Yasmine S Touil

Icolas Auzeil

François Boulinguez

Hanane Saighi

Anne Regazzetti

Daniel Scherman

Dr Guy G Chabot
email: guy.chabot@parisdescartes.fr

Fisetin disposition and metabolism in mice: Identification of geraldol as an active metabolite

Keywords: Lewis lung carcinoma cells, MS/MS, tandem mass spectrometry, MTT, 1-(4,5-dimethylthiazol-2-yl)-3,5-diphenyltetrazolium, flavonoid, fisetin, pharmacokinetics, metabolism, geraldol, cytotoxicity

Although the natural flavonoid fisetin (3,3',4',7-tetrahydroxyflavone) has been recently identified as an anticancer agent with antiangiogenic properties in mice, its in vivo pharmacokinetics and metabolism are presently not characterized. Our purpose was to determine the pharmacokinetics and metabolism of fisetin in mice and determine the biological activity of a detected fisetin metabolite. After fisetin administration of an efficacious dose of 223 mg/kg i.p. in mice, the maximum fisetin concentration reached 2.5 µg/ml at 15 min and the plasma concentration declined biphasically with a rapid half-life of 0.09 h and a terminal half-life of 3.1 h. Three metabolites were detected, one of which was a glucuronide of fisetin (M1), whereas another glucuronide (M2) was a glucuronide of a previously unknown fisetin metabolite (M3). HPLC MS/MS analysis indicated that M3 was a methoxylated metabolite of fisetin (MW=300 Da). The UV spectrum of M3 was identical to that of fisetin and standard 3,4',7-trihydroxy-3'-methoxyflavone (geraldol). In addition, because M3 co-eluted with standard geraldol in 4 different chromatographic ternary gradient conditions, M3 was therefore assigned to geraldol. Of interest, this metabolite was shown to achieve higher concentrations than fisetin in Lewis lung tumors. We also compared the cytotoxic and antiangiogenic activities of fisetin and geraldol in vitro and it was found that the latter was more cytotoxic than the parent compound toward tumor cells, and that it could also inhibit endothelial cells migration and proliferation. In conclusion, these results suggest that fisetin metabolism plays an important role in its in vivo anticancer activities.

Introduction

Among the numerous products available from plants, the flavonoid superfamily plays a central role by their interesting pharmacological properties for several diseases, including cancer prevention and therapy [START_REF] Havsteen | The biochemistry and medical significance of the flavonoids[END_REF][START_REF] Hill | Vascular collapse after flavone acetic acid: a possible mechanism of its anti-tumour action[END_REF][START_REF] Lopez-Lazaro | Flavonoids as anticancer agents: structure-activity relationship study[END_REF][START_REF] Middleton | The effects of plant flavonoids on mammalian cells: implications for inflammation, heart disease, and cancer[END_REF]. We, and others, have recently identified fisetin (3,3',4',7tetrahydroxyflavone, Figure 1), a flavonoid found in several fruits, vegetables, nuts and wine [5;6], as a lead compound possessing biological properties of potential interest in the prevention and/or treatment of cancer in vivo [START_REF] Khan | A novel dietary flavonoid fisetin inhibits androgen receptor signaling and tumor growth in athymic nude mice[END_REF][START_REF] Tripathi | Anti-cancer activity of a combination of cisplatin and fisetin in embryonal carcinoma cells and xenograft tumors[END_REF][START_REF] Touil | Improved antiangiogenic and antitumour activity of the combination of the natural flavonoid fisetin and cyclophosphamide in Lewis lung carcinoma-bearing mice[END_REF]. Fisetin possesses antioxidant and antiinflammatory activity [10;11] and was found to be cytotoxic and antiangiogenic in vitro [12;13]. Fisetin is cytotoxic to various human cancer cell lines including leukemia (HL60) [START_REF] Lee | Wogonin and fisetin induce apoptosis in human promyeloleukemic cells, accompanied by a decrease of reactive oxygen species, and activation of caspase 3 and Ca 2+ -dependent endonuclease[END_REF], breast (MCF7) [START_REF] Fotsis | Flavonoids, dietary-derived inhibitors of cell proliferation and in vitro angiogenesis[END_REF], colon (HT29) [START_REF] Kuntz | Comparative analysis of the effects of flavonoids on proliferation, cytotoxicity, and apoptosis in human colon cancer cell lines[END_REF], liver (SK-HEP-1, Caco-2) [15;16], neuroblastoma (SHEP, WAC-2) [START_REF] Fotsis | Flavonoids, dietary-derived inhibitors of cell proliferation and in vitro angiogenesis[END_REF], prostate (LNCaP, PC3) [START_REF] Haddad | Novel antiproliferative flavonoids induce cell cycle arrest in human prostate cancer cell lines[END_REF], and also to endothelial cells [START_REF] Fotsis | Flavonoids, dietary-derived inhibitors of cell proliferation and in vitro angiogenesis[END_REF]. Fisetin appears to interact with several molecular targets, including cyclindependent kinases [START_REF] Lu | Fisetin inhibits the activities of cyclin-dependent kinases leading to cell cycle arrest in HT-29 human colon cancer cells[END_REF][START_REF] Sung | Fisetin, an inhibitor of cyclin-dependent kinase 6, down-regulates nuclear factor-kappaB-regulated cell proliferation, antiapoptotic and metastatic gene products through the suppression of TAK-1 and receptor-interacting protein-regulated IkappaBalpha kinase activation[END_REF][START_REF] Lu | Crystal structure of a human cyclin-dependent kinase 6 complex with a flavonol inhibitor, fisetin[END_REF], DNA topoisomerases I and II [21;22], urokinase [START_REF] Jankun | Nutraceutical inhibitors of urokinase: potential applications in prostate cancer prevention and treatment[END_REF], actin [START_REF] Böhl | Flavonoids affect actin functions in cytoplasm and nucleus[END_REF],

and androgen receptor signaling [START_REF] Khan | A novel dietary flavonoid fisetin inhibits androgen receptor signaling and tumor growth in athymic nude mice[END_REF]. It has also recently been found that fisetin induces a forced exit from mitosis by targeting the mitotic spindle checkpoint involving the inhibition of Aurora B activities required for the maintenance of normal spindle checkpoint signaling [START_REF] Salmela | Dietary flavonoid fisetin induces a forced exit from mitosis by targeting the mitotic spindle checkpoint[END_REF]. In vivo, fisetin has shown antiangiogenic activity after topical application in a rabbit model of corneal neovascularization [START_REF] Joussen | Treatment of corneal neovascularization with dietary isoflavonoids and flavonoids[END_REF]. After systemic administration in mice, fisetin has shown interesting antitumor activity in several cancer models, including prostate, teratocarcinoma and lung carcinoma [START_REF] Khan | A novel dietary flavonoid fisetin inhibits androgen receptor signaling and tumor growth in athymic nude mice[END_REF][START_REF] Tripathi | Anti-cancer activity of a combination of cisplatin and fisetin in embryonal carcinoma cells and xenograft tumors[END_REF][START_REF] Touil | Improved antiangiogenic and antitumour activity of the combination of the natural flavonoid fisetin and cyclophosphamide in Lewis lung carcinoma-bearing mice[END_REF].

Although fisetin has shown activity in vitro and in vivo in mice, there is presently no information concerning the fisetin levels achieved in this preclinical species that could allow in vitro and in vivo comparisons. Moreover, the fisetin molecule bearing 4 hydroxyl substituents, including a catechol on the phenyl B ring, is therefore probably extensively metabolized in vivo. In the present study, at an effective dose of 223 mg/kg given by the i.p. route, we were therefore interested to determine the pharmacokinetics and metabolism of fisetin in vivo in mice.

We report here the pharmacokinetics of fisetin and its main metabolites in mice. In addition to glucuronide conjugates, we identified a 3'-methoxylated metabolite of fisetin (3,4',7-trihydroxy-3'-methoxyflavone or geraldol) as a major metabolite of fisetin. This methoxylated metabolite was found to be more cytotoxic that the parent compound and could also inhibit endothelial cells migration and proliferation.

Materials and Methods

Chemicals and reagents

Fisetin (3,3',4',7-tetrahydroxyflavone), kaempferol (3,4',5,7-tetrahydroxyflavone), βglucuronidase, dimethylsulfoxide (DMSO), polyethylene glycol 200 (PEG200) were purchased from Sigma-Aldrich (Saint-Quentin Fallavier, France), and geraldol (3,4',7trihydroxy-3'-methoxyflavone) was obtained from Extrasynthese (Genay, France). For HPLC studies, stock solutions of fisetin, kaempferol and geraldol were prepared in methanol and stored at -20°C protected from light. Methanol (HPLC grade) was purchased from Carlo Erba (Val de Reuil, France), glacial acetic acid was from Prolabo (Paris, France), and water was purified using a Milli-Q apparatus from Millipore Corporation. For cell culture, stock solutions of fisetin and geraldol were freshly prepared in DMSO and further diluted in cell culture medium. Matrigel and bFGF were obtained from BD Biosciences (Le Pont de Claix, France). All other chemicals were obtained from commercial sources and were of the highest purity available.

Fisetin pharmacokinetics in mice

Female 8 weeks old C57BL/6J mice (mean body weight 18-22 g), were purchased from Janvier (Le Genest-St-Isle, France). After an overnight fasting period, mice were injected intraperitonelly (i.p.) with fisetin (223 mg/kg body weight) dissolved in 100 µl of PEG200/DMSO (7:3; v:v). This dose level was chosen because it was non toxic (based on body weights) when administered i.p. (daily X 5) for 2 consecutive weeks and also because this dose was found active in Lewis lung carcinoma bearing mice [START_REF] Touil | Improved antiangiogenic and antitumour activity of the combination of the natural flavonoid fisetin and cyclophosphamide in Lewis lung carcinoma-bearing mice[END_REF]. Blood samples (n=3 per sampling time) were collected by cardiac puncture with heparinized syringes after sacrificing the animals by CO 2 at the following times: pre-administration (time 0), and at 0.25, 0.5, 1, 1.5, 2, 4, 8, 15 and 24 h post-dosing. Blood samples were transferred into polypropylene tubes and centrifuged at 12,000g for 10 min to obtain the plasma which was kept at -20°C until HPLC analysis. For the determination of fisetin and metabolites in tumors, pieces of Lewis lung carcinoma (LLC) were injected subcutaneously into the right flank of female C57BL/6J mice (5 mm 3 ) on day 0. Fifteen days later, when the tumors reached a volume of 500-1000 mm

Sample preparation and extraction

Plasma samples, tumor or normal tissues homogenates (prepared with 1 ml of PBS using a Teflon potter at a speed of 50 rpm with 3 strokes), were extracted by mixing (Vortex mixer, 1 min) with a 5-fold volume of cold methanol containing kaempferol (3,4',5,7tetrahydroxyflavone, Fig. 1), as an internal standard (final concentration 3.3 µg/ml). The mixture was put on ice for one hour to precipitate the proteins, and the samples were centrifuged at 12,000g for 10 min at 4°C. The upper phase was transferred to another polypropylene tube and 100 µl was injected onto a Shimadzu HPLC-UV system described below.

Standard curves and extraction

Stock solutions of fisetin, geraldol and kaempferol (cf., Fig. 1) were prepared in methanol at 100 µg/ml and stored at -20 °C in the dark. Working standard solutions of increasing concentrations of fisetin and geraldol were prepared on the day of analysis by diluting the stock solution. The standard curves of fisetin and geraldol in mouse plasma, tumors, or normal tissues, were built using the ratio of the area of fisetin or geraldol over the area of the internal standard (kaempferol) and were linear within the concentration range used (0.01 to 20 µg/ml) with correlation coefficient >0.99. The percent extraction from plasma, tumors, or normal tissues was 91% and the lower limit of quantification was 0.01 µg/ml. The apparent concentration of the metabolites was calculated based on the fisetin standard curve.

HPLC system for pharmacokinetic studies

For pharmacokinetic studies we used a HPLC-UV system (Shimadzu CLASS-VP ® , version 5.3), equipped with a C 18 column (Beckman Ultrasphere ODS, 5 µm; 4.6 × 25 mm) thermostated at 20°C, and a UV detector set at 360 nm. The mobile phase was composed of 52% methanol, 48% aqueous acetic acid (2%), at a flow rate of 1 ml/min.

Pharmacokinetic parameters determination

Fisetin plasma concentrations were analyzed using non compartmental analysis and also using a bicompartmental model with extravascular administration. The following non compartmental pharmacokinetics parameters were derived using standard methods [START_REF] Gibaldi | Noncompartmental analysis based on statistical moment theory[END_REF] B, 75-46% C. For each gradient, the logarithm of the retention factor (log k) of M3 and geraldol were calculated using retention time and dead time. The retention factor relates to retention time, but is preferred because it is dimensionless and independent of unexpected variations in mobile phase flow. In contrast to retention time, the log k can unequivocally be ascribed to structural changes, as previously used for flavonoids by Stefova et al., 2007 [28].

To further ascertain the identity of M3, we also injected an equimolar mixture of geraldol and M3 using all 4 gradient systems.

In vitro cytotoxic activity of fisetin and metabolite

The murine Lewis lung carcinoma (LLC) cell line, the normal NIH 3T3 murine fibroblast cell line and the EAhy 926 endothelial cell line were grown in Dulbecco's modified Eagle's medium (DMEM) containing 2 mM L-glutamine, 10% fetal bovine serum, 100 U/ml penicillin and 100 µg/ml streptomycin (37°C, 5% CO 2 ). Exponentially growing cells were plated onto 96-well plates at 5000 cells per well in 200 µl. Twenty-four h later, cells were exposed for 24 or 48 h to fisetin or geraldol at increasing concentrations (0 to 1 mM).

Viability was assessed using the MTT test and absorbance was read at 562 nm in a microplate reader (BioKinetics Reader, EL340). Appropriate controls with DMEM only and MTT were run to subtract background absorbance. Experiments were run in quadruplicate and repeated 3 times. Control cells were exposed to 1% DMSO which was not cytotoxic. The results are presented as the inhibitory concentrations for 50% of cells (IC 50 ) for a 24 or 48 h exposure time relative to controls.

Cell migration assay (scratch wound assay)

EAhy 926 endothelial cells were grown to confluence and a wound was introduced by clearing an area of the monolayer using a 100 µl pipet tip. After wounding, digital photographs were recorded of wounded areas of each well at a magnification of X 100 (time 0 h). Medium was changed, and bFGF (10 ng/ml), fisetin or geraldol was added at increasing concentrations (0, 22, 44 and 88 µM) to the media containing 2.5 % of FBS. After a 24 h incubation period, digital photographs were recorded of the cleared areas of each well.

Migration was evaluated by manually drawing the distance of the wound area (d) at T 0h and T 24h time on a computer screen and distances values were obtained using the ImageJ software [START_REF] Abramoff | Image processing with ImageJ[END_REF]. The results were expressed as a percent of the controls using the following formula: 100

× [1-(dT 0h -dT 24h of treated cells)/( dT 0h -dT 24h of control cells)]. Experiments were performed in triplicate for each concentration and were repeated 3 times.

Formation of capillary-like structures

Analysis of capillary-like structure formation was performed using EAhy 926 endothelial cells grown on Matrigel. Fifty µl of the Matrigel solution was applied to each well on a 96well plate and incubated for 30 min at 37°C. EAhy 926 cells (1 × 10 4 ) were suspended in 100 µl of medium, plated onto the gel matrix and incubated at 37°C. Adherent cells received bFGF (10 ng/ml) and fisetin or geraldol at increasing concentrations (0, 22, 44 and 88 µM).

After a 24 h exposure time, in vitro angiogenesis was assessed by counting the capillary-like structures in each well at X 100 magnification under a light microscope (Zeiss). The in vitro anti-angiogenic effect was calculated by the following formula: 100 × [1-(number of capillary-like tube of treated cells) / (number of capillary-like tube of control cells)].

Experiments were performed in duplicate for each condition and repeated 3 times.

Statistical analysis

Results are expressed as the mean ± SEM of at least 3 independent experiments. Statistical differences between means were assessed using the Student t test for unpaired data. If unequal variance was observed, the Welch's correction was applied. A P value ≤ 0.05 was considered significant.

Results

Plasma and tissue pharmacokinetics of fisetin and its metabolites

A representative chromatogram depicting the separation of fisetin and its metabolites in mouse plasma is presented in Figure 2. In these chromatographic conditions (reversed-phase mode), fisetin eluted at 40 min and the internal standard (kaempferol) at 53 min. Fisetin plasma pharmacokinetics after an i.p. administration at a dose of 223 mg/kg is presented in Figure 3 and Table 1. The maximum plasma concentration reached 2.53 µg/ml at 15 min post dosing and declined thereafter with a first rapid alpha half-life of 0.09 h (5.4 min), followed by a longer terminal disposition half-life beta of 3.12 h. The total area under the plasma versus time curve (AUC) was 4.0 µg × h/ml, the apparent intraperitoneal clearance (CL/F) was 1.12 L/h, and the Vss/F was 5.1 L.

As shown in Figure 2, in addition to the fisetin peak, several other peaks not present in control plasma were also detected. Of those peaks, two metabolites eluted before fisetin (M1 at 26 min and M2 at 35 min), whereas another metabolite (M3) eluted after fisetin with a retention time of 48 min. Figure 4 presents the relative concentrations of these metabolites as a function of time using fisetin as a reference. It can be observed that metabolites M1 and M2 could achieve apparent higher concentrations than the parent compound, whereas metabolite M3 apparent concentrations were lower compared to fisetin. Pharmacokinetic parameters of the metabolites are presented in Table 1.

To obtain some information on fisetin and metabolites levels in normal tissues, mice were sacrificed 30 minutes after i.p. injection of fisetin and liver, intestines, kidneys, lungs and spleen were harvested. Fisetin and metabolites levels presented in Table 2 show that fisetin concentrations were highest in liver, intestines and kidneys. Metabolite M1 highest concentrations were mainly found in plasma and kidneys. For metabolites M2 and M3, the highest concentrations were in kidneys and intestines.

We next submitted plasma samples to β-glucuronidase digestion, and the M1 and M2 metabolite peaks were completely abolished, whereas the fisetin and the M3 peaks were markedly increased as shown in Figure 5 (compare with Fig. 2 without β-glucuronidase). This observation indicated that M1 and M2 were glucuronides of fisetin and metabolite M3, respectively.

Identification of metabolite M3

To identify metabolite M3, the peak at 48 min was subjected on line to mass spectrometry analysis and UV spectrum determination. The mass spectrum of M3 presented a molecular ion m/z value of 301 Da [M+H] + , thus corresponding to a molecular weight of 300 Da (Figure 6-A). In the same conditions, fisetin presented a M+H m/z value of 287 Da corresponding to a molecular weight of 286 Da (data not shown). Therefore, the gain of 14 atomic mass units observed with M3 over the parent compound corresponded to the addition of a CH 2 on the fisetin molecule, indicating that metabolite M3 was a methylated compound of fisetin.

However, because fisetin possesses 4 hydroxyl groups, the exact localization of the methoxyl on the fisetin molecule could not easily be identified.

In addition, the UV spectrum of M3 was found superimposable to that of fisetin with identical maxima at 245, 320 (shoulder) and 360 nm (Figure 6-B). Among the possible methylated fisetin candidates, the 3,4',7-trihydroxy-3'-methoxyflavone or geraldol (cf. Figure 1), therefore appeared as a possible candidate. Geraldol UV spectrum was indeed found superimposable to that of M3 and fisetin (Figure 6-B). Also the mass spectrum of authentic geraldol was also superimposable to the mass spectrum of M3 (data not shown).

Authentic geraldol was also tested for its HPLC retention time and it was found to co-elute with metabolite M3 in 4 different ternary elution systems (Table 3), thus further substantiating that M3 was indeed geraldol. In addition, the logarithm of the retention factor (log k) of M3

and geraldol yielded the same values, further substantiating the identity of M3 as geraldol.

Considering M3 molecular mass identical to geraldol, its UV spectrum superimposable to both fisetin and geraldol spectra, its retention times and log k identical to authentic geraldol in 4 ternary gradient systems, allowed us to ascribe M3 as being geraldol.

Tumor concentration of fisetin and its metabolites

At 15 min after the administration of fisetin i.p. (223 mg/kg) to tumor bearing mice (Lewis lung carcinoma), the plasma and tumor concentrations of fisetin and its main metabolites were also determined by HPLC. Glucuronides M1 and M2 presented higher concentrations in the plasma compared to tumors (compare Figures 7A and7B). However, it was of interest to note that geraldol was the metabolite that reached the highest concentrations in tumors (Figure 7-B). Indeed, geraldol concentrations in tumors were 4.3 times higher than the fisetin concentration in this tissue at this sampling time. Considering this observation, we were next interested to investigate further the geraldol cytotoxic and antiangiogenic properties.

Cytotoxic activity of fisetin and geraldol on Lewis lung carcinoma (LLC) cells, endothelial cells (EAhy 926), and normal cells ( IH 3T3)

The effect of fisetin and metabolite geraldol on the viability of Lewis carcinoma cells, endothelial cells (EAhy 926), and normal NIH 3T3 cells were also tested (Figure 8). A dosedependent decrease in viability of the three cell lines after a 24 h exposure time to both flavonoids could be observed. The most cytotoxic flavonoid on LLC cells was found to be geraldol with an IC 50 (the concentration producing 50 % reduction of viability) of 24 µM, about 2.5-fold lower than that of fisetin (59 µM) (Fig. 8-A). Concerning the effect on EAhy 926 endothelial cells viability, fisetin and geraldol showed similar IC 50 (76 and 72 µM, respectively) (Fig. 8-B). However, it was of interest that the normal NIH 3T3 cells were significantly less sensitive to fisetin and geraldol than either LLC or endothelial cells, with IC 50 of 195 µM and 128 µM, respectively (Fig. 8-C).

Fisetin and geraldol effect on endothelial cells migration in vitro

We next compared the effect of various concentrations [START_REF] Constantinou | Flavonoids as DNA topoisomerase antagonists and poisons: structure-activity relationships[END_REF][START_REF] Lotito | Metabolic conversion of dietary flavonoids alters their anti-inflammatory and antioxidant properties[END_REF], 88 µM) of fisetin and its metabolite geraldol on EAhy 926 endothelial cells migration using the scrape wound assay.

Figure 9 illustrates that at 24 h post-wounding of confluent EAhy 926 endothelial cells, the control bFGF (10 ng/ml) treated cells migrated and totally filled the scraped area, as expected.

Although fisetin and geraldol could inhibit significantly endothelial cells migration at 44 µM (Figure 9), fisetin was found more active than geraldol at this concentration, and also at the lower concentration of 22 µM (Figure 10). Although the inhibition of endothelial cells migration was dose-dependent for both flavonoids, the calculated IC 50 for the antimigration effect showed that fisetin had a significantly (p<0.001) stronger antimigration effect compared to geraldol (43 ± 0.8 µM versus 64 ± 0.8 µM, respectively) (Figure 10).

Fisetin and geraldol effect on the capillary-like structure formation on Matrigel

To examine whether geraldol could directly inhibit angiogenesis, its effect on bFGF-mediated tube formation by endothelial cells was investigated at concentrations ranging from 3 to 300 µM. As expected, EAhy 926 endothelial cells plated on Matrigel with bFGF (10 ng/ml) formed a capillary-like network within 24 h (data not shown). Fisetin could prevent the formation of the capillary-like network in a dose-dependent fashion with an IC 50 of 51 ± 7 µM, whereas no significant antiangiogenic effect of geraldol could be observed at the highest achievable in vitro concentration of 88 µM, because geraldol was not soluble at higher concentrations (data not shown).

4.

Discussion

The natural flavonoids are attracting interest in cancer prevention and therapy because these compounds are relatively non toxic compared to other chemotherapeutic agents used in cancer therapy [START_REF] Havsteen | The biochemistry and medical significance of the flavonoids[END_REF][START_REF] Hill | Vascular collapse after flavone acetic acid: a possible mechanism of its anti-tumour action[END_REF][START_REF] Lopez-Lazaro | Flavonoids as anticancer agents: structure-activity relationship study[END_REF][START_REF] Middleton | The effects of plant flavonoids on mammalian cells: implications for inflammation, heart disease, and cancer[END_REF]. Although the dietary flavonoid fisetin has recently been shown to be an interesting anticancer active agent in mice [START_REF] Khan | A novel dietary flavonoid fisetin inhibits androgen receptor signaling and tumor growth in athymic nude mice[END_REF][START_REF] Tripathi | Anti-cancer activity of a combination of cisplatin and fisetin in embryonal carcinoma cells and xenograft tumors[END_REF][START_REF] Touil | Improved antiangiogenic and antitumour activity of the combination of the natural flavonoid fisetin and cyclophosphamide in Lewis lung carcinoma-bearing mice[END_REF] and to possess antiangiogenic properties in vitro [12;13] and in vivo [START_REF] Touil | Improved antiangiogenic and antitumour activity of the combination of the natural flavonoid fisetin and cyclophosphamide in Lewis lung carcinoma-bearing mice[END_REF], the blood levels achieved by this compound after systemic administration and its metabolism have not previously been investigated in mice. The purpose of this study was therefore to study fisetin pharmacokinetics in mice, to determine fisetin principal metabolic routes and to evaluate the biological activity of a detected metabolite.

Concerning the pharmacokinetics of fisetin in mouse plasma, we found that it could reach a concentration of 2.5 µg/ml at 15 min and that it displayed a relatively long terminal half-life in plasma of 3.12 h after an i.p. administration of 223 mg/kg. Although the plasma concentrations are relatively low, it is possible that the long residence time could be responsible for the remarkable in vivo antitumor effect in mice at this dose level [START_REF] Touil | Improved antiangiogenic and antitumour activity of the combination of the natural flavonoid fisetin and cyclophosphamide in Lewis lung carcinoma-bearing mice[END_REF].

Nonlinear pharmacokinetics may also be involved in the fisetin long residence time at this dose level, as nonlinear pharmacokinetics has already been reported for other flavonoids [START_REF] Chabot | Pharmacodynamics and causes of dose-dependent pharmacokinetics of flavone-8-acetic acid (LM-975; NSC-347512) in mice[END_REF].

Potential accumulation of fisetin and/or metabolites in tumour tissue could also play a role in the fisetin antitumour activity. Compared to fisetin pharmacokinetics in rat in which negligible levels of free fisetin and a short half-life of 2.7 minutes were reported [START_REF] Shia | Metabolism and pharmacokinetics of 3,3',4',7-tetrahydroxyflavone (fisetin), 5-hydroxyflavone, and 7-hydroxyflavone and antihemolysis effects of fisetin and its serum metabolites[END_REF], it thus appears that the mouse is eliminating fisetin at a much slower rate (half-life = 3.12 hours), probably due to a lower conjugation capacity through glucuronidation in mice. Indeed, such significant species differences in clearance has also been observed for the flavonoid analogue 5,6-dimethylxanthenone-4-acetic acid (DMXAA), which is also mainly metabolized through glucuronidation, where in vitro intrinsic clearance in mice was found to be 15-fold lower that in rats, and in vivo plasma clearance was 1.8-fold smaller in mice compared to rat plasma clearance [START_REF] Zhou | Species differences in the metabolism of the antitumour agent 5,6-dimethylxanthenone-4-acetic acid in vitro: implications for prediction of metabolic interactions in vivo[END_REF].

We also observed that metabolites M1 and M2, which were identified as glucuronides, displayed longer terminal half-lives compared to fisetin. This appears unusual since these two metabolites are glucuronides of fisetin and M3, and shorter half-lives could have been expected. However such a pharmacokinetic behaviour has also been reported for fisetin in rats where a half-life of 2.7 min was found for the aglycone, whereas the glucuronides were showing a half-life of 40.7 min [START_REF] Shia | Metabolism and pharmacokinetics of 3,3',4',7-tetrahydroxyflavone (fisetin), 5-hydroxyflavone, and 7-hydroxyflavone and antihemolysis effects of fisetin and its serum metabolites[END_REF]. Such a longer half-life for genistein sulfates/glucuronides has also been observed in rats compared to the parent aglycone [START_REF] Piskula | Factors affecting flavonoids absorption[END_REF].

In this study, we have also identified a methylated metabolite of fisetin which was assigned to the 3,4',7-trihydroxy-3'-methoxyflavone (geraldol). This metabolite, reported here for the first time to our knowledge, is probably generated through catechol-O-methyl transferase (COMT) metabolism because fisetin possesses a catechol function on the B ring.

COMT catalyzes the transfer of a methyl group from S-adenosyl-L-methionine to polyphenols possessing an o-diphenolic (catechol) moiety. COMT can be found as membrane bound and soluble forms and is expressed in brain and most peripheral tissues including liver, kidneys and small intestine in the mouse, although there are large differences between species [34;35].

Methylation has already been reported for other flavonoids possessing a catechol function, e.g., quercetin, catechin, caffeic acid, and luteolin (reviewed in [START_REF] Manach | Polyphenols: food sources and bioavailability[END_REF]). Methylation of flavonoids generally occurs predominantly in the 3'-position of the polyphenol, but a minor proportion of 4'-O-methylated product can also be formed.

Although our mass spectrometry data indicated the presence of a methoxyl group on the fisetin molecule, the exact position of this substituent could not be determined by this technique alone. The formal identification of the methoxylated metabolite of fisetin was therefore further investigated using UV spectroscopy which is particularly useful for the identification of flavonoids [START_REF] Mabry | The systematic identification of flavonoids[END_REF]. It is known that methylation of hydroxyl groups in positions 3, 5, 7, or 4' produces hypsochromic shifts (shorter wavelengths) of the characteristic absorption bands in the UV-spectra of flavones, but does not significantly affect the shape of the UV-spectra when it occurs in other positions [START_REF] Markham | Techniques of flavonoid identification[END_REF]. In our case, metabolite M3 did not present a hypsochromic shift compared to the parent compound and their UV spectra were superimposable. Therefore, the methylation of the 3, 4', and the 7 positions could therefore be ruled out as possible methylated positions of fisetin.

We further ascertained the identity of metabolite M3 as being the 3,4',7-trihydroxy-3'methoxyflavone or geraldol using the log k value obtained by reversed-phase HPLC. It was already demonstrated that log k values are significantly different for the same flavone if methylated in the 3' or the 4' position [START_REF] Stefova | QSRR of flavones: evaluation of substituent contributions to RP HPLC retention[END_REF]. Using the same approach, the log k values obtained with 4 different ternary mobile phases were strictly identical for geraldol and metabolite M3, therefore substantiating further that M3 was indeed the 3'-methyl-fisetin, and could not be the 4'-methyl-fisetin. Concerning the possible methylation on position 3 of fisetin, this would have resulted in a decrease in the retention time in the reversed-phase HPLC mode, owing to its inductive effect which increases the influence of the polar carbonyl group [START_REF] Stefova | QSRR of flavones: evaluation of substituent contributions to RP HPLC retention[END_REF]. Methylation of the hydroxyl group in position 3 results in a lack of hydrogen bonding between 3-OH and the carbonyl group (at C4) which results in an increased polarity of the molecule and consequently a decreased in retention time. In contrast, an increase in retention time was observed compared to fisetin, which further rules out a methylation at the 3 position of fisetin. Considering metabolite M3 molecular mass M3 identical to geraldol, its UV spectrum identical to fisetin and geraldol, and identical retention times to geraldol in 4 different ternary gradient systems, the identity of M3 was therefore assigned to geraldol.

Of particular interest, it was also observed that this 3'-methoxylated metabolite of fisetin could achieve higher concentrations in tumors compared to fisetin, an observation probably linked to the fisetin in vivo remarkable anticancer activity already noted by some investigators [START_REF] Khan | A novel dietary flavonoid fisetin inhibits androgen receptor signaling and tumor growth in athymic nude mice[END_REF][START_REF] Tripathi | Anti-cancer activity of a combination of cisplatin and fisetin in embryonal carcinoma cells and xenograft tumors[END_REF][START_REF] Touil | Improved antiangiogenic and antitumour activity of the combination of the natural flavonoid fisetin and cyclophosphamide in Lewis lung carcinoma-bearing mice[END_REF]. This high tumor geraldol levels could result from either an increased in situ formation or this metabolite, or from hydrolysis of its glucuronide that could regenerate the aglycone.

Indeed, a high COMT activity in the tumor is possible because an increased expression of this enzyme has recently been observed in cancer [START_REF] Hevir | Disturbed expression of phase I and phase II estrogen-metabolizing enzymes in endometrial cancer: lower levels of CYP1B1 and increased expression of S-COMT[END_REF]. Concerning the possible regeneration of the aglycone from its corresponding glucuronide, it has been reported that Lewis lung tumors express a high β-glucuronidase activity that could hydrolyze the glucuronide to its geraldol aglycone [START_REF] Dobrossy | Elevation of lysosomal enzymes in primary Lewis lung tumor correlated with the initiation of metastasis[END_REF].

From a metabolic standpoint, the methylation of fisetin in vivo could be viewed as a favourable metabolic route, because methylation usually results in a higher metabolic stability of flavonoids that could favour its retention in vivo [START_REF] Wen | Methylated flavonoids have greatly improved intestinal absorption and metabolic stability[END_REF]. Because geraldol can be glucuronidated, as shown in this work, and could therefore be secreted in bile, bacterial glucuronidase present in the intestines could hydrolyze this metabolite and give back the aglycone (geraldol) that could be absorbed from the intestines, therefore possibly contributing to a longer residence time in vivo for this metabolite. In addition, the presence of a methoxy group on the A or B ring of the flavonoid also seems to protect the structure from bacterial degradation in faeces, therefore probably contributing to longer exposure times in vivo by allowing the reabsorption from the intestines of the intact methylated metabolite [START_REF] Lin | Degradation of flavonoid aglycones by rabbit, rat and human fecal flora[END_REF].

However, we observed in this work that the geraldol formed in vivo is rapidly cleared from the plasma as shown by its short half-life, although this metabolite is less polar and probably more metabolically stable than fisetin. We have no obvious explanation for this observation, except that upon geraldol formation in vivo, this metabolite could be rapidly metabolized to other metabolites (e.g., glucuronides and/or sulfates), which could perhaps explain its rapid clearance.

The fisetin antiproliferative/cytotoxic activity determined in this study on Lewis lung carcinoma cells is in line with its cytotoxic activity reported on other cancer cell lines, e.g., in prostate [START_REF] Haddad | Novel antiproliferative flavonoids induce cell cycle arrest in human prostate cancer cell lines[END_REF], liver [START_REF] Chen | Wogonin and fisetin induction of apoptosis through activation of caspase 3 cascade and alternative expression of p21 protein in hepatocellular carcinoma cells SK-HEP-1[END_REF], colon [START_REF] Lu | Fisetin inhibits the activities of cyclin-dependent kinases leading to cell cycle arrest in HT-29 human colon cancer cells[END_REF], and leukemia cell lines [START_REF] Lee | Wogonin and fisetin induce apoptosis in human promyeloleukemic cells, accompanied by a decrease of reactive oxygen species, and activation of caspase 3 and Ca 2+ -dependent endonuclease[END_REF]. With regard to the comparison of the biological activity of fisetin and its metabolite geraldol, it was of interest to note that the metabolite was more cytotoxic than the parent compound on Lewis lung carcinoma cells showing a 2.5-fold lower IC 50 value. This increased cytotoxicity of the metabolite associated with the higher concentrations of geraldol in tumor could give fisetin a selective anticancer activity advantage in vivo. Also of interest, both fisetin and geraldol were found several folds less cytotoxic towards normal NIH 3T3 cells compared to tumor cells, and this could give these compounds an important in vivo advantage in terms of therapeutic index, because they would be less toxic to normal cells compared to cancer cells. This relative non toxicity of flavonoids to normal cells was also noted for other normal cell lines [START_REF] Fotsis | Flavonoids, dietary-derived inhibitors of cell proliferation and in vitro angiogenesis[END_REF]. This tumor cell selectivity was also observed on prostate cancer cells that were shown to be more vulnerable to fisetin compared to normal prostate cells [START_REF] Haddad | Novel antiproliferative flavonoids induce cell cycle arrest in human prostate cancer cell lines[END_REF]. This relatively uncommon cancer cell selectivity could therefore confer fisetin a valuable advantage for in vivo anticancer treatment.

In addition, it should be mentioned that because of the presence of 4 hydroxyl substituents on the fisetin molecule, several glucuronide conjugates were also detected at high concentrations in mouse plasma in the present mouse study, and also in rat plasma [START_REF] Shia | Metabolism and pharmacokinetics of 3,3',4',7-tetrahydroxyflavone (fisetin), 5-hydroxyflavone, and 7-hydroxyflavone and antihemolysis effects of fisetin and its serum metabolites[END_REF]. These metabolites could also play a role in the overall antiangiogenic effects observed in vivo in mice, because some flavonoid sulfates and/or glucuronides have been reported to display bioactivities sometimes superior to their aglycones (free forms) [START_REF] Fang | Morin sulfates/glucuronides exert anti-inflammatory activity on activated macrophages and decreased the incidence of septic shock[END_REF][START_REF] Meng | Activation of aminoflavone (NSC 686288) by a sulfotransferase is required for the antiproliferative effect of the drug and for induction of histone -H2AX[END_REF][START_REF] Lotito | Metabolic conversion of dietary flavonoids alters their anti-inflammatory and antioxidant properties[END_REF][START_REF] Tribolo | Comparative effects of quercetin and its predominant human metabolites on adhesion molecule expression in activated human vascular endothelial cells[END_REF]. Future studies aimed at determining potential high partition of metabolites and/or accumulation in tumour tissue should also be considered, as well as a more complete pharmacokinetic profile of fisetin and its metabolites in tumours should also be assessed.

The interesting bioactivity of geraldol shown in the present work has also to be put in perspective of a recently published work on quercetin (3',3,4',5,7-pentahydroxyflavone), a close parent of fisetin bearing an additional 5-hydroxyl substituent. A 3'-O-methylated metabolite of quercetin, i.e., isorhamnetin, was shown to possess potent inhibitory activity of cyclo-oxygenase-2 (COX-2), an enzyme mechanistically linked to carcinogenesis [START_REF] Jones | Characterisation of metabolites of the putative cancer chemopreventive agent quercetin and their effect on cyclo-oxygenase activity[END_REF]. Also in line with this observation, quercetin metabolites have been found to possess antioxidant properties [START_REF] Da Silva | Quercetin metabolites inhibit copper ion-induced lipid peroxidation in rat plasma[END_REF][START_REF] Manach | Quercetin is recovered in human plasma as conjugated derivatives which retain antioxidant properties[END_REF][START_REF] Merfort | Radical scavenger activity of three flavonoid metabolites studied by inhibition of chemiluminescence in human PMNs[END_REF], and to inhibit xanthine oxidase and lipoxygenase [START_REF] Day | Conjugation position of quercetin glucuronides and effect on biological activity[END_REF].

Regarding the effect of fisetin and geraldol on the formation of capillary-like structures, the parent compound fisetin has previously been shown to be active in this model [START_REF] Touil | Improved antiangiogenic and antitumour activity of the combination of the natural flavonoid fisetin and cyclophosphamide in Lewis lung carcinoma-bearing mice[END_REF], whereas geraldol could not achieve an active concentration in this model probably due to its inherently low water solubility. It is however possible that longer exposure time to geraldol (> 24 h) could nonetheless participate to an antiangiogenic response in vitro. Considering the above results, it is therefore possible that fisetin antitumor action in vivo could involve a dual mechanism of action, i.e., the parent compound fisetin would exert its strong antiangiogenic action directly, whereas its main metabolite geraldol would then exert its strong cytotoxicity against tumor cells.

In conclusion, we have determined fisetin pharmacokinetics in mice, for the first time to our knowledge, and have also identified geraldol, a previously unknown metabolite. In addition, we have shown that the metabolite geraldol possesses interesting biological activities in terms of selective cytotoxicity on cancer cells, antiproliferative and antimigration effects on endothelial cells. Fisetin and geraldol anticancer and antiangiogenic properties therefore deserve to be optimized in preclinical models, as a single administration or in combination therapy with cytotoxic anticancer agents.

Legends for Figures

Figure 1 .

 1 Figure 1. Chemical structures of fisetin, geraldol and kaempferol. R 1 =OH, R 2 =H, fisetin

Figure 2 .

 2 Figure 2. Representative HPLC chromatogram showing the separation of fisetin and its main

Figure 3 .

 3 Figure 3. Pharmacokinetic profile of fisetin in mouse plasma. After an intraperitoneal

Figure 4 .

 4 Figure 4. Plasma concentration of the main fisetin metabolites (M1, M2 and M3) as a

Figure 5 .

 5 Figure 5. Representative HPLC chromatogram showing the beta-glucuronidase digestion of

Figure 6 .

 6 Figure 6. Mass and UV spectra of metabolite M3. A) Representative mass spectrum of

Figure 7 .

 7 Figure 7. A) Plasma and B) tumor concentrations of fisetin and its main metabolites M1, M2

Figure 8 .

 8 Figure 8. Comparative cytotoxicity of fisetin and geraldol on various cell lines.

Figure 9 .

 9 Figure 9. Effect of fisetin and its metabolite geraldol on endothelial cell migration (scratch

Figure 10 .

 10 Figure 10. Comparative antimigration effects of fisetin and its metabolite geraldol.

Figure 1 ,Figure 2 ,Figure 3 ,

 123 Figure 1, Touil et al.

Figure 10 ,

 10 Figure 10, Touil et al.

  3 , mice were injected with fisetin (223 mg/kg, i.p.) dissolved in 100 µl of polyethylene glycol 200/DMSO (7:3; v:v), and 15 min later, the mice were sacrificed and the tumors were harvested, rinsed with ice-cold PBS and transferred into a polypropylene tube and kept at -

20°C until analysis. All animal experiments have been carried out in accordance with institutional and French regulations concerning the protection of animals used for experimental and other scientific purposes, and with the European Commission regulations.

. Identification of metabolites 2.7.1 Hydrolysis of glucuronide conjugates. To

	2.7hydrolyze glucuronide conjugates, plasma
	or tumor homogenate samples were adjusted to pH 5.0 with an equal volume of 50 mM
	acetate buffer, and incubated at 37°C for 2 h in the presence of 1000 units of β-glucuronidase
	(Sigma No. G-0751). The hydrolyzed samples were submitted to the above described protocol
	of methanolic extraction and injected onto the HPLC system.
	: area
	under the plasma concentration versus time curve from time zero to the time of the last
	measurable concentration (AUC 0-t ) calculated by the trapezoidal method; maximum
	concentration (C max ); mean residence time (MRT); terminal half-life; apparent intraperitoneal
	clearance (CL/F) and volume of distribution at steady state (Vss/F). Pharmacokinetics
	parameters were also determined by fitting the fisetin plasma concentration versus time to a
	two-compartmental model with extravascular administration using the Graph Pad Prism
	software.

2.7.2 Identification of metabolite M3 using UV and mass spectrometry. Metabolite

	M3
	was analyzed online using an HPLC-MS/MS system composed of an HPLC apparatus
	(Surveyor  , ThermoElectron) equipped with a UV diode array detector and coupled with a
	mass spectrometer (LCQ-Advantage  , ThermoElectron). A C 18 reversed-phase column (EC.
	250/2 Nucleodur Sphinx RP, 5 µm, 250 mm X 2.1 mm, Macherey-Nagel) thermostated at
	30°C was used. The metabolites (injection volume: 15 µl) were resolved with the following
	ternary gradient using methanol (A), acetonitrile (B) and aqueous 0.1% TFA (C) at a flow rate
	of 0.2 ml/min: from 0 to 50 min, 7-35% A, 4-19 % B, 89-46 % C; from 50 to 60 min, 35% A,
	19% B, 6% C (gradient 1). The mass spectrometer consisted of an electrospray ionization
	system used in positive ion polarity mode and ion trap mass analyzer. The operating
	parameters where as follows: the spray needle voltage was set at 4.0 kV and the spray was
	stabilized with a nitrogen sheath gas. Electrospray capillary voltage was 4.0 V, nitrogen was
	used as auxilliary gas, and the capillary temperature was 250°C. The Xcalibur ® software was
	used for data acquisition and analysis.

2.7.3 Identification of M3 using retention times in various gradient systems. To ascertain

	the identity of M3, this metabolite and authentic geraldol were separately injected using the
	HPLC system with gradient 1 described in section 2.7.2, and three other ternary gradients
	composed of methanol (A), acetonitrile (B) and aqueous 0.1% TFA (C) (flow rate 0.2 ml/min)
	as follows: gradient 2-from 0 to 70 min, 7-35% A, 4-19% B, 89-46% C; gradient 3-from 0 to
	50 min, 15-35% A, 10-19% B, 75-46% C; gradient 4-from 0 to 70 min, 15-35% A, 10-19%

Table 1 . Fisetin and metabolites pharmacokinetic parameters. 1

 1

		Cmax (µg/ml) AUC (µg/ml)×h	MRT (h)	Half-lives (h)
	Fisetin	2.53	4.00	4.51	3.12
	Metabolite M1	6.54	40.71	11.27	7.81
	Metabolite M2	2.22	13.63	9.95	6.89
	Metabolite M3	0.07	0.17	0.65	0.45

1

Cmax, maximum plasma concentration; AUC, area under the plasma concentration versus time curve from time zero to the time of the last measurable concentration; MRT, mean residence time.

Table 2 . Fisetin and its metabolites distribution in plasma and normal tissues in mice.

 2 Mice were administered fisetin intraperitoneally at a dose of 223 mg/kg and tissues were harvested 30 minutes later for HPLC analyses, as detailed in the Materials and Methods section. Values are the mean ± SEM of 3 independent determinations.

			Concentration (µg/ml or µg/g tissue)
	Tissues	Fisetin	M1	M2	M3
	Plasma	1.14 ± 0.12	6.12 ± 0.47	1.93 ± 0.29	0.10 ± 0.01
	Liver	39.15 ± 12.85	0.66 ± 0.22	2.02 ± 1.31	2.23 ± 0.38
	Intestines	80.11 ± 33.82	0.12 ± 0.08	4.17 ± 3.24	5.05 ± 0.49
	Kidneys	83.69 ± 10.45	4.23 ± 1.88	49.03 ± 22.65	14.25 ± 6.54
	Lungs	8.00 ± 3.31	0.69 ± 0.46	5.67 ± 5.69	1.57 ± 0.67
	Spleen	2.00 ± 0.50	1.03 ± 1.37	1.30 ± 1.01	1.71 ± 1.67

Table 3 . Retention times of fisetin and geraldol in various HPLC ternary gradient systems Gradient Retention times in minutes (log k) b

 3

	system a	Fisetin	Geraldol	Metabolite M3
	1	40.0 (0.561)	47.7 (0.590)	47.7 (0.591)
	2	49.4 (0.596)	60.1 (0.628)	60.1 (0.628)
	3	28.6 (0.504)	39.0 (0.557)	39.0 (0.557)
	4	32.8 (0.528)	46.6 (0.586)	46.6 (0.586)
	a The ternary gradient systems used were composed of methanol (A), acetonitrile (B) and
	water containing 0.1% TFA, as follows: gradient 1-from 0 to 50 min: 7-35% A, 4-19% B, 89-
	46% C ; gradient 2-from 0 to 70 min: 7-35% A ; 4-19% B ; 89-46% C ; gradient 3-from 0 to
	50 min : 15-35% A ; 10-19% B ; 75-46% C ; gradient 4-from 0 to 70 min : 15-35% A ; 10-
	19% B ; 75-46% C. The reversed-phase HPLC system used is described in the Methods
	section.

b The retention factor k was calculated as in

Stefova et al., 2007 [27]

Acknowledgements. This research was supported by the Institut national de la santé et de la recherche médicale (Inserm), by the Centre national de la recherche scientifique (CNRS), and by a grant from the French National Cancer Institute (Institut ational du Cancer, Boulogne-Billancourt, France).


Clément Zinoune

Philippe Bonnifait

Javier Ibañez-Guzmán

Detection of Missing Roundabouts in Maps for Driving Assistance Systems

Keywords:

Detection of Missing Roundabouts in Maps for Driving Assistance Systems

Clément Zinoune1,2 , Philippe Bonnifait 1 , Javier Ibañez-Guzmán 2 Abstract-Passenger vehicles are evolving into sensorbased computer controlled platforms with different levels of autonomy. Digital maps representing road networks are being used as an a priori source of information to provide context and to anticipate oncoming situation. On top of it, world models are built for machine understanding. However, these can have local errors, affecting location based functions. A common one is due to the rapid deployment of roundabouts, as they are introduced widely, with navigation maps ignoring their presence. This work introduces a novel approach to make the vehicle able to detect the presence of a roundabout while it is driven. It is then possible to update the map by the vehicle itself. The approach is based on graphical pattern recognition methods using a Bayesian classifier. The approach has been demonstrated experimentally using data acquired in real-traffic conditions.

I. INTRODUCTION

Continuous progress in information and communication technologies has meant that modern vehicles are being transformed into sensor-based platforms with often advanced wireless connectivity capabilities. Most vehicle manufacturers are today working on the deployment of Advanced Driving Assistance Systems (ADAS). The rationale is to facilitate the comfort of driving tasks, reduce the number of accidents as well as to improve traffic flow whilst including costs as a major constraint.

Statistics have shown that the introduction of ADAS functions improves safety. For example, the introduction of an "Automatic Intelligent Speed Assistant" could reduce up to 15% of road fatalities [START_REF]Traffic Accident Causation in Europe Project (TRACE)[END_REF]. However, ADAS require more and more detailed information about the driving context the vehicle is going to be facing in the near future. This is provided in some part by sensors mounted on the vehicle like radar or camera especially for the close vehicle's surroundings because they are necessarily limited by their fields of view and the occlusions due to obstacles in the scene (other vehicles for instance). The high cost of these sensors is also a drawback for their use in standard vehicles. Digital maps are gradually used to get a wider scale context information. For example, as they store road geometry information, they may be used to help to control the vehicle speed and bend the front lights when a curve is approaching [START_REF] Chowdhary | Driver assistance applications based on automotive navigation system infrastructure[END_REF]. Moreover, a large amount of road attributes are kept in the digital maps which makes possible to inform the driver of the current speed limit [START_REF] Janssen | Vehicle surround sensing based on information fusion of monocular video and digital map[END_REF], to change the perception field of view in accordance to the kind of environment the vehicle is evolving in (rural or urban) [START_REF] Unterholzner | Adaptive state space control of a camera platform for an autonomous ground vehicle[END_REF], or to turn off the cruise control system as soon as the vehicle is approaching a crossing or a toll gate. This is obviously in addition to the standard use of maps and global navigation satellite systems (GNSS) for guidance purposes.

However, digital maps include multiple errors originating in the collected data or in changes that have occurred since the compilation of the maps and their distribution to the vehicles. This has resulted in serious integrity issues that not only are a common client complain of users of navigation devices but also reduce the integrity of safety related applications. Within this context, it is very important to be able to determine when the maps are erroneous and, if possible, to correct them using data which is collected by the subject vehicle. Confidence in the detected errors and in the corrections to make should increase as the trajectory is repeated by the vehicle and shared with other users.

Map errors can be classified in 3 categories: geometrical, topological and attributes. Geometrical errors make that the map doesn't match with reality. Topological errors refer to connection errors between nodes of the map. Incorrect attributes mean that the semantic information extracted from the map (like speed limit) doesn't correspond to reality. We consider in the following only geometrical errors.

In practice, map engines are in charge of providing the pertinent map information to the ADAS functions through an Electronic Horizon [START_REF] Durekovic | Architectures of map-supported ADAS[END_REF] for instance. In order to monitor the map quality, a sub-system is introduced between the map engine and the ADAS (Fig. 1). It collects the GPS and proprioceptive information to make its own representation of the travelled trajectory which is then used to detect errors in the map. Once an error has been detected, the faulty link ID is then stored in a look-aside database (LADB). This "don't use the map" information will be exploited by the ADAS next time the vehicle will travel in the same area.

In this paper, we focus on one particular kind of geometrical errors, the missing "roundabouts". Roundabouts (i.e. traffic circles) are particularly interesting elements of the road networks that affect the content of a map. They are introduced extensively instead of crossroad intersections because they divide by four the number of potential collision points, they reduce the vehicle speed and improve traffic flow [START_REF] Ewing | The built environment and traffic safety: A review of empirical evidence[END_REF]. Further, they may not be represented in digital navigation maps. These also have significant consequences on ADAS. For example, a cruise control system must be turned off when the vehicle approaches a roundabout which is possible only if it has been registered in the map. Fig. 1. Map monitoring architecture. The system informs the map-aided ADAS whether the map is wrong thanks to data previously collected and stored in a look-aside database.

On the one hand, road changes including new roundabouts may be detected by using image processing methods like active contour [START_REF] Ravanbakhsh | Road roundabout extraction from very high resolution aerial imagery[END_REF] or gradient [START_REF] De Gunst | Knowledge-based updating of maps by interpretation of aerial images[END_REF] on aerial images. On the other hand, the vehicle itself can be used to create maps by converting the vehicle's trajectory into road segments [START_REF] Chen | High-precision lanelevel road map building for vehicle navigation[END_REF]. This generally assumes a very accurate (and so often expensive) localisation system like bi-frequency GPS combined with inertial measurements units or lidars.

Methods based on usual vehicles equipments can also be found in the literature. In [START_REF] Leitão | Road singularities detection and classification[END_REF], a camera mounted underneath the vehicle's mirror is used to extract and classify road singularities using a neural network. [START_REF] Ekpenyong | Updating of road network databases: Spatio-temporal trajectory grouping using snap-drift neural network[END_REF] uses also a neural network architecture to classify roads travelled by several vehicles into classes like road, local street, roundabout or traffic light stop according to a set of parameters (speed, rate of acceleration, curvature or change in travel direction). [START_REF] Agamennoni | Robust and accurate road map inference[END_REF] proposes algorithms to infer the presence of road or roundabouts based on offline and centralized GPS trace mining.

In this paper, a method for detecting the presence of roundabouts is studied, using a ego-localization systems which exploits the vehicle embedded sensors. The idea is to recognize roundabouts in the passed vehicle's path. The immediate trace of the position estimates is first converted in an adequately sampled window. Then, an efficient descriptor is proposed and exploited in a Bayesian classifier that has been chosen to take profit of its low computational complexity and make embedded application possible. Once a roundabout has been detected, this information is compared to the digital map content. If the roundabout is not already recorded in the map, an error is detected and recorded in the LADB.

The remainder of the paper is organized as follows: Section II formulates the proposed framework including the localization algorithm, the preprocessing necessary to feature recognition, the descriptors, the defined classes as well as the estimation of the roundabout characteristics. Section III, includes the experimental content, it validates the proposed approach using data acquired from a test vehicle operating in real traffic conditions. It includes the experimental apparatus, and an analysis of the algorithm performances. Finally, Section IV concludes the paper.

II. FRAMEWORK

The algorithm presented here uses as input information coming from standard sensors mounted in the vehicle and a standard global positioning system (GPS). The odometer, the yaw rate, the speed and the speed difference between the rear wheels are used here. They have the advantage to be broadcast on one single bus in the vehicle (CAN-bus) so to be easily read and recorded. Proprioceptive sensors are very efficient to provide a precise estimate of the travelled path on a short horizon of time. GPS provides the global position of the vehicle which makes possible to georeference the roundabouts as soon as they are detected. Merging the two modalities allows localizing precisely and globally.

A. Localization

The vehicle localisation task is done by a loosely coupled Extended Kalman Filter (EKF). It uses GPS fixes when available to correct the proprioceptive estimation [START_REF] Bonnifait | Data fusion of four ABS sensors and GPS for an enhanced localization of carlike vehicles[END_REF]. The state representation is shown in [START_REF]Traffic Accident Causation in Europe Project (TRACE)[END_REF].

X k+1 = f (X k , v k ) + α k Y k = H.X k + β k (1)
X k = x k y k ω k b k ψ k T Y k = x gps k y gps k ω g k r k T (2)
The state, observation and input vectors are detailed in [START_REF] Chowdhary | Driver assistance applications based on automotive navigation system infrastructure[END_REF], where index k denotes the k th time step. In the state vector X, x y are the vehicle's coordinates in a East-North-Up (ENU) reference frame. ψ and ω stand for the vehicle's heading (clockwise from the North direction) and yaw rate (rotation rate around the Up axis) respectively. Finally, b is the vehicle's gyroscope bias as defined in (4). The observation vector Y contains the vehicle's position according to the GPS observation x gps y gps , the yaw rate measured by the vehicle's gyroscope ω g and the speed difference between the rear vehicle's wheels r. The measured input is the vehicle's speed v.

The model and observation noises α and β respectively are supposed to be white and zero mean.

The state transition function f depends on the input v and on the state itself. It is defined in [START_REF] Janssen | Vehicle surround sensing based on information fusion of monocular video and digital map[END_REF] where T is the sampling period and l is the vehicle's rear track. The observation model H used here is given in [START_REF] Ewing | The built environment and traffic safety: A review of empirical evidence[END_REF].

f (X k , v k ) =       x k + T v k sin(ψ k ) y k + T v k cos(ψ k ) ω k b k ψ k -T ω k       (3) ω g = ω + b (4) r = 2 l ω (5)
H =     1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 2/l 0 0     (6)
For a moving vehicle, the observability of each state variable can be easily demonstrated.

The vehicle's state is computed in real time and its position over the last L meter is kept in a sliding window. This track is used in the following to ascertain the vehicle has crossed a roundabout.

B. Pre-processing

The first step of feature recognition is pre-processing. The area of interest is isolated from the whole vehicle track using the concept of sliding window, e.g. only the last L meters of the vehicle's trajectory are used to search for features.

The vehicle's odometer is used to spot the time since which the vehicle covered L meters. The vehicle positions are stored in a buffer memory and dismissed as soon as it goes out of the sliding window. The track present in the buffer memory at any time is called buffer track.

Pre-processing consists in changing the reference frame of the buffer track to reduce the diversity of the possible roundabout shapes. The shapes and roundabout orientations are then made independent. For this purpose, we propose to use an egocentric approach. The buffer track is transformed into the current vehicle frame using the rotation matrix Z defined in [START_REF] Ravanbakhsh | Road roundabout extraction from very high resolution aerial imagery[END_REF]. As ψ denotes the vehicle's heading with respect to the North (vertical axis), this rotation matrix is slightly different from the usual one.

Z k = sin (ψ k ) -cos (ψ k ) cos (ψ k ) sin (ψ k ) (7)

C. Descriptor

In classification and recognition tasks, the choice of a descriptor is crucial. In this paper, we focus on pieces of vehicle's trajectory as graphical 2D objects. The descriptor should then illustrate these two dimensions independently.

Our choice of a descriptor was motivated by the similarities between our problem and the handwriting recognition domain [START_REF] Abdl | Handwriting identification: a direction review[END_REF], [START_REF] Plamondon | Online and off-line handwriting recognition: a comprehensive survey[END_REF]. Let the descriptor be composed of two halves: left and down profiles. The left (resp. down) vector stores the distances between the left (resp. down) side of the interval and the first point met on this line (resp. column) as shown in Fig. 2. The profiles contain n equally spaced elements.

One issue when one deals with features such as roundabouts is the diversity of the elements called roundabouts. This comes, on the one hand from the number of exits of the roundabout and the one chosen by the driver, and on the other hand the roundabout radius. The first difficulty will be solved by defining a suitable number of classes, as developed later on. Secondly, to prevent scale effects, the descriptor is normalized using [START_REF] De Gunst | Knowledge-based updating of maps by interpretation of aerial images[END_REF]. The descriptor of one buffer track is therefore a vector D of 2n elements. As soon as the vehicle goes straight, the projection of the buffer track along the Y axis becomes very small. The left profile sampling shown in Fig. 2 is made inconvenient and may introduce errors in the classifier. Moreover, in this situation, the vehicle cannot have crossed any roundabout, thus, buffer tracks that have a vertical range less than threshold ∆Y min are neglected.

D =   c 1 c 2 • • • c n max i:1→n (c i ) l 1 l 2 • • • l n max i:1→n (l i )   (8)

D. Classes

Consistent classes must be defined to make the recognition process efficient. It is not suitable to create only two main classes: one containing every buffer track that contains a roundabout and the other with anything else. Indeed, a too wide variety of shapes inside the same class (due to the large diversity of roundabout shapes as said previously) makes them inconsistent and the learning and recognition steps very tough. The solution found here has been to split the "roundabout" class into sub-classes. Each one being associated to one kind of buffer track containing a roundabout (one for the vehicle went straight, one for the vehicle took the third exit, ...). Likewise, several classes associated to non-roundabout buffer tracks have been designed.

Table I illustrates the typical buffer tracks of every class. These have been manually designed to represent the variety of the buffer tracks as well as possible. Thus, roundabout at which the vehicle went straight, turned left and made a U-turn are represented. Also, a class is set for each one of them when the roundabout is at the beginning and at the end of the buffer track to improve the robustness of the classifier. This makes the first eight classes. The same way to proceed is applied to non-roundabout classes: Nearly straight trajectories are in classes nine and ten, left turns are in classes eleven, fourteen and fifteen and right turns are in classes twelve and thirteen. Once these representative samples have been manually isolated, their corresponding descriptors are computed as developed in the previous paragraph.

E. Recognition

Let us consider a closed frame of discernment Ω and a set of classes {w i } (i = 1 → k) being a partition of Ω. According to total probability law and to the Bayes' theorem, the conditional probability of a sample D to belong to the i th class is given by [START_REF] Chen | High-precision lanelevel road map building for vehicle navigation[END_REF]. In this equation, it is assumed that every class has the same prior probability.

P (w i |D) = P (D|w i ) k j=1 P (D|w j ) (9)
We consider sample D as a vector containing 2n random variables. Let us assume that, within a given class w i , samples are normally distributed around the representative sample D i . Thus, if D belongs to w i then D ∼ N (D i , P) ∨ i ∈ {1, ..., k} where P is the covariance matrix.

The posterior probability of w i to be the correct class associated to the sample D is then given by [START_REF] Leitão | Road singularities detection and classification[END_REF].

P (w i |D) = exp -(D-Di) T •P -1 •(D-Di) 2 k j=1 exp - (D-Dj) T •P -1 •(D-Dj) 2 (10)
The sample D is then associated to the maximum posterior probability class. This method minimises error probability.

F. Roundabout placement

As soon as a roundabout has been detected in a buffer track, the final step is to find its centre and its radius. This allows to convert the discovery of a roundabout back into absolute coordinates and to identify it clearly into the LADB. This step is based on the clustering of the successive rotation centres. It is easy to prove that if three points are not aligned, a unique circle passing by them exists. The centre and radius of every points trio is computed along the whole buffer track as presented by blue crosses in Fig. 3. Considering the fact that the curvature radius remains almost constant while the vehicle is in the roundabout, the true centre belongs to the densest cluster. As shown by the red square and circle, the selected centre is the barycentre of this cluster. This method is a good compromise between simplicity and robustness.

The centre coordinates are converted back into absolute ENU using the reciprocals of ( 7) and then into global latitude/longitude coordinates.

Usually, the roundabout information is actually one of possible value of the attribute road type linked to a road segment (as motorway, parking, tunnel,...). If a roundabout has been detected for the first time by the vehicle and is missing in the digital map, its location is added to the digital map and a "don't use the digital map here" flag is raised for ADAS. Then, next time the vehicle goes through this place, ADAS will be able to reject the map information.

III. RESULTS

A. Experimental setup

The method has been tested in real conditions thanks to a Renault Espace passenger vehicle driven in various conditions, urban, suburban and rural environments. The localisation system on board was a standard single frequency Ublox 4T receiver. A Ixsea LandIns Inertial Navigation System (INS) tightly coupled to a Novatel GPS receiver has been used as position ground truth.

We also had access to more than 300 vehicle's internal states variables of the vehicle via the CAN-bus. More precisely, we have used the odometer, the speed, the rear wheel speed difference and the yaw rate.

The few parameters of the algorithm have been tuned as follows. First the length L of the buffer track must be larger than the perimeter of the biggest roundabout the vehicle may cross. We have set L = 250m which corresponds to a roundabout radius of 40 meters which is also the value chosen for the maximum detectable radius R M ax in the filter described before. Then, the minimum Y range defined in this filter is set so that ∆Y min = 10m which corresponds to minimum detectable radius of 5 meters for U-turns and 10 meters for straight crossings. Finally, as the covariance matrix P of the classifier is not sensitive, the identity matrix has been used.

B. Method performance evaluation

The most relevant way to assess the method is to use it on real data. A large amount of data representing more than 150 km have been recorded in various environments. Rural, suburban and urban areas have been covered totalling up 37 different roundabouts. Some of them have been crossed several times by different ways and under different traffic conditions.

Firstly, the performance of the localization sub-system has been assessed thanks to the ground truth provided by the INS. The maximum distance error is below 15 meters and equals 5 meters in average. The consistency of the filter is highlighted in Fig. 4. It shows that the true position (in green) and the measured position (in red) are always inside the 3-sigmas covariance ellipses (blue ellipses) which corresponds to a 99.7% probability. One can notice that when the vehicle is stopped (bottom side of the picture), these ellipses decrease due to a larger number of observations for the same position.

Secondly, the classifier performance has been deduced from more than 100 km of driving sequences. The classes assigned by the classifier have been manually compared to true classes. The results have been merged to provide the overall roundabout detection rates. These are summarized in Table II. One can notice that every buffer track classified as roundabout actually contains a roundabout, i.e. there are no false positives. Moreover, data used here contains ambiguous manoeuvres like U-turn and left turns with no roundabout. However some roundabouts are missed by the classifier. This is due, on the one hand, to the fact that the vehicle crossed them nearly straight which makes the roundabout almost invisible in the buffer track. On the other hand, the evaluation of a buffer track is independent from one time step to the other. A roundabout miss-detected when it was at the beginning of the track may be detected a few time steps later. This justifies the uses of two classes for one roundabout shape as denoted in II-D.

Fig. 5 represents a part of the outputs of the algorithm. One can notice that the roundabouts have been correctly detected and their centres located on the map. A direct application of the algorithm is shown in Fig. 6. The vehicle has been driven on a recently modified road. On road D926, a new roundabout has been built and another one is still under construction. We can see the roundabout centres (red stars) detected by the algorithm while the vehicle was driven along the blue path. It can also be noticed on this picture that the U-turn is successfully classified as a non-roundabout. The algorithm makes then possible to detected that the map is obsolete and to store this information for next passages in the LADB. We see in this situation that a cruise control system must be henceforth turned off on road D924.

The background map used in Fig. 5 and Fig. 6 to illustrate the results is taken from OpenStreetMap project [START_REF] Hentschel | Autonomous robot navigation based on openstreetmap geodata[END_REF], [17].

The method has been coded and tested in Matlab and runs more than 10 times faster than the real data. Then, even if it has not been implemented yet, it makes conceivable an embedded application.

IV. CONCLUSIONS AND FUTURE WORK

The presented approach is based on the notion of integrity monitoring of digital maps used in ADAS functions for passenger vehicles. To prevent malfunctions, the detection of one typical road geometrical error has been addressed, namely, missing representation of roundabouts in digital maps. The shape of a path followed by a vehicle when traversing a roundabout has a typical signature, thus it has been possible to apply a Bayesian classifier on data representing the traversed path. For this purpose, only data from existing vehicle sensors was used (i.e. odometer, gyroscope, speedometers and GPS). Further, the resulting algorithm has low computational complexity, so it can be easily incorporated into embedded applications. The proposed solution has been extensively tested on data acquired in real-traffic conditions showing promising results by detecting the roundabouts that the vehicle traversed whatever their diameter, shape or the driver trajectory within the roundabout. It was possible to detect recently built roundabouts and to add them onto a look-aside database as these were not yet present in commercially available digital maps. The on-line correction of digital road maps is applicable in particular to commuting trajectories, even after the first run. Thus the performance of map-based ADAS functions is made more reliable. Further to this work, other aspects of map integrity will be assessed drawing also advantage from perception system based on camera which appears widely in passenger vehicles. Example of application of the algorithm in rural environment. Here the road network has changed recently, one roundabout has been created and another one is being built. These are not registered in the digital map (red lines) yet. The vehicle track is in blue and the centres of the roundabouts found by our algorithm are in red stars.

Fig. 2 .

 2 Fig.2. An example of buffer track (in blue) with its associated descriptor. The first half is left profile (in green), the second one is down profile (in red).

Fig. 3 .

 3 Fig. 3. Determination of the roundabout centre and radius in a buffer track. Blue line represents the buffer track itself and blue crosses are its successive rotation centres. Red square denotes the centre retained associated to its circle (red line).

Fig. 4 .

 4 Fig.[START_REF] Unterholzner | Adaptive state space control of a camera platform for an autonomous ground vehicle[END_REF]. Detail of vehicle's path. The position estimations are associated to their covariance ellipses (blue line, crosses and ellipses). GPS fixes used in the filter are denoted in red and ground truth is in green.

Fig. 5 .

 5 Fig. 5. The position estimates of the vehicle in urban area is represented by a blue line. The vehicle is travelling anticlockwise. The roundabout centres found by the program are shown by red stars.

Fig. 6 .

 6 Fig.6.Example of application of the algorithm in rural environment. Here the road network has changed recently, one roundabout has been created and another one is being built. These are not registered in the digital map (red lines) yet. The vehicle track is in blue and the centres of the roundabouts found by our algorithm are in red stars.

TABLE I CLASSES

 I USED FOR ROUNDABOUT INFERENCE

Heudiasyc UMR CNRS 7253, Université de Technologie de Compiègne, France. philippe.bonnifait@hds.utc.fr, clement.zinoune@utc.fr

Renault S.A.S, France. javier.ibanez-guzman@renault.com