Pharmacokinetics of tiludronate in horses: A field population study. (9th January 2018) - Popot, M. A.; Jacobs, M.; Garcia, P.; Loup, B.; Guyonnet, J.; Toutain, P. L.; Bailly‐Chouriberry, L.; Bonnaire, Y.
Summary: Background: Tiludronate is a bisphosphonate drug marketed to treat different bone conditions in horses. Objectives: The goal of this study was to measure the plasma concentrations of tiludronate in a population of race and sport horses under field conditions, and using pharmacokinetic population modelling, to estimate detection times for doping control. Study design: Prospective cohort. Methods: This study was conducted under field conditions on 39 race or sport horses diagnosed with bone conditions based on a lameness examination and treated with tiludronate. Each horse received 1 mg/kg of tiludronate (Tildren ® ) intravenously (i.v.). Blood samples (from 1 to 4 per horse with a total of 93 samples) were collected around 10, 20, 30, 40 and 50 days after tiludronate administration. Tiludronate was quantified by HPLC/ESI‐MS n . Tiludronate concentrations were analysed using nonlinear mixed‐effects modelling (population approach). Monte Carlo simulations were then used to compute a prediction interval to estimate the corresponding quantile of horses predicted to have concentrations below some potential screening limits. Results: This study highlighted pharmacokinetic differences between healthy experimental horses and the population of horses being treated in the field as well as the effect of level of training on plasma tiludronate. Different detection times were computed corresponding to different possible screening limits. Main limitations: The number of horses in each group was limited, and the specific disease being treated with tiludronate is unknown. Conclusions: This population pharmacokinetic study on tiludronate will enable racing and other sports authorities to provide a detection time reflecting field conditions for the medication control of tiludronate. More generally, our study design and the data modelling serve as an example of how to generate detection times directly from the target horse population rather than from experimental horses.