232 resultados para clinical pharmacology
Resumo:
Pharmacologists have generally been prejudiced against prostanoids, uncritically accepting their suppression as desirable therapy, especially for ‘quick-fix’ analgesia. This myopic perception for a long time ignored (a) the essentiality of prostanoid precursors in nutrition, (b) the physiological protective functions of natural prostaglandins (PGs) (vasculature, stomach, kidney), (c) resolution of inflammation after the expression of COX-2 and (d) increasing therapeutic use of either synthetic PGs (for erectile dysfunction, opthalmic disorders, inducing parturition, etc) or their natural precursors, e.g., ω3-rich polyunsaturated oils, to treat arthritis. Experimental studies in rats have indicated that prostaglandins (E series) are (i) useful, perhaps auto-regulators of established immunoreactivity and (ii) able to amplify (or even induce) anti-inflammatory activity with other agents. Furthermore, anti-prostanoid therapy (APT) can be arthritigenic!!, interfering with the acquisition of tolerance to some arthritigens. For patients with rheumatoid arthritis this additional side-effect of APT, barely recognised to date, may actually perpetuate their arthritis by impairing prostanoid-mediated remission processes. Hopefully, recent adverse publicity about COX-2 inhibitory drugs might stimulate serious re-assessment of some traditional anti-inflammatory therapies with low APT activity for the management of both acute pain (non-addictive cannabinoids, celery seed, etc.) and chronic inflammation, e.g., Lyprinol® (a mussel lipid extract).
Resumo:
Background: Lean bodyweight (LBW) has been recommended for scaling drug doses. However, the current methods for predicting LBW are inconsistent at extremes of size and could be misleading with respect to interpreting weight-based regimens. Objective: The objective of the present study was to develop a semi-mechanistic model to predict fat-free mass (FFM) from subject characteristics in a population that includes extremes of size. FFM is considered to closely approximate LBW. There are several reference methods for assessing FFM, whereas there are no reference standards for LBW. Patients and methods: A total of 373 patients (168 male, 205 female) were included in the study. These data arose from two populations. Population A (index dataset) contained anthropometric characteristics, FFM estimated by dual-energy x-ray absorptiometry (DXA - a reference method) and bioelectrical impedance analysis (BIA) data. Population B (test dataset) contained the same anthropometric measures and FFM data as population A, but excluded BIA data. The patients in population A had a wide range of age (18-82 years), bodyweight (40.7-216.5kg) and BMI values (17.1-69.9 kg/m(2)). Patients in population B had BMI values of 18.7-38.4 kg/m(2). A two-stage semi-mechanistic model to predict FFM was developed from the demographics from population A. For stage 1 a model was developed to predict impedance and for stage 2 a model that incorporated predicted impedance was used to predict FFM. These two models were combined to provide an overall model to predict FFM from patient characteristics. The developed model for FFM was externally evaluated by predicting into population B. Results: The semi-mechanistic model to predict impedance incorporated sex, height and bodyweight. The developed model provides a good predictor of impedance for both males and females (r(2) = 0.78, mean error [ME] = 2.30 x 10(-3), root mean square error [RMSE] = 51.56 [approximately 10% of mean]). The final model for FFM incorporated sex, height and bodyweight. The developed model for FFM provided good predictive performance for both males and females (r(2) = 0.93, ME = -0.77, RMSE = 3.33 [approximately 6% of mean]). In addition, the model accurately predicted the FFM of subjects in population B (r(2) = 0.85, ME -0.04, RMSE = 4.39 [approximately 7% of mean]). Conclusions: A semi-mechanistic model has been developed to predict FFM (and therefore LBW) from easily accessible patient characteristics. This model has been prospectively evaluated and shown to have good predictive performance.
Estimation of pharmacokinetic parameters from non-compartmental variables using Microsoft Excel((R))
Resumo:
This study was conducted to develop a method, termed 'back analysis (BA)', for converting non-compartmental variables to compartment model dependent pharmacokinetic parameters for both one- and two-compartment models. A Microsoft Excel((R)) spreadsheet was implemented with the use of Solver((R)) and visual basic functions. The performance of the BA method in estimating pharmacokinetic parameter values was evaluated by comparing the parameter values obtained to a standard modelling software program, NONMEM, using simulated data. The results show that the BA method was reasonably precise and provided low bias in estimating fixed and random effect parameters for both one- and two-compartment models. The pharmacokinetic parameters estimated from the BA method were similar to those of NONMEM estimation.
Resumo:
The aim of this report is to describe the use of WinBUGS for two datasets that arise from typical population pharmacokinetic studies. The first dataset relates to gentamicin concentration-time data that arose as part of routine clinical care of 55 neonates. The second dataset incorporated data from 96 patients receiving enoxaparin. Both datasets were originally analyzed by using NONMEM. In the first instance, although NONMEM provided reasonable estimates of the fixed effects parameters it was unable to provide satisfactory estimates of the between-subject variance. In the second instance, the use of NONMEM resulted in the development of a successful model, albeit with limited available information on the between-subject variability of the pharmacokinetic parameters. WinBUGS was used to develop a model for both of these datasets. Model comparison for the enoxaparin dataset was performed by using the posterior distribution of the log-likelihood and a posterior predictive check. The use of WinBUGS supported the same structural models tried in NONMEM. For the gentamicin dataset a one-compartment model with intravenous infusion was developed, and the population parameters including the full between-subject variance-covariance matrix were available. Analysis of the enoxaparin dataset supported a two compartment model as superior to the one-compartment model, based on the posterior predictive check. Again, the full between-subject variance-covariance matrix parameters were available. Fully Bayesian approaches using MCMC methods, via WinBUGS, can offer added value for analysis of population pharmacokinetic data.
Resumo:
Objective: To describe empiric community-acquired pneumonia (CAP) management in Australian hospital emergency departments (EDs) and evaluate this against national guidelines, including use of the pneumonia severity index and antibiotic selection. Design: A multicentre, cross-sectional, retrospective audit, April 2003 to February 2005. Setting: 37 Australian hospitals: 22 principal referral hospitals, six large major city hospitals, four large regional hospitals, four medium hospitals and one private hospital. Participants: Adult patients with a diagnosis of CAP made in the ED. Data on 20 consecutive CAP ED presentations were collected in participating hospitals. Outcome measures: Documented use of the pneumonia severity index, initial antibiotic therapy prescribed in the ED, average length of stay, inpatient mortality, and concordance with national guidelines. Results: 691 CAP presentations were included. Pneumonia severity index use was documented in 5% of cases. Antibiotic therapy covering common bacterial causes of CAP was prescribed in 67% of presentations, although overall concordance with national guidelines was 18%. Antibiotic prescribing was discordant due to inadequate empiric antimicrobial cover, allergy status (including contraindication to penicillin), inappropriate route of administration and/or inappropriate antibiotic choice according to recommendations. There was no significant difference between concordant and discordant antibiotic prescribing episodes in average length of stay (5.0 v 5.7 days; P=0.22) or inpatient mortality (1.6% v 4.1%; chi(2) = 1.82; P=0.18). Conclusions: Antibiotic therapy for CAP prescribed in Australian EDs varied. Concordance with national CAP guidelines was generally low. Targeted interventions are required to improve concordance.
Resumo:
The pharmacokinetic disposition of metformin in late pregnancy was studied together with the level of fetal exposure at birth. Blood samples were obtained in the third trimester of pregnancy from women with gestational diabetes or type 2 diabetes, 5 had a previous diagnosis of polycystic ovary syndrome. A cord blood sample also was obtained at the delivery of some of these women, and also at delivery of others who had been taking metformin during pregnancy but from whom no blood had been taken. Plasma metformin concentrations were assayed by a new, validated, reverse-phase HPLC method, A 2-compartment, extravascular maternal model with transplacental partitioning of drug to a fetal compartment was fitted to the data. Nonlinear mixed-effects modeling was performed in'NONMEM using FOCE with INTERACTION. Variability was estimated using logarithmic interindividual and additive residual variance models; the covariance between clearance and volume was modeled simultaneously. Mean (range) metformin concentrations in cord plasma and in maternal plasma were 0.81 (range, 0.1-2.6) mg/L and 1.2 (range, 0. 1-2.9) mg/L, respectively. Typical population values (interindividual variability, CV%) for allometrically scaled maternal clearance and volume of distribution were 28 L/h/70 kg (17.1%) and 190 L/70 ka (46.3%), giving a derived population-wide half-life of 5.1 hours. The placental partition coefficient for metformin was 1.07 (36.3%). Neither maternal age nor weight significantly influenced the pharmacokinetics. The variability (SD) of observed concentrations about model-predicted concentrations was 0.32 mg/L. The pharmacokinetics were similar to those in nonpregnant patients and, therefore, no dosage adjustment is warranted. Metformin readily crosses the placenta, exposing the fetus to concentrations approaching those in the maternal circulation. The sequelae to such exposure, ea, effects on neonatal obesity and insulin resistance, remain unknown.
Resumo:
Indomethacin (IND) is the drug of choice for the closure of a patent ductus arteriosus (PDA) in neonates. This paper describes a simple, sensitive, accurate and precise microscale HPLC method suitable for the analysis of IND in plasma of premature neonates. Samples were prepared by plasma protein precipitation with acetonitrile containing the methyl ester of IND as the internal standard (IS). Chromatography was performed on a Hypersil C-18 column. The mobile phase of methanol, water and orthophosphoric acid (70:29.5:0.5, v/v, respectively), was delivered at 1.5 mL/min and monitored at 270 nm. IND and the IS were eluted at 2.9 and 4.3 min, respectively. Calibrations were linear (r > 0.999) from 25 to 2500 mu g/L. The inter- and intra-day assay imprecision was less than 4.3% at 400-2000 mu g/L, and less than 22.1% at 35 mu g/L. Inaccuracy ranged from -6.0% to +1.0% from 35 to 2000 mu g/L. The absolute recovery of IND over this range was 93.0-113.3%. The IS was stable for at least 36 h when added to plasma at ambient temperature. This method is suitable for pharmacokinetic studies of IND and has potential for monitoring therapy in infants with PDA when a target therapeutic range for IND has been validated. (c) 2005 Elsevier B.V. All rights reserved.
Resumo:
Alternative measures to trough concentrations [non-trough concentrations and limited area under the concentration-time curve (AUC)] have been shown to better predict tacrolimus AUC. The aim of this study was to determine if these are also better predictors of adverse outcomes in long term liver transplant recipients. The associations between tacrolimus trough concentrations (C-0), non-trough concentrations (C-1, C-2, C-4, C-6/8), and AUC(0-12) and the occurrence of hypertension, hyperkalaemia, hyperglycaemia and nephrotoxicity were assessed in 34 clinically stable liver transplant patients. The most common adverse outcome was hypertension, prevalence of 36%. Hyperkalaemia and hyperglycaemia had a prevalence of 21% and 13%, respectively. A sequential population pharmacokinetic/pharmacodynamic approach was implemented. No significant association between predicted C-0, C-1, C-2, C-4, C-6/8 or AUC(0-12) and adverse effects could be found. Tacrolimus concentrations and AUC measures were in the same range in patients with and without adverse effects. Measures reported to provide benefit, preventing graft rejection and minimizing acute adverse effects in the early post-transplant period, were not able to predict adverse effects in stable adult liver recipients whose trough concentrations were maintained in the notional target range.
Resumo:
The flavivirus West Nile virus (WNV) has spread rapidly throughout the world in recent years causing fever, meningitis, encephalitis, and fatalities. Because the viral protease NS2B/NS3 is essential for replication, it is attracting attention as a potential therapeutic target, although there are currently no antiviral inhibitors for any flavivirus. This paper focuses on elucidating interactions between a hexapeptide substrate (Ae-KPGLKR-p-nitroanilide) and residues at S1 and S2 in the active site of WNV protease by comparing the catalytic activities of selected mutant recombinant proteases in vitro. Homology modeling enabled the predictions of key mutations in VWNV NS3 protease at S1 (V115A/F, D129A/ E/N, S135A, Y150A/F, S160A, and S163A) and S2 (N152A) that might influence substrate recognition and catalytic efficiency. Key conclusions are that the substrate P1 Arg strongly interacts with S1 residues Asp-129, Tyr-150, and Ser-163 and, to a lesser extent, Ser-160, and P2 Lys makes an essential interaction with Asn-152 at S2. The inferred substrate-enzyme interactions provide a basis for rational protease inhibitor design and optimization. High sequence conservation within flavivirus proteases means that this study may also be relevant to design of protease inhibitors for other flavivirus proteases.
Resumo:
High-performance liquid chromatography coupled by an electrospray ion source to a tandem mass spectrometer (HPLC-EST-MS/ MS) is the current analytical method of choice for quantitation of analytes in biological matrices. With HPLC-ESI-MS/MS having the characteristics of high selectivity, sensitivity, and throughput, this technology is being increasingly used in the clinical laboratory. An important issue to be addressed in method development, validation, and routine use of HPLC-ESI-MS/MS is matrix effects. Matrix effects are the alteration of ionization efficiency by the presence of coeluting substances. These effects are unseen in the chromatograrn but have deleterious impact on methods accuracy and sensitivity. The two common ways to assess matrix effects are either by the postextraction addition method or the postcolumn infusion method. To remove or minimize matrix effects, modification to the sample extraction methodology and improved chromatographic separation must be performed. These two parameters are linked together and form the basis of developing a successful and robust quantitative HPLC-EST-MS/MS method. Due to the heterogenous nature of the population being studied, the variability of a method must be assessed in samples taken from a variety of subjects. In this paper, the major aspects of matrix effects are discussed with an approach to address matrix effects during method validation proposed. (c) 2004 The Canadian Society of Clinical Chemists. All rights reserved.