Pharmacokinetics of terbinafine and five known metabolites in children, after oral administration

Humbert, Henri; Denouël, J.; Cabiac, M. D.; Lakhdar, H.; Sioufi, A.

doi:10.1002/(sici)1099-081x(199810)19:7<417::aid-bdd111>3.0.co;2-t

Cited by 17 publications

(5 citation statements)

References 13 publications

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“…Many studies have assessed the pharmacokinetics of terbinafine following oral administration in humans. 15,16,[23][24][25] Related studies have also been carried out in animals, including African penguins and red-tailed hawks, 13,14 horses and Greyhounds. 17 However, to date, no information about the pharmacokinetics of terbinafine after IV or oral administration in cats has been reported.…”

Section: Discussionmentioning

confidence: 99%

“…The pharmacokinetics of terbinafine has been evaluated in humans, horses, dogs, penguins and hawks. 13–17 Although some studies on the determination of terbinafine in cat plasma and hair have been described, 18–20 little is known about the pharmacokinetics of terbinafine in cats. Given the use of terbinafine in cats, the purposes of this study were to assess the pharmacokinetics of terbinafine in cats after intravenous (IV) and oral administration, and to determine the oral bioavailability of terbinafine hydrochloride tablets.…”

Section: Introductionmentioning

confidence: 99%

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Single dose pharmacokinetics of terbinafine in cats

Wang

Ding

Liu

et al. 2012

Journal of Feline Medicine and Surgery

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The pharmacokinetics of terbinafine was studied in six healthy fasted cats following a single intravenous and oral administration at a dose of 10 mg/kg and 30 mg/kg, respectively, according to a two-period crossover design. Plasma terbinafine concentrations were determined using a reverse phase liquid chromatographic method. The pharmacokinetic parameters were calculated by non-compartmental analysis with WinNonlin 5.2.1 software. After intravenous administration, the terminal half-life and area under the curve from time 0 to infinity were 10.40 ± 4.56 h, 15.20 ± 3.61 h·µg/ml, respectively. After oral dosing, the mean maximum concentration was 3.22 ± 0.60 µg/ml, reached at 1.33 ± 0.41 h. The terminal half-life, area under the curve from time 0 to infinity and apparent volume of distribution were 8.01 ± 3.46 h, 13.77 ± 4.99 h·µg/ml, 25.63 ± 6.29 l/kg, respectively. The absolute bioavailability of terbinafine hydrochloride tablets after oral administration was 31.00 ± 10.85%. Although bioavailability was low, excellent penetration at the site of infection and low minimum inhibitory concentrations values provided terbinafine with good efficacy against dermatophyte infections.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single dose pharmacokinetics of terbinafine in cats

Wang

Ding

Liu

et al. 2012

Journal of Feline Medicine and Surgery

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“…A significant negative correlation between the mean blood concentration of ketoconazole and age has been reported (185). Terbinafine AUC values are significantly higher in children than in young adults (194), although in a separate study, higher plasma concentrations and lower half-lives of the terminal elimination phase were reported (127). For fluconazole, the volume of distribu-tion is greater in neonates and decreases in young adults (31), the mean plasma half-life in children (22 h) is shorter than in adults (37 h) (59), and total clearance is greater in children (1.16 liters/h) than in adults (1.07 liters/h) (163).…”

Section: Agementioning

confidence: 59%

Human Pharmacogenomic Variations and Their Implications for Antifungal Efficacy

2006

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SUMMARY Pharmacogenomics is defined as the study of the impacts of heritable traits on pharmacology and toxicology. Candidate genes with potential pharmacogenomic importance include drug transporters involved in absorption and excretion, phase I enzymes (e.g., cytochrome P450-dependent mixed-function oxidases) and phase II enzymes (e.g., glucuronosyltransferases) contributing to metabolism, and those molecules (e.g., albumin, A1-acid glycoprotein, and lipoproteins) involved in the distribution of antifungal compounds. By using the tools of population genetics to define interindividual differences in drug absorption, distribution, metabolism, and excretion, pharmacogenomic models for genetic variations in antifungal pharmacokinetics can be derived. Pharmacogenomic factors may become especially important in the treatment of immunocompromised patients or those with persistent or refractory mycoses that cannot be explained by elevated MICs and where rational dosage optimization of the antifungal agent may be particularly critical. Pharmacogenomics has the potential to shift the paradigm of therapy and to improve the selection of antifungal compounds and adjustment of dosage based upon individual variations in drug absorption, metabolism, and excretion.

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“…Moreover, there was no information found in literature on prochloraz, propiconazole and terbinafine excretion in urine or faces. (Fustinoni et al, 2014;Oerlemans et al, 2019) Terbinafine ---urine (human) No unchanged terbinafine was excreted in the urine (Jensen, 2009) N-desmethylcarboxyterbinafine 53.8 1.98 urine (human) (Humbert et al, 1998) Voriconazole Voriconazole 2.0 50.0 urine (human) (Levêque et al, 2006) WBE AF intake (PNDI) estimates as well as AF per-capita NHS per-postcode prescription (PNDP) are presented in Figures 7 and 8. Average WBE-estimated fluconazole PNDIs were 330 ± 125, 627 ± 1231, 309 ± 12, 163 ± 40 and 8 ± 2 mg day -1 1000inh -1 , and prescription PNDPs were 33, 6, 18, 19, and 12 mg day -1 1000inh -1 for city A, B, C and D. Similarly, average WBE-estimated ketoconazole PNDIs were 3001 ± 891 and 2145 ± 682 mg day -1 1000inh -1 , and prescription PNDPs were 74 and 69 mg day -1 1000inh -1 for city B and D. It is apparent that PNDIs are much higher than PNDPs.…”

Section: Estimation Of Community Wide Daily Intake Of Afsmentioning

confidence: 99%

An integrated One Health framework for holistic evaluation of risks from antifungal agents in a large-scale multi-city study using post-acquisition mass spectral data mining and wastewater-based epidemiology pipelines

Kasprzyk-Hordern

Proctor

Jagadeesan

et al. 2023

Preprint

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A new analytical framework for retrospective mass spectral data mining for antifungal (AFs) agents was developed as part of One Health framework for holistic evaluation of risks from AFs. Post-acquisition data mining of mass spectral repository obtained with a maXis HD QToF mass spectrometer, retrospective quantification and confirmation using bbCID method was undertaken. A large scale, multi-city spatiotemporal profiling programme of antifungal agents was undertaken in the Avon River catchment, South-West England encompassing five contrasting towns/cities served by five major wastewater treatment plants (WWTPs) covering a large geographical area of 2000 km2 and a population of ~1.5 million accounting for > 75% of the overall population in the studied catchment. Key drivers of AFs in the catchment were identified with communal WWTP discharges being the main driver for human AFs (e.g., fluconazole and ketoconazole) and agricultural runoff being the main driver for pesticide AFs (e.g., prochloraz, prothioconazole and tebuconazole). Daily mass loads (DLs) of AF were study/city dependent with several instances of large quantities of AFs (direct disposal) observed. Wastewater-Based Epidemiology (WBE) pipelines were developed to estimate community-wide exposure to antifungal agents and to triangulate the WBE data with per-postcode prescription. Average WBE-estimated human used fluconazole and ketoconazole PNDIs (population normalised daily intake per person) exceeded on average 300 mg day-1 1000inh-1 and 2000 mg day-1 1000inh-1. This is much higher than PNDPs (population normalised daily prescriptions) <40 mg day-1 1000inh-1 and <80 mg day-1 1000inh-1 for fluconazole and ketoconazole respectively. This was expected due to both prescription and OTC (over-the-counter) usage as well as both oral and topical (non-metabolic) application. WBE applied to pesticide-based AFs: prothioconazole and tebuconazole provided variable PNDI estimates across the catchment. Prothioconazole PNDIs were <40,000 mg day-1 1000inh -1, which gave intake per kg of body weight at: 0.43, 0.26, 0.07 mg kg-1 in City A, B and C and is likely due to accounting for external/non-human sources. This is higher than the acceptable daily intake (ADI) set at 0.01 mg kg-1bw day-1, which warrants further study. Intake per kg of body weight estimated using tebuconazole as a xenobiotic chemical residue (XCR) was: 0.86, 1.39, 0.12, 0.13 and 2.7 mg kg-1 in City A-E respectively and is likely due to accounting for external/non-human sources. Intake calculated using the metabolite is in line with ADI: 0.02 and 0.01 mg kg-1 in City B and C respectively, which aligns well with ADI set at 0.03 mg kg-1bw day-1. The environmental risk assessment of the target AAs indicates low/medium risk from fluconazole, prochloraz and tebuconazole, medium risk from epoxiconazole, prothioconazoles metabolite, and tebuconazole, and high risk for prothioconazole in river water. High risk was estimated from fluconazole, epoxiconazole, prothioconazole and its metabolite, tebuconazole, ketoconazole in influent (and/or effluent) samples, which is important due to possible raw sewage discharge via sewer overflows. Due to lack of information on PNEC (predicted no-effect concentration) for other AFs, risk assessment could not be undertaken, which warrants further study.

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Pharmacokinetics of terbinafine and five known metabolites in children, after oral administration

Cited by 17 publications

References 13 publications

Single dose pharmacokinetics of terbinafine in cats

Single dose pharmacokinetics of terbinafine in cats

Human Pharmacogenomic Variations and Their Implications for Antifungal Efficacy

An integrated One Health framework for holistic evaluation of risks from antifungal agents in a large-scale multi-city study using post-acquisition mass spectral data mining and wastewater-based epidemiology pipelines

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