'oMFA and CoMSIA models. All molecules were superimposed on the template structure by atom-based, multitit and the SYBYL QS.AR rigid body field fit alignments. The statistical quality of the QSAR models was assessed using the parameters r ,„^, /•; and /-J^.j. In addition to steric and electronic fields. ClogP was also taken as descriptor to account for lipophilicity. The resulting models exhibited a good conventional r ,„^ and cross-validated r^ values up to 0.910 and 0.502 for CoMFA and 0.987 and 0..S,^4 for CoMSI.A. Robust cross-validation by 2 groups was performed 25 times to eliminate chance correlation. The CoMFA models exhibited good external predictivity as compared to that of CoMSI.A models. These .^D-QSAR models are very useful for design of novel HDAC inhibitors.
Introduction HDAC inhibitors typically possess a metal-binding group, a hydrophobic cap functionality that interacts with the amino acid Histone deacetylase (HDAC) inhibitors have gained considerable residues at the entrance of the /V'-acetyl lysine binding channel interest due to their ability to modulate transcriptional activity.' and an aliphatic spacer connecting the cap and the metal binding HDAC-mediatcd transcriptional activity represents a common group. The factors contributing to the biological activity can be molecular mechanism of alteration in chromin structure and analyzed through the use of dilTerent physicochemical descriptors blockage of normal cell dilTerentiation. As a result, this class of in the generation of quantitative structure activity relationship inhibitors can block angiogencsis and cell cvcling. and promote (QSAR) models. Dtie to the flexibility of the spacer group between apoptosis and cell dilTerentiation. HDAC has become a novel the metal binding and cap groups, it is difficult to choose a suitable target for the discovery of drugs for the treatment of cancer conformation to achieve a meaningful superimposition. Only a and other diseases." The number of HDAC enzyme subtypes few QSAR studies have been reported until now.^""" Wang el al.'" has expanded considerably over the past few years. ofTering reported QSAR models on TSA- and SAH A-like hydroxamic acid opportunities for the development of HD.AC inhibitors with and suggested that the shape and area of molecules are important improved specificity. for their biological activity. Siinilarly Xie el al." reported a QSAR A number of natural inhibitors such as trichostatin A (TSA)." study on a data set of 124 compounds which showed that the cyclic tetrapeptide trapoxin (TPX).' HC toxin'" and apicidin" van dcr Waals surface area and hydrophobicity are important have been so far reported. Among them. TSA has been identified parameters required for the biological activity. as a potent and specific HDAC inhibitor. Synthetic inhibitors In order to gain further insight into the structural requirements like sodium phenyl butyrate.'^ sodium valproate," subcranilo of HDAC inhibitors, we have performed a three-dimensional hydroxamic acid (SAHA).'^ straight chain TSA and SAHA like quantitative structure activity relationship (.'5D-QSAR) study analogues""" and oxamflatin"* have been reported. TSA and its using comparative molecular field analysis (CoMFA)'" and com- analogues are considered to be mimics of the histone acetyl lysine paralive molecular similarity indices analysis (CoMSIA)." In side cliain. Crystal structures of histone deacetylase-like protein CoMFA, it is assumed that the interaction between an inhibitor (HDLP) with TSA and SAHA revealed that hydroxamic acid- and its molecular target is preliminarily noncovalent and .shape- ba.sed HD.AC inhibitors bind to the deacetylase core by inserting dependent in nature. A QSAR can be derived correlating the their aliphatic chains into the HDLP pocket. Their hydroxamic dilTerences in steric and electrostatic fields surrounding a set of acid group reaches the polar bottom of the pocket, where it molecules to the biological activity. This method can be used coordinates with the zinc ion.''' to develop a .^D pharmacophore model" that describes the structure activity relationship (S.AR). The CoMSI.A method of I'odihi C)>llci;c III I'luimuuy. Bluinin i'iilyapevlh Deeiiu'd i'liin-r.'^ilv.."iD-QS.A R was introduced by Klcbe"^ in 1994. in which a common Urumhvunc. I'uiiv. 41 lOiS. JiiJiti. E-nuiil: vivivipx^.iigiiuiil cam: f'iix: +V/- probe atom and similarity indices are calculated at regularly spaced :u-:^4Jv.Mj: III, y'jj^:i)-:y4.r:3^ 'Dcpailiiicnl oj Cliemislry. i'liiversity iij I'lnw. Puiie. 41 lOlhS, Iniliii grid points for prealigned molecules. CoMSIA considers five t VMK dcdicalcs this nianiiSLTipt to .Shridevi V. Kiilkarni. ditlerent fields: electronic, steric, hydrophobic held and hydrogen
2858 1 Org. Biomol. Chem., 2006, 4, 2858-2868 bond donor and acceptor liclds. and is less alignnienl-sensitixe The acti\it\ data used in this study may ha\e contributions llian C'oMFA. CoMFA and CoMSlA lia\e been widely applied in from other factors than just .steric and electrostatic interactions. drug design.""-'' The ClogP. the calculated logtirithm of partition coefficient, was calculated and added to the CoMF.A table. Inclusion of ClogP in the CoMFA model with field lit alignment increased internal (/;; = Results and discussion 0.502) as well as external predicti\ily (r,,j = 0.6_vi). We performed The 3D-Q.SAR models for li\dro\annc acid analogues were all further studies with ClogP in addition to CoMFA fields. deri\ed using CoMFA and C'oMSIA leelmiques. The negati\e The model generated with FF I alignment (Fig. 2) with a good logarithm of \C\„ (plC\„) was used as the biological acti\it\ in internal predictixe ability ir^ = 0.502) and a small standard the M)-QSAR study (Table 1). Conformation of the molecules error of estimatation (SEE = 0.260) was .selected as the best used ill llie study was obtained by a sysleiiiatic search and the model to explain SAR and to carry out further analysis. Results lowest energy contbrnier was selected and niinimi/ed using Powell obtained from the RMS I. MF I and Fl' 1 alignments with method to rnisO.OOl kcal mol 'A '. ClogP as additional descriptors are shown m Table .^. Observed Alignment of the molecules was carrieil out using three and predicted biological activities of the training and test sets techniques, namely RMS fitting (atom-based), multilit (tlexihle are plotted in Fig. ? and 4. respectively To further assess the ruling) and S^BYLQ.S.AR rigid body held litting. The most acti\e robustness and statistical confidence of the derived .•^D-QSAR molecule I 7 was used as a tenijilate molecule lor alignment (I- ig. I). model, bootstrapping analysis was performed and average of 100 runs is 0.919 (f^,). Cross-validation with 2 groups was performed to a.scertain the true predictivity of the model and repeated 25 times: the mean ;••, was 0.299. ,A negative value of r in a randomized biological activity test revealed that the results were not based on chance correlations. The results of these ci"i>ss-\alidation tests are show n in Table 5.
RMS I l''i(;. 1 M(>lecii]e 17 willi alums used lor sii[XTinipi>sition are niarkeil, Aloiiis I 4 were used for RMS I aligiimciu and atoms s s were used for RMS II.
CoMFA
CoMb.A models were generated using a training set of 40 molecules (I able 1). with a column liltering \alue in mini of 2.0. A test .set of 17 molecules (Table 1) was u.sed to check the external predictivity of the models. A preliminary study was performed on the atom-based align ment to study importance of each held indi\idually The cross- \alidatcd r; \alue from the electrostatic held only was higher than that of the steric field only analysis. All further analyses were Fig. 2 Sijpcriniposilion of aff molecules u.siiig FF I. performed with steric and electrostatic fields calculated at each grid point simultaneousK. The atom-based alignment RMS 1 ga\e 0.405 with four compoiient.s. a con\cntional r- (/;,„„) of 0.9."!4. a predicti\e r (r,^.j) of 0.210 and an F value of 122.99.\ The alignment of the molecules using atom-based selection RMS 11 shows good internal predicli\ity with an r of 0..^ 14. Howe\ei. the model exhibited rather a poor external predictnity with an r^ of 0.1.^2. This is because the alignment did nol ha\e CONHOU as template for supenmposilion. CoMb.A models generated for multifu alignment. MF 1 showed r' of 0.24."^ with one componenl. r ,^ of 0..S71. c;., of -O.OIS. F \alue of 50.MO. Realignment of the molecules b\ field fit {¥}• I'"i^. .^ (rrapfi of ohscr\cd ycrsu.^ predicled acli\itiL's of ifie Irainini: set ll with lespecl to the liclds of template molecule (molecule 17) Irom II I CoMlA analysis. yieldeil r of 0.47S with se\en components. /• ,, of 0.9K7. ;;^., of O.yzi. F \alue of 174.6S7. NKxIels generated for multiht (MF II) I he results of .iD-QS.AR using Co.Ml A. are represented as a and field fit (lb II) alignmeiils using RMS 11 data set had poor "coefficient contour" map. I'he conlour nia]is t)btained from the external predicti\ity (Table 2). held fit model arc used to explain the SAR of molecules m the
Tins jouinal is The RoyrT Society of Chr-'^-istiv J','06 Org. Biomol. Chew., 2006, 4, 2858-2868 | 2859 - < — 2
2860 I Org. Biomol. Chem., 2006, 4, 2858-2868 Org. e/omo/. C/iem., 2006, 4, 2858-2868 | 2861 - < -3 -^
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2862 I Org. Biomol. Chem., 2006, 4, 2858-2868 Table 2 Sunimarv ol'CoMKA results with steric and electrostatic fields
Alignments
RMSI" FFl' MF !• RMSir FF ir MFII'
1.1 „ 0.405 0.47X 0.243 0.514 0.484 0.326 Components 4 7 1 4 5 5 SEP 0.675 0.602 0.756 0.613 0.628 0.731 '^,.« 0.934 0.987 0.571 0.961 0.988 0.904 SEE 0.227 0.155 0.553 0.176 0.099 0.123 Fvalue 122.995 174.687 50.610 168.022 551.325 123.24 Steric contribution 0.440 0.408 0.449 0.415 0.374 0.409 Electrostatic 0.560 0.592 0.501 0.585 0.626 0.591 0.210 0.327 -0.018 0.132 0.035 -0.120 ^. 0.959 0.990 0.676 0.975 0.994 0.945 ' Alignment by RMS fit.' Alignment by field fit.' Alignment by multifit. •* A cross-validated r value was obtained from the 'leave-one-out' method.
Table 3 Summai^ of CoMFA results with the additional descriptor floiiP
RMSI" FFl' MFI'
'i" 0.464 0.502 0.252 » Components 3 3 2 SEP 0.641 0.609 0.752 r 0.874 0.910 0.649 SEE 0.308 0.260 0507 F value X3.44I 121.227 34.152 Steric contribution 0.409 0.372 0.440 74 • M » Electrostatic 0.481 0.533 0.456 Otmtm4 * ° Alignment by RMS fit. ' Alignment by field fit. '' Alignment by multifit. Fig. 6 displays the electrostatic contour plot. The blue contours '' A cross-validated r value was obtained from the 'leave-one-out' method. describe regions v\here positively charged groups enhance activity (80'/'" contribution), and red contours de.scribe regions where neg present study. The CoMFA contour maps are shown in Fig. 5 and atively charged groups enhance the activity {20'y;i contribution). 6. The field values were calculated at each grid point as the scalar product of the associated QSAR coefficient and the standard CoMSIA deviation of all the values in the corresponding column of the data table (STDDEV*COEFF) and are plotted as a percentage The CoMSIA analysis was performed using steric, electrostatic, contribution to the QSAR equation. hydrophobic, and hydrogen bond donor and acceptor descriptors. Fig. 5 displays the steric contour plot. The green contours Only a few combinations of descriptors were used, which are describe regions where sterically favorable groups enhance activity complimentary to previously generated CoMFA models. Only {SO"Ai contribution), and yellow contours describe regions of FF I alignment was used for CoMSIA analysis. All the results unfavorable steric effects (20'M. contribution). of CoMSIA are shown in Table 4. CoMSIA models show lower Table 4 Results of CoMSIA analvsis SB" SEH" SEA" SEHA" SEHAD" 0.297 0.343 0.419 0.441 0.5.34 '•:,' Components 7 5 8 5 7 ri.m 0.984 0.976 0.991 0.975 0.987 SEE 0.118 0.138 0.091 0.140 0.109 F value 274.093 276.292 404.253 270.407 350.063 Steric contribution 0.406 0.280 0.316 0.218 0.179 Electrostatic 0.594 0.415 0.430 0.311 0.272 Hydrophobic 0.000 0.304 0.000 0.224 0.196 Hydrogen bond acceptor 0.000 0.000 0.254 0.246 0.197 Hydrogen bond donor 0.000 0.(X)0 0.000 0.000 0.155 i-^^j 0.054 0.286 0.304 0.389 0.464 /~^ 0.990 0.981 0.993 0.989 0.989 ' S = steric; E = electroststic: H = hydrophobic. A = hHydrogen bond acceptor. D = hydrogen bond donor ' The same as in Table 2. This journal is •.: The Royal Society of Chemistry 2006 Org. Biomol. Chem., 2006,4, 2858-2868 | 2863 Tahk'5 Results of aiuKsis witli cross \alidalion lor groups and of elcctroslatic iiUeraclions for the present scries of molecules. randomized bioloaical acliMiics The model with steric. electronic and hydrogen bond acceptor descriptors has a good predictivily (c;;,.^! of 0.304. .Addition of r, for 2 grou 1S" Randomized r' '• a hydrophobic descriptor to thi.s model caused an increase in CoMlA' CoMSIA' CdMFA CoMSlA' the /", ((1.4411. tombination of all fields gave a C'oMSI.A model with proper btilance of all statistical terms. The models showed Mean (1.2')') (I.27.S •-0.35') -(f240 higher ;•, (0..534) and a coiisidertible („, _, (0.464) values. The model, SD 0.117 O.OX? 0.1)23 0.064 Hiah ().43X 0.424 -0.082 -0.063 characteri/ed by a good ); ,, (0.'JX7) and a lower .SEE (0. lO'J). was Losv 0.(164 0.116 -(1.463 -0.3M selected as the best model to generate contour maps and explain the S.AR. To further as.sess and validate the derived .3D-0S.AR " Cross-\alidated /- for 2 groups with optimum nunihcr of components. a\erage ot'25 runs.' Cross-\alidated /- with randomized biological activity. model, bootstrapping analysi.s was performed and average result a\erage of 25 runs ' CoMKA model generated by licld fit. ''C'oMSl.A of 100 runs is 0.989 (/;,). To ascertain the true piedictivity of the analysis by combined steric. electronic. h\drophobic and Indrogcn bond model. c!oss-\alidalioii with 2 gioiips was performed 23 times donor and acceptor tields. and the mean r^ was 0.27.^. A negative value of r in a randomized biological activity test levcaled that the results were not ba.sed on chance correlations. The results of CoMSI.A are summarized in Table 4 and the observed versus predicted biological activities of the training and test sets are plotted in Fig. 7 and 8. 1 6f. 1 6 4 45 i 55 » 65 7 75 3 85 SI Kifj. 7 (iraph of observed IVIAK.V predicted .ictivities of the training set I'i};. 5 CoMF.A steric STnEV*COF;FF contour plots Irom the licld lit. from FF I CoMSIA anafvsi.s. Sterically favored areas are represented by green pohhedra. StericalK disfavorable are represented by yellow polyliedra. The active molecule P is shoun in ball-and-stick representation. 9 85 ^ 8 I 75 n 1 ' l« 6 55 5 55 65 7 75 85 Fig. 8 (iraph of observed icr.Mo predicted activities of the lest set fi\>m FF I CoMSIA analysis. Fi};. 6 C'oMF.A electrostatic STDFV'C'OEFF' contoiu" plots from the I he steric. electrostatic, hvdrophobic and hydrogen bond donor lield fit. Positive-charge favored areas are represented b\ blue poKhedra. and acceptor ciintours of C'oMSI.A are shown in Fig. 9 ]}. Negative-charge favored areas are represented bv red polvliedra. The active respectiveh. The steric fields (green, more steric bulk favored: molecule I " is shown in ball-and-slick represem;i[i(>n yellow, steric bulk disfavored), electrostatic lields (blue, posi tive charge favored: levl. negative charge favored), hvdrophobic predictive properties than those ol'CoMFA IIUHICIS. In all IIUHICIS lields (yellow. hvdro|ihobic I'avured: white, hvdrophobic disfa the electronic licld is liic comnion I'ticlor indicating the imporlancc vored), hydrogen bond acceptor lields (magentti, favored: red. 2864 ! Org. Biomol. Chem., 2006,4, 2858-2868 Fhis journal is "he Royal Society of Che:iiis;ry 2iju6 Kij;. II C'oMSIA hydrophobic liekls. M-llow indicates regions where hvdrophobie siihstituents enhance activitv; uhite indicates hydrophobic KiK- 9 CoMSlA sloric llcld.s. bellow indicalos stcricallv uiilinorablo substituents reduce acti\it>. The acti\e molecule 17 is shown in a region; green indicates a ,slericall\ l'a\orable region. The active molecule ball-and-stick representation. 1 7 is shoun ill a ball-and-stick representation. I''i};. 10 CoMSI.A electrostatic tields. I\)siti\e-cliarge ra\ored areas are Hg. 12 (.'oMSi.A hydrogen bond ;icceptor fields. Magenta indicates represented by blue polyhedra. Negative-charge favored areas are repre regions where hydrogen bond acceptor substituents enhance activ ity: red sented b> red polyhedra. The active molecule 17 i.s shown in a ball-and-stick indicates hydrogen bond acceptor substituents reduce activity. The active representation. molecule 17 is shown in a ball-and-stick representation. disfav ored) and h\drogcn bond donor liclds (cvan. Ia\ orcd: ptirplc, The PLS analysis on RMS I model was performed using a disfavored). thresholii cokimn filtering value of 2.0 kcal niol '. The results of dilTeient alignments are reported in Table 2. The analysis shoued tliat electrostatic (ields play a major role in binding to Intorprctation of'QS.VR iiiodcLs the IID.AC active site. The CoMKA models weie vtilidated by 1 he CoMlvX and C'oMSI.A analyses were performed on a series predicting the activity of the external lest set. The results obtained of HDAC inhibitors. The conformations of the molectiles were show that RMS I alignment produces a statistically signiticant generated from a systematic search of all the rotalahle bonds with model. RMS II has improved internal predict!vity but the external a uniform increment. The rotation of the spacer produces main predictiv ity of the model was reduced. Tlie.se analyses indicate that low energy conformations. The lowest cncrgv conlormatiiMi of the the hvdro.xamic acid group is very important for alignment. The all the tnolecules was used in the stiidv. hydioxaimc acid is important for en/yme inhibitory activity, as it coordinates the zinc ion through CO and OH groups. It also (i)MFA forms hydrogen bonds between its NH and OH groups and the tw 11 charge relay systems Hisl .il/Asp 166 and 1 Iisl.i2/.\spl7.^ ami ,\lignmem of the molecules \s itiiportam for CoMIA studies. In between its CO and the Tyr2^)7 hydroxy I gioiip.'' the present study we liave aligned these ligands onto the template Mtiltitit alignment decreased the predictiv it\ whereas lield lit structure (molecule 17) tisinii two different stratCLiies. tilignment showed improved predictions in both cases. Slight This journal is Org. e/omo/. Chem., 2006,4, 2858-2868 | 2865 (oMSIA CoMSLA analysis was performed for field fit alignment a.s it gave the best model in CoM FA. .All the C'oMSI A fields were consideretl for analysis. A combination of steric. electrostatic, hydrophobic and hydrogen bond donor and acceptor fields gave the best QSAR model with good internal as well as external predictivity. The model was further validated using 2 groups method and randomization tests. Fig. 9 ]?• show the C'oMSIA contour maps with the nio.s! active molecule. The steric contour maps of C'oMSI.A (Fig. 9) are also similar to C'oMFA steric maps. The electrostatic contour maps are shown in Fig. 10. CoMSI.A slunvs blue contours over the hydroxamic acid group, which signifies tiie importance of this group. The red polyhedra over the phenyl ring indicate that the presence of electron-rich functional groups at this position increase the activity. .Analysis of CoMSI.A hydrophobic maps I'if;. 13 C iiMSIA hydrogen bond donor ticlds. C \aii indicates regions (I'ig. 11) indicates that a lipophilic-favorable yellow region is where h\drogen bond donorsuhstituentsenlianee aeliMt\: purple indieLites found near the aromatic ring. This indicates that lipophilicity hsdroL'en bond donor suhstituems reduce acti\it\. The aeli\e nu>leeule 1 7. oi' the cap portion of the molecule is important for activitv. A is shown in a ball-and-stick represenlalion. hydrophiljc-favorable white contour is observed surrounding the hydroxamate functional group. In the present study, hydrogen bond acceptor fields (fig. 12) provide further support for the \ariation in the alignment rules leads to dramatic ditTcrcnccs role of the positively-charged hydroxamate group. Hydrogen bond m the e\tetiial predictions. The goixi internal and e\teiiial acceptor-unfavorable red polyhedra observed at the phenyl ring prcdictimis with h'F I alignnicnl support the use of these atoms nuhcale that the presence ofelection-rich groups improves activ ity. tor superimposition. Hydrogen bond donor maps (Fig. 13) show hydrogen bond donor- All additional descriptor was addetl to the C'oMFA tabic to favorable cyan contours near the hydroxyl group of hydroxamic stud) the influence of other factors on the CoMFA results. acids. Inclusion of C'logP in the CoMf'A table impro\ed the statistical resLilts (ifthe hF I model. H\drophobicit\ is important for HD.AC The role of both the steric and electrostatic contribution can inhibition. be clearly explained by analy/mg the molecules 25. 26 and 2S. PFS anal\sis on FF I was also performed using the 2 grcnip Molecule 25. due to the double bond in its spacer chain, loses its cross-\alidation procedure ( I'able 5). The /-, xakics obser\ed orientation towards the stericallv favorable region. Molecule 26 during 25 cross-\alidation runs were less than those obscr\ed from shows lesser biological activitv. due to an adtiitional niethvl group 'leave one out'. In no cases were these \alucs were negative, and adjacent to the hydroxamic acid group, which is also oriented they showed good internal consistency. The field fit alignment towards the stericallv unfavourable region. Molecule 28. with FF I was used to analyze the CoMlA contour maps as this an electron-withdrawing chloro group substituted on the para model exhibited good internal as well as external predictivit\'. The position of the side chain, enhances the potency of the molecule, C'oMFA steric and electrostatic maps are shown in F'ig. 5 and 6. due to the proper orientation towards steric- and electrostatic- respecti\ely. favorable regions. Molecules .32. ,vi and .37 are less active. The bulky phenyl ring substituents of these molecules are oriented Fig. 5 depicts the steric contour plot. I he lipophilic fragment towards the stericallv unfavourable yellow region. Molecule 15 has o( the molecule was surrounded by the stericallv favorable green comparable biological activity to that of the most active molecule contours. The niost-acti\e molecule 17 has a phenyl ring embedded 17, becau.se of the proper steric and electrostatic interactions of its in this green region. Other sterically-fa\orable green contours are hydri>phobic phenyl groups. obser\ed near the spacer chain. I his green contour is surrounded by the unfavorable yellow region. A substitution on the spacer atoms adjacent to the phenyl ring (as that of TS.A) is favored, but any substitution on the carbon next to the hydro.xamic Kxperimental acitl functional group is unfavorable. 'I his also suggests that the Biological datj orientation of the bulky groups at these positions is important for activ ity. A data set consisting of 57 hydroxamic acid analogues was taken I'ig. d displays tlie electrostatic contour plots. .A red contour from the literature." " The structure of the compounds and their was fouiul near the phenyl ring of compound 1'?. suggesting that biological data are given in fable I. In this QS.AR study, the a high election density in this region increases the activ ity. A large biological activity of each compound has been expressed as the negative-charge unfavorable blue contour was found to surround negative logarithm of normalized IC',,, (plC\,i). These normalized the spacer chain. This indicates that substitutions in this region IC,„ values, and the negative logarithm of normalized IC*,, (plC,,,) with high electron density reduce activ ii\ and en\pliasi/.es the of compounds were taken from the literature and used in the necessity of positively charged groups: hvdroxaniale is essential present stuily.-' I hus. the data correlated lineailv to the free energy for HDAC activitv. change A Irainiiiii set of 40 molecules (Table I) was used for 2866 i Org. Blomol. Chem., 2006,4, 2858-2868 '\M "-ociety of Cliemistry 2006 tlic generation ol'QSAR models. Tlie training set molecules were sp' carbon atom with van der Waals radius of l.?2 A and + selected in such a way that they contains information in terms of 1.0 charge was served as the probe atom to calculate steric and both their structural features and biological activity ranges. The electrostatic fields. The steric and electrostatic contributions were most active molecules were included, so that they can provide truncated to ±30 kcal mol '. The electrostatic contributions were critical information on pharmacophore requirements. Several ignored at lattice intersections with maximum steric interactions. moderately active and inactive molecules were also included, to CoMSI.A calculates similarity itidicc.s at the intersections of spread out the range of activities. A test set of 17 molecules (40 5b. the surrounding lattice. Five physicoehemical properties steric, Table 1) was used to access the predictive ability of the generated electrostatic, hydrophobic, hydrogen bond donor and acceptor models. The test molecules represent a range of biological activity were evaluated, using a common probe atom with I A radius and similar to the training .set. charge, hydrophobicity and hydrogen bond property of-I-1.0. The attenuation factor was set to default vale, 03. Computational details All computational studies were performed using SYBYL. 6.9.1'" Calculation of C'logP with a standard Tripos force field." The compounds were con structed from the fragments in the SYBYL database with standard The ClogP values for .^7 molecules were calculated using bond lengths and bond angles. Geometry optimization was carried C'logP/C'MR application within Sybyl 6.9.1. The.se methods are out using the standard Tripos forcelield with distance dependent- developed by the Biobyte Corporation. dielectric function and energy gradient of 0.001 kcal mol ' A '. The initial conformations were obtained from a systematic search. Partial least square (PLS) analysis The lowest energy conformers were selected and minimi/ed tising the Powell method till root-mean-square (mis) deviation 0.(101 rhe CoMFA and CoMSI.A descriptors were used as independent kcal mol ' A ' was achieved. Partial atomic charges required variables and pIC^,, as dependent variables in the PLS" regression for calculation of the electrostatic interaction were computed analysis to derive the ."^D-QSAR models using the standard by a semiempirical molecular orbital method using AMI in the implementation in the Svbyl package. Initially. PLS was carried MOPAC program. out in conjugation with the "lea\e-onc-out' (LOO)" option to determine the optimum number of components. .Alignment rules. The results from cross-validation analysis were expres.sed as the cross-validated r value (r^). which is defined as; The "alignment rule", i.e.. the positioning of a molecular model within the fixed lattice, is by far the most important input variable PRESS in C'oMbA. since the relative interaction energies depend strongly on relative molecular positions. The most active molecule 17 was E<>--K„_)^- used as a template for aligning the other molecules. In the present study, we have superimposed molecules bv three where PRESS = Z (Y ~ Y •,,„,,)'. alignment rules: (1) atom-based alignment. (2) multifit alignment. The number of coiuponents that result in the highest c; and {?•:) lield fit alignment. lowest standard error of predictions (SEP) were taken as the Alignment (1) was done by atom-based fitting of the atoms to optimum. Equal weights were a.ssigned to steric and electrostatic the most active molecule. 17. (a) The hydroxamic acid group of fields using CoMFA_STD scaling option. To speed up the analysis the molecules was used for rnis titting (RMS I) and (b) carbonyl and reduce the noise, a minimum filter value "a" of 2.0 kcal mol ' group and phenyl ring atoms for RMS II. both as shown in Fig. 1. was u.sed. The LOO method of cross-validation is rather obsolete Alignment (2) of the molecules was carried out by llexible fitting and it generally gives high r value. Final analysis was performed (multifit) of atoms, of the molecules to the template molecule 17. to calculate the r „,^ with a number of cross-v alidation groups set to This involved energy calculations and fitting onto the template zero using the optimum number of components. To further assess molecule by applying force (force constant 20 kcal mol ') and the robustness and statistical conlidence of the derived model.s, subsequent energy niininu/ation. bootstrapping analysis (100 runs) was performed. The statistical results obtained for CoMFA analysis are shown in Table 2 and .^. .Alignment (.") was carried out using the S\B\'L QS.AR rigid body field tit command within SYBYL and using compound 17 To perform a more rigorous statistical lest, cross-validation as template molecule. The superimposition of all the molecules is using 2 groups was carried out for the field fit analysis of CoMFA shown in Fig. 2. and CoMSI.A. In this case, the data set is randomly divided into two groups, and the activity of the compounds from one group is CoMFA and CoMSl.A interaction energies. predicted using the model from the other group. The process of group cross-validation was performed 25 times. The final /-, value For each alignment, the steric and electrostatic potential fields for was calculated by taking the mean of 25 run.s. These r. values were CoMFA were calculated at each lattice intersection of a regularlv compared with r^ obtained from LOO for each PLS analysis. The spaced grid box. The lattice spacing was set to a value of 2.0 .A in statistical results obtained from cross-validation with 2 groups for all .\. ) and Z directions. I he van der Waals potential (Leniiard- CoMFA and CoMSFX analyses are shown in Fable 5. Joncs. (i 12) and the cokiuibic term, which represent, respectively, To check the probability of chance correlation. PLS analysis steric and electrostatic fields, were calculated using the Tripos force was performed by randomization of the biological activity This field. .A distance-dependent dielectric constant of 1.0 was used. A was done bv randomlv chanainc the biokmical activity data and Org. Biomol. Chem., 2006, 4, 2858-2868 | 2867 performing PLS analysis to calculate the r^ value lor field fit of References CoMFA and CoMSlA. The process was repeated 100 times. 1 M. J. Pazin and J. T Kadonaga. Cell. 1997. 89. ?.2> ,128. 2 P. A. Mark.s. R. A. Rifkind. V. M. Richon. R. Breslow. T Miller and Predictive r value (rj^,^) W. K. Kelly. Neil. Rer. Cmuer. 2001. 1. 194-202. 3 W. K. Kelly. O. A. OC'iinnor and P. A. Marks, fi.x/ieri Opiii. Iinesi. /)n/,e,v..2(X)2. II. 1695 17I.'!. To test the predictive power of the derived CoMFA and CoMSIA 4 P. A. Marks. V. M. Richon. R. Breslow and R. A. RilTcind. Curr. Opin. models, biological activities of the test .set molecules were predicted Omol.2m\. 13.477 483. using models derived from training set. The plot of predicted versus 5 P A. Marks. V. M. Richon. R. Breslow and R. A. Rifkind. Clin. Cancer observed activity of test set are shown in Fig. 3 4 and 7 8 based /?<'.v.. 2001. 7. 759-760. 6 C. J. Phiel. K Zhang. E. Y. Huang. M. G. Giienther. M. A. Lazar and on CoMFA and CoMSIA. respectively P S. Klein. J. Biul. Cliem.. 2001. 76. 36734-36741. The predictive r value was calculated using the following 7 R T. Meinke and P Liberator. Ciirr Med. Clieni. 2(H)I. 8. 211 235. formula: 8 M. Yoshida. M. Kijima. M. Akita and T. Beppu. J Biol. Cliem.. 1990. 265. 17174 17179. SD - PRESS 9 M. Kijima. M. Yoshida. K.Suguta.S. HorinoiichiandT. Beppu. 7. Biol. Chem.. 1993. 268. 22429 22435. SD 10 R. E. Shute. B. Dunlapand D. H. Rich./ Meil. Chem.. 1987. 30. 71-78. 11 J. W. Han. S. H. Ahn. S. Y Wang and G. V. Seo. Cancer «<.v.. 2000. 60. where SD is the sum of squared deviation between the biological 6068-6074. activities of the test set molecule and the mean activity of the 12 S. D. Gore and M. A. Carducci. Kxperi Opin. Inve.si. Drugs.. 2000. 9. 292.3-2934. training set molecules and PRESS is the sum of squared deviations 13 M. Gottlicher. S. Minucci. R Zhu. O. H. Kramer. A. Schimpf. S. between the observed and the predicted activities of the test Giavara. J. P. Sleeman. F. Lo Coco. C. Ner\i. P. G. Pelicci and T. molecules. Hainzel. EM BO J. 2001. 20. 6969-6978. 14 L. M. Butler. [> B. Agiis. H. 1. Scher. B. Higgins. A. Rose. C Cordon- Cardo. H. T. Thaler. R. A. Rifkind. P Mark.s and V. M. Richon. Cancer ft'.v..2000. 60. 5165-5170. Conclusions 15 M. Jung. G. Brosch. [> Kiille. H. Stherf C. Cierhiiuser and P Loidl. J. Med. Cliem.. 1999.42,4669-4679. The CoMFA and CoMSIA methods have been applied to derive 16 S. W. Remi.szewski. L. C. Sambucetti. P Atadja. K. \V. Bair. W. D. 3D-QSAR models for hydroxamic acid HDAC inhibitors. The Cornell. M. A. Green. K. L. Howell. M. Jung. P. Kwon. N. Trogani and models obtained using these methods showed high correlative and H. Walker. J. Med. Chem.. 2002,45. 753-757. 17 S. H. Woo. S. Frechette. E. A. Khalil. G. Bouchain. A. Vaisburg. N. predictive abilities. A high bootstrapped r value indicates that a Bernstein. O. Moradei. S. Leit. M. Allan. M. Fournel. M.-C. Trachy- similar relationship exits in all molecules. Inclusion of ClogP as Bourget. Z. Li. J. M. Beslerman and 1). Delormc. / Med. Chem.. 2002, an additional descriptor increased the statistical significance of the 45,2877-2885. model, indicating that lipophilicity enhances the HDAC inhibitory 18 Y. B. Kim. K. H. Lee, K. Sugita. M. Yoshida and S. Horinouchi, Oncogene. 1999. 18. 2461 2470. activity DitTerenl alignments were considered for the study. 19 M. S. Finnin. J. R. Dongian. A. Cohen. V. M. Richon. R. A. Rifkina, The atom-based alignment with the hydro.xamic acid functional R A. Mark.s. R. Bre.slow and N. R Pavletich. .\'alnre. 1999.401.188-193. group gives a better result than other atom-based alignment. It 20 D.-F Wang. O. G. Wiest. P Helquist. H.-Y Lan-Hargest and N. L. emphasizes the importance of the interaction of hydroxamic acid Wiech. Bioorg. Med. Chem. Lett.. 2(K)4. 14. 707 711. 21 Y Guo. J. Xiao. Z. Guo. F Chu. Y Cheng and S. Wu. Bioorg. Med with the enzyme residues. Out of all these alignments, the field Chem.. 2005, 13(18). 5424-5434. fit alignment (along with ClogP) resulted in the best CoMFA 22 S. Massa, A. Mai. G. Sbardella. M. Esposito. R. Ragno. P. Loidl and model. The same alignment was also considered for CoMSIA G. Brosch. J. Med. Chem.. 2003. 46. 512-524. where all five fields were considered in dilTerent combinations. 23 A. Xie. C. Liao, Z. Li. Z. Ning. W. Hu. X. Lu. L. Shi and J. Zhou. Curr Med. Chem.: .Ami-Cancer Agenl.s. 2004, 4, 273-299. The model generated using all five fields gave a statistically- 24 R. D. Cramer. III. J. D. Bunce and D. E. Patterson. Quanl. Suiiet.-Act. significant model and explained the observed biological activities. Relat.. 1988,7. 18-25. The contour maps from both the models are similar in explaining 25 G. Klebeand U. Abraham./ Compur AidedMol. Des.. 1999.13. I-IO. influence of substitution on activity The substitution by electron 26 V. Hariprasad and V. M. Kulkarni. / Compiil. Aided Mol. /Jc.v.. 1996. 10. 284 292. rich functional groups on the phenyl ring may improve activity. 27 S. S. Kulkarni and V. M. Kulkarni, / Med Chem . 1999, 42, 373-380. Hydrogen bond acceptor and donor groups also enhance the 28 S. V. Murthy and V. M. Kulkarni, Bioorg. Med. Chem.. 2002,10, 2267- activity Overall, electrostatic interactions play a major role in 2282. binding to the HDAC active site. 29 M. T. Mahindra and V. M. Kulkarni. / Compul. Aided Mol. flc.v.2002 , 16. 181 2(H). 30 SYBYL 6.'>.1. Tripos Associates Inc.. St Hanley Road. St Loui.s. MO. USA. 2003. Acknowledgements 31 M. Clark. R. D. Cramer. Ill and N. V. Opdenbosh. / Compw. Chem.. 1989. 10.982 1012. 32 W J. Dunn. U. Wolds, S. Hellbergand J. Gasteiger. Quum. Slrmi.-Act. The authors are thankful to Dr Shivajirao S. Kadam, Principal Relat.. 1984.3, 131-1.37. Poona College of Pharinacv lor constant encouraccnient. 33 S. Wold. Technomelrics. 1978.4. .397 405. 2868 i Org. Biomol. Chem., 2006, 4, 2858-2868 The Royal Society of Chemistry 2006