Bioavailability of Flumequine and Diclofenac in Mice Exposed to a Metal-drug Chemical Cocktail. Evaluation of the Protective Role of Selenium.

RESEARCH ARTICLE Bioavailability of flumequine and diclofenac in mice exposed to a metal-drug chemical cocktail. Evaluation of the protective role of selenium. Noemí Aranda-Merino 1 | Antonio Marín-Garrido 1 | Cristina Román-Hidalgo 1 | María Ramos-Payán 1 | Nieves Abril 2 | Rut Fernández-Torres 1 | Miguel ´ Angel Bello-L opez 1 1 Departamento de Química Analítica, Facultad de Química, Universidad de Sevilla, Sevilla, Spain 2 Departamento de Bioquímica y Biología Molecular, Edificio Severo Ochoa, Campus Universitario de Rabanales, Universidad de C ordoba, C ordoba, Spain Correspondence Rut Fernández-Torres and Miguel ´ Angel Bello- L opez, Departamento de Química Analítica, Facultad de Química, Universidad de Sevilla, 41012 Sevilla, Spain. Email: [email protected] and [email protected] Funding information Spanish Ministry of Science and Innovation (MCIN), Grant/Award Numbers: I+D+i PID2021-123073NB-C21, I+D+i PID2021-123073NB-C22 Abstract Background and Purpose: Organisms, including humans, are subjected to the simultaneous action of a wide variety of pollutants, the effects of which should not be considered in isolation, as many synergies and antagonisms have been found between many of them. Therefore, this work proposes an in vivo study to evaluate the effect of certain metal contaminants on the bioavailability and metabolism of pharmacologically active compounds. Because the most frequent entry vector is through ingestion, the influence of the gut microbiota and the possible protective effects of selenium has been additionally evaluated. Experimental Approach: A controlled exposure experiment in mammals (Mus musculus)toa“chemical cocktail”consisting of metals and pharmaceuticals (diclofenac and flumequine). The presence of selenium has also been evaluated as an antagonist. Mouse plasma samples were measured by UPLC-QTOF. A targeted search of 48 metabolites was also performed. Key Results: Metals significantly affected the FMQ plasma levels when the gut microbiota was depleted. Hydroxy FMQ decreased if metals were present. Selenium minimized this decrease. The 3-hydroxy DCF metabolite was not found in any case. Changes in some metabolic pathways are discussed. Conclusions and Implications: The presence of metals in the mouse diet as well as the prior treatment of mice with an antibiotic mixture (Abxs), which deplete the gut microbiota, has a decisive effect on the bioavailability and metabolism of the tested pharmaceuticals and dietary selenium minimize some of their effects. KEYWORDS diclofenac, drug metabolism, flumequine, metals, mice Abbreviations: Abx, pretreatment with an antibiotics mixture; DCF, diclofenac; FMQ, flumequine; PACs, pharmacologically active compounds; QTOF, quadrupole time of flight. Received: 2 February 2023 Revised: 16 November 2023 Accepted: 7 December 2023 DOI: 10.1111/bph.16312 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2023 The Authors. British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society. Br J Pharmacol. 2024;1–17. wileyonlinelibrary.com/journal/bph 1 1|INTRODUCTION Biological responses of organisms to environmental factors, such as chemical or biochemical pollutants, can be assessed using environmental bioindicators or laboratory models (Gago-Tinoco et al., 2014; García-Sevillano et al., 2014). Because humans and mice share a central route of entry, the digestive tract, from which ingested contaminants are distributed throughout the body via blood flow to different parts of the body (G omez-Ariza et al., 2011), the use of mammals as laboratory models allows the results obtained to be transferred to humans, despite the fact that mice and humans have different gut microbiota communities. The analysis and distribution of pollutants and their metabolites in living organisms is of great importance because of their different harmfulness, based not only on their concentration. Pollutants may interact through antagonistic or synergistic mechanisms that must be considered for proper evaluation of biological responses (García-Barrera et al., 2012). The use of controlled exposure experiments to environmental pollutants of different chemical groups (a “chemical cocktail”) to assess the actual effects of pollutants in the environment (Van Den Brink et al., 2005) would be a good approximation for proper evaluation of the biological response of organisms. In recent years, the traditional concern about “classical”pollutants, such as metals and pesticides, is shifting to a much more diverse and broad group of substances that constitute the so-called emerging pollutants. Their effect on biota, including humans, has been, and is being, extensively studied, not only as harmful but also as biologically active substances. In this context, pharmacologically active compounds (PACs) constitute a large group of emerging pollutants with potentially harmful effects on the environment and human health. It has been found that conventional wastewater treatment plants (WWTPs) do not adequately remove the residues of PACs in wastewater, resulting in their persistent incorporation to environmental waters (Deblonde et al., 2015) and thus are included in the existing biota. Many analytical methods have been developed to determine PACs and their main metabolites in all types of environmental samples, from water and sediments to animals and plants. However, studies on the effects of the presence of chemical cocktails of PACs and metals on biological responses are scarce (Cedergreen, 2014), despite the fact that studies in earthworms (Eisenia fetida) (Huang et al., 2009) have shown the effects of the presence of ciprofloxacin, an antibiotic widely used for human and veterinary therapeutic purposes, on copper distribution, accumulation and toxicity in this organism. Although there are several papers on the metabolism of PACs (Anad on et al., 2008), serious and rigorous information on the synergistic or antagonistic effects of other chemicals on the metabolism of pharmaceutically active compounds (PACs) is very limited (Rodríguez- Moro et al., 2022). On the other hand, the main way of entry of major pollutants into the animals is their ingestion. For this reason, the influence of gut absorption should also be considered. The human microbiome comprises a complex, dynamic and equilibrated bacterial community that plays a pivotal role in health, interfering with the host's vital physiological, metabolic and immunological functions (Kandel Gambarte & Wolansky, 2022; Martel et al., 2022). Alterations in microbial composition and diversity, known as “dysbiosis,”are associated with an increased risk of certain diseases such as allergies, intestinal inflammatory problems, diabetes, obesity and also neurological and cognitive disorders via the gut–brain axis, (Borre et al., 2014; Collado et al., 2015; Hsiao et al., 2013; Stilling et al., 2014). Some researchers have shown that gut microorganisms can directly or indirectly affect the bioavailability of oral drugs such as by changing intestinal properties. Gut microbial enzyme activity can directly influence the bioavailability of oral drugs by affecting their metabolism (Goldman et al., 1974), first-pass effect (McCabe et al., 2015) or enterohepatic recirculation (Liang et al., 2015). Thus, it is important to consider the influence of gut microbiota in the presence of other chemicals such as metals or metalloids (Zhang et al., 2021). In addition, recent studies demonstrate the influence of gut microbiota on selenium status and expression of selenoproteins in mice (Hrdina et al., 2009) and that dietary selenium affects the expression of selenoproteins influenced by the gut microbiota in mouse (Kasaikina et al., 2011) and other mammals (Lv et al., 2015). However, these studies focused on the evaluation of enzymatic activity and the determination of low molecular weight selenium species such as inorganic selenium, selenomethionine, and selenocysteine and selenoproteins. What is already known •Pollutants may interact through antagonistic or synergistic mechanisms on biological responses of animals. •Gut absorption could affect bioavailability and metabolism of PACs. What does this study add •Information on the synergistic or antagonistic effects of metals on the metabolism of PACs. •Evaluation of the protective role of seluniumand gut microbiota on PACs metabolism changes. What is the clinical significance •Possible synergistic and antagonistic effects on mammalians PACs metabolism must be considered. •Physiological undesirable effects of metals and gut microbiota could be minimized by dietary selenium. 2ARANDA-MERINO ET AL. 14765381, 0, Downloaded from https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bph.16312 by Universidad De Sevilla, Wiley Online Library on [19/04/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License The combined effects of exposure to the pesticide dichlorodiphenyldichloroethylene (DDE) and selenium on the metabolome of Mus musculus have been studied (Rodríguez-Moro et al., 2019). Likewise, some exposure experiments have been carried out for Se–Cd and Hg–Se mixtures (García-Sevillano et al., 2015). Moreover, there are studies that establish a relationship between oral exposure of metals and gut microbiota in mice (Zhai et al., 2017) and that the latter acts as a barrier against chronic exposure to heavy metals (Collado et al., 2015). Some metabolomics, metallomics and metataxonomics studies involving the influence of microbiota and the antagonistic effect of Se on metal toxicity have been previously published (Arias-Borrego et al., 2022; Callej on-Leblic, Selma-Royo, Collado, Abril, & García-Barrera, 2021; Callej on-Leblic, Selma-Royo, Collado, G omez-Ariza, et al., 2021; Parra-Martínez et al., 2022). However, the consequences of pharmacological bioavailability and metabolism under these conditions have not been previously described. We consider this study a good approach to addressing living beings' exposure to a wide variety of pollutants released into the environment and the multiple pathways through which they enter organisms in our soil, oceans, and rivers. In this paper, the bioavailability and metabolism in mice of two commonly used pharmaceuticals (diclofenac and flumequine) with a high environmental relevance are studied. Mice were exposed to a cocktail of metal drugs, including cadmium, arsenic and mercury, and the potential antagonist effect of selenium as well as the influence of the gut microbiota was evaluated. The purpose of this study is to initiate a line of research that will analyse synergism and antagonism between different types of substances and examine their effects on mammalian physiology and metabolism. 2|METHODS 2.1 |Chemicals and reagents Diclofenac, 3-hydroxydiclofenac, 4-hydroxydiclofenac, 5-hydroxydi- clofenac, flumequine, 13 C 3 -flumequine, d 4 -diclofenac, CdCl 2 and As 2 O 3 were purchased from Sigma-Aldrich (Madrid, Spain); HgCl 2 was supplied by Supelco-Merck (Madrid, Spain) and Flumesyva powder (10% [w/w] flumequine) was acquired from Laboratorios SYVA (Le on, Spain). Acetonitrile and methanol (LC-MS grade) and formic acid (analytical grade) were purchased from VWR (Barcelona, Spain). For all preparations and dilutions, ultrapure deionized water (Milli-Q Plus water system, Millipore, Billerica, MA, USA) was used. Stock solutions of diclofenac, 3-hydroxydiclofenac, 5-hydroxydiclofenac and d 4 -diclofenac (1000 μgmL 1 ), flumequine (400 μgmL 1 ) and 13 C 3 -flumequine (500 μgmL 1 ) were prepared by dissolving the substances in methanol; 4-hydroxydiclofenac (100 μgmL 1 ) was supplied as a solution in acetonitrile. All the stock solutions were stable for at least 2 months at 4C. Working solutions were prepared daily by adequate dilutions using ultrapure water. 2.2 |Chromatographic separation and mass spectrometry determination Chromatographic analysis was conducted in an Acquity ® UPLC (Waters, Milford, MA, USA) coupled to a Xevo ® G2S QTOF mass spectrometer (Waters, Micromass, Manchester, UK). An Acquity ® BEH C18 column (50 mm 2.1 mm i.d., 1.7 μm particle size) at 25C using water (A) and methanol (with 0.1% [v/v] formic acid) (B) as mobile phase at a flow rate of 0.4 mlmin 1 for 17 min was used. The following gradient elution programme was applied to achieve the separation: (1) t=0 min 70% A; (2) t=3.0 min 45% A; (3) t=10 min 45% A; (4) t=13 min 10% A; (5) t=15 min 70% A and (6) t=17 min 70% A; 10 μl of the extracted sample were injected into the column, and detection was carried out in positive/negative ionization, with an electrospray source at 2.0/2.0 kV capillary voltage, 120C source temperature and 400C desolvation temperature. Data were acquired using a reference spray (lockSpray ® ) to certify accuracy and reproducibility. Leucine enkephalin m/z [M +H] + 556.2771 and m/z [M H]  554.2615 were used as references. The lockSpray ® frequency was set at 30 s for mass correction of the analyte. The lockSpray ® capillary was set at 1.6 kV. Under these conditions, the target analytes of three diclofenac hydroxylated metabolites with similar structures and comparable m/z values were clearly distinguished. Additionally, target analytes were isolated with low limits of detection and quantification, unambiguous determination and/or quantitative analysis of selected compounds. Table 1shows the corresponding retention times for the target analytes along with their theoretical and experimental m/z. The software MassLynx ® (version 4.1) was used to process the data, whereas target metabolites were identified using Chromalynx ® XS (Waters, Micromass, Manchester, UK), which identifies previously defined metabolites. In this case, a targeted search of 48 candidates was performed based on previous metabolites reported in the bibliography (Fu et al., 2017; Parshikov & Sutherland, 2012; Sarda et al., 2014). This identification was based on exact mass (error <5 ppm), chlorine isotopic patterns for diclofenac (DCF), metabolites and the MS/MS fragments obtained in the second function by comparing them to those previously reported in literature. The targeted search was conducted in both positive and negative ionization. TABLE 1 Retention times and theoretical and experimental m/z for the target analytes (ESI + ionization). t R (min) m/z theor. m/z exp. 13 C 3 -flumequine 4.09 265.0979 265.0974 Flumequine 4.09 262.0879 262.0879 d 4 -diclofenac 12.32 300.0558 300.0492 Diclofenac 12.41 296.0245 296.0241 3OH-diclofenac 6.35 312.0194 312.0186 4OH-diclofenac 6.62 312.0194 312.0179 5OH-diclofenac 7.21 312.0194 312.0179 ARANDA-MERINO ET AL.3 14765381, 0, Downloaded from https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bph.16312 by Universidad De Sevilla, Wiley Online Library on [19/04/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2.3 |Data and statistical analysis This work complies with BJP's recommendations and requirements (Curtis et al., 2022) on experimental design and analysis. Statistical analysis (one-way ANOVA and nonparametric analysis) of data from plasma of the studied compounds in different groups of mice submitted to the exposure test was carried out using Minitab ® 16.1.0 software (Minitab Inc., State College, PA, USA). The group size represents the number of independent values, and statistical analysis was performed based on these independent values. All obtained data were used in the statistical analysis. To test whether there were significant differences in FMQ and DCF levels found in different exposure groups, statistical data were analysed using Minitab ® Statistical Software. First, to compare two sets of variables, a normality test was used to determine whether each dataset was well modelled by a normal distribution. If the normality test had a Pvalue >0.05, a oneway ANOVA statistical test with Tukey comparison was performed using mean values of plasma concentration levels. When P< 0.05, a less accurate nonparametric test (Mood's median test) was applied using the median of the concentration values in the dataset. Twelve groups consisting of n =5 animals were considered for statistical analysis. 2.4 |Exposure experiences This investigation was performed with the consent of the Ethical Committee of the University of C ordoba (Spain) and the regional government (Code Num. 02-01-2019-001), in accordance with current European Union regulations. Animal studies are reported in compliance with the ARRIVE guidelines (Percie du Sert et al., 2020) and with the recommendations made by the British Journal of Pharmacology (Lilley et al., 2020). Male M. musculus mice 8 weeks old of the consanguineous strain BALB/c (Charles River, Barcelona, Spain) were used as model organism in mammalian exposure experiments. Studies were designed to generate groups of equal size, using randomisation and blinded analysis. Mice were divided into six test groups: (1) control group with a regular diet, (2) control group with diet supplemented with selenium, (3) diclofenac and flumequine supplemented regular diet, (4) diclofenac, flumequine and selenium supplemented diet, (5) arsenic, cadmium, mercury, diclofenac and flumequine supplemented diet and (6) arsenic, cadmium, mercury, diclofenac, flumequine and selenium supplemented diet. In addition, the influence of gut microbiota dysbiosis on the bioavailability and/or metabolism of the pharmaceuticals has been assessed in different groups (group B) within each of the aforementioned groups. Thus, a total of 12 groups consisting of five animals were considered (see Table 2for details). Mice were housed in individual cages under a controlled environment (photoperiod 12-h light/dark cycle, temperature 25 ± 2C), with ad libitum access to drinking water and food, at the Experimental Animal Services (SAEx) from the University of C ordoba (Spain), under its qualified staff control. Individual caging allowed determination of actual water and food consumed and to infer the exact dose of compounds. After the acclimatisation period (3 days), the mice were submitted to a pre-exposure period for 7 days, during which some of the animals (B groups) received a combination of antibiotics (Abx) in the drinking water inducing gut microbiota depletion prior to the exposure assay. The antibiotic cocktail consisted of ampicillin 1%, metronidazole 1%, neomycin 1%, vancomycin 0.5% and an antifungal (amphotericin B, 10 mgL 1 ) (Callej on-Leblic, Selma-Royo, Collado, Abril, & García-Barrera, 2021; Zarrinpar et al., 2018). The duration of the assay was 2 weeks, and all mice (except control groups 1 and 2) received up to five pollutants: PACs in the food and/or metals in the drinking water. Animals from exposure groups 4 and 6 also received selenium (0.65 mgkg 1 ) via a supplemental feed (Altromin Natrium-Selenit, Altromin Spezialfutter GmbH & Co., Lage, Germany). Estimated daily intakes (mgkg 1 ) for PACs and metals were as follows: flumequine (625), diclofenac (20), arsenic (3), cadmium (0.1) and mercury (1). The dose was chosen after an exhaustive review of the literature (Callej on-Leblic, Selma-Royo, Collado, G omez- Ariza, et al., 2021; García-Sevillano et al., 2013; Kashida et al., 2006; TABLE 2 Mice exposure test groups (for details, see text). GROUP Abx Se supplemented Pharmaceuticals Metals cocktail 1A 1B + 2A CONTROLS + 2B ++ 3A + 3B ++ 4A ++ 4B ++ + 5A ++ 5B +++ 6A +++ 6B ++ + + 4ARANDA-MERINO ET AL. 14765381, 0, Downloaded from https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bph.16312 by Universidad De Sevilla, Wiley Online Library on [19/04/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License L opez-Pacheco et al., 2019; Rodríguez-Moro et al., 2020; Trombini et al., 2021) and based on our own expertise. The doses used are consistent with environmentally relevant concentrations and are a compromise between semirealistic conditions and the concentrations expected to have pharmacological effects. For the selection of doses, we considered the literature, our previous works and the possibility of bioaccumulation along the food chain (Chormare & Kumar, 2022; Nagpal et al., 2018; Nilsen et al., 2019; Zenker et al., 2014) and used doses below their LD 50 to minimize possible synergistic effects that could enhance their toxicity. After exposure, mice were individually anaesthetised by isoflurane (1.5%) inhalation and exsanguinated by cardiac puncture into a heparinized tube before being killed by cervical dislocation. Plasma samples were collected by centrifugation (725 g, 30 min, 4C) and stored at 80C until their analysis. 2.5 |Vortex assisted sample plasma extraction procedure To 50 μl of plasma sample in an Eppendorf tube, 5 μl of 1000 μgL 1 aqueous solution of 13 C 3 -flumequine and d 4 -diclofenac (internal standards, IS) and 500 μl of acetonitrile were added. The tube was subjected to vortex agitation (VORX-005-001, Labbox Labware; Premia de Dalt, Barcelona, Spain) for 3 min, followed by centrifugation (4C) at 10,000 g (Biocen 22R, Orto alresa; Daganzo, Madrid, Spain) for 15 min. The supernatant was decanted, 500 μl of acetonitrile were added, and the agitation/centrifugation steps were repeated. Combined supernatants were evaporated to dryness at room temperature using a speed vacuum system (Genevac™MiVac Duo, Fisher Scientific, Madrid, Spain) and stored at 80C until analysis. The extracts were reconstituted by the addition of 50 μl of a 1:1 methanol:water mixture and vortex agitation for 2 min. Reconstituted extracts were centrifuged (10,000 g,4 C) for 15 min, and 10 μlof the supernatant were injected into the chromatographic system for the analysis. 3|RESULTS 3.1 |Optimization of the chromatographic-mass spectrometry (UPLC-ESI-QTOF-MS) procedure For the quantitation of aqueous standards of flumequine (FMQ), diclofenac and 3-hydroxy, 4-hydroxy and 5-hydroxydiclofenac, internal standard calibration using 13 C 3 -flumequine and d 4 -diclofenac were used. As the mobile phase, mixtures of water:methanol and water:acetonitrile were tested. Formic acid, acetic acid and ammonium formate were checked as additives in the mobile phase. Two chromatographic columns were tested: Acquity ® UPLC BEH C18 (1.7-μm particle size, 500 2.1 mm) and Cortecs ® UPLC C18 (1.6-μm particle size, 500 2.1 mm). Additionally, ESI ionization mode was evaluated in both positive and negative modes. Optimization of these experimental parameters led to the conditions shown earlier in the experimental section. Figure 1shows a representative ion chromatogram for a 10 ngml 1 aqueous standard solution containing all selected compounds and internal standards. 3.2 |Optimization of plasma samples vortex assisted extraction method Blank mouse plasma samples were spiked (100 ngml 1 ) with selected compounds and the internal standards to optimize their extraction procedure. A preliminary study tested acetonitrile, methanol and their mixtures with 1:1(v/v) and 1:3 (v/v) water as extraction solvents. The mixtures provided lower recovery than the pure solvent, and the highest recovery was obtained using acetonitrile. Then, this solvent was selected for subsequent optimization procedures. Different recoveries were obtained using variable volumes of acetonitrile: 30%–70% for 500 μl, 35%–85% for 1000 μl and 45–99% using 2500 μl, so this last condition was selected as optimal. No significant differences were observed in recoveries using extraction (vortex agitation) times between 3 and 5 min. Thus, an extraction time of 3 min was selected as optimal. On the other hand, three different volumes (50, 100 and 150 μl) were evaluated to reconstitute the extracts after drying of the combined supernatants using a speed vacuum system. No significant difference was observed in recoveries in the results obtained, and 50 μl was selected as the optimal reconstitution volume in order to achieve better sensitivity for the analytical procedure. Finally, methanol, acetonitrile and 1:1 mixtures with water as reconstituting solvent was tested providing a 1:1 mixture methanol:water with better sensitivity and peak shape. Table S1 shows all data from the optimization procedure. 3.3 |Validation of the proposed analytical procedure The method was validated based on EURACHEM (European Analytical Chemistry) guide (Magnusson & Örnemark, 2014) considering the following quality parameters: linearity, sensitivity (instrumental and method limits of detection and quantitation), matrix effect (suppression or enhancement of ionization and overall matrix effect), precision and recovery (trueness). Blank plasma samples from laboratory mice were used as a matrix for method validation purposes. A comparison of external calibration and matrix-matched calibration curves was conducted by spiking blank plasma extracts at 10 concentration levels (ranging from 0.5 to 500 ngml 1 ) of selected analytes. In both cases, using 13 C 3 -flumequine and d4-diclofenac as internal standards, no matrix effect was detected. Thus, external calibrations using internal standards were used to validate the proposed procedure. The corresponding linearity percentage and linear range values obtained for the chromatographic procedure are shown in Table 3. ARANDA-MERINO ET AL.5 14765381, 0, Downloaded from https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bph.16312 by Universidad De Sevilla, Wiley Online Library on [19/04/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Instrumental detection and quantitation limits were calculated as the minimum concentrations of analyte peaks with signal- to-noise ratios of 3 and 10, respectively. Limits of detection and quantitation of the methods were carried out on blank plasma samples (n =6). To evaluate the precision of the method, repeatability (within a single day) and intermediate precision (within three days) were studied at two concentration levels (50 and 500 ngml 1 ) and n =6. A precision study was performed using actual concentration predictions from the validation standards selected for the analytical assay and calculating the relative standard deviation (RSD) (Table 4). In all cases, the values obtained in all cases were less than 15% RSD, which is usually considered an acceptance criterion for bioanalytical methods (Naidis & Turpeinen, 2009). TABLE 3 Linear ranges and instrumental and method limits of detection and quantitation for the selected analytes. Compound Linearity a (%) Linear Range a (ngml 1 ) ILOD b (ngml 1 ) ILOQ b (ngml 1 ) MLOD b (ngml 1 ) MLOQ b (ngml 1 ) FMQ 99 0.76 (0.9)-500 (2.3) 0.24 0.80 0.23 0.76 DCF 99 3.0 (3.2)-500 (6.1) 1.1 3.7 0.9 3.0 3OH-DCF 98 3.7(7.3)-500 (4.5) 1.2 4.0 1.1 3.7 4OH-DCF 98 3.4 (6.0)-500 (6.8) 1.2 4.1 1.1 3.4 5OH-DCF 97 4.1(8.2)-500 (5.3) 1.3 5.2 1.2 4.1 a Concentration (%RSD). Ten concentration levels; n =3. b n=6. TABLE 4 Evaluation of the precision (% RSD) for the proposed procedure at two concentration levels. (internal standard concentrations 100 ngml 1 , n=6). Repeatability Intermediate precision 50 ngml 1 500 ngml 1 50 ngml 1 500 ngml 1 Flumequine 1.21 3.22 2.53 4.62 Diclofenac 4.53 8.51 5.23 6.38 3OH-diclofenac 7.25 4.65 14.3 17.1 4OH-diclofenac 11.3 6.58 7.25 10.2 5OH-diclofenac 5.26 2.63 16.3 6.50 FIGURE 1 Extracted ion chromatograms (XIC) for a 10 ngml 1 aqueous standard solution containing the selected compounds and the internal standards. 6ARANDA-MERINO ET AL. 14765381, 0, Downloaded from https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bph.16312 by Universidad De Sevilla, Wiley Online Library on [19/04/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Accuracy was evaluated by recovery assays on spiked samples submitted to the extraction procedure and measured using external calibration using internal standards. Recoveries were calculated at three concentration levels for the analysed compounds. Spiked blank plasma samples at 10, 100 and 500 ngml 1 final concentrations (in triplicate) were submitted to the extraction procedure, and the results obtained were evaluated (Table 5). 4|DISCUSSION 4.1 |Bioavailability of flumequine It has been reported (Plakas et al., 2000) that after oral dosing, peak plasma concentrations of flumequine are reached within14 h. Absorption and elimination half-lives are 4.9 and 22 h, respectively. After ingestion, more than 80% of FMQ remains unmetabolized, with the major metabolite being 7-hydroxyflumequine. However, in several studies, the parent drug was usually the only residue detected (Haagsma et al., 1993; Samuelsen & Ervik, 1997). FMQ is excreted in urine and faeces as glucuronide conjugates of the parent drug and 7-hydroxyflumequine. In our study, FMQ plasma concentrations were very high, probably due to the high dose administered during the experiment. Table 6shows the FMQ concentrations found in the plasma samples of the different groups corresponding to the exposure tests. It is remarkable that exposure to selected metals resulted in a higher dispersion in flumequine plasma levels for exposed animals (Groups 5 and 6). From a qualitative point of view, group means for individuals with Abxs pretreatment (B groups) appear slightly higher except for the 3B group (without metals) that showed the lowest FMQ levels for all tests. Furthermore, selenium generally does not have a major effect on global plasma levels. Figure S1 shows the scatter plot of the data for individuals in each exposure group. Statistical significance was analysed based on the normal distribution of datasets using either one-way ANOVA or nonparametric analysis. Table 7shows the corresponding results obtained. As can be seen, significant differences in FQM plasma levels were found only in two experiments (shaded cells) involving mice with prior Abxs pretreatment (groups 3B, 4B and 5B). The presence of metals significantly affected FQM plasma levels when the gut microbiota was depleted with a marked decrease when metals were not present in the diet. The absence of dietary supplemental selenium affects animals submitted to Abx pretreatment and fed diets lacking the metal. This suggests that the ingestion of selenium supplemented feed favours FMQ absorption for animals submitted to Abxs pretreatment, but an antagonistic effect was observed in the presence of the metal cocktail. 4.2 |Bioavailability of diclofenac and their hydroxy-metabolites Diclofenac is eliminated primarily by metabolism (Davies & Anderson, 1997; Todd & Sorkin, 1988). The biotransformation of diclofenac takes place partly through glucuronidation of the intact molecule but mainly through single and multiple hydroxylation and/or methoxylation, giving rise to several phenolic metabolites (3-hydroxy-, 4-hydroxy-, 5-hydroxy-, 4,5-dihydroxy- and 3-hydroxy-4-methoxydi- clofenac), which are largely converted to glucuronic conjugates. Two of these metabolites (3-hydroxy and 4-hydroxy) are biologically active, but to a much lesser degree than the parent drug (Wiesenberg-Boettcher et al., 1991). The two main DCF metabolites are 4-hydroxydiclofenac (more than 50%), which is produced by the CYP2C9 enzyme, and 5-hydroxydiclofenac, which seems to be the product of some enzymes: CYP2C8, CYP2C18, CYP2C19, CYP2B6, among others (Dorado et al., 2008); 60%–70% of administered diclofenac is eliminated in the urine, and 30% is excreted in the faeces. The terminal half-life of diclofenac is approximately 2 h, although the apparent half-life, including all metabolites, is 25.8–33 h (Todd & Sorkin, 1988). TABLE 5 Recovery percentages obtained at three concentration levels for the selected analytes from spiked blank plasma samples (n =3). 10 ngml 1 100 ngml 1 500 ngml 1 FMQ 98 ± 1 97 ± 3 95 ± 4 DCF 97 ± 3 99 ± 4 96 ± 2 3OH-DCF 99 ± 2 98 ± 1 97 ± 3 4OH-DCF 99 ± 3 98 ± 2 98 ± 4 5OH-DCF 88 ± 1 97 ± 3 97 ± 5 TABLE 6 Bioavailability of flumequine in the exposed mice: average plasma concentrations from five individuals and standard deviations (data from measurements in triplicate). Group Exposure conditions Flumequine (mg L 1 ) SD (mg L 1 ) 3A Pharmaceuticals 30 0.5 3B Abx +pharmaceuticals 25 0.6 4A Se +pharmaceuticals 37 0.5 4B Abx +Se +pharmaceuticals 47 0.2 5A Pharmaceuticals +metals 32 13 5B Abx +pharmaceuticals +metals 42 11 6A Se +pharmaceuticals +metals 40 16 6B Abx +Se +pharmaceuticals +metals 48 8.5 ARANDA-MERINO ET AL.7 14765381, 0, Downloaded from https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bph.16312 by Universidad De Sevilla, Wiley Online Library on [19/04/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License It is remarkable that no 3-hydroxydiclofenac metabolite was detected in any of the plasma samples in our study, which is in accordance with previously reported studies in mice, including an extensive metabolite screening using 14 C-diclofenac (Sarda et al., 2012). Table 8 shows the average concentrations of diclofenac and OH- metabolites in plasma samples corresponding to the exposure test of the different groups. In general, a high intersubject variability in compound levels was obtained for all exposure conditions, so that only general trends could be extracted. Figures S2–S4 show the scatter plots of the data for individuals in each exposure group. Trends showed that selenium supplemented mice had the highest plasma DCF concentrations regardless of dietary metal presence (groups 4A, 6A and 6B), but if the gut microbiota was depleted (4B), the plasma levels were similar to those that were not supplemented with selenium. Animals in these exposure groups also showed the highest plasma levels of 4-hydroxydiclofenac, indicating that the presence of dietary selenium increases the levels of this metabolite. Moreover, when the gut microbiota was depleted (4B), 4-hydroxydiclofenac levels were in the same order as in the animals exposed only to the PACs cocktail (3A). This is in accordance with the early report by Saitta et al. (2014), who demonstrated that the gut microbiota increases the metabolism (deglucuronation) and delays the excretion of diclofenac, and can lead to an increase in its toxicity (enterohepatic circulation). According to the literature, the major metabolite in humans is 4-OH DCF (Dorado et al., 2003), but in our experience, the highest concentrations was found for 5-OH diclofenac. Therefore, its plasma levels significantly decrease in mice exposed to metals (groups 5A and 5B) but dietary selenium minimizes these decreases (groups 6A and 6B). Abxs pretreatment (B groups) does not affect this metabolic pathway (B groups) except for the case of dietary selenium (group 4B). TABLE 7 Influence of the different exposure conditions on the flumequine plasma contents (in parentheses experimental conditions for the analysed groups). Statistical analysis of significance accordingly to normality distribution of sets of data: one-way ANOVA or nonparametric analysis (shaded cells indicate groups with statistically significant differences) (median and mean values in mg L 1 ) (for details, see text). Metals effects Metals effect Metals effect (selenium) Metals effect (depleted microbiota) Metals effect (selenium & depleted microbiota) Median Pvalue Median Pvalue Mean Pvalue Median Pvalue 5A 37 1.000 6A 39 1.000 5B 42 0.024 6B 52 0.280 3A 36 4A 42 3B 25 4B 44 Effects when metals were not present Selenium effect Microbiota effect Microbiota effect (selenium) Selenium effect (depleted microbiota) Mean Pvalue Median Pvalue Median Pvalue Median Pvalue 3A 30 0.419 3A 37 0.302 4A 42 1.000 3B 20 0.004 4A 37 3B 20 4B 44 4B 44 Effects when metals were present Selenium effect Microbiota effect Microbiota effect (selenium) Selenium effect (depleted microbiota) Median Pvalue Mean Pvalue Median Pvalue Mean Pvalue 5A 37 1.000 5A 33 0.230 6A 39 0.248 5B 42 0.377 6A 40 5B 42 6B 52 6B 48 TABLE 8 Bioavailability of diclofenac, 4-hydroxdiclofenac and 5-hydroxydiclofenac for the exposed mice: average plasma concentrations from five individuals and standard deviations (data from measurements in triplicate) (all data in μgL 1 ). Group Exposure conditions DCF 4-OH DCF 5-OH DCF Conc. SD Conc. SD Conc. SD 3A Pharmaceuticals 322 253 22 19 85 61 3B Abx +pharmaceuticals 257 273 12 7.3 55 30 4A Se +pharmaceuticals 646 429 42 38 154 88 4B Abx +Se +pharmaceuticals 302 109 19 9.3 9.1 2.6 5A Pharmaceuticals +metals 229 158 6.3 1.8 16 9.0 5B Abx +pharmaceuticals +metals 343 191 14 3.5 31 6.8 6A Se +pharmaceuticals +metals 836 937 27 6.1 57 28 6B Abx +Se +pharmaceuticals +metals 647 314 38 21 72 36 8ARANDA-MERINO ET AL. 14765381, 0, Downloaded from https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bph.16312 by Universidad De Sevilla, Wiley Online Library on [19/04/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Statistical analysis of DCF plasma levels (Table 9) reveals that the metal cocktail does not significantly affect its bioavailability. However, significant differences are obtained in groups with depleted microbiota in the Se-supplemented diet group compared with the groups with depleted microbiota. It seems that microbiota depletion significantly affects DCF levels. Statistical analysis of the influence of different exposure conditions on 4-hydroxydiclofenac plasma content (Table 10) revealed a metal effect in depleted microbiota and Se-supplemented groups. Greater differences were observed in the data for groups submitted to the metal-drug chemical cocktail, with significant differences in plasma levels produced by Abx pretreatment and selenium TABLE 9 Influence of the different exposure conditions on the diclofenac plasma contents (in parentheses experimental conditions for the analysed groups). Statistical analysis of significance accordingly to normality distribution of sets of data: one factor ANOVA or nonparametric analysis (shaded cells indicate groups with statistically significant differences) (median and mean values in μgL 1 ) (for details, see text). Metals effects Metals effect Metals effect (selenium) Metals effect (depleted microbiota) Metals effect (selenium & depleted microbiota) Median Pvalue Median Pvalue Mean Pvalue Median Pvalue 5A 342 0.248 6A 239 0.158 5B 363 0.398 6B 747 0.280 3A 370 4A 646 3B 257 4B 296 Effects when metals were not present Selenium effect Microbiota effect Microbiota effect (selenium) Selenium effect (depleted microbiota) Mean Pvalue Median Pvalue Median Pvalue Median Pvalue 3A 322 0.101 3A 369 0.302 4A 646 0.004 3B 91 0.343 4A 646 3B 91 4B 296 4B 296 Effects when metals were present Selenium effect Microbiota effect Microbiota effect (selenium) Selenium effect (depleted microbiota) Median Pvalue Mean Pvalue Median Pvalue Mean Pvalue 5A 342 0.248 5A 278 0.230 6A 239 0.248 5B 363 0.158 6A 239 5B 363 6B 747 6B 647 TABLE 10 Influence of the different exposure conditions on the 4-hydroxydiclofenac plasma contents (in parentheses experimental conditions for the analysed groups). Statistical analysis of significance accordingly to normality distribution of sets of data: one-way ANOVA or nonparametric analysis (shaded cells indicate groups with statistically significant differences) (median and mean values in μgL 1 ) (for details, see text). Metals effects Metals effect Metals effect (selenium) Metals effect (depleted microbiota) Metals effect (selenium & depleted microbiota) Median Pvalue Median Pvalue Mean Pvalue Median Pvalue 5A 5.3 0.248 6A 32 1.000 5B 14 0.210 6B 32 0.031 3A 15 4A 32 3B 10 4B 16 Effects when metals were not present Selenium effect Microbiota effect Microbiota effect (selenium) Selenium effect (depleted microbiota) Mean Pvalue Median Pvalue Median Pvalue Median Pvalue 3A 22 0.349 3A 15 0.302 4A 32 0.058 3B 11 0.058 4A 33 3B 11 4B 16 4B 16 Effects when metals were present Selenium effect Microbiota effect Microbiota effect (selenium) Selenium effect (depleted microbiota) Median Pvalue Mean Pvalue Median Pvalue Mean Pvalue 5A 5.3 0.248 5A 6.5 0.005 6A 32 1.000 5B 14 0.039 6A 32 5B 14 6B 32 6B 34 ARANDA-MERINO ET AL.9 14765381, 0, Downloaded from https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bph.16312 by Universidad De Sevilla, Wiley Online Library on [19/04/2024]. 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Evaluation of the protective role of selenium. British Journal of Pharmacology,1–17. https://doi.org/10.1111/bph.16312 ARANDA-MERINO ET AL.17 14765381, 0, Downloaded from https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bph.16312 by Universidad De Sevilla, Wiley Online Library on [19/04/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License