167 resultados para Pharmacokinetic parameters

em QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast


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WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT center dot The cytotoxic effects of 6-mercaptopurine (6-MP) were found to be due to drug-derived intracellular metabolites (mainly 6-thioguanine nucleotides and to some extent 6-methylmercaptopurine nucleotides) rather than the drug itself. center dot Current empirical dosing methods for oral 6-MP result in highly variable drug and metabolite concentrations and hence variability in treatment outcome. WHAT THIS STUDY ADDS center dot The first population pharmacokinetic model has been developed for 6-MP active metabolites in paediatric patients with acute lymphoblastic leukaemia and the potential demographic and genetically controlled factors that could lead to interpatient pharmacokinetic variability among this population have been assessed. center dot The model shows a large reduction in interindividual variability of pharmacokinetic parameters when body surface area and thiopurine methyltransferase polymorphism are incorporated into the model as covariates. center dot The developed model offers a more rational dosing approach for 6-MP than the traditional empirical method (based on body surface area) through combining it with pharmacogenetically guided dosing based on thiopurine methyltransferase genotype. To investigate the population pharmacokinetics of 6-mercaptopurine (6-MP) active metabolites in paediatric patients with acute lymphoblastic leukaemia (ALL) and examine the effects of various genetic polymorphisms on the disposition of these metabolites. Data were collected prospectively from 19 paediatric patients with ALL (n = 75 samples, 150 concentrations) who received 6-MP maintenance chemotherapy (titrated to a target dose of 75 mg m(-2) day(-1)). All patients were genotyped for polymorphisms in three enzymes involved in 6-MP metabolism. Population pharmacokinetic analysis was performed with the nonlinear mixed effects modelling program (nonmem) to determine the population mean parameter estimate of clearance for the active metabolites. The developed model revealed considerable interindividual variability (IIV) in the clearance of 6-MP active metabolites [6-thioguanine nucleotides (6-TGNs) and 6-methylmercaptopurine nucleotides (6-mMPNs)]. Body surface area explained a significant part of 6-TGNs clearance IIV when incorporated in the model (IIV reduced from 69.9 to 29.3%). The most influential covariate examined, however, was thiopurine methyltransferase (TPMT) genotype, which resulted in the greatest reduction in the model's objective function (P

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The steady-state pharmacokinetic profile of indomethacin was examined in twelve healthy volunteers (4 m, 8 f; 20-34 y) and in 12 elderly subjects (7 m, 5 f; 70-88 y). Two formulations of indomethacin were examined, providing duplicate data for each subject group. The subjects received each formulation of indomethacin (25 mg tid) for 6 days in a single blind crossover fashion. On day 7, after an overnight fast, a final 25 mg dose of indomethacin was given and plasma concentrations measured over the following 12 h. Kinetic parameters Cpmin, Tmax and AUC (0-12 h) were determined. There were no differences in the pharmacokinetic parameters between young and elderly subjects or between data for the two formulations of indomethacin. AUC values (micrograms.ml-1.h), for example, for the two formulations in the young subjects were 5.85 and 6.85 while the values for the elderly subjects were 6.55 and 6.50 respectively. When each treatment period was considered independently there was a significant difference between young and elderly subjects with regard to compliance. The rates of non compliance (over and under compliance) using a capsule count technique were, however, low with a mean maximum value of 5.8% being recorded for the elderly subjects.

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Aims: To characterize the population pharmacokinetics of ranitidine in critically ill children and to determine the influence of various clinical and demographic factors on its disposition. Methods: Data were collected prospectively from 78 paediatric patients (n = 248 plasma samples) who received oral or intravenous ranitidine for prophylaxis against stress ulcers, gastrointestinal bleeding or the treatment of gastro-oesophageal reflux. Plasma samples were analysed using high-performance liquid chromatography, and the data were subjected to population pharmacokinetic analysis using nonlinear mixed-effects modelling. Results: A one-compartment model best described the plasma concentration profile, with an exponential structure for interindividual errors and a proportional structure for intra-individual error. After backward stepwise elimination, the final model showed a significant decrease in objective function value (-12.618; P <0.001) compared with the weight-corrected base model. Final parameter estimates for the population were 32.1lh for total clearance and 285l for volume of distribution, both allometrically modelled for a 70kg adult. Final estimates for absorption rate constant and bioavailability were 1.31h and 27.5%, respectively. No significant relationship was found between age and weight-corrected ranitidine pharmacokinetic parameters in the final model, with the covariate for cardiac failure or surgery being shown to reduce clearance significantly by a factor of 0.46. Conclusions: Currently, ranitidine dose recommendations are based on children's weights. However, our findings suggest that a dosing scheme that takes into consideration both weight and cardiac failure/surgery would be more appropriate in order to avoid administration of higher or more frequent doses than necessary.

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Objective: To characterize the population pharmacokinetics of canrenone following administration of potassium canrenoate (K-canrenoate) in paediatric patients.

Methods: Data were collected prospectively from 37 paediatric patients (median weight 2.9?kg, age range 2 days–0.85 years) who received intravenous K-canrenoate for management of retained fluids, for example in heart failure and chronic lung disease. Dried blood spot (DBS) samples (n?=?213) from these were analysed for canrenone content and the data subjected to pharmacokinetic analysis using nonlinear mixed-effects modelling. Another group of patients (n?=?16) who had 71 matching plasma and DBS samples was analysed separately to compare canrenone pharmacokinetic parameters obtained using the two different matrices.

Results: A one-compartment model best described the DBS data. Significant covariates were weight, postmenstrual age (PMA) and gestational age. The final population models for canrenone clearance (CL/F) and volume of distribution (V/F) in DBS were CL/F (l/h)?=?12.86?×? (WT/70.0)0.75?×?e [0.066?×? (PMA?-?40]) and V/F (l)?=?603.30?×? (WT/70)?×?(GA/40)1.89 where weight is in kilograms. The corresponding values of CL/F and V/F in a patient with a median weight of 2.9?kg are 1.11?l/h and 20.48?l, respectively. Estimated half-life of canrenone based on DBS concentrations was similar to that based on matched plasma concentrations (19.99 and 19.37?h, respectively, in 70?kg patient).

Conclusion: The range of estimated CL/F in DBS for the study population was 0.12–9.62?l/h; hence, bodyweight-based dosage adjustment of K-canrenoate appears necessary. However, a dosing scheme that takes into consideration both weight and age (PMA/gestational age) of paediatric patients seems more appropriate.

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The purpose of this study was to mathematically characterize the effects of defined experimental parameters (probe speed and the ratio of the probe diameter to the diameter of sample container) on the textural/mechanical properties of model gel systems. In addition, this study examined the applicability of dimensional analysis for the rheological interpretation of textural data in terms of shear stress and rate of shear. Aqueous gels (pH 7) were prepared containing 15% w/w poly(methylvinylether-co-maleic anhydride) and poly(vinylpyrrolidone) (PVP) (0, 3, 6, or 9% w/w). Texture profile analysis (TPA) was performed using a Stable Micro Systems texture analyzer (model TA-XT 2; Surrey, UK) in which an analytical probe was twice compressed into each formulation to a defined depth (15 mm) and at defined rates (1, 3, 5, 8, and 10 mm s-1), allowing a delay period (15 s) between the end of the first and beginning of the second compressions. Flow rheograms were performed using a Carri-Med CSL2-100 rheometer (TA Instruments, Surrey, UK) with parallel plate geometry under controlled shearing stresses at 20.0°?±?0.1°C. All formulations exhibited pseudoplastic flow with no thixotropy. Increasing concentrations of PVP significantly increased formulation hardness, compressibility, adhesiveness, and consistency. Increased hardness, compressibility, and consistency were ascribed to enhanced polymeric entanglements, thereby increasing the resistance to deformation. Increasing probe speed increased formulation hardness in a linear manner, because of the effects of probe speed on probe displacement and surface area. The relationship between formulation hardness and probe displacement was linear and was dependent on probe speed. Furthermore, the proportionality constant (gel strength) increased as a function of PVP concentration. The relationship between formulation hardness and diameter ratio was biphasic and was statistically defined by two linear relationships relating to diameter ratios from 0 to 0.4 and from 0.4 to 0.563. The dramatically increased hardness, associated with diameter ratios in excess of 0.4, was accredited to boundary effects, that is, the effect of the container wall on product flow. Using dimensional analysis, the hardness and probe displacement in TPA were mathematically transformed into corresponding rheological parameters, namely shearing stress and rate of shear, thereby allowing the application of the power law (??=?k?n) to textural data. Importantly, the consistencies (k) of the formulations, calculated using transformed textural data, were statistically similar to those obtained using flow rheometry. In conclusion, this study has, firstly, characterized the relationships between textural data and two key instrumental parameters in TPA and, secondly, described a method by which rheological information may be derived using this technique. This will enable a greater application of TPA for the rheological characterization of pharmaceutical gels and, in addition, will enable efficient interpretation of textural data under different experimental parameters.