INDIRECT ESTIMATION OF THE UNBOUND FRACTION OF CYCLOSPORINE IN PLASMA

Citation
F. Akhlaghi et al., INDIRECT ESTIMATION OF THE UNBOUND FRACTION OF CYCLOSPORINE IN PLASMA, Therapeutic drug monitoring, 20(3), 1998, pp. 301-308
Citations number
21
Categorie Soggetti
Pharmacology & Pharmacy","Public, Environmental & Occupation Heath",Toxicology,Biology
Journal title
ISSN journal
01634356
Volume
20
Issue
3
Year of publication
1998
Pages
301 - 308
Database
ISI
SICI code
0163-4356(1998)20:3<301:IEOTUF>2.0.ZU;2-O
Abstract
The unbound fraction (f(U)) of cyclosporine in plasma is approximately 0.02. The measurement of cyclosporine f(U) requires a laborious equil ibrium dialysis procedure, which is not practical in a clinical settin g. A mathematical model was developed to estimate cyclosporine f(U) fr om concentrations of serum lipoproteins, the major binding proteins fo r cyclosporine. Values of f(U) were determined ex vivo in 126 plasma s amples obtained from 58 recipients of heart and lung transplants, usin g equilibrium dialysis. Concentrations of serum lipids, measured using standard enzymatic techniques, were used as concentration markers for serum lipoproteins. Patients were randomly assigned to either of two equal-sized groups. One group (subgroup 1) was used to evaluate the pa rameters of the model, and the other group isubgroup 2) was used to ex amine its predictive performance. The parameters were estimated using least squares non-linear regression. A model incorporating concentrati ons of serum HDL- and LDL-cholesterol, serum albumin, and time after t ransplantation gave the best fit. For subgroup 2, mean prediction erro r (ME), a measure of bias, and root mean squared error (RMSE) and medi an absolute error (MAE), measures of precision, and their 95% confiden ce intervals were estimated. For the best fit model, ME was 0.07 x 10( -2) (-0.065 x 10(-2) - 0.1 x 10(-2)), indicating that the model provid ed an unbiased estimate of the value of cyclosporine f(U). Root mean s quared error and MAE were 0.536 x 10(-2) (0.398 x 10(-2) - 0.645 x 10( -2)) and 0.27 x 10(-2) (0.226 x 10(-2) - 0.409 x 10(-2)), respectively . Prediction error was normally distributed; approximately 30% of the prediction errors were <10% and <5% of prediction errors were >50%. Th is model has shown a reasonable predictive performance in the patients with cardiac transplants studied; however, its predictive performance will need to be validated in a larger number of recipients of transpl ants of various types.