232 resultados para clinical pharmacology

em University of Queensland eSpace - Australia


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Drugs and metabolites are eliminated from the body by metabolism and excretion. The kidney makes the major contribution to excretion of unchanged drug and also to excretion of metabolites. Net renal excretion is a combination of three processes - glomerular filtration, tubular secretion and tubular reabsorption. Renal function has traditionally been determined by measuring plasma creatinine and estimating creatinine clearance. However, estimated creatinine clearance measures only glomerular filtration with a small contribution from active secretion. There is accumulating evidence of poor correlation between estimated creatinine clearance and renal drug clearance in different clinical settings, challenging the 'intact nephron hypothesis' and suggesting that renal drug handling pathways may not decline in parallel. Furthermore, it is evident that renal drug handling is altered to a clinically significant extent in a number of disease states, necessitating dosage adjustment not just based on filtration. These observations suggest that a re-evaluation of markers of renal function is required. Methods that measure all renal handling pathways would allow informed dosage individualisation using an understanding of renal excretion pathways and patient characteristics. Methodologies have been described to determine individually each of the renal elimination pathways. However, their simultaneous assessment has only recently been investigated. A cocktail of markers to measure simultaneously the individual renal handling pathways have now been developed, and evaluated in healthy volunteers. This review outlines the different renal elimination pathways and the possible markers that can be used for their measurement. Diseases and other physiological conditions causing altered renal drug elimination are presented, and the potential application of a cocktail of markers for the simultaneous measurement of drug handling is evaluated. Further investigation of the effects of disease processes on renal drug handling should include people with HIV infection, transplant recipients (renal and liver) and people with rheumatoid arthritis. Furthermore, changes in renal function in the elderly, the effect of sex on renal function, assessment of living kidney donors prior to transplantation and the investigation of renal drug interactions would also be potential applications. Once renal drug handling pathways are characterised in a patient population, the implications for accurate dosage individualisation can be assessed. The simultaneous measurement of renal function elimination pathways of drugs and metabolites has the potential to assist in understanding how renal function changes with different disease states or physiological conditions. In addition, it will further our understanding of fundamental aspects of the renal elimination of drugs.

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Free drug measurement and pharmacodymanic markers provide the opportunity for a better understanding of drug efficacy and toxicity. High-performance liquid chromatography (HPLC)-mass spectrometry (MS) is a powerful analytical technique that could facilitate the measurement of free drug and these markers. Currently, there are very few published methods for the determination of free drug concentrations by HPLC-MS. The development of atmospheric pressure ionisation sources, together with on-line microdialysis or on-line equilibrium dialysis and column switching techniques have reduced sample run times and increased assay efficiency. The availability of such methods will aid in drug development and the clinical use of certain drugs, including anti-convulsants, anti-arrhythmics, immunosuppressants, local anaesthetics, anti-fungals and protease inhibitors. The history of free drug measurement and an overview of the current HPLC-MS applications for these drugs are discussed. Immunosuppressant drugs are used as an example for the application of HPLC-MS in the measurement of drug pharmacodynamics. Potential biomarkers of immunosuppression that could be measured by HPLC-MS include purine nucleoside/nucleotides, drug-protein complexes and phosphorylated peptides. At the proteomic level, two-dimensional gel electrophoresis combined with matrix-assisted laser desorption/ionisation time-of-flight (TOF) MS is a powerful tool for identifying proteins involved in the response to inflammatory mediators. (C) 2003 Elsevier Science B.V. All rights reserved.

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The aim of this review is to analyse critically the recent literature on the clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplant recipients. Dosage and target concentration recommendations for tacrolimus vary from centre to centre, and large pharmacokinetic variability makes it difficult to predict what concentration will be achieved with a particular dose or dosage change. Therapeutic ranges have not been based on statistical approaches. The majority of pharmacokinetic studies have involved intense blood sampling in small homogeneous groups in the immediate post-transplant period. Most have used nonspecific immunoassays and provide little information on pharmacokinetic variability. Demographic investigations seeking correlations between pharmacokinetic parameters and patient factors have generally looked at one covariate at a time and have involved small patient numbers. Factors reported to influence the pharmacokinetics of tacrolimus include the patient group studied, hepatic dysfunction, hepatitis C status, time after transplantation, patient age, donor liver characteristics, recipient race, haematocrit and albumin concentrations, diurnal rhythm, food administration, corticosteroid dosage, diarrhoea and cytochrome P450 (CYP) isoenzyme and P-glycoprotein expression. Population analyses are adding to our understanding of the pharmacokinetics of tacrolimus, but such investigations are still in their infancy. A significant proportion of model variability remains unexplained. Population modelling and Bayesian forecasting may be improved if CYP isoenzymes and/or P-glycoprotein expression could be considered as covariates. Reports have been conflicting as to whether low tacrolimus trough concentrations are related to rejection. Several studies have demonstrated a correlation between high trough concentrations and toxicity, particularly nephrotoxicity. The best predictor of pharmacological effect may be drug concentrations in the transplanted organ itself. Researchers have started to question current reliance on trough measurement during therapeutic drug monitoring, with instances of toxicity and rejection occurring when trough concentrations are within 'acceptable' ranges. The correlation between blood concentration and drug exposure can be improved by use of non-trough timepoints. However, controversy exists as to whether this will provide any great benefit, given the added complexity in monitoring. Investigators are now attempting to quantify the pharmacological effects of tacrolimus on immune cells through assays that measure in vivo calcineurin inhibition and markers of immuno suppression such as cytokine concentration. To date, no studies have correlated pharmacodynamic marker assay results with immunosuppressive efficacy, as determined by allograft outcome, or investigated the relationship between calcineurin inhibition and drug adverse effects. Little is known about the magnitude of the pharmacodynamic variability of tacrolimus.

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Hydroxychloroquine (HCQ) is an antimalarial drug that is also used as a second-line treatment of rheumatoid arthritis (RA). Clinically, the use of HCQ is characterized by a long delay in the onset of action, and withdrawal of treatment is often a result of inefficacy rather than from toxicity. The slow onset of action can be attributed to the pharmacokinetics (PK) of HCQ, and wide interpatient variability is evident. Tentative relationships between concentration and effect have been made, but to date, no population PK model has been developed for HCQ. This study aimed to develop a population PK model including an estimation of the oral bioavailability of HCQ. In addition, the effects of the coadministration of methotrexate on the PK of HCQ were examined. Hydroxychloroquine blood concentration data were combined from previous pharmacokinetic studies in patients with rheumatoid arthritis. A total of 123 patients were studied, giving the data cohort from four previously published studies. Two groups of patients were included: 74 received hydroxychloroquine (HCQ) alone, and 49 received HCQ and methotrexate (MTX). All data analyses were carried out using the NONMEM program. A one-compartment PK model was supported, rather than a three-compartment model as previously published, probably because of the clustering of concentrations taken at the end of a dosing interval. The population estimate of bioavailability of 0.75 (0.07), n = 9, was consistent with literature values. The parameter values from the final model were: (Cl) over bar = 9.9 +/- 0.4 L/h, (V) over bar 605 +/- 91 L, (k(d)) over bar = 0.77 +/- 0.22 hours(-1), (t(tag)) over bar = 0.44 +/- 0.02 hours. Clearance was not affected by the presence of MTX, and, hence, steady-state drug concentrations and maintenance dosage requirements were similar. A population PK model was successfully developed for HCQ.

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Aim To develop a population pharmacokinetic model for mycophenolic acid in adult kidney transplant recipients, quantifying average population pharmacokinetic parameter values, and between- and within-subject variability and to evaluate the influence of covariates on the pharmacokinetic variability. Methods Pharmacokinetic data for mycophenolic acid and covariate information were previously available from 22 patients who underwent kidney transplantation at the Princess Alexandra Hospital. All patients received mycophenolate mofetil 1 g orally twice daily. A total of 557 concentration-time points were available. Data were analysed using the first-order method in NONMEM (version 5 level 1.1) using the G77 FORTRAN compiler. Results The best base model was a two-compartment model with a lag time (apparent oral clearance was 271 h(-1), and apparent volume of the central compartment 981). There was visual evidence of complex absorption and time-dependent clearance processes, but they could not be successfully modelled in this study. Weight was investigated as a covariate, but no significant relationship was determined. Conclusions The complexity in determining the pharmacokinetics of mycophenolic acid is currently underestimated. More complex pharmacokinetic models, though not supported by the limited data collected for this study, may prove useful in the future. The large between-subject and between-occasion variability and the possibility of nonlinear processes associated with the pharmacokinetics of mycophenolic acid raise questions about the value of the use of therapeutic monitoring and limited sampling strategies.

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Patient outcomes in transplantation would improve if dosing of immunosuppressive agents was individualized. The aim of this study is to develop a population pharmacokinetic model of tacrolimus in adult liver transplant recipients and test this model in individualizing therapy. Population analysis was performed on data from 68 patients. Estimates were sought for apparent clearance (CL/F) and apparent volume of distribution (V/F) using the nonlinear mixed effects model program (NONMEM). Factors screened for influence on these parameters were weight, age, sex, transplant type, biliary reconstructive procedure, postoperative day, days of therapy, liver function test results, creatinine clearance, hematocrit, corticosteroid dose, and interacting drugs. The predictive performance of the developed model was evaluated through Bayesian forecasting in an independent cohort of 36 patients. No linear correlation existed between tacrolimus dosage and trough concentration (r(2) = 0.005). Mean individual Bayesian estimates for CL/F and V/F were 26.5 8.2 (SD) L/hr and 399 +/- 185 L, respectively. CL/F was greater in patients with normal liver function. V/F increased with patient weight. CL/F decreased with increasing hematocrit. Based on the derived model, a 70-kg patient with an aspartate aminotransferase (AST) level less than 70 U/L would require a tacrolimus dose of 4.7 mg twice daily to achieve a steady-state trough concentration of 10 ng/mL. A 50-kg patient with an AST level greater than 70 U/L would require a dose of 2.6 mg. Marked interindividual variability (43% to 93%) and residual random error (3.3 ng/mL) were observed. Predictions made using the final model were reasonably nonbiased (0.56 ng/mL), but imprecise (4.8 ng/mL). Pharmacokinetic information obtained will assist in tacrolimus dosing; however, further investigation into reasons for the pharmacokinetic variability of tacrolimus is required.

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A retrospective review was undertaken in 744 patients who were dose-individualized with gentamicin once daily to evaluate a change in gentamicin clearance as a potential predictor of nephrotoxicity. The definition of nephrotoxicity was chosen to be a change in creatinine clearance greater than 20%. Similarly, a change in gentamicin clearance of greater than 20% was also considered a possible index of nephrotoxicity. Four criteria were developed to assess the usefulness of gentamicin clearance as a predictor of nephrotoxicity. Following the application of the inclusion/exclusion criteria, 132 patients were available for the analysis. The sensitivity, specificity, positive predictive value, and negative predictive value were assessed for each of the criteria. Receiver operating characteristic (ROC) curves were produced to determine if an optimum value in the change of gentamicin clearance could be found to maximize sensitivity and specificity. The overall incidence of nephrotoxicity based on a decrease in creatinine clearance by 20% or more was 3.8%. Women were overrepresented in the nephrotoxic group [71.4% versus 40.1% (P = 0.0025)]. Patients with nephrotoxicity had statistically longer treatment periods, increased cumulative dose, and more dosing predictions (P < 0.05 in each case). The sensitivity of the criteria ranged from 43 to 46%, and specificity ranged from 93 to 99%. The positive and negative predictive values ranged from 63 to 94% and 86 to 89%, respectively. In those patients in whom nephrotoxicity was predicted from a change in gentamicin clearance, this change occurred on average 3 days before the change in creatinine clearance (P < 0.05). A change in gentamicin clearance to predict nephrotoxicity may be a useful addition to current monitoring methods, although it is not the complete answer.

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