Article pubs.acs.org/JAFC
Modeling the Effect of Phase II Conjugations on Topoisomerase I Poisoning: Pilot Study with Luteolin and Quercetin Luca Dellafiora,† Pedro Mena,‡,§ Daniele Del Rio,*,‡,§ and Pietro Cozzini*,† †
Molecular Modeling Laboratory, Department of Food Science, ‡The Laboratory of Phytochemicals in Physiology, Human Nutrition Unit, Department of Food Science, and §LS9 Bioactives and Health, Interlaboratory Group, Department of Food Science, University of Parma, 43125 Parma, Italy ABSTRACT: Topoisomerases are targeted by several drugs in cancer chemotherapy acting as key enzymes in cell viability. Some flavonoids and their glycosides may exert health protective effects through the poisoning of topoisomerases. However, previous studies did not consider the substantial modifications taking place after ingestion neglecting that only metabolites can interact with the internal compartments of the human body. Since the high number of possible metabolites hinders their systematic analysis, an in silico approach can be a valuable tool to prioritize compounds by identifying candidates for further characterization. Specifically focusing on luteolin and quercetin, among the most ubiquitous flavonoids in the human diet, this work reports a computational procedure to model the effect of hepatic phase II conjugative metabolism on poisoning of human Topoisomerase I. As a general effect, glucuronidation and sulphation might enhance and quench poisoning activity, respectively. Among all, quercetin-3-O-glucuronide represents a promising candidate to be analyzed more thoroughly. KEYWORDS: Topoisomerase I, poisoning, molecular modeling, flavones, metabolites, activity
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mediated Topos poisoning.12,13 A further factor to be taken into account is that, after consumption,6 flavonoids are subjected to extensive phase II metabolism, and thus it would be their glucuronide and sulfate metabolites that might act as potential chemopreventive agents, rather than their parent compounds. The role of these circulating metabolites in poisoning Topo I has not been determined, yet. Therefore, considering that luteolin and quercetin are widely consumed through a normal balanced diet, it is relevant to investigate whether their phase II metabolites are able to act as Topo I poisons. Nevertheless, the systematic characterization of metabolized forms of polyphenols based on in vitro assays may be a hard challenge due to the high number of hits and, sometimes, to the lack of commercially available standards at affordable costs. It becomes thus necessary a prioritizing system to drive experimental trials in a focused manner. The computational approach efficiently meet this need and because the Topos poisoning activity unequivocally involves protein/DNA complex-ligand recognitions, the use of 3D molecular modeling becomes paramount.14 Hence, the present work presents a pilot study on luteolin and quercetin, with the aim of verifying the feasibility of modeling the effects of their phase II conjugation on Topo I poisoning by using already verified and effective technique.15 In addition to plant-based glycosides (used for training procedure), several phase-II human metabolites, still untested to the best of our knowledge, were analyzed (luteolin3′-O-glucuronide, luteolin-3′-O-sulfate, luteolin-4′-O-glucuronide, luteolin-7-O-glucuronide, luteolin-7-sulfate, quercetin-3-
INTRODUCTION Topoisomerases (Topos) are ubiquitous enzymes that control DNA supercoiling and intertwining. They allow DNA relaxing by introducing single- (Topo I) or double-strand breakage (Topo II) through respectively one or two transesterification reactions. When Topos activity is altered, abnormal DNA structures and delay of replication and transcription inevitably occur.1 Both “canonical inhibition” and poisoning can disrupt Topos activity. The former requires the prevention/alteration of protein−DNA interaction, the latter turns the enzymes into a DNA damaging agent since poisoning compounds hinder DNA religation by stabilizing the so-called “cleavable complex”.2−4 Since Topos are involved in key steps of cell viability, topoisomerase inhibitors and poisons are widely used as antibacterial and anticancer agents.5 Flavonoids are low molecular weight secondary metabolites widely distributed in the plant kingdom. They are ubiquitous constituents of fruit- and vegetable-rich diets where they have potential protective effects against the onset of chronic conditions such as cancer and cardiovascular disease.6 Among flavonoid subgroups, the most potent Topo I poisons are flavones and flavonols.2 Quercetin, principally as glycoside conjugates, is an abundant flavonol in the human diet, occurring in products such as apples, red grapes, citrus, onions, garlic, and tea,7 and its consumption has been linked to lower rates of certain cancers.8 The flavone luteolin also occurs almost exclusively as glycoside conjugates and is found in carrots, peppers, celery, olive oil, peppermint, thyme, rosemary, and oregano.7 Its consumption has been associated with a range of biological activities.9 Both quercetin and luteolin have been reported to poison DNA Topo I and II,2,10,11 which supports their possible chemopreventive activity. However, results are conflicting and some works in the literature do not support flavonoids© 2014 American Chemical Society
Received: Revised: Accepted: Published: 5881
January 24, 2014 May 26, 2014 May 29, 2014 May 29, 2014 dx.doi.org/10.1021/jf501548g | J. Agric. Food Chem. 2014, 62, 5881−5886
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Figure 1. Molecules forming the training-set. waters and ligands were removed and 30 poses for each compound were generated. No constraints were set up and the explorable space was defined in a radius of 10 Å from the centroid of the binding pocket. For each GOLD docking search, a maximum number of 100 000 operations were performed on a population of 100 individuals with a selection pressure of 1.1. Operator weights for crossover, mutation, and migration were set to 95, 95, and 10, respectively. The number of islands was set to 5 and the niche to 2. The hydrogen bond distance was set to 2.5 Å and the vdW linear cutoff to 4.0. Ligand flexibility options “flip pyramidal N”, “flip amide bonds”, and “flip ring corners” were allowed. Rescoring Procedure and Hydropathic Analysis. The software HINT (Hydropatic INTeraction)19 was used as postprocessing tool to rescore all poses generated by GOLD. The HINT scoring function calculates the favor of protein−ligand recognition trough a sum of hydrophobic atom constants which derived from experimental LogPo/ w values (the partition coefficient of a molecule in 1-octanol/water). Thus, HINT appears as a “natural”20 and intuitive force-field by providing empirical and quantitative evaluation of protein−ligand interaction as a sum of all single atom−atom contributions using the following equation:
O-glucuronide, quercetin-3′-O-glucuronide, quercetin-3-sulfate, quercetin-4′-O-glucuronide, quercetin-7-O-glucuronide, and quercetin-7-sulfate)16,17 and ranked according to physicochemical prioritization criteria.
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MATERIAL AND METHODS
The procedure involved docking simulations followed by rescoring procedures using a proper scoring function. Our intention was to develop a procedure easy to perform, without excesses of software and hardware requirements and avoiding complex mathematical and statistical analysis. All experimental data were retrieved from literature2 and query set contained metabolites observed under in-vivo-like conditions whose might be the predominant occurring form in circulatory system.16,17 Molecular Modeling. The model used for docking simulation was derived from the high resolution structure of human Topo I retrieved from Protein Data Bank (http://www.rcsb.org; PDB code 1K4T).18 Protein structure and small molecules were processed using the molecular modeling software Sybyl, version 8.1 (www.tripos.com). All atoms were checked for atom- and bond-type assignments. Aminoand carboxyl-terminal groups were set as protonated and deprotonated, respectively. Hydrogen atoms were computationally added to the protein and energy-minimized using the Powell algorithm whit a coverage gradient of ≤0.5 kcal (mol Å)−1 and a maximum of 1500 cycles. Docking Simulations. The docking simulations of all compounds were performed with the program GOLD version 5.1 (CCDC; Cambridge, U.K.; http://www.ccd.cam.ac.uk) on a double-quad cores machine equipped with 1.86 GHz processor. All crystallographic
HINT score =
∑i ∑ j bij = ∑i ∑ j (aiSiajSjTijR ij + rij)
where bij is the interaction score between atoms i and j, a is the hydrophobic atomic constant, S represents the solvent accessible surface area, Tij is a logic function assuming +1 or −1 values, depending on the nature of the interatomic interaction, Rij and rij are functions of the distance between atoms i and j. 5882
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Positive and high HINT score correlates with favorable binding free energy, allowing the evaluation of the thermodynamic benefit of predicted complexes.21−24 Because different conformations of the same molecule may coexist contributing to the whole energy of binding, a Boltzman average calculation was applied. In this way the contributions of different conformations to the predicted interaction were taken into account.25 Thus, from here, we referred to HINT score (HS) as Boltzman averaged score. Replicas of Docking Simulation and Rescoring Procedure. Because GOLD uses a Lamarkian genetic algorithm, results may slightly change from run to run. This implies that computing twice the same molecule is impossible to record exactly the same score, because two identical poses (i.e., the exact xyz coordinates for each atom) are virtually impossible to obtain. This may be a nontrivial issue potentially disruptive in place of rescoring procedures. GOLD results are intrinsically ranked by its internal scoring functions. Because of this, it is not possible to separate GOLD’s pose generation from its ranking, and we were forced to apply HINT only to rescore pregenerated poses. Thus, in order to avoid not-causative fluctuations due to random component of docking algorithm, each analysis was repeated 5-fold and the mean value was considered.
as ranges that totally overlap cannot be resolved. Conversely, luteolin-4′-O-glucoside (less active) and luteolin-8-C-glucoside (more active) were unequivocally separated from luteolin. Likewise quercetin-3-O-rhamnoside and quercetin-3-O-rutinoside clearly diverged down from quercetin. The training-set underwent docking simulations and rescoring procedure in order to assess the applicability of the procedure to this specific case study. Overall, good correlation between computed and experimental data was obtained (Figure 2). Specifically, computed results were in agreement with
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RESULTS Training Procedure and Training-Set Results. When the validation of computational models may be not mandatory, as in this instance due to the lack of sufficient and inherent experimental data, training procedures are compulsory.26 In the current study, the procedure exploited experimental data retrieved from the literature.2 Specifically, a training-set has been formed by luteolin, luteolin-8-C-glucoside, luteolin-4′-Oglucoside, luteolin-7-O-glucoside, quercetin, quercetin-3-Orhamnoside, and quercetin-3-O-rutinoside (Figure 1). Data and results concerning the training procedure are reported in Table 1. Taking into account the high variability and, in some
Figure 2. Correlation between computed results and experimental data. All experimental data were retrieved from the literature and express relative potency of compounds with respect to the reference standard camptothecin.2
experimental data and the procedure was trained according to the fact that the augmentation and reduction of HSs correlated to conjugation-dependent enhancing and quenching of poisoning activity, respectively. However, it was not possible to compare the relative activity of luteolin and luteolin-7-Oglucoside due to the same reasons reported above (Table 1). Globally the procedure proved to be able to evaluate the relative effect of conjugations on poisoning activity of luteolin and quercetin. Query-Set Results. The query-set underwent the same procedure of the training-set. Results were reported in Table 2.
Table 1. Results of the Training-Seta compound luteolin luteolin-8-Cglucoside luteolin-4′-Oglucoside luteolin-7-Oglucoside quercetin quercetin-3-Orhamnoside quercetin-3-Orutinoside
experimental activity
activity category
mean HS
predicted effect
55 ± 8.2 69 ± 5.6
/ ↑
683 ± 28.0 804 ± 107.0
/ ↑
19 ± 4.3
↓
562 ± 76.0
↓
51 ± 14.0
↑↓
747 ± 136.0
↑↓
57 ± 15.6 15 ± 12.0
/ ↓
718 ± 42.0 240 ± 140.0
/ ↓
5 ± 1.8
↓
383 ± 194.0
↓
Table 2. Results of the Query-Seta
a
Enhancing and quenching effects with respect to the reference aglyconic compounds (in bold) are represented by up- or down-arrow, respectively “↑↓” indicates indefinable relative potency. Hint scores are expressed as mean value of 5 replicates. All experimental data were retrieved from the literature and express relative potency of compounds respect to the reference standard camptothecin.2
cases, the proximity of experimental data, the absolute and quantitative rank of compounds was not easy to resolve. However, it was possible to perform a semiquantitative and relative comparison; instead of a global potency rank, we defined a binary categorization describing conjugation-dependent effects of enhancing and quenching on luteolin and quercetin, respectively. For instance, luteolin and luteolin-7-Oglucoside shared proximal values and the high variability of luteolin-7-O-glucoside made their relative potency indefinable,
compound
mean HS
predicted effect
luteolin luteolin-3′-O-glucuronide luteolin-3′-sulfate luteolin-4′-O-glucuronide luteolin-7-O-glucuronide luteolin-7-sulfate quercetin quercetin-3-O-glucuronide quercetin-3′-O-glucuronide quercetin-3′-sulfate quercetin-4′-O-glucuronide quercetin-7-O-glucuronide quercetin-7-sulfate
683 ± 28 756 ± 103 738 ± 24 899 ± 163 891 ± 199 354 ± 139 718 ± 42 1766 ± 112 782 ± 177 636 + 10 756 ± 105 719 ± 41 618 ± 190
/ ↑↓ ↑ ↑ ↑ ↓ / ↑ ↑ ↓ ↑ − ↓
a
Enhancing and quenching effects with respect to aglyconic reference compounds (in bold) are represented by up- or down-arrow, respectively. “↑↓” indicates indefinable relative potency. “−” indicates comparable effect. HINT scores are expressed as mean value of 5 replicates. 5883
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chose GOLD, which is one of the most popular commercial docking software, and HINT, a natural and intuitive force-field to evaluate the favor of protein−ligand recognition. In addition, the use of such “linear” tools, avoiding complex mathematical and statistical models, contributes to reduce the risk of traps, such as overfitting phenomena and others erroneous or misleading results. The coupling of GOLD to perform docking simulations and HINT as rescoring function, has already proved to be a well working procedure to evaluate protein− ligand interaction,33,35,36 thus no further validation was needed. Rather, as a proof of concept, we presented (i) the feasibility of a model able to evaluate the semiquantitative effect of conjugations on luteolin and quercetin by using the few inherent data available in literature and (ii) the application of such model to prioritize quercetin and luteolin metabolites for further experimental characterization. We focused on luteolin and quercetin due to their massive occurrence in plant-derived food and we took into account conjugated metabolites because glucuronidated and sulfated derivatives are the main circulating forms in vivo. Once assessed the capability to model the effect of conjugation on poisoning activity of luteolin and quercetin, glucuronidated and sulfated metabolites underwent the same procedure. In principle such approach can be applied to any other type of small molecule (and protein), but the procedural performance must be assessed case by case trough cross-check with experimental data. Flavonoids able to effectively poison Topo I require hydroxyl group substitution at the C-3, C-7, C-3′, and C-4′ positions, a keto group at C-4, and a Δ2,3 double bond (3,3′,4′,7tetrahydroxy-substituted flavones).12 Quercetin and luteolin, as well as other flavonoids like apigenin, myricetin, fisetin, and morin, match these criteria and have been shown to poison Topo I.2,11 However, the presence of sugar moieties has been pointed out to quench the poisoning effect of flavones and flavonols,2 except for compounds glycosylated at the 8 position, as in the case of luteolin-8-C-glucoside (Table 1). In addition, the glucuronide form of SN-38, a potent topoisomerase I inhibitor derived from the pro-drug irinotecan (a derivative of camptothecin), has only 1% of the antitumor activity of its aglyconic active form.37 Based on this observation, a limited poisoning effect of phase II metabolites containing a glucuronide conjugates should be expected. Nevertheless, most of the glucuronidated derivatives of the tested flavonoids showed enhanced Topo I poisoning effect with respect to their parent compounds (Table 2). On the contrary, sulfate derivatives, with the only exception of luteolin-3′-sulfate, showed a quenched effect when compared to aglycones. In contrast to the marked role of the type of conjugation, its position in the flavonoid structure did not play a key role in the poisoning effect of these phenolic metabolites, with the exception of quercetin-3-O-glucuronide. The docking results revealed that the HINT score for quercetin-3-O-glucuronide was notably higher while the magnitude of the predicted effect was quite similar for most of the metabolites (Table 2). This might indicate that glucuronidation of flavonols at the C-3 position enhances their bioactivity as Topo I poisons. In this specific case, the substitution of the hydroxyl group by the glucuronide moiety occurs at the nonbenzene ring, the C ring. Thus, considering the essential role that the phenolic hydroxyl group plays in the activity of the chemotherapeutic SN-38,37 this structural feature may at least partially explain the enhanced effect of quercetin-3-O-glucuronide. Overall, these results
Overall, glucuronidation seemed to exert enhancing effects on both luteolin and quercetin, whereas sulphation, with the exception of luteolin-3′-sulfate, had the opposite effect. In addition, it is of interest to notice the out of range score of quercetin-3-O-glucuronide, indicating that this compound might be much more active than the others. Conversely, quercetin-7-O-glucuronide appeared virtually identical to quercetin and it could exert similar activity. Finally, once again, it was not possible to resolve the relative effect of luteolin-3′-O-glucuronide versus luteolin.
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DISCUSSION Topos I, as well as other topoisomerases, are key enzymes in cell viability which act on genome stability and accessibility during replication and transcription. Therefore, they are commonly targeted by antibacterial and anticancer chemicals. There are at least two mechanisms to disrupt Topo I activity: canonical inhibition by preventing protein−DNA recognition and poisoning by stabilizing DNA−protein complexes and hindering the DNA religation step. Enzyme poisoning rather than inhibition drives drug action. Several compounds act as Topo poisons, with many of them highly specific for Topo I, others for Topo II, and some promiscuous for both isoforms.27 Currently camptothecin derivatives are the only anticancer drugs targeting Topo I approved by the FDA (Food and Drug Administration, http://www.fda.gov), although several promising compounds are under development.5 Within this frame, flavonoids such as quercetin and luteolin might play critical roles. It is well-known that this class of compounds commonly occurs in edible parts of fruits and vegetables and exerts a broad spectrum of health-protective properties by interacting with several protein targets. Because advances in system biology suggest that multitarget compounds may prove higher efficacy rather than host-specific drugs,28 these ubiquitous compounds might be of great relevance in cancer prevention, and TopoI poisoning may contribute to their health benefits. To better understand the underlying mechanisms of luteolin and quercetin putatively beneficial action, their metabolites, truly circulating in the bloodstream after their dietary intake, should be evaluated. Considering the wide array of possible metabolic phase II conjugation and the lack of commercially available standards, an in silico approach might be a useful prioritizing tool, acting by highlighting candidates for further characterization. In silico techniques may be widely accessible and successfully applied also outside the boundaries of medicinal chemistry. The use of straightforward procedures (in terms of hardware and software requirements) may facilitate both the transition process and procedural automatizm in the field of the biological activity evaluation of phytocompounds. Several works in the literature show that computer driven analysis can contribute to decipher the mechanisms of action of a wide array of highly functional phytocomponents (e.g., refs 29−31). In addition, virtual screening has already proven to be a promising tool to make feasible the bioactivity evaluation of huge spectra of molecules of dietary origin,32 including their metabolites,33 or to discover novel enzymatic inhibitors.34 However, to the best of our knowledge, relevant human metabolites of quercetin and luteolin have not been analyzed yet for their Topo I poisoning activity. For these reasons, we presented a virtual screening procedure performed on machines equipped with common hardware architecture similar to that of current portable devices (including laptops and smartphones). Concerning software, we 5884
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Journal of Agricultural and Food Chemistry indicate that quercetin-3-O-glucuronide is a promising candidate to be tested in further in vitro studies. Sulfate and glucuronide conjugates are the major circulating metabolites of both quercetin and luteolin,17,38−40 representing the physiological forms that may exert biological activities attributed to the consumption of their glycosidic precursors abundant in plant food. In the case of quercetin, quercetin-3′O-sulfate and quercetin-3-O-glucuronide have been reported to be the predominant metabolites in plasma after the consumption of quercetin glycoside containing foods or supplements.17,38−40 Therefore, in view of their putative quite opposite poisoning effect on Topo I, the study of their bioactivity on carcinogenic cell models could shed light on the real potential of dietary quercetin as Topo I poison. On the other hand, deconjugation processes consequent to cellular phenolic phase II conjugates uptake should also be taken into account, since this may condition their bioactivity, as already reported.41 In conclusion, the feasibility of an in silico model to evaluate conjugation-dependent effects of luteolin and quercetin on TopoI poisoning has been demonstrated by using literature experimental data. However, the applicability of the current model does not go beyond the prediction of potential effects and expanding the applicability of this model to other metabolic modifications and to other phenolic structures would be desirable. Further experimental results obtained on this specific biological feature will ultimately assess the validity of the applied model system. In addition, it is to be pointed out that in vivo effects cannot be inferred solely by using neither in silico models, nor cell-based or cell-free in vitro assays. However, such methods may suggest reasonable mechanisms of action that will then need to be deeply investigated. This applied in silico model falls within the framework of “prospective” studies. The abuse of “retrospective” docking analysis, intended as aimed only at reproducing already published results, has been reported by several groups (e.g., ref 42). In this sense, our “pilot study” approach explored the feasibility of a good model that is neither definitive, nor a standalone analysis. Rather, it should be placed side by side to experimental works in order to help in prioritizing compounds by predicting putative activity. Finally, the fact that different conjugations were linked, in this study, to opposite effects should be considered extremely relevant from a nutritional viewpoint, also beyond the results obtained within the specific applied model. The variability of hepatic phase II conjugation among subjects, as well as the interaction of dietary phytochemicals in this context, should be considered more complex than previously thought, and the putative beneficial nutritional effects of flavonoids, postulated by several epidemiological observations, should be evaluated taking carefully considering these observations.
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ABBREVIATIONS USED
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REFERENCES
Article
FDA, Food and Drug Administration; HS, HINT score; PDB, Protein Data Bank; Topo, topoisomerase
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AUTHOR INFORMATION
Corresponding Authors
*Tel: +39-0521-903830. Fax: +39-0521-903832. E-mail: [email protected]. *Tel: +39-0521-905669. Fax: +39-0521-905556. E-mail: pietro. [email protected]. Notes
The authors declare no competing financial interest. 5885
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camptothecin analog. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 15387− 13592. (19) Kellogg, E. G.; Abraham, D. J. Hydrophobicity: is LogP(o/w) more than the sum of its parts? Eur. J. Med. Chem. 2000, 37, 651−661. (20) Amadasi, A.; Spyrakis, F.; Cozzini, P.; Abraham, D. J.; Kellogg, G. E.; Mozzarelli, A. Mapping the energetics of water-protein and water-ligand interactions with the ″natural″ HINT forcefield: predictive tools for characterizing the roles of water in biomolecules. J. Mol. Biol. 2006, 358, 289−309. (21) Cozzini, P.; Fornabaio, M.; Marabotti, A.; Abraham, D. J.; Kellogg, G. E.; Mozzarelli, A. Simple, intuitive calculations of free energy of binding for protein-ligand complexes. 1. Models without explicit constrained water. J. Med. Chem. 2002, 45, 2469−2483. (22) Fornabaio, M.; Cozzini, P.; Mozzarelli, A.; Abraham, D. J.; Kellogg, G. E. Simple, intuitive calculations of free energy of binding for protein-ligand complexes. 2. Computational titration and pH effects in molecular models of neuraminidase-inhibitor complexes. J. Med. Chem. 2003, 46, 4487−4500. (23) Fornabaio, M.; Spirakis, F.; Mozzarelli, A.; Cozzini, P.; Abraham, D. J.; Kellogg, G. E. Simple, intuitive calculations of free energy of binding for protein-ligand complexes. 3. The free energy contribution of structural water molecules in HIV-1 protease complexes. J. Med. Chem. 2004, 47, 5507−4516. (24) Marabotti, A.; Spyrakis, F.; Facchiano, A.; Cozzini, P.; Alberti, S.; Kellogg, G. E.; Mozzarelli, A. Energy-based prediction of amino acid-nucleotide base recognition. J. Comput. Chem. 2008, 29, 1955− 1969. (25) Salsi, E.; Bayden, A. S.; Spyrakis, F.; Amadasi, A.; Campanini, B.; Bettati, S.; Dodatko, T.; Cozzini, P.; Kellogg, G. E.; Cook, P. F.; Roderick, S. L.; Mozzarelli, A. Design of O-acetylserine sulfhydrylase inhibitors by mimicking nature. J. Med. Chem. 2010, 53, 345−356. (26) Combes, R. D. In silico methods for toxicity prediction. Adv. Exp. Med. Biol. 2012, 745, 96−116. (27) Khadka, D. B.; Cho, W. J. Topoisomerase inhibitors as anticancer agents: a patent update. Expert Opin. Ther. Pat. 2013, 23, 1033−1056. (28) Russo, P.; Del Bufalo, A.; Cesario, A. Flavonoids acting on DNA topoisomerases: recent advances and future perspectives in cancer therapy. Curr. Med. Chem. 2012, 19, 5287−5293. (29) Lambert, J.; Yennawar, N.; Gu, Y.; Elias, R. Inhibition of secreted phospholipase A2 by proanthocyanidins: a comparative enzymological and in silico modeling study. J. Agric. Food Chem. 2012, 60, 7417−7420. (30) Puiggròs, F.; Sala, E.; Vaqué, M.; Ardévol, A.; Blay, M.; Fernández-Larrea, J.; Arola, L.; Bladé, C.; Pujadas, G.; Salvadó, M. In vivo, in vitro, and in silico studies of Cu/Zn-superoxide dismutase regulation by molecules in grape seed procyanidin extract. J. Agric. Food Chem. 2009, 57, 3934−3942. (31) Shimmyo, Y.; Kihara, T.; Akaike, A.; Niidome, T.; Sugimoto, H. Flavonols and flavones as BACE-1 inhibitors: structure-activity relationship in cell-free, cell-based and in silico studies reveal novel pharmacophore features. Biochim. Biophys. Acta 2008, 1780, 819−825. (32) Li, Y.; Frenz, C.; Li, Z.; Chen, M.; Wang, Y.; Li, F.; Luo, C.; Sun, J.; bohlin, L.; Li, Z.; Yang, H.; Wang, C. Virtual and in vitro bioassay screening of phytochemical inhibitors from flavonoids and isoflavones against xanthine oxidase and cyclooxygenase-2 for gout treatmen. Chem. Biol. Drug. Des. 2013, 81, 537−544. (33) Dellafiora, L.; Mena, P.; Cozzini, P.; Brighenti, F.; Del Rio, D. Modelling the possible bioactivity of ellagitannin-derived metabolites. In silico tools to evaluate their potential xenoestrogenic behavior. Food Funct. 2013, 4, 1442−1451. (34) Wang, J.; Tan, H.; Li, Y.; Ma, Y.; Li, Z.; Guddat, L. Chemical synthesis, in vitro acetohydroxyacid synthase (AHAS) inhibition, herbicidal activity, and computational studies of isatin derivatives. J. Agric. Food Chem. 2011, 59, 9892−9900. (35) Amadasi, A.; Mozzarelli, A.; Meda, C.; Maggi, A.; Cozzini, P. Identification of xenoestrogens in food additives by an integrated in silico and in vitro approach. Chem. Res. Toxicol. 2009, 22, 52−63.
(36) Cozzini, P.; Dellafiora, L. In silico approach to evaluate molecular interaction between mycotoxins and the estrogen receptors ligand binding domain: a case study on zearalenone and its metabolites. Toxicol. Lett. 2012, 214, 81−85. (37) Kaneda, N.; Nagata, H.; Furuta, T.; Yokokura, T. Metabolism and pharmacokinetics of the camptothecin analogue CPT-11 in the mouse. Cancer Res. 1990, 50, 1715−1720. (38) Chen, T.; Li, L. P.; Lu, X. Y.; Jiang, H. D.; Su, Z. Absorption and Excretion of Luteolin and Apigenin in Rats after Oral Administration of Chrysanthemum morifolium Extract. J. Agric. Food Chem. 2007, 55, 273−277. (39) Cialdella-Kam, L.; Nieman, D. C.; Sha, W.; Meaney, M. P.; Knab, A. M.; Shanely, R. A. Dose-response to 3 months of quercetincontaining supplements on metabolite and quercetin conjugate profile in adults. Br. J. Nutr. 2013, 109, 1923−1933. (40) Jaganath, I. B.; Mullen, W.; Edwards, C. A.; Crozier, A. The relative contribution of the small and large intestine to the absorption and metabolism of rutin in man. Free Radic. Res. 2006, 40, 1035−1046. (41) Menéndez, C.; Dueñas, M.; Galindo, P.; González-Manzano, S.; Jiménez, R.; Moreno, L.; Zarzuelo, M. J.; Rodríguez-Gómez, I.; Duarte, J.; Santos-Buelga, C.; Pérez-Vizcaíno, F. Vascular deconjugation of quercetin glucuronide: The flavonoid paradox revealed? Mol. Nutr. Food Res. 2011, 55, 1780−1790. (42) Leach, A. R.; Shoichet, B. K.; Peishoff, C. E. Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. J. Med. Chem. 2006, 49, 5851−5855.
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Modeling the Effect of Phase II Conjugations on Topoisomerase I Poisoning: Pilot Study with Luteolin and Quercetin Luca Dellafiora,† Pedro Mena,‡,§ Daniele Del Rio,*,‡,§ and Pietro Cozzini*,† †
Molecular Modeling Laboratory, Department of Food Science, ‡The Laboratory of Phytochemicals in Physiology, Human Nutrition Unit, Department of Food Science, and §LS9 Bioactives and Health, Interlaboratory Group, Department of Food Science, University of Parma, 43125 Parma, Italy ABSTRACT: Topoisomerases are targeted by several drugs in cancer chemotherapy acting as key enzymes in cell viability. Some flavonoids and their glycosides may exert health protective effects through the poisoning of topoisomerases. However, previous studies did not consider the substantial modifications taking place after ingestion neglecting that only metabolites can interact with the internal compartments of the human body. Since the high number of possible metabolites hinders their systematic analysis, an in silico approach can be a valuable tool to prioritize compounds by identifying candidates for further characterization. Specifically focusing on luteolin and quercetin, among the most ubiquitous flavonoids in the human diet, this work reports a computational procedure to model the effect of hepatic phase II conjugative metabolism on poisoning of human Topoisomerase I. As a general effect, glucuronidation and sulphation might enhance and quench poisoning activity, respectively. Among all, quercetin-3-O-glucuronide represents a promising candidate to be analyzed more thoroughly. KEYWORDS: Topoisomerase I, poisoning, molecular modeling, flavones, metabolites, activity
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mediated Topos poisoning.12,13 A further factor to be taken into account is that, after consumption,6 flavonoids are subjected to extensive phase II metabolism, and thus it would be their glucuronide and sulfate metabolites that might act as potential chemopreventive agents, rather than their parent compounds. The role of these circulating metabolites in poisoning Topo I has not been determined, yet. Therefore, considering that luteolin and quercetin are widely consumed through a normal balanced diet, it is relevant to investigate whether their phase II metabolites are able to act as Topo I poisons. Nevertheless, the systematic characterization of metabolized forms of polyphenols based on in vitro assays may be a hard challenge due to the high number of hits and, sometimes, to the lack of commercially available standards at affordable costs. It becomes thus necessary a prioritizing system to drive experimental trials in a focused manner. The computational approach efficiently meet this need and because the Topos poisoning activity unequivocally involves protein/DNA complex-ligand recognitions, the use of 3D molecular modeling becomes paramount.14 Hence, the present work presents a pilot study on luteolin and quercetin, with the aim of verifying the feasibility of modeling the effects of their phase II conjugation on Topo I poisoning by using already verified and effective technique.15 In addition to plant-based glycosides (used for training procedure), several phase-II human metabolites, still untested to the best of our knowledge, were analyzed (luteolin3′-O-glucuronide, luteolin-3′-O-sulfate, luteolin-4′-O-glucuronide, luteolin-7-O-glucuronide, luteolin-7-sulfate, quercetin-3-
INTRODUCTION Topoisomerases (Topos) are ubiquitous enzymes that control DNA supercoiling and intertwining. They allow DNA relaxing by introducing single- (Topo I) or double-strand breakage (Topo II) through respectively one or two transesterification reactions. When Topos activity is altered, abnormal DNA structures and delay of replication and transcription inevitably occur.1 Both “canonical inhibition” and poisoning can disrupt Topos activity. The former requires the prevention/alteration of protein−DNA interaction, the latter turns the enzymes into a DNA damaging agent since poisoning compounds hinder DNA religation by stabilizing the so-called “cleavable complex”.2−4 Since Topos are involved in key steps of cell viability, topoisomerase inhibitors and poisons are widely used as antibacterial and anticancer agents.5 Flavonoids are low molecular weight secondary metabolites widely distributed in the plant kingdom. They are ubiquitous constituents of fruit- and vegetable-rich diets where they have potential protective effects against the onset of chronic conditions such as cancer and cardiovascular disease.6 Among flavonoid subgroups, the most potent Topo I poisons are flavones and flavonols.2 Quercetin, principally as glycoside conjugates, is an abundant flavonol in the human diet, occurring in products such as apples, red grapes, citrus, onions, garlic, and tea,7 and its consumption has been linked to lower rates of certain cancers.8 The flavone luteolin also occurs almost exclusively as glycoside conjugates and is found in carrots, peppers, celery, olive oil, peppermint, thyme, rosemary, and oregano.7 Its consumption has been associated with a range of biological activities.9 Both quercetin and luteolin have been reported to poison DNA Topo I and II,2,10,11 which supports their possible chemopreventive activity. However, results are conflicting and some works in the literature do not support flavonoids© 2014 American Chemical Society
Received: Revised: Accepted: Published: 5881
January 24, 2014 May 26, 2014 May 29, 2014 May 29, 2014 dx.doi.org/10.1021/jf501548g | J. Agric. Food Chem. 2014, 62, 5881−5886
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Figure 1. Molecules forming the training-set. waters and ligands were removed and 30 poses for each compound were generated. No constraints were set up and the explorable space was defined in a radius of 10 Å from the centroid of the binding pocket. For each GOLD docking search, a maximum number of 100 000 operations were performed on a population of 100 individuals with a selection pressure of 1.1. Operator weights for crossover, mutation, and migration were set to 95, 95, and 10, respectively. The number of islands was set to 5 and the niche to 2. The hydrogen bond distance was set to 2.5 Å and the vdW linear cutoff to 4.0. Ligand flexibility options “flip pyramidal N”, “flip amide bonds”, and “flip ring corners” were allowed. Rescoring Procedure and Hydropathic Analysis. The software HINT (Hydropatic INTeraction)19 was used as postprocessing tool to rescore all poses generated by GOLD. The HINT scoring function calculates the favor of protein−ligand recognition trough a sum of hydrophobic atom constants which derived from experimental LogPo/ w values (the partition coefficient of a molecule in 1-octanol/water). Thus, HINT appears as a “natural”20 and intuitive force-field by providing empirical and quantitative evaluation of protein−ligand interaction as a sum of all single atom−atom contributions using the following equation:
O-glucuronide, quercetin-3′-O-glucuronide, quercetin-3-sulfate, quercetin-4′-O-glucuronide, quercetin-7-O-glucuronide, and quercetin-7-sulfate)16,17 and ranked according to physicochemical prioritization criteria.
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MATERIAL AND METHODS
The procedure involved docking simulations followed by rescoring procedures using a proper scoring function. Our intention was to develop a procedure easy to perform, without excesses of software and hardware requirements and avoiding complex mathematical and statistical analysis. All experimental data were retrieved from literature2 and query set contained metabolites observed under in-vivo-like conditions whose might be the predominant occurring form in circulatory system.16,17 Molecular Modeling. The model used for docking simulation was derived from the high resolution structure of human Topo I retrieved from Protein Data Bank (http://www.rcsb.org; PDB code 1K4T).18 Protein structure and small molecules were processed using the molecular modeling software Sybyl, version 8.1 (www.tripos.com). All atoms were checked for atom- and bond-type assignments. Aminoand carboxyl-terminal groups were set as protonated and deprotonated, respectively. Hydrogen atoms were computationally added to the protein and energy-minimized using the Powell algorithm whit a coverage gradient of ≤0.5 kcal (mol Å)−1 and a maximum of 1500 cycles. Docking Simulations. The docking simulations of all compounds were performed with the program GOLD version 5.1 (CCDC; Cambridge, U.K.; http://www.ccd.cam.ac.uk) on a double-quad cores machine equipped with 1.86 GHz processor. All crystallographic
HINT score =
∑i ∑ j bij = ∑i ∑ j (aiSiajSjTijR ij + rij)
where bij is the interaction score between atoms i and j, a is the hydrophobic atomic constant, S represents the solvent accessible surface area, Tij is a logic function assuming +1 or −1 values, depending on the nature of the interatomic interaction, Rij and rij are functions of the distance between atoms i and j. 5882
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Positive and high HINT score correlates with favorable binding free energy, allowing the evaluation of the thermodynamic benefit of predicted complexes.21−24 Because different conformations of the same molecule may coexist contributing to the whole energy of binding, a Boltzman average calculation was applied. In this way the contributions of different conformations to the predicted interaction were taken into account.25 Thus, from here, we referred to HINT score (HS) as Boltzman averaged score. Replicas of Docking Simulation and Rescoring Procedure. Because GOLD uses a Lamarkian genetic algorithm, results may slightly change from run to run. This implies that computing twice the same molecule is impossible to record exactly the same score, because two identical poses (i.e., the exact xyz coordinates for each atom) are virtually impossible to obtain. This may be a nontrivial issue potentially disruptive in place of rescoring procedures. GOLD results are intrinsically ranked by its internal scoring functions. Because of this, it is not possible to separate GOLD’s pose generation from its ranking, and we were forced to apply HINT only to rescore pregenerated poses. Thus, in order to avoid not-causative fluctuations due to random component of docking algorithm, each analysis was repeated 5-fold and the mean value was considered.
as ranges that totally overlap cannot be resolved. Conversely, luteolin-4′-O-glucoside (less active) and luteolin-8-C-glucoside (more active) were unequivocally separated from luteolin. Likewise quercetin-3-O-rhamnoside and quercetin-3-O-rutinoside clearly diverged down from quercetin. The training-set underwent docking simulations and rescoring procedure in order to assess the applicability of the procedure to this specific case study. Overall, good correlation between computed and experimental data was obtained (Figure 2). Specifically, computed results were in agreement with
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RESULTS Training Procedure and Training-Set Results. When the validation of computational models may be not mandatory, as in this instance due to the lack of sufficient and inherent experimental data, training procedures are compulsory.26 In the current study, the procedure exploited experimental data retrieved from the literature.2 Specifically, a training-set has been formed by luteolin, luteolin-8-C-glucoside, luteolin-4′-Oglucoside, luteolin-7-O-glucoside, quercetin, quercetin-3-Orhamnoside, and quercetin-3-O-rutinoside (Figure 1). Data and results concerning the training procedure are reported in Table 1. Taking into account the high variability and, in some
Figure 2. Correlation between computed results and experimental data. All experimental data were retrieved from the literature and express relative potency of compounds with respect to the reference standard camptothecin.2
experimental data and the procedure was trained according to the fact that the augmentation and reduction of HSs correlated to conjugation-dependent enhancing and quenching of poisoning activity, respectively. However, it was not possible to compare the relative activity of luteolin and luteolin-7-Oglucoside due to the same reasons reported above (Table 1). Globally the procedure proved to be able to evaluate the relative effect of conjugations on poisoning activity of luteolin and quercetin. Query-Set Results. The query-set underwent the same procedure of the training-set. Results were reported in Table 2.
Table 1. Results of the Training-Seta compound luteolin luteolin-8-Cglucoside luteolin-4′-Oglucoside luteolin-7-Oglucoside quercetin quercetin-3-Orhamnoside quercetin-3-Orutinoside
experimental activity
activity category
mean HS
predicted effect
55 ± 8.2 69 ± 5.6
/ ↑
683 ± 28.0 804 ± 107.0
/ ↑
19 ± 4.3
↓
562 ± 76.0
↓
51 ± 14.0
↑↓
747 ± 136.0
↑↓
57 ± 15.6 15 ± 12.0
/ ↓
718 ± 42.0 240 ± 140.0
/ ↓
5 ± 1.8
↓
383 ± 194.0
↓
Table 2. Results of the Query-Seta
a
Enhancing and quenching effects with respect to the reference aglyconic compounds (in bold) are represented by up- or down-arrow, respectively “↑↓” indicates indefinable relative potency. Hint scores are expressed as mean value of 5 replicates. All experimental data were retrieved from the literature and express relative potency of compounds respect to the reference standard camptothecin.2
cases, the proximity of experimental data, the absolute and quantitative rank of compounds was not easy to resolve. However, it was possible to perform a semiquantitative and relative comparison; instead of a global potency rank, we defined a binary categorization describing conjugation-dependent effects of enhancing and quenching on luteolin and quercetin, respectively. For instance, luteolin and luteolin-7-Oglucoside shared proximal values and the high variability of luteolin-7-O-glucoside made their relative potency indefinable,
compound
mean HS
predicted effect
luteolin luteolin-3′-O-glucuronide luteolin-3′-sulfate luteolin-4′-O-glucuronide luteolin-7-O-glucuronide luteolin-7-sulfate quercetin quercetin-3-O-glucuronide quercetin-3′-O-glucuronide quercetin-3′-sulfate quercetin-4′-O-glucuronide quercetin-7-O-glucuronide quercetin-7-sulfate
683 ± 28 756 ± 103 738 ± 24 899 ± 163 891 ± 199 354 ± 139 718 ± 42 1766 ± 112 782 ± 177 636 + 10 756 ± 105 719 ± 41 618 ± 190
/ ↑↓ ↑ ↑ ↑ ↓ / ↑ ↑ ↓ ↑ − ↓
a
Enhancing and quenching effects with respect to aglyconic reference compounds (in bold) are represented by up- or down-arrow, respectively. “↑↓” indicates indefinable relative potency. “−” indicates comparable effect. HINT scores are expressed as mean value of 5 replicates. 5883
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chose GOLD, which is one of the most popular commercial docking software, and HINT, a natural and intuitive force-field to evaluate the favor of protein−ligand recognition. In addition, the use of such “linear” tools, avoiding complex mathematical and statistical models, contributes to reduce the risk of traps, such as overfitting phenomena and others erroneous or misleading results. The coupling of GOLD to perform docking simulations and HINT as rescoring function, has already proved to be a well working procedure to evaluate protein− ligand interaction,33,35,36 thus no further validation was needed. Rather, as a proof of concept, we presented (i) the feasibility of a model able to evaluate the semiquantitative effect of conjugations on luteolin and quercetin by using the few inherent data available in literature and (ii) the application of such model to prioritize quercetin and luteolin metabolites for further experimental characterization. We focused on luteolin and quercetin due to their massive occurrence in plant-derived food and we took into account conjugated metabolites because glucuronidated and sulfated derivatives are the main circulating forms in vivo. Once assessed the capability to model the effect of conjugation on poisoning activity of luteolin and quercetin, glucuronidated and sulfated metabolites underwent the same procedure. In principle such approach can be applied to any other type of small molecule (and protein), but the procedural performance must be assessed case by case trough cross-check with experimental data. Flavonoids able to effectively poison Topo I require hydroxyl group substitution at the C-3, C-7, C-3′, and C-4′ positions, a keto group at C-4, and a Δ2,3 double bond (3,3′,4′,7tetrahydroxy-substituted flavones).12 Quercetin and luteolin, as well as other flavonoids like apigenin, myricetin, fisetin, and morin, match these criteria and have been shown to poison Topo I.2,11 However, the presence of sugar moieties has been pointed out to quench the poisoning effect of flavones and flavonols,2 except for compounds glycosylated at the 8 position, as in the case of luteolin-8-C-glucoside (Table 1). In addition, the glucuronide form of SN-38, a potent topoisomerase I inhibitor derived from the pro-drug irinotecan (a derivative of camptothecin), has only 1% of the antitumor activity of its aglyconic active form.37 Based on this observation, a limited poisoning effect of phase II metabolites containing a glucuronide conjugates should be expected. Nevertheless, most of the glucuronidated derivatives of the tested flavonoids showed enhanced Topo I poisoning effect with respect to their parent compounds (Table 2). On the contrary, sulfate derivatives, with the only exception of luteolin-3′-sulfate, showed a quenched effect when compared to aglycones. In contrast to the marked role of the type of conjugation, its position in the flavonoid structure did not play a key role in the poisoning effect of these phenolic metabolites, with the exception of quercetin-3-O-glucuronide. The docking results revealed that the HINT score for quercetin-3-O-glucuronide was notably higher while the magnitude of the predicted effect was quite similar for most of the metabolites (Table 2). This might indicate that glucuronidation of flavonols at the C-3 position enhances their bioactivity as Topo I poisons. In this specific case, the substitution of the hydroxyl group by the glucuronide moiety occurs at the nonbenzene ring, the C ring. Thus, considering the essential role that the phenolic hydroxyl group plays in the activity of the chemotherapeutic SN-38,37 this structural feature may at least partially explain the enhanced effect of quercetin-3-O-glucuronide. Overall, these results
Overall, glucuronidation seemed to exert enhancing effects on both luteolin and quercetin, whereas sulphation, with the exception of luteolin-3′-sulfate, had the opposite effect. In addition, it is of interest to notice the out of range score of quercetin-3-O-glucuronide, indicating that this compound might be much more active than the others. Conversely, quercetin-7-O-glucuronide appeared virtually identical to quercetin and it could exert similar activity. Finally, once again, it was not possible to resolve the relative effect of luteolin-3′-O-glucuronide versus luteolin.
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DISCUSSION Topos I, as well as other topoisomerases, are key enzymes in cell viability which act on genome stability and accessibility during replication and transcription. Therefore, they are commonly targeted by antibacterial and anticancer chemicals. There are at least two mechanisms to disrupt Topo I activity: canonical inhibition by preventing protein−DNA recognition and poisoning by stabilizing DNA−protein complexes and hindering the DNA religation step. Enzyme poisoning rather than inhibition drives drug action. Several compounds act as Topo poisons, with many of them highly specific for Topo I, others for Topo II, and some promiscuous for both isoforms.27 Currently camptothecin derivatives are the only anticancer drugs targeting Topo I approved by the FDA (Food and Drug Administration, http://www.fda.gov), although several promising compounds are under development.5 Within this frame, flavonoids such as quercetin and luteolin might play critical roles. It is well-known that this class of compounds commonly occurs in edible parts of fruits and vegetables and exerts a broad spectrum of health-protective properties by interacting with several protein targets. Because advances in system biology suggest that multitarget compounds may prove higher efficacy rather than host-specific drugs,28 these ubiquitous compounds might be of great relevance in cancer prevention, and TopoI poisoning may contribute to their health benefits. To better understand the underlying mechanisms of luteolin and quercetin putatively beneficial action, their metabolites, truly circulating in the bloodstream after their dietary intake, should be evaluated. Considering the wide array of possible metabolic phase II conjugation and the lack of commercially available standards, an in silico approach might be a useful prioritizing tool, acting by highlighting candidates for further characterization. In silico techniques may be widely accessible and successfully applied also outside the boundaries of medicinal chemistry. The use of straightforward procedures (in terms of hardware and software requirements) may facilitate both the transition process and procedural automatizm in the field of the biological activity evaluation of phytocompounds. Several works in the literature show that computer driven analysis can contribute to decipher the mechanisms of action of a wide array of highly functional phytocomponents (e.g., refs 29−31). In addition, virtual screening has already proven to be a promising tool to make feasible the bioactivity evaluation of huge spectra of molecules of dietary origin,32 including their metabolites,33 or to discover novel enzymatic inhibitors.34 However, to the best of our knowledge, relevant human metabolites of quercetin and luteolin have not been analyzed yet for their Topo I poisoning activity. For these reasons, we presented a virtual screening procedure performed on machines equipped with common hardware architecture similar to that of current portable devices (including laptops and smartphones). Concerning software, we 5884
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Journal of Agricultural and Food Chemistry indicate that quercetin-3-O-glucuronide is a promising candidate to be tested in further in vitro studies. Sulfate and glucuronide conjugates are the major circulating metabolites of both quercetin and luteolin,17,38−40 representing the physiological forms that may exert biological activities attributed to the consumption of their glycosidic precursors abundant in plant food. In the case of quercetin, quercetin-3′O-sulfate and quercetin-3-O-glucuronide have been reported to be the predominant metabolites in plasma after the consumption of quercetin glycoside containing foods or supplements.17,38−40 Therefore, in view of their putative quite opposite poisoning effect on Topo I, the study of their bioactivity on carcinogenic cell models could shed light on the real potential of dietary quercetin as Topo I poison. On the other hand, deconjugation processes consequent to cellular phenolic phase II conjugates uptake should also be taken into account, since this may condition their bioactivity, as already reported.41 In conclusion, the feasibility of an in silico model to evaluate conjugation-dependent effects of luteolin and quercetin on TopoI poisoning has been demonstrated by using literature experimental data. However, the applicability of the current model does not go beyond the prediction of potential effects and expanding the applicability of this model to other metabolic modifications and to other phenolic structures would be desirable. Further experimental results obtained on this specific biological feature will ultimately assess the validity of the applied model system. In addition, it is to be pointed out that in vivo effects cannot be inferred solely by using neither in silico models, nor cell-based or cell-free in vitro assays. However, such methods may suggest reasonable mechanisms of action that will then need to be deeply investigated. This applied in silico model falls within the framework of “prospective” studies. The abuse of “retrospective” docking analysis, intended as aimed only at reproducing already published results, has been reported by several groups (e.g., ref 42). In this sense, our “pilot study” approach explored the feasibility of a good model that is neither definitive, nor a standalone analysis. Rather, it should be placed side by side to experimental works in order to help in prioritizing compounds by predicting putative activity. Finally, the fact that different conjugations were linked, in this study, to opposite effects should be considered extremely relevant from a nutritional viewpoint, also beyond the results obtained within the specific applied model. The variability of hepatic phase II conjugation among subjects, as well as the interaction of dietary phytochemicals in this context, should be considered more complex than previously thought, and the putative beneficial nutritional effects of flavonoids, postulated by several epidemiological observations, should be evaluated taking carefully considering these observations.
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ABBREVIATIONS USED
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REFERENCES
Article
FDA, Food and Drug Administration; HS, HINT score; PDB, Protein Data Bank; Topo, topoisomerase
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