5 resultados para Nonmem

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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BACKGROUND:: The interaction of sevoflurane and opioids can be described by response surface modeling using the hierarchical model. We expanded this for combined administration of sevoflurane, opioids, and 66 vol.% nitrous oxide (N2O), using historical data on the motor and hemodynamic responsiveness to incision, the minimal alveolar concentration, and minimal alveolar concentration to block autonomic reflexes to nociceptive stimuli, respectively. METHODS:: Four potential actions of 66 vol.% N2O were postulated: (1) N2O is equivalent to A ng/ml of fentanyl (additive); (2) N2O reduces C50 of fentanyl by factor B; (3) N2O is equivalent to X vol.% of sevoflurane (additive); (4) N2O reduces C50 of sevoflurane by factor Y. These four actions, and all combinations, were fitted on the data using NONMEM (version VI, Icon Development Solutions, Ellicott City, MD), assuming identical interaction parameters (A, B, X, Y) for movement and sympathetic responses. RESULTS:: Sixty-six volume percentage nitrous oxide evokes an additive effect corresponding to 0.27 ng/ml fentanyl (A) with an additive effect corresponding to 0.54 vol.% sevoflurane (X). Parameters B and Y did not improve the fit. CONCLUSION:: The effect of nitrous oxide can be incorporated into the hierarchical interaction model with a simple extension. The model can be used to predict the probability of movement and sympathetic responses during sevoflurane anesthesia taking into account interactions with opioids and 66 vol.% N2O.

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Bone research is limited by the methods available for detecting changes in bone metabolism. While dual X-ray absorptiometry is rather insensitive, biochemical markers are subject to significant intra-individual variation. In the study presented here, we evaluated the isotopic labeling of bone using 41Ca, a long-lived radiotracer, as an alternative approach. After successful labeling of the skeleton, changes in the systematics of urinary 41Ca excretion are expected to directly reflect changes in bone Ca metabolism. A minute amount of 41Ca (100 nCi) was administered orally to 22 postmenopausal women. Kinetics of tracer excretion were assessed by monitoring changes in urinary 41Ca/40Ca isotope ratios up to 700 days post-dosing using accelerator mass spectrometry and resonance ionization mass spectrometry. Isotopic labeling of the skeleton was evaluated by two different approaches: (i) urinary 41Ca data were fitted to an established function consisting of an exponential term and a power law term for each individual; (ii) 41Ca data were analyzed by population pharmacokinetic (NONMEM) analysis to identify a compartmental model that describes urinary 41Ca tracer kinetics. A linear three-compartment model with a central compartment and two sequential peripheral compartments was found to best fit the 41Ca data. Fits based on the use of the combined exponential/power law function describing urinary tracer excretion showed substantially higher deviations between predicted and measured values than fits based on the compartmental modeling approach. By establishing the urinary 41Ca excretion pattern using data points up to day 500 and extrapolating these curves up to day 700, it was found that the calculated 41Ca/40Ca isotope ratios in urine were significantly lower than the observed 41Ca/40Ca isotope ratios for both techniques. Compartmental analysis can overcome this limitation. By identifying relative changes in transfer rates between compartments in response to an intervention, inaccuracies in the underlying model cancel out. Changes in tracer distribution between compartments were modeled based on identified kinetic parameters. While changes in bone formation and resorption can, in principle, be assessed by monitoring urinary 41Ca excretion over the first few weeks post-dosing, assessment of an intervention effect is more reliable approximately 150 days post-dosing when excreted tracer originates mainly from bone.

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The goals of the present study were to model the population kinetics of in vivo influx and efflux processes of grepafloxacin at the serum-cerebrospinal fluid (CSF) barrier and to propose a simulation-based approach to optimize the design of dose-finding trials in the meningitis rabbit model. Twenty-nine rabbits with pneumococcal meningitis receiving grepafloxacin at 15 mg/kg of body weight (intravenous administration at 0 h), 30 mg/kg (at 0 h), or 50 mg/kg twice (at 0 and 4 h) were studied. A three-compartment population pharmacokinetic model was fit to the data with the program NONMEM (Nonlinear Mixed Effects Modeling). Passive diffusion clearance (CL(diff)) and active efflux clearance (CL(active)) are transfer kinetic modeling parameters. Influx clearance is assumed to be equal to CL(diff), and efflux clearance is the sum of CL(diff), CL(active), and bulk flow clearance (CL(bulk)). The average influx clearance for the population was 0.0055 ml/min (interindividual variability, 17%). Passive diffusion clearance was greater in rabbits receiving grepafloxacin at 15 mg/kg than in those treated with higher doses (0.0088 versus 0.0034 ml/min). Assuming a CL(bulk) of 0.01 ml/min, CL(active) was estimated to be 0.017 ml/min (11%), and clearance by total efflux was estimated to be 0.032 ml/min. The population kinetic model allows not only to quantify in vivo efflux and influx mechanisms at the serum-CSF barrier but also to analyze the effects of different dose regimens on transfer kinetic parameters in the rabbit meningitis model. The modeling-based approach also provides a tool for the simulation and prediction of various outcomes in which researchers might be interested, which is of great potential in designing dose-finding trials.

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A new technique was evaluated to identify changes in bone metabolism directly at high sensitivity through isotopic labeling of bone Ca. Six women with low BMD were labeled with 41Ca up to 700 days and treated for 6 mo with risedronate. Effect of treatment on bone could be identified using 41Ca after 4-8 wk in each individual. INTRODUCTION: Isotopic labeling of bone using 41Ca, a long-living radiotracer, has been proposed as an alternative approach for measuring changes in bone metabolism to overcome current limitations of available techniques. After isotopic labeling of bone, changes in urinary 41Ca excretion reflect changes in bone Ca balance. The aim of this study was to validate this new technique against established measures. Changes in bone Ca balance were induced by giving a bisphosphonate. MATERIALS AND METHODS: Six postmenopausal women with diagnosed osteopenia/osteoporosis received a single oral dose of 100 nCi 41Ca for skeleton labeling. Urinary 41Ca/40Ca isotope ratios were monitored by accelerator mass spectrometry up to 700 days after the labeling process. Subjects received 35 mg risedronate per week for 6 mo. Effect of treatment was monitored using the 41Ca signal in urine and parallel measurements of BMD by DXA and biochemical markers of bone metabolism in urine and blood. RESULTS: Positive response to treatment was confirmed by BMD measurements, which increased for spine by +3.0% (p = 0.01) but not for hip. Bone formation markers decreased by -36% for bone alkaline phosphatase (BALP; p = 0.002) and -59% for procollagen type I propeptides (PINP; p = 0.001). Urinary deoxypyridinoline (DPD) and pyridinoline (PYD) were reduced by -21% (p = 0.019) and -23% (p = 0.009), respectively, whereas serum and urinary carboxy-terminal teleopeptides (CTXs) were reduced by -60% (p = 0.001) and -57.0% (p = 0.001), respectively. Changes in urinary 41Ca excretion paralleled findings for conventional techniques. The urinary 41Ca/40Ca isotope ratio was shifted by -47 +/- 10% by the intervention. Population pharmacokinetic analysis (NONMEM) of the 41Ca data using a linear three-compartment model showed that bisphosphonate treatment reduced Ca transfer rates between the slowly exchanging compartment (bone) and the intermediate fast exchanging compartment by 56% (95% CI: 45-58%). CONCLUSIONS: Isotopic labeling of bone using 41Ca can facilitate human trials in bone research by shortening of intervention periods, lowering subject numbers, and having easier conduct of cross-over studies compared with conventional techniques.

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BACKGROUND: Propofol and sevoflurane display additivity for gamma-aminobutyric acid receptor activation, loss of consciousness, and tolerance of skin incision. Information about their interaction regarding electroencephalographic suppression is unavailable. This study examined this interaction as well as the interaction on the probability of tolerance of shake and shout and three noxious stimulations by using a response surface methodology. METHODS: Sixty patients preoperatively received different combined concentrations of propofol (0-12 microg/ml) and sevoflurane (0-3.5 vol.%) according to a crisscross design (274 concentration pairs, 3 to 6 per patient). After having reached pseudo-steady state, the authors recorded bispectral index, state and response entropy and the response to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy. For the analysis of the probability of tolerance by logistic regression, a Greco interaction model was used. For the separate analysis of bispectral index, state and response entropy suppression, a fractional Emax Greco model was used. All calculations were performed with NONMEM V (GloboMax LLC, Hanover, MD). RESULTS: Additivity was found for all endpoints, the Ce(50, PROP)/Ce(50, SEVO) for bispectral index suppression was 3.68 microg. ml(-1)/ 1.53 vol.%, for tolerance of shake and shout 2.34 microg . ml(-1)/ 1.03 vol.%, tetanic stimulation 5.34 microg . ml(-1)/ 2.11 vol.%, laryngeal mask airway insertion 5.92 microg. ml(-1) / 2.55 vol.%, and laryngoscopy 6.55 microg. ml(-1)/2.83 vol.%. CONCLUSION: For both electroencephalographic suppression and tolerance to stimulation, the interaction of propofol and sevoflurane was identified as additive. The response surface data can be used for more rational dose finding in case of sequential and coadministration of propofol and sevoflurane.