Pharmacokinetic-pharmacodynamic modelling of the antihistaminic (H1) effect of bilastine.

OBJECTIVE: To model the pharmacokinetic and pharmacodynamic relationship of bilastine, a new histamine H(1) receptor antagonist, from single- and multiple-dose studies in healthy adult subjects. METHODS: The pharmacokinetic model was developed from different single-dose and multiple-dose studies. In the single-dose studies, a total of 183 subjects received oral doses of bilastine 2.5, 5, 10, 20, 50, 100, 120, 160, 200 and 220 mg. In the multiple-dose studies, 127 healthy subjects received bilastine 10, 20, 40, 50, 80, 100, 140 or 200 mg/day as multiple doses during a 4-, 7- or 14-day period. The pharmacokinetic profile of bilastine was investigated using a simultaneous analysis of all concentration-time data by means of nonlinear mixed-effects modelling population pharmacokinetic software NONMEM version 6.1. Plasma concentrations were modelled according to a two-compartment open model with first-order absorption and elimination. For the pharmacodynamic analysis, the inhibitory effect of bilastine (inhibition of histamine-induced wheal and flare) was assessed on a preselected time schedule, and the predicted typical pharmacokinetic profile (based on the pharmacokinetic model previously developed) was used. An indirect response model was developed to describe the pharmacodynamic relationships between flare or wheal areas and bilastine plasma concentrations. Finally, once values of the concentration that produced 50% inhibition (IC(50)) had been estimated for wheal and flare effects, simulations were carried out to predict plasma concentrations for the doses of bilastine 5, 10 and 20 mg at steady state (72-96 hours). RESULTS: A non-compartmental analysis resulted in linear kinetics of bilastine in the dose range studied. Bilastine was characterized by two-compartmental kinetics with a rapid-absorption phase (first-order absorption rate constant = 1.50 h(-1)), plasma peak concentrations were observed at 1 hour following administration and the maximal response was observed at approximately 4 hours or later. Concerning the selected pharmacodynamic model to fit the data (type I indirect response model), this selection is attributable to the presence of inhibitory bilastine plasma concentrations that decrease the input response function, i.e. the production of the skin reaction. This model resulted in the best fit of wheal and flare data. The estimates (with relative standard errors expressed in percentages in parentheses) of the apparent zero-order rate constant for flare or wheal spontaneous appearance (k(in)), the first-order rate constant for flare or wheal disappearance (k(out)) and bilastine IC(50) values were 0.44 ng/mL/h (14.60%), 1.09 h(-1) (15.14%) and 5.15 ng/mL (16.16%), respectively, for wheal inhibition, and 11.10 ng/mL/h (8.48%), 1.03 h(-1) (8.35%) and 1.25 ng/mL (14.56%), respectively, for flare inhibition. The simulation results revealed that bilastine plasma concentrations do not remain over the IC(50) value throughout the inter-dose period for doses of 5 and 10 mg. However, with a dose of 20 mg of bilastine administered every 24 hours, plasma concentrations remained over the IC(50) value during the considered period for the flare effect, and up to 20 hours for the wheal effect. CONCLUSION: Pharmacokinetic and pharmacodynamic relationships of bilastine were reliably described with the use of an indirect response pharmacodynamic model; this led to an accurate prediction of the pharmacodynamic activity of bilastine.

Pharmacokinetic-pharmacodynamic modeling of the hydroxy lerisetron metabolite L6-OH in rats: an integrated parent-metabolite model.

PURPOSE: The twofold aim of this study was to characterize in vivo in rats the pharmacokinetics (PK) and pharmacodynamics (PD) of L6-OH, a metabolite of lerisetron with in vitro pharmacological activity, and evaluate the extent to which L6-OH contributes to the overall effect. METHODS: The PK of L6-OH was determined directly postmetabolite i.v. dose (PK-1), and also simultaneously for L (lerisetron concentration) and for generated L6-OH after lerisetron dose (200 microg kg(-1), i.v.), using Nonlinear Mixed Effects Modeling with an integrated parent-metabolite PK model (PK-2). Surrogate effect was measured by inhibition of serotonin-induced bradycardia. Protein binding was assayed via ultrafiltration and all quantification was performed via liquid chromatography-electrospray ionization-mass spectrometry. RESULTS: L6-OH showed elevated plasma and renal clearances, and volume of distribution (PK-1). The in vivo potency (PD) of L6-OH was high (EC(50) = 0.098 ng mL(-1) and EC(50unbound) = 0.040 ng mL(-1)). Total clearance for L (PK-2) in the presence of generated L6-OH (CL(L) = CL(-->L6-OH) + CL(n)) was 0.0139 L min(-1). Most of this clearance was L6-OH formation (F(c) = 99.6%), but only an 8.6% fraction of L6-OH was released into the bloodstream. The remainder undergoes biliar and fecal elimination. The parameters estimated from PK-2 were used to predict concentrations of L6-OH (Cp(L6)) generated after a lerisetron therapeutic dose (10 microg kg(-1)) in the rat. These concentrations are needed for the PD model and are below the quantification limit. Cp(L6max) was less than the EC(50) of L6-OH. CONCLUSIONS: We conclude that after lerisetron administration, L6-OH is extensively formed in the rat but it is quickly eliminated; therefore, besides being equipotent with the parent drug, the L6-OH metabolite does not influence the effect of lerisetron.

Age-related changes in pharmacokinetics and pharmacodynamics of lerisetron in the rat: a population pharmacokinetic model.

BACKGROUND: The importance of studying the effects of age on the pharmacokinetics and pharmacodynamics of lerisetron - a new 5-hydroxytryptamine-3 (serotonin) receptor antagonist - comes from the facts that lerisetron will be administered to patients that are being treated with cytotoxic drugs and that the elderly frequently suffer from neoplastic diseases. OBJECTIVE: The present study was designed to explore the effects of age on the pharmacokinetics and pharmacodynamics of lerisetron by using an aged rat model. A mixed-effects population study was carried out in order to analyze the sparse data and to create covariate models which could be used to derive dosage recommendations. METHODS: Fischer 344 rats (n = 44) were divided into three groups, depending on their age: 5, 13, and 25 months. Blood samples were collected before administration of 200 micro g/kg of lerisetron for measurements of albumin, alpha(1)-acid glycoprotein, and unbound fraction of lerisetron. The lerisetron plasma concentrations were measured by high-performance liquid chromatography. A two-compartment model was fitted to the data using the nonlinear mixed-effects computer program WinNonMix. The population analysis was performed with the complete set of the collected data, and the potential sources of variability in the population parameters were investigated. Additionally, a pharmacodynamic study was performed. The effect of lerisetron (inhibition of the von Bezold-Jarisch reflex) was evaluated in young, adult, and senescent Fischer 344 rats. RESULTS: The mean values of the individual Bayes estimates of the parameters showed a decrease in total clearance and distribution volume of the central compartment in old rats. The lerisetron free (unbound) fraction remained unchanged among the groups, and there were no significant differences in alpha(1)-acid glycoprotein levels. The concentration-effect relationship was best described by a sigmoid E(max) model. Since the drug concentration in plasma at half-maximal effect (EC(50)) decreased in old rats, an increased sensitivity to the effect of lerisetron in old animals could be expected. CONCLUSION: Both pharmacokinetic changes (decreased volume of distribution and clearance and increased elimination half-life) and pharmacodynamic alterations (decrease in total and unbound EC(50)) may be responsible for the different responses to lerisetron observed in old rats.

'In vitro' binding of propofol to serum lipoproteins in thyroid dysfunction.

OBJECTIVE: To determine a regression relationship between the unbound fraction (fu percent) of propofol, a highly lipophilic intravenous anaesthetic agent, and demographic and biochemical variables in a thyroid dysfunction population. METHODS: Serum samples from patients with hypo- (n=33) and hyperthyroidism (n=33) and also from healthy volunteers (control group; n=9) were spiked with propofol to a total (bound + unbound propofol) concentration of 10 microg/ml. The unbound concentration was determined using ultrafiltration followed by high-performance liquid chromatography with fluorimetric detection and the unbound fraction percent (fu) and the binding ratio [bound/free concentration (B/F)] were calculated. Albumin, alpha(1)-acid glycoprotein, cholesterol, triglycerides, HDL, LDL, VLDL lipoproteins, and T3 and T4 hormone concentrations were measured. B/F has a linear relationship with lipoprotein concentration--unlike that for fu which is curvilinear - so, we developed a linear regression model of B/F for propofol with the above biochemical variables and with gender. RESULTS: The fu in hypothyroidism was significantly lower than in the healthy control (mean +/- standard deviation 0.74 +/- 0.13% vs 0.87 +/- 0.12%, P < 0.01) but the fu was no different in hyperthyroid subjects than controls (0.94 +/- 0.16% vs 0.87 +/- 0.12%, P > 0.05). HDL, LDL and VLDL lipoproteins were significant predictors of B/F (P < 0.05) and were capable of explaining 54% of the variability in the 'in vitro' binding of propofol in thyroid disease. CONCLUSION: These results could help explain the interindividual variability in the protein binding of propofol in thyroid dysfunction patients and suggest the inclusion of lipoproteins in covariate model development for the pharmacokinetic/pharmacodynamic parameters of propofol.

Prediction of unbound propofol concentrations in a diabetic population.

Propofol is a short-acting general intravenous anesthetic characterized by a wide interindividual variability in the response after the same dose. Its binding to serum proteins exceeds 98%, so small changes in protein concentrations can be amplified in the unbound fraction of the drug and hence possibly in the effect. It is then likely that part of the variability in the response could be attributed to differences in protein levels among individuals and particularly among those with pathologies such as diabetes. The aim of this study was to establish predictive regression models in a diabetes mellitus (DM) population between unbound:bound propofol ratios and demographic and biochemical indices. Unbound:bound propofol ratios can be routinely obtained in the clinic as opposed to the free fraction of the drug. In DM patients (30 women and 37 men aged between 17 and 78 y) with mellitus type 1 (n = 37) and type 2 (n = 30) diabetes, the authors measured the lipoproteins (HDL, LDL, and VLDL), cholesterol, triglycerides, albumin, alpha1-acid glycoprotein (AAG), free fatty acids (FFA), glycosylated hemoglobin, and the unbound fraction (Fu) and the bound/free ratio (B/F) of propofol. A linearized regression model between the above variables--as well as age, sex, and type of diabetes--and Fu was then developed. Patients had blood drawn and sera separated by centrifugation and spiked with propofol to a concentration of 10 microg/mL. The Fu was determined via ultrafiltration. Multiple linear regression analysis was used to identify significant predictor variables of Fu in this population and two models were originated: one with lipoprotein serum concentrations as explanatory variables (Model A) and another that depended on cholesterol and triglycerides (Model B). Both models presented high correlation coefficients (r2 = 0.71 and 0.68, respectively; P < 0.0001), and each was used to predict Fu in an independent group of 15 DM patients of similar characteristics and biochemical indices as the model development group. Bias and precision were for Model A, 0.9% and 7.8%, and for Model B, 3.0% and 8.7%, respectively. Both models were compared with each other and to a naive predictor (the mean) and each was better than the naive model in predicting the unbound fraction of propofol. Model A and model B could be used in estimating Fu of propofol in DM patients based on the more routine clinical measures of lipoprotein serum concentrations or cholesterol and triglyceride levels.