In Silico Prediction of the Toxic Potential of Lupeol - ACS Publications

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IN SILICO PREDICTION OF THE TOXIC POTENTIAL OF LUPEOL Manuel Antonio Ruiz-Rodríguez, Angelo Vedani, Ana Lidia Flores-Mireles, Manuel Humberto Chairez-Ramirez, Jose Alberto Gallegos-Infante, and Ruben F. Gonzalez-Laredo Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.7b00070 • Publication Date (Web): 27 Jun 2017 Downloaded from http://pubs.acs.org on June 29, 2017

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Chemical Research in Toxicology

IN SILICO PREDICTION OF THE TOXIC POTENTIAL OF LUPEOL

Manuel A. Ruiz-Rodríguez,1,2* Angelo Vedani2, Ana L. Flores-Mireles,3 Manuel H. Cháirez-Ramírez,1 José A. Gallegos-Infante,1 and Rubén F. González-Laredo1*

1

Tecnológico Nacional de México-Instituto Tecnológico de Durango, Department of Chemical and Biochemical Engineering. Blvd. Felipe Pescador 1830 Ote., 34080 Durango, México

2

Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland

3

Department of Molecular Microbiology and Center for Women’s Infectious Disease Research, Washington University School of Medicine, Saint Louis, MO 63110-1093, USA

*

Corresponding authors, E-Mail: [email protected]; [email protected] Phones +52(618)8185402 ext 113 and +52(618)8405954 TecNM-Instituto Tecnológico de Durango, Blvd. Felipe Pescador 1830 Ote., 34080 Durango, México

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ABSTRACT Lupeol is a natural triterpenoid found in many plant species such as mango. This compound is the principal active component of many traditional herbal medicines. In the past decade, a considerable number of publications dealt with lupeol and its analogues due to the interest in their pharmacological activities against cancer, inflammation, arthritis, diabetes, and heart disease. To identify further potential applications of lupeol and its analogues, it is necessary to investigate their mechanisms of action, particularly, their interaction with off-target proteins that may trigger adverse effects or toxicity. In this study, we simulated and quantified the interaction of lupeol and 11 of its analogues towards a series of 16 proteins, known or suspected to trigger adverse effects employing the VirtualToxLab. This software provides a thermodynamic estimate of the binding affinity and the results were challenged by molecular-dynamics simulations, which allow probing the kinetic stability of the underlying protein–ligand complexes. Our results indicate that there is a moderate toxic potential for lupeol and some of its analogues, by targeting and binding to nuclear receptors involved in fertility, which could trigger undesired adverse effects.

KEYWORDS Lupeol, herbal medicines, in silico, toxicology, VirtualToxLab, endocrine disruption, molecular mechanisms

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INTRODUCTION Lupeol and its analogues are triterpenes found in a diversity of vegetables and fruits. For instance, lupeol is found in mangoes, cabbage, green pepper, strawberry, olives and grapes.1,2 Interestingly, this group of compounds is the active component in many plants used in traditional medicine by native cultures in North America, Japan, China, Latin America and Caribbean islands.2-4 Importantly, it has been shown that lupeol and its analogues display therapeutic properties against cancer, inflammation, arthritis, diabetes and heart disease. Therefore, these compounds have raised interest as drugs to treat such conditions.5 In some cases it has been possible to determinate their action mechanism against these diseases. Lupeol is a multi-target agent which affects different protein receptors depending on the disease that is treated with this compound. In the case of inflammation, lupeol affects the molecular pathways of the nuclear factor kappa B(NFκB), cFLIP, Fas, Kras, phosphatidylinositol-3-kinase PI3K/Akt and Wnt/β-catenin in a variety of cells.6 In cancer treatments, lupeol inhibits DNA topoisomerase II, protein kinases and serine proteases, causing the death of cancer cells.7,8 Lupeol has also been reported to inhibit growth in melanoma and leukemia cells and inhibit tumor promotion in mouse skin by modulating various signaling pathways.9-11 The topical application of lupeol at 200 µg/animal has been reported to prevent DNA strand breaks in mice skin caused by 7,12dimethylbenz[a]anthracene (DMBA).12 Furthermore, in skin mouse models, lupeol has inhibited the genotoxicity effect of Benzo[a]pyrene (B[a]P), which is a binding mutagen. Additionally, lupeol was able to significantly decrease B[a]P-induced clastogenicity, by pretreating mice with lupeol [1 mg/animal] for seven days prior to B[a]P administration.13 Many studies have focused on understanding the properties of lupeol and its analogues such as determination of phytochemical properties, synthesis and biological activity, using mice, dogs and cancer cell lines as test models in order to find promising applications to cure diseases. However, prior to their potential application in humans, it is necessary to establish if they might trigger undesired effects. Traditionally, initial bioassays are performed using mouse models but these experiments are both laborious and expensive with the inconvenience that results may not simply be translated to humans.14,15 Therefore, in order to better understand the toxicological effects of a new drug, it is necessary to develop new approaches to overcome these problems. Computational approaches for in silico toxicology determinations turn into an efficient alternative to predict drug-protein interactions without the aforementioned drawbacks.


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One of these new tools is VirtualToxLab (cf. http://www.virtualtoxlab.org), which is an in silico concept for estimating the toxic potential — endocrine and metabolic disruption, aspects of carcinogenicity and cardiotoxicity of drugs, chemicals and natural products.16-20 This software calculates the toxic potential (TP) which is defined in this document as the ability to trigger adverse effects in base of an automated protocol that simulates and quantifies the binding affinities of small molecules towards a series of 16 proteins, known or suspected to trigger adverse effects. The binding affinity is defined as the attraction between a ligand (chemical compound tested) and one of the 16 proteins suspected to trigger adverse effects. This union is quantified considering covalent and non-covalent bonds, intermolecular interactions such as hydrogen bonding, electrostatic interactions, hydrophobic and Van der Waals forces. Among these proteins there are 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). In this study, we simulated and quantified the interaction of lupeol and 11 of its analogues towards the 16 proteins currently included in the VirtualToxLab. As the simulations are conducted at the atomic level, this allows for a mechanistic interpretation of the underlying binding modes. This protocol is independent from any training data and makes the approach universal with respect to the applicability domain. Moreover, the platform provides individual binding affinities and an estimate for the overall toxic potential.21



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MATERIALS AND METHODS The three-dimensional structures of lupeol and 11 of its analogues were constructed and energy-minimized using the BioX software.22 The chemical structures of all investigated compounds are shown in Figure 1. In a first step, the configuration of a compound (position, orientation, conformation) in aqueous solution is sampled and quantified using the Aquarius software (cf. ref. 18: Fig. 3, left). Then, a similar protocol with allowance for induced fit is employed at the protein (cf. ref. 18: Fig. 3, center) using Alignator23 and Cheetah.18,24 In a next step, the change in free energy, ∆G, of the small molecule when binding from the aqueous environment to the protein is estimated.18 Finally, the toxic potential of lupeol and its analogues were determined using VirtualToxLab. The technology and mathematical models underlying in VirtualToxLab have been recently described in detail18 and shall, therefore, only be briefly summarized in this document. VirtualToxLab is based on a client–server protocol and consists of a graphical user interface (building and uploading compounds, downloading and visualizing results) that calculates the toxic potential (TP) and binding affinity (IC50 values) of a chemical compound in base to the values of quantitative structure–activity relationship (QSAR) and multiple docking with 16 protein susceptible to trigger adverse effects from chemical compounds. The philosophy underlying the VirtualToxLab is to estimate the toxic potential of a compound through the normalized individual binding affinities towards a series of protein models known or suspected to trigger adverse effects. In addition, the standard deviation of the predictions, the predictive power of the individual models and the underlying protein superfamilies are considered. Then, the compound-protein interaction is adjusted and compared by statistical relative importance using the standard deviation of the individual predictions and the quality of the subjacent models (for example the number of modeled structures and the range of activity covered). An extended description of the mathematical models used in VitualToxLab is described by Vedani et al.18 The TP is based only in thermodynamic properties and does not consider adsorption, disruption, metabolism and elimination properties (ADME). A very low value of TP does not necessary indicate that a compound is safe to use. This value only indicates a low statistical probability that this compound can trigger an adverse effect on the basis of the chemical compound submitted to VirtualToxLab can bond to one or more of the tested proteins.


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The values of TP may range from 0.0 (none) to 1.0 (extreme). The toxic potential is a nonlinear complex function that should not be overestimated but interpreted as a toxic alert. Besides the results of toxic potential, the program provides the calculations of the concentration that can inhibit one of the proteins used as reference in the VirtualToxLab. Only proteins with the highest TP value were modeled by molecular dynamics (MD) to determine the stability of the protein-compound relationship for 10 ns. The MD simulations were carried out employing Desmond25 as embedded in the VirtualDesignLab.26 A force field OPLS3 was set to 10 ns during the MD simulations, at constant pressure and temperature MTK and considering water as solvent. The visualization of the results was performed using Visual Molecular Dynamics (VMD).27 To assess the physicochemical properties of the investigated compounds, we employed the QikProp software28 accessible through the VirtualDesignLab.26 For a complementary assessment of the investigated compound’s harmful feature, we have employed third-party software components: Toxtree29 based on the Cramer rules,30 MolInspiration31 based on a molecular-fragment database32 and Lazar (endpoints, carcinogenesis-rodents and mutagenesis-Salmonella typhimurium),33 using structure–activity relationships.34 All the calculations were done using a Linux server of 1,024 nodes.



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RESULTS AND DISCUSSIONS The binding affinities of lupeol and its analogues were determined against all 16 target proteins currently embedded in this platform (Table 1). Our results suggested that nuclear receptors were the primary targets, showing that the highest affinities were towards androgen (AR), the glucocorticoid (GR), the progesterone (PR), and estrogen receptors (ER). The estimated toxic potential ranged from 0.377 (low) to 0.569 (moderate) as represented in Figure 2. The results from MD simulations showed the lead compound. Lupeol was the most active compound presenting PR-, ER- and AhR-binding affinities in nanomolar concentrations (see Table 1). Lupeol binds most strongly to the PR (24.30 nM) and the ERα (67.90 nM), but also to the aryl hydrocarbon receptor (AhR: 419 nM) and the mineralocorticoid receptor (MR: 696 nM). The affinities of other targets were greater than 1.0 µM. Kinetic stability of lupeol-PR and -ER complexes was determined according to results shown in figure 3 (PR) and figure 4 (ERα). In both ligand-protein complexes, the ligand is stabilized through weak intermolecular interactions due to the lipophilic feature of lupeol bearing just a single hydroxyl group, which confers the hydrogen-bond accepting or donating properties of the ligand. At the lupeol-PR complex, a strong hydrogen bond is formed with Thr894 while at the ERα the interaction is formed with Glu353. The main interactions are, therefore, formed with aliphatic and aromatic amino-acid side chains lining the binding pocket. The 30-hydroxylated derivative of lupenone displays a remarkable binding affinity (31.4 nM) towards the AR and appears to be kinetically stable (Figure 5). The interaction is stabilized by hydrophobic interactions through two hydrogen bonds with Asn705 and Gln711. In the case of betulin, the results indicate a strong and kinetically stable binding with the glucocorticoid receptor (GR: 15.3 nM) (Figure 6). This compound is stabilized through three hydrogen bonds with Asn564, Arg611 and Cys736 residues along with hydrophobic interactions. Particularly, the interaction of the extra hydroxyl group (C28), compared to lupeol, appears to create a stronger binding. On the other hand, the interaction with Arg611 is kinetically labile (Figure 7: center), which appears to reduce the binding affinity by, possibly, a factor of 5–10. Some analogues of lupeol displayed a moderate binding affinity towards the aryl hydrocarbon receptor (AhR), among them betulinic acid amide had the lowest IC50 with 131 nM. While the MD simulation


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suggests a stable ligand–protein complex, the lack of a hydrogen-bond acceptor for the aliphatic hydroxyl group of betulinic acid amide (Figure 7) would reduce the binding affinity by a factor of 5–10, leading to a moderate binding of the AhR and therefore to a smaller carcinogenic potential. 30-hydroxylupenone displays a computed binding affinity of 485 nM towards cytochrome P450-2D6 (2D6), which might trigger a metabolic response, e.g. drug–drug interactions.35 Apart from the interaction with the FeIII ion, the hydroxyl group is stabilized by two hydrogen bonds with Gln244 and Ser304, respectively. The MD simulation underlines the stability of the protein–ligand complex (Figure 8). Computational assessment of a compound’s biological activity or toxicity should always be discussed along with its ADME properties (adsorption, distribution, metabolism, elimination) and bioavailability as a prerequisite for understanding its potential beneficial or undesired effects.18 The physicochemical properties of the investigated compounds were assessed by the QikProp software28 accessible through the VirtualDesignLab (Table 2).16 All compounds had a similar molecular weight (425470), but significantly different lipophilic properties (log P: 4.6-7.8). They are not very soluble in water (log S: 5.1-8.9) but are orally bioavailable (94-100%) and can penetrate cell membranes (Caco permeability: 3104400). Their polar surface area ranges from 20 to 62 Å2. They might also penetrate the blood-brain barrier (MDCK permeability: 190-2500; but log BB: -0.50-0.12). In conclusion, all compounds could be present at considerable concentrations in the systemic circulation, which might trigger adverse effects. The results for the compounds discussed above are given in Table 3. Calculations with ToxTree showed low estimated toxicity for lupeol and its analogues, while MolInspiration showed probabilities of binding nuclear receptors between 0.72 (lupeol) to 0.93 (betulinic acid). The results from ToxTree would seem rather unexpected, particularly when compared with results from MolInspiration, but the former software is based on rather simple rules,29 e.g. the occurrence of more than one aromatic ring, the presence of a substituted aromatic ring or any atom other than C, H, O, N or divalent S. If none of these conditions are met, a low probability results (denoted as “–” in Table 3). In MolInspiration, the most interesting value is the probability to bind a nuclear receptor, which is indeed preeminent for all investigated compounds, and is in agreement with the results obtained from the VirtualToxLab. There is consensus with respect to bind the hERG ion channel, which was not observed in both methods. MolInspiration, which includes more enzymes than the four CYP450 entities currently employed in the



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VirtualToxLab, weights slightly higher the binding of enzymes. In addition, the results from MolInspiration suggest that lupeol and its analogues are somewhat susceptible to attack and possibly to degradation by proteases. Over the years, numerous specialized QSAR-based tools have been and are still being actively developed; some of them have been settled to determine the toxicity of man-made chemicals. One of those tools to which VirtualToxLab could be compared is ToxCast (http://www.epa.gov/ncct/toxcast/).36−39 This tool uses QSAR models for ER and AR to evaluate chemicals for potential endocrine disruption. Some advantages of ToxCast is that besides the QSAR models, it uses decision forest (DF) models to give a backup to its results and do not relay only in the QSAR information to determine if a chemical compound could be harmful. VirtualToxLab does not use DF models but in order to give a backup to its results, the interaction compound-protein is adjusted and compared by statistical relative importance (weight toxic potential wTP) using the standard deviation of the individual predictions and the quality of the subjacent models. Besides, VirtualToxLab does the calculations of toxic potential using the results from QSAR towards 16 proteins susceptible of triggering adverse effects, while ToxCast performance calculations of QSAR are only on AR and ER. Unlucky, there were not available results of ToxCast for lupeol and its analogues to be compared with the results obtained from VirtualToxLab in this study. Computing time is another aspect to consider when discussing an in silico study. In our case, it ranged from 41 to 66 (CPU) hours. Considering that VirtualToxLab is able of parallel computing, the time needed to determine TP of bigger molecules could be a limiting factor, since at present time the software runs in a Linux server of 1,024 nodes. Exhaustive sampling would imply a hundred times longer protocol, which is currently not feasible. Even then, it could not be guaranteed that the correct binding was identified, as induced fit adaptation of the protein conformation to the ligand’s topology might still not be properly addressed. Falsepositive results may occur when underestimating a compound’s desolvation energy, which in turn, leads to too high binding affinities. Thermodynamically feasible but kinetically unstable, binding modes are typically detected by MD simulations. Although VirtualToxLab employs a sophisticated protocol to assess the toxic potential of chemical compounds, both false-positive and false-negative results may occur. False-negative results are more frequent as the technology tests “only” 16 pathways that may eventually trigger a toxic response, while many more


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adverse mechanisms exist.18 Another source for underestimating a compound’s harmful feature is associated with the fact that exhaustive sampling of ligand binding to a macromolecular target is not possible at the currently available computing capabilities. In such case, the correct binding mode might be unidentified, particularly for large and very flexible molecules. The need for validation of the VirtualToxLab calculations by MD simulations arises from the fact that the Monte-Carlo sampling technology employed in the VirtualToxLab (i.e. software Cheetah18,24) computes a thermodynamic value for a compound’s strength to bind a given target protein. While this is a necessary condition for binding, it is not sufficient as the ligand–protein interaction must be stable over a reasonable period of time in order to trigger an (e.g. agonistic) effect. This kinetic aspect (i.e. the timedependent stability of a ligand–protein complex) can be assessed through MD simulations. The time span for which a MD simulation should be conducted is debatable; in this study we used a reasonable span time of 10–8 sec (10 ns), which allows to safely monitor the stability of key ligand–protein interactions (e.g. hydrogen bonds) as well as the response of the protein to ligand binding (induced fit). Simulations at longer times (e.g. 100 ns) could be desired, but is not reasonable considering actual hardware limitations and computing times.16-18 The toxic potential calculated by VirtualToxLab of lupeol or its analogues is based in the proteinchemical compound interactions. Is for this reason that some harmful ADME properties studied in vitro are not possible to compare directly with the results from VirtualToxLab. Reports on levels tested of lupeol and its analogs have been concentrated in Table 4. Until now, there are no reports on lupeol toxicity even at concentrations as higher as 2 g/kg animal, which is puzzling considering that these compounds have shown cytotoxic effects against cancer cells by inhibiting topoisomerase, a vital enzyme in cellular replication. The administration of lupeol did not show mortality, not matter if lupeol and its analogs were administrated by intravenous, cutaneous or oral ways. The maximum administration time for lupeol was 30 days in intravenous doses at 2 mg/animal without reporting mortality or toxicity effects in mice. 40 This may suggest that these compounds are not toxic in animals at doses as high as 500, 2000 and 3000 mg/kg for betulinic acid,41 lupeol42 and betulin,43 respectively.



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Saleem et al.2 showed that lupeol docks with the human androgen receptor using a computer docking-ligand model. This was further confirmed in vitro where lupeol interacted with the estrogen-receptor alfa (ERα) causing its expression in MDA-MB-231 breast cancer cells.44 Gupta et al.45 reported a 100% reduction on fertility of albino rats after the application of lupeol acetate. Likewise, traditional healers of Chhattisgarh in India use an extract from Echinops echinatus for the treatment of sexual disorders, also a paste prepared by mixing the root bark powder with the juice of Datura stramonium and Blumea lacera leaves is used to avoid premature ejaculation.46 Additionally, male rats treated with Echinops echinatus extracts, which contains high concentration of lupeol, reduced the level of testosterone and the testicular weight.47 These reports on lupeol and its analogues present interactions with protein receptors related with fertility, which corroborates our results were lupeol interacted with the estrogen-receptor alfa (ERα). Another possible adverse effect reported on lupeol that was not tested with VirtualToxLab is that lupeol is a competitive inhibitor of trypsin and chymotrypsin (Ki values 22 and 8 µM, respectively),48 indicating that chymotrypsin could be an interesting enzyme to be included as a protein model of VirtualToxLab. In conclusion, our studies have shown that lupeol and its analogues do not display a high toxic potential and binding to nuclear receptors according to VitrtualToxLab and other computer programs used in this report (possibly, with the exception of MolInspiration). However, lupeol and its analogues bind towards the ERα receptor, which could generate adverse effects; therefore, this would be considered when directing new research in the use of lupeol and its analogues as potential new drugs to treat illnesses. Adverse effects might be triggered via diverse mechanisms, but only few of them have been simulated in this study. In contrast to classic synthetic drugs, natural products often display a complex topology complemented with a larger number of hydrogen-bond functionalities. Therefore, it is less likely that they meet other (off-) targets binding patterns than rather simple chemotherapeutic compounds. Instead, the unique configuration of natural compounds might privilege them to trigger more complex agonistic mechanisms.


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ACKNOWLEDGEMENTS This article is dedicated to the memory of our dear friend and coauthor Prof. Dr. Angelo Vedani (1951-2016). Authors gratefully acknowledge the software support from the Biographics Laboratory 3R and the invaluable technical assistance of Dr. Martin Smieško and his team. Manuscript revision and suggestions from Joseph J. Karchesy and Jeffrey R. Bacon are recognized. Sources of funding: Manuel A. Ruiz-Rodríguez gratefully acknowledges a graduate-student scholarship from the Mexican Science and Technology Council (CONACyT) and financial support from the University of Basel. Conflicts of interest: none

ABBREVIATIONS 1A2, cytochrome P450-1A2; 2C9, cytochrome P450-2C9; 2D6, cytochrome P450-2D6; 3A4, cytochrome P450-3A4; ADME, adsorption, disruption, metabolism and elimination properties; AhR, aryl hydrocarbon receptor; AR, androgen receptor; Arg611, arginine 611; Asn564, asparagine 564; Asn705, asparagine 705; B(NFκB), nuclear factor kappa beta; B[a]P, benzo[a]pyrene; C28, carbon atom number 28; Caco, cancer coli (epithelial cell line); cFLIP, cellular FLICE-like inhibitory protein; CYP450, cytochrome P450; Cys736, cysteine 736; DF, decision forest; DMBA, 7,12-dimethylbenz[a]anthracene; DNA, deoxyribonucleic acid; Erα, estrogen alpha receptor; Fas, Fas cell surface death receptor; Gln244, glutamine 244; Gln711, glutamine 711; Glu353, glutamic acid 353; GR, glucocorticoide receptor; hERG, human ether-à-go-go-related gene K(+) ion channel; IC50, half maximal inhibitory concentration; Kras, Kras protein; LXR, Liver-X receptor; MD, molecular dynamics; MDA-MB-231, M. D. Anderson Cancer Center-MB-231 (Human breast cancer cell line); MDCK, Madin-Darby canine kidney (epithelial cell line); MR, mineralocorticoid receptor; MTK, Martyna, Tobias, and Klein: Constant pressure and temperature conditions; OPLS3, optimized Potentials for Liquid Simulations 3; PI3K/Akt, phosphatidylinositol-3-kinase; PPARγ, peroxisome proliferator-activated receptor gamma; PR, progesterone receptor; QSAR, quantitative structure-activity relationship; Ser304, serine 304; Thr894, threonine 894; TP, Toxic potential; TRα, Thyroid receptor alpha; TRβ, Thyroid receptor beta; VMD, visual Molecular Dynamics; Wnt/β-catenin, Wnt/β-Catenin (Signaling Pathway).



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REFERENCES (1) Saleem, M., Alam, A., Arifin, S., Shah, M. S., Ahmed, B., and Sultana, S. (2001) Lupeol, a triterpene, inhibits early responses of tumor promotion induced by benzoyl peroxide in murine skin. Pharmacol. Res. 43, 127-134. (2) Saleem, M. (2009) Lupeol, a novel anti-inflammatory and anti-cancer dietary triterpene. Cancer Lett. 285, 109-115. (3) Beveridge, T. H., Li, T. S., and Drover, J. C. (2002) Phytosterol content in American ginseng seed oil. J. Agric. Food Chem. 50, 744-750. (4) Kakuda, R., Iijima, T., Yaoita, Y., Machida, K., Kikuchi, M. (2002) Triterpenoids from Gentiana scabra. Phytochemistry 59, 791-794. (5) Pranav, K., Kulpreet, B., and Shukla, Y. (2008) Lupeol: Connotations for chemoprevention. Cancer Lett. 263, 1-13. (6) Saleem, M., Murtaza, I., Tarapore, R. S., Suh, Y., Adhami, V. M., Johnson, J. J., Siddiqui, I. A., Khan, N., Asim, M., Hafeez, B. B., Shekhani, M. T., Li, B., and Mukhtar, H. (2009) Lupeol inhibits proliferation of human prostate cancer cells by targeting β-catenin signaling. Carcinogenesis 30, 808-817. (7) Hodges, L. D., Kweifio-Okai, G., and Macrides, T. A. (2003) Antiprotease effect of anti-inflammatory lupeol esters. Mol. Cell. Biochem. 252, 97-101. (8) Wada, S., Iida, A., and Tanaka, R. (2001) Screening of triterpenoids isolated from Phyllanthus flexuosus for DNA topoisomerase inhibitory activity. J. Nat. Prod. 64, 1545-1547. (9) Aratanechemuge, Y., Hibasami, H., Sanpin, K., Katsuzaki, H., Imai, K., and Komiya, T. (2004) Induction of apoptosis by lupeol isolated from mokumen (Gossampinus malabarica L. Merr) in human promyelotic leukemia HL-60 cells. Oncol. Rep. 11, 289-292. (10) Hata, K., Hori, K., Ogasawara, H., and Takahashi, S. (2003) Anti-leukemia activities of Lup-28-al20(29)-en-3-one, a lupane triterpene. Toxicol. Lett. 143, 1-7. (11) Hata, K., Hori, K., and Takahashi, S. (2003) Role of p38 MAPK in lupeol induced B16 2F2 mouse melanoma cell differentiation. J. Biochem. 134, 441-445. (12) Nigam, N., Prasad, S., and Shukla, Y. (2007) Preventive effects of lupeol on DMBA induced DNA alkylation damage in mouse skin. Food Chem. Toxicol. 45, 2331-2335.


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(39) Kavlock, R., Chandler, K., Houck, K., Hunter, S., Judson, R., Kleinstreuer, N., Knudsen, T., Martin, M., Padilla, S., Reif, D., Richard, A., Rotroff, D., Sipes, N., and Dix, D. (2012) Update on EPA’s ToxCast Program: Providing high throughput decision support tools for Chemical Risk Management. Chem. Res. Toxicol. 25, 1287−1302. (40) Lee, T. K., Poon, R. T. P., Wo, J. Y., Ma, S., Guan, X. Y., Myers, J. N., Allevogt, P., and Yuen, A. P. W. (2007) Lupeol suppresses cisplatin-induced nuclear factor-kB activation in head and neck squamous cell carcinoma and inhibits local invasion and nodal metastasis in an orthotopic nude mouse model. Cancer Res. 67, 8800-8809. (41) Pisha, E., Chai, H., Lee, I., Chagwedera, T., Farnsworth, N., Cordell, G., Beecher, C., Fong, H., Kinghorn, A., Brown, D., Wani, M., Wall, M., Hieken, T., Das Gupta, T., and Pezzuto, J. (1995) Discovery of betulinic acid as a selective inhibitor of human melanoma that functions by induction of apoptosis. Nature Med. 1, 1046-1051. (42) Bani, S., Kaul, A., Khan, B., Ahmad, S. F., Suri, K. A., Gupta, B. D., Satti, N. K., and Qazi, G. N. (2006) Suppression of T lymphocyte activity by lupeol isolated from Crataeva religiosa. Phytother. Res. 20, 279287. (43) Tolmacheva, I., Shelepen-kina, L., Vikharev, Y., Anikina, L., Grishko, V., and Tolstikov, A. (2005) Synthesis and biological activity of s-containing betulin derivatives. Chem. Nat. Compd. 416, 701-705. (44) Lambertini, E., Lampronti, I., Penolazzi, L., Khan, M. T. H., Ather, A., Giorgi, G., Gambari, R., and Piva, R. (2005) Expression of estrogen receptor-gene in breast cancer cells treated with transcription factor decoy is modulated by Bangladeshi natural plant extracts. Oncol. Res. 14, 69-79. (45) Gupta, R., Bhatnager, A., Joshi, Y., Sharm, M., Khushalani, V., and Kachhawa, J. (2005) Induction of antifertility with lupeol acetate in male albino rats. Pharmacology 75, 57-62. (46) Padashetty, S., and Mishra, S. (2007) An HPTLC method for the evaluation of two medicinal plants commercially available in the Indian market under the common trade name Brahmadandi. Chromatographia 66, 447-449.



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(47) Agrawal, M., and Nahata, A. (2012) Protective effects of Echinops echinatus on testosterone-induced prostatic hyperplasia in rats. Eur. J. Integr. Med. 4, 177-185. (48) Rajic, A., Akihisa, T., Ukiya, M., Yasukawa, K., Sandeman, R., Chandler, D., and Polya, G. (2001) Inhibition of trypsin and chymotrypsin by anti-inflammatory triterpenoids from Compositae flowers. Planta Med. 67, 599-604. (49) Geetha, T., Varalakshmi, P., and Latha, R. M. (1998) Effect of triterpenes from Crataeva nurvala stem bark on lipid peroxidation in adjuvant induced arthritis in rats. Pharmacol. Res. 37, 191-195. (50) Geetha, T., and Varalaxmi, P. (1998) Anti-inflammatory activity of lupeol and lupeol linoleate in adjuvant-induced arthritis. Fitoterapia 69, 3-19. (51) Saleem, M., Afaq, F., Adhami, V. M., and Mukhtar, H. (2004) Lupeol modulates NF-kappa B and PI3K/Akt pathways and inhibits skin cancer in CD-1 mice. Oncogene 23, 5203-5214. (52) Patocka, J. (2003) Biologically active pentacyclic triterpenes and their current medicine signification. J. Appl. Biomed. 1, 7-12. (53) Sudhahar, V., Ashok Kumar, S., Varalakshmi, P., and Sujatha, V. (2008) Protective effect of lupeol and lupeol linoleate in hypercholesterolemia associated renal damage. Mol. Cell Biochem. 317, 11–20. (54) Preetha, S., Kanniappan, M., Selvakumar, E., Nagaraj, M., and Varalakshmi, P. (2006) Lupeol ameliorates aflatoxin B1-induced peroxidative hepatic damage in rats. Comp. Biochem. Physiol., Part C: Toxicol. Pharmacol. 143, 333-339. (55) Murtaza, I., Saleem, M., Adhami, V. M., Hafeez, B. B., and Mukhtar, H. (2009) Suppression of cFLIP by lupeol, a dietary triterpene, is sufficient to overcome resistance to TRAIL-mediated apoptosis in chemoresistant human pancreatic cancer cells. Cancer Res. 69, 1156–65. (56) Saleem, M., Kaur, S., Kweon, M., Adhami, V., Afaq, F., and Mukhtar, H. (2005) Lupeol, a fruit and vegetable based triterpene, induces apoptotic death of human pancreatic adenocarcinoma cells via inhibition of Ras signaling pathway. Carcinogenesis 26, 1956-1964.


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(57) Sudhahar, V., Kumar, S. A., and Varalakshmi, P. (2006) Role of lupeol and lupeol linoleate on lipemicoxidative stress in experimental hypercholesterolemia. Life Sci. 78, 1329-1335. (58) Saleem, M., Maddodi, N., Abu Zaid, M., Khan, N., bin Hafeez, B., Asim, M., Suh, Y., Setaluri, V., and Mukhtar, H. (2008) Lupeol inhibits growth of highly aggressive human metastatic melanoma cells in vitro and in vivo by inducing apoptosis. Clin. Cancer Res. 14, 2119-2127. (59) Sudhahar, V., Veena, C. K., and Varalakshmi, P. (2008) Antiurolithic effect of lupeol and lupeol linoleate in experimental hyperoxaluria. J. Nat. Prod. 71, 1509-1512. (60) Ai-Rehaily, A., El-Tahir, K., Mossa, J., and Rafatullah, S. (2001). Pharmacological studies of various extracts and the major constituent, lupeol, obtained from hexane extract of Teclea nobilis in Rodents. Nat. Prod. Sci. 7, 76-82. (61) Geetha, T., and Varalakshmi, P. (2001) Anti-inflammatory activity of lupeol and Lupeol linoleate in rats. J. Ethnopharmacol. 76, 77-80. (62) Latha, R., Lenin, M., Rasool, M., and Varalakshmi, P. (2001) A novel derivative pentacyclic triterpene and omega-3 fatty acid. Prostaglandins Leukot. Essent. Fatty Acids 64, 81-85. (63) Sudhahar, V., Ashokkumar, S., and Varalakshmi, P. (2006) Effect of lupeol and lupeol linoleate on lipemic - hepatocellular aberrations in rats fed a high cholesterol diet. Mol. Nutr. Food Res. 50, 12121219. (64) Sunitha, S., Nagaraj, M., and Varalakshmi, P. (2001) Hepatoprotective effect of lupeol and lupeol linoleate on tissue antioxidant defence system in cadmium-induced hepatotoxicity in rats. Fitoterapia 7, 516-523. (65) You, Y., Nam, N., Kim, Y., Bae, K., and Ahn, B. (2003) Antiangiogenic activity of lupeol from Bombax ceiba. Phytother. Res. 17, 341-344. (66) Vidya, L., Lenin, M., and Varalakshmi, P. (2002) Evaluation of the effect of triterpenes on urinary risk factors of stone formation in pyridoxine deficient hyperoxaluric rats. Phytother. Res. 16, 514-518.



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(67) Hata, K., Ogihara, K., Takahashi, S., Tsuka, T., Minami, S., and Okamoto, Y. (2010) Effects of lupeol on melanoma in vitro and in vivo: fundamental and clinical trials. In Kamihira, M., Katakura, Y., and Ito, A. (eds) Animal Cell Technology: Basic & Applied Aspects. Springer, Dordrecht, vol 16, 339-344. (68) Al-Yahya, M., Mossa, J., Ageel, A., and Rafatullah, S. (1994) Pharmacological and safety evaluation studies on Lepidium sativum L., seeds. Phytomedicine 2, 155-159. (69) Jäger, S., Laszczyk, M., and Scheffler, A. (2008) A preliminary pharmacokinetic study of betulin, the main pentacyclic triterpene from extract of outer bark of birch (Betulae alba cortex). Molecules 13, 32243235. (70) Rzeski, W., Stepulak, A., Szymanski, M., Juszczak, M., Grabarska, A., Sifringer, M., Kaczor, J., and Kandefer-Szerszen, M. (2009) Betulin Elicits anti-cancer effects in tumour primary cultures and cell lines in vitro. Basic Clin. Pharmacol. Toxicol. 105, 425–432. (71) Drag-Zalesinska, M., Kulbacka, J., Saczk, J., Wysocka, T., Zabel, M., Surowiak, P., and Drag, M. (2009) Esters of betulin and betulinic acid with amino acids have improved water solubility and are selectively cytotoxic toward cancer cells. Bioorg. Med. Chem. Lett. 19, 4814-4817. (72) Lin, W., Sahasivam, S., and Lin, F. (2009) The dose dependent effects of betulin on porcine chondrocytes. Process Biochem. 44, 678-684. (73) Ciurlea, S., Tiulea, C., Csanyi, E., Berko, S., Toma, C., Dehelean, C., and Loghin, F. (2010) A pharmacotoxicological evaluation of a betulin topical formulation tested on c57bl/6j mouse experimental nevi and skin lesions. Studia Universitatis Vasile Goldis Arad, Seria Stiintele Vietii 20, 5-9. (74) Kommera, H., Kaluderovic, G. N., Kalbitz, J., and Paschke, R. (2010) Synthesis and anticancer activity of novel betulinic acid and betulin derivatives. Arch. Pharm. 343, 449-457. (75) Szuster-Ciesielska, A., Plewka, K., Daniluk, J., and Kandefer-Szerszen, M. (2011) Betulin and betulinic acid attenuate ethanol-induced liver stellate cell activation by inhibiting reactive oxygen species (ROS), cytokine (TNF-α, TGF-β) production and by influencing intracellular signaling. Toxicology 280, 152-163.


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(76) Wert, L., Alakurtti, S., Corral, M., Sánchez-Fortún, S., Yli-Kauhaluoma, J., and Alunda, J. (2011) Toxicity of betulin derivatives and in vitro effect on promastigotes and amastigotes of Leishmania infantum and L. donovani. J. Antibiot. 7, 475-481. (77) Zuco, V., Supino, R., Righetti, S., Cleris, L., Marchesi, E., Gambacorti-Passerini, C., and Formelli, F. (2002) Selective cytotoxicity of betulinic acid on tumor cell lines, but not on normal cells. Cancer Lett. 175, 17–25. (78) Sami, A., Taru, M., Salme, K., and Jari, Y. (2006) Pharmacological properties of the ubiquitous natural product betulin. Eur. J. Pharm. Sci. 29, 1-13.



Table 1. Binding affinities (IC50 nanomolar) of the investigated compounds against all 16 target proteins in the VirtualToxLab.16-18 "Not binding" refers to a binding affinity > 100 µM

PPARγ

TRα

TRβ

PR

MR

30-Hydroxylupenone

2.87

6090.00

not binding

15400.00

4850.00

412.00

0.30

not binding

6.37

11.90

356.00

not binding

not binding

0.97

not binding

not binding

30-Hydroxylupeol

31.40

365.00

58800.00

19800.00

2910.00

1490.00

47.50

1820.00

64.80

35.30

8190.00

48.10

2520.00

441.00

2760.00

5980.00

Betulin

56.90

3810.00

not binding

3720.00

17900.00

2550.00

213.00

not binding

15.30

469.00

27500.00

1310.00

1240.00

36.30

15900.00

335.00

Betulin aldehyde

414.00

306.00

not binding

1890.00

21400.00

5060.00

68.90

344.00

1820.00

81.40

3530.00

586.00

1810.00

20.60

7080.00

527.00

Betulinic acid

6790.00

not binding

not binding

61300.00

13800.00

643.00

3540.00

not binding

42700.00

3450.00

17500.00

36600.00

384.00

1300.00

not binding

not binding

Betulinic acid amide

1240.00

131.00

58100.00

63600.00

5490.00

6680.00

82.70

326.00

30.20

26.50

5110.00

100.00

10800.00

22.00

2350.00

14100.00

Betulonic acid

74900.00

not binding

not binding

not binding

20700.00

2770.00

12100.00

not binding

860.00

10500.00

71700.00

46200.00

4590.00

7650.00

47300.00

not binding

Lupenone

7130.00

1670.00

not binding

8170.00

5100.00

8610.00

3170.00

4770.00

2020.00

138.00

9170.00

10900.00

5790.00

1400.00

2490.00

5550.00

Lupeol

3150.00

419.00

not binding

5670.00

7300.00

4660.00

67.90

502.00

1330.00

144.00

5320.00

696.00

2980.00

24.30

5750.00

6760.00

Lupeol acetate

70600.00

2910.00

not binding

31000.00

3650.00

12000.00

1430.00

2470.00

8890.00

3280.00

12000.00

21300.00

3270.00

3160.00

9640.00

8130.00

Lupeol-30-aldehyde

1120.00

238.00

not binding

22900.00

52100.00

1370.00

82.10

1150.00

1690.00

39.60

6660.00

1840.00

1900.00

66.50

5280.00

44900.00

Methylbetulinate

3880.00

not binding

not binding

1630.00

4660.00

17400.00

98.40

34700.00

820.00

244.00

not binding

19000.00

4880.00

113.00

not binding

not binding


LXR

hERG

GR

ERβ

ERα

CYP3A4

CYP2D6

CYP2C9

CYP1A2

AhR

Molecule

AR

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Table 2. Physicochemical properties of selected compounds employed in this study. Preferred values are given in square brackets. For details, see ref. 28.

Compound

MW [g/Mol]

log P

log S

log BB

Oral Abs [%]

Caco Perm [nm/sec]

MDCK Perm [nm/sec]

PSA 2 [Å ]

Lupeol Lupeol acetate 30-Hydroxylupeol Lupeol-30-aldehyde Lupenone 30-Hydroxylupenone Betulin Betulinic acid Betulinic acid amide Betulin aldehyde Betulonic acid Methylbetulinate

427 469 443 441 425 441 443 457 456 441 455 470

7.0 7.8 5.7 5.9 6.9 5.8 5.8 6.1 4.6 5.9 6.1 6.5

–7.8 –8.9 –6.6 –7.6 –7.6 –6.9 –6.6 –6.5 –5.1 –6.7 –6.8 –7.4

0.12 0.08 –0.40 –0.50 0.18 –0.37 –0.35 –0.40 –0.45 –0.27 –0.38 –0.10

100 100 100 100 100 100 100 94 100 100 94 100

4400 4200 1600 1300 4300 1500 1800 334 620 1800 310 3000

2500 2300 820 660 2400 790 950 190 560 950 176 1600

20 34 42 52 25 47 40 57 62 47 63 47

MW: molecular weight {MW | 130 ≤ MW ≤ 725} log P: predicted octanol/water partition coefficient {log P | –2.0 ≤ log P ≤ +6.5} log S: predicted aqueous solubility in mol/dm3 {log S | –6.5 ≤ log S ≤ –0.5} log BB: predicted brain/blood partition coefficient {–3.0 ≤ log BB ≤ –1.2} Oral Abs: Predicted human oral absorption on a 0 to 100% scale {80% is high} Caco Perm: Predicted apparent Caco-2 cell permeability in nm/sec {500 is great} MDCK Perm: Predicted apparent MDCK cell permeability in nm/sec {500 is great} PSA: polar surface area {PSA | 7 ≤ PSA ≤ 200}



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 35

Table 3. Consensus scoring employing ToxTree,29 MolInspiration31 and Lazar33 ToxTree: General harmfulness — low (–), intermediate (+), high (++)

Compound

ToxTree

MI/NRL

MI/ICM

MI/PI

MI/EI

Laz/Car

Laz/Mut

Lupeol Lupeol acetate 30-Hydroxylupeol Lupeol-30-aldehyde Lupenone 30-Hydroxylupenone Betulin Betulinic acid Betulinic acid amide Betulin aldehyde Betulonic acid Methylbetulinate

– – – – – – – – – – – –

0.76 0.72 0.80 0.87 0.75 0.74 0.85 0.93 0.80 0.79 0.88 0.83

0.10 0.08 0.06 0.13 –0.01 –0.04 –0.04 0.03 0.01 0.03 –0.06 0.02

0.19 0.10 0.16 0.24 0.01 0.05 0.09 0.14 0.18 0.19 0.04 0.10

0.45 0.44 0.55 0.61 0.41 0.46 0.51 0.55 0.53 0.53 0.47 0.46

3 3 3 3 4 3 3 3 3 3 3 3

0 0 0 0 0 0 0 0 0 0 0 0

MI/NRL: MolInspiration — probability to bind to a nuclear receptor {range | –1.0 ≤ range ≤ +1.0} MI/ICM: MolInspiration — probability to modulate an ion channel {range | –1.0 ≤ range ≤ +1.0} MI/PI: MolInspiration — probability to inhibit a protease {range | –1.0 ≤ range ≤ +1.0} MI/EI: MolInspiration — probability to inhibit an enzyme {range | –1.0 ≤ range ≤ +1.0} Laz/Car: Lazar — carcinogenicity {six tests: range | 0 ≤ range ≤ 6} Laz/Mut: Lazar — mutagenicity {two tests: range | 0 ≤ range ≤ 2}


Page 25 of 35

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Table 4. Toxicity reported for lupeol and its analogues Compound

Dose tested

Model

Lupeol

50 mg/kg

Mice

Via of administration Oral

Lupeol

200 mg/kg

Mice

Oral

None

1

50

Lupeol

2000 mg/kg

Mice

Oral

None

14

41

Lupeol

2 mg/animal

Mice

Cutaneous

None

2

51

Lupeol

2 g/kg

Mice

Oral

None

4

52

Lupeol

50 mg/kg

Rats

Oral

None

15

53,57,59,63

Lupeol

100 mg/kg

Mice

Oral

None

7

54

Lupeol

50 µmol/L

Human PaC cell

Medium

None

2

55

Lupeol

2 mg/animal

Mice

Injection

None

30

40

Lupeol

50 µM

Medium

None

2

56

Lupeol

80 µmol/L

Human pancreatic adenocarcinoma cells Melanoma cells

Medium

None

3

58

Lupeol

10 mg/kg

Rats

Oral

None

4

60

Lupeol

100 mg/kg

Rats

Oral

None

7

54

Lupeol

50 mg /kg

Rats

Oral

None

10

61,62

Lupeol

150 mg/kg

Rats

Oral

None

3

64

Lupeol

50 µg/L

None

1

65

1.5 mg/animal

Human melanoma cells Mice

Medium

Lupeol

Topical

None

0.6

1

Lupeol

50 mg/kg

Rats

Oral

None

28

59

Lupeol

35 mg/kg

Rats

Intra-tumoral

None

21

66

Lupeol

0.75–1.5mg/tumor site 0.5-3 g/kg

Dogs

1

67

Mice

Intra-tumoral injection Oral

None

Lupeol

None

1

68

Lupeol linolate

50 mg/kg

Rats

Oral

None

15

53.63

Lupeol linolate

35 mg/kg

Rats

Intra-tumoral

None

21

66

Lupeol linolate

50 mg/kg

Mice

Oral

None

18

50

Lupeol linolate

150 mg/kg

Rats

Oral

None

3

64

Betulin

3000 mg/kg.

Rats

Injected

None

14

43

Betulin

540 mg/kg; mouse

Mice

Injected

None

28

69

Betulin

300 mg/kg

Dogs

Injected

None

28

69

Betulin

5 and 10 uM

Humans cells

Medium

None

4

70

Betulin

42 uM

Humans cells

Medium

None

3

71

Betulin

5.12 ug/ml

Medium

None

7

72

Betulin

5%

Porcine chondrocytes Mice

Topical

None

21

73

Betulin

15.34 uM

Medium

None

4

74

Betulin

500 mg/kg

Human cells Mice

Oral

None

28

42

Betulin

10 uM

Mice liver cells

Medium

None

1

75

Betulin

50 uM

Hamster cells

Medium

2

76

Betulin

50 uM

Mice cells

Medium

2

76

Betulinic acid

100 mg/kg

Human cells (cancer) in mice

3

77

Betulinic acid

500 mg/kg

Mice

Oral (chosen dose was the maximum reachable with selected solvent) Oral

None (one derivate of Betulin showed toxicity) None (one derivate of Betulin showed toxicity) None

None

28

78

Betulinic acid

13.10 uM

None

4

74

5 uM 20 mg/kg

Human cancer cells Mice liver cells Mice

Medium

Betulinic acid 30-hydroxy lupenone

Medium Topical

None None

1 0.2

75 35

cancer

Adverse effects None


Administration time (days) 18

References 49


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FIGURE LEGENDS Figure 1. Chemical structures of lupeol and its investigated analogues.

Figure 2. Toxic potential of the 12 investigated compounds as computed with the VirtualToxLab.16,18

Figure 3. Left: binding of lupeol to the progesterone receptor. The ligand and key amino-acid residues are shown in licorice; the protein is depicted by its inner surface (colored by z-depth). Hydrophobic residues are shown in space-filling mode (brown) and water molecules represented by blue beads. Hydrogen bonds are indicated by yellow dashed lines. The image was generated employing the VMD software.27 Right: structure of lupeol oriented as in the left image.

Figure 4. Left: binding of lupeol to the estrogen receptor α. The ligand and key amino-acid residues are shown in licorice; the protein is depicted by its inner surface (colored by z-depth). Hydrophobic residues are shown in space-filling mode (brown) and water molecules represented by blue beads. Hydrogen bonds are indicated by yellow dashed lines. The image was generated employing the VMD software.27 Right: structure of lupeol oriented as in the left image.

Figure 5. Left: binding of 30-hydroxylupenone to the androgen receptor. The ligand and key amino-acid residues are shown in licorice; the protein is depicted by its inner surface (colored by z-depth). Hydrophobic residues are shown in space-filling mode (brown) and water molecules represented by blue beads. Hydrogen bonds are indicated by yellow dashed lines. The image was generated employing the VMD software.27 Right: structure of 30-hydroxylupenone oriented as in the left image.

Figure 6. Left: binding of betulin to the glucocorticoid receptor. The ligand and key amino-acid residues are shown in licorice; the protein is depicted by its inner surface (colored by z-depth). Hydrophobic residues are shown in space-filling mode (brown) and water molecules represented by blue beads. Hydrogen bonds are indicated by yellow dashed lines. The image was generated employing the VMD software.27 Right: structure of betulin oriented as in the left image.


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Figure 7. Left: binding of betulinic acid amide to the aryl hydrocarbon receptor. The ligand and key aminoacid residues are shown in licorice; the protein is depicted by its inner surface (colored by z-depth). Hydrophobic residues are shown in space-filling mode (brown) and water molecules represented by blue beads. Hydrogen bonds are indicated by yellow dashed lines. The image was generated employing the VMD software.27 Right: structure of betulinic acid amide oriented as in the left image.

Figure 8. Left: binding of 30-hydroxylupenone to CYP450 2D6. The ligand, the heme and key amino-acid residues are shown in licorice; the protein is depicted by its inner surface (colored by z-depth). The FeIII ion is represented as a pink sphere. Hydrophobic residues are shown in space-filling mode (brown) and water molecules represented by blue beads. Hydrogen bonds are indicated by yellow dashed lines. The image was generated employing the VMD software.27 Right: structure of 30-hydroxylupenone oriented as in the left image.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 1. Chemical structures of lupeol and its investigated analogues. 477x323mm (300 x 300 DPI)


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Figure 2. Toxic potential of the 12 investigated compounds as computed with the VirtualToxLab.16,18 165x103mm (299 x 299 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 3. Left: binding of lupeol to the progesterone receptor. The ligand and key amino-acid residues are shown in licorice; the protein is depicted by its inner surface (colored by z-depth). Hydrophobic residues are shown in space-filling mode (brown) and water molecules represented by blue beads. Hydrogen bonds are indicated by yellow dashed lines. The image was generated employing the VMD software.27 Right: structure of lupeol oriented as in the left image. 220x144mm (300 x 300 DPI)


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Figure 4. Left: binding of lupeol to the estrogen receptor α. The ligand and key amino-acid residues are shown in licorice; the protein is depicted by its inner surface (colored by z-depth). Hydrophobic residues are shown in space-filling mode (brown) and water molecules represented by blue beads. Hydrogen bonds are indicated by yellow dashed lines. The image was generated employing the VMD software.27 Right: structure of lupeol oriented as in the left image. 219x144mm (300 x 300 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 5. Left: binding of 30-hydroxylupenone to the androgen receptor. The ligand and key amino-acid residues are shown in licorice; the protein is depicted by its inner surface (colored by z-depth). Hydrophobic residues are shown in space-filling mode (brown) and water molecules represented by blue beads. Hydrogen bonds are indicated by yellow dashed lines. The image was generated employing the VMD software.27 Right: structure of 30-hydroxylupenone oriented as in the left image. 218x143mm (300 x 300 DPI)


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Figure 6. Left: binding of betulin to the glucocorticoid receptor. The ligand and key amino-acid residues are shown in licorice; the protein is depicted by its inner surface (colored by z-depth). Hydrophobic residues are shown in space-filling mode (brown) and water molecules represented by blue beads. Hydrogen bonds are indicated by yellow dashed lines. The image was generated employing the VMD software.27 Right: structure of betulin oriented as in the left image. 221x144mm (300 x 300 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 7. Left: binding of betulinic acid amide to the aryl hydrocarbon receptor. The ligand and key aminoacid residues are shown in licorice; the protein is depicted by its inner surface (colored by z-depth). Hydrophobic residues are shown in space-filling mode (brown) and water molecules represented by blue beads. Hydrogen bonds are indicated by yellow dashed lines. The image was generated employing the VMD software.27 Right: structure of betulinic acid amide oriented as in the left image. 218x144mm (300 x 300 DPI)


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Figure 8. Left: binding of 30-hydroxylupenone to CYP450 2D6. The ligand, the heme and key amino-acid residues are shown in licorice; the protein is depicted by its inner surface (colored by z-depth). The FeIII ion is represented as a pink sphere. Hydrophobic residues are shown in space-filling mode (brown) and water molecules represented by blue beads. Hydrogen bonds are indicated by yellow dashed lines. The image was generated employing the VMD software.27 Right: structure of 30-hydroxylupenone oriented as in the left image. 216x144mm (300 x 300 DPI)


In Silico Prediction of the Toxic Potential of Lupeol - ACS Publications

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