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Volume dynamics is a two-compartment dynamical model using hemoglobin (Hb) derived plasma diluted level as input data and urine output as input variable through consecutive repeated measurements of Hb concentration in the blood during infusion. It could be applied to evaluate and guide crystalloid fluid rehydration for patients with dehydration or hypovolemia and during anesthesia or surgery. Volume dynamics could be also used to quantificate of strains, hypovolume, and the change of fluid distribution and elimination caused by anesthesia or surgery. The factors which influence the volume resuscitation are complex, including gender, age, hemodynamic state [mean arterial pressure (MAP)], health and stress state, renal function, consciousness, surgical or anesthesia state and so on, which may affect the half-life, distribution, and volume of the fluid. This article summarizes and analyzes the pathophysiological changes of crystalloids fluid in vivo, in order to provide reference for volume management in critically ill patients.
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A non-linear mixed-effects model is proposed to assess the impact of acarbose over time on postprandial glycaemia in a single rat. The model is based on two compartments, one representing the entry of glucose in the blood and the other its exit. The rat was submitted to two treatments: ingestion of starch and ingestion of starch plus acarbose. The model showed great suitability, with inferences on the behavior of glucose levels in response to treatments and supplying a richer description than just the area under the curve. The marginal curves for the two treatments are similar during the first moments; however, after reaching the peak of glucose concentration, they progressively became separate due to acarbose treatment and reached the initial levels more quickly. The proposed model, albeit with a single sample unit, showed similar results to those with larger samples; in other words, acarbose significantly attenuates glycaemia after ingestion of starch.
Neste estudo, foi proposto um modelo não linear de efeitos mistos para verificar o impacto da acarbose ao longo do tempo na glicemia pós -prandial de um único rato. Adotou-se um modelo de dois compartimentos: um representando a entrada de glicose no sangue e outro, a saída. O rato foi submetido a dois tratamentos: ingestão de amido e de amido com adição de acarbose. O modelo proposto apresentou um ótimo ajuste, permitindo fazer inferências do comportamento da glicose para os tratamentos e fornecendo uma descrição muito mais rica do que simplesmente a área sob a curva. As curvas marginais para os dois tratamentos foram semelhantes nos primeiros tempos observados, porém, após o pico de concentração de glicose, elas se distanciaram progressivamente com o tratamento da acarbose atingindo os níveis iniciais mais rapidamente. O modelo adotado, com uma única unidade amostral, mostrou resultados similares a outros estudos com maior número de unidades amostrais, isto é, a acarbose pode atenuar consideravelmente a glicemia após ingestão de amido.
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Rats , Acarbose , Diabetes Mellitus , GlucoseABSTRACT
Objective To simplify the calculation of T>MIC and evaluate meropenem regimens based on the simple mathematical model of T>MIC reported in literature for two-compartment model.Methods Six meropenem regimens were designed according to the recommended dose with the infusion duration of 0.5 h and 3 h.Four different MIC susceptibility breakpoint of meropenem against clinical microbiologic flora were used to formulate different T>MIC.Each T>MIC was calculated by both the simple and two-compartment model of T>MIC.Meropenem regimens were evaluated for different bacterial infections based on the simple model of T>MIC with 40%~100% of the T>MIC% as the target.The t-test suggested no significant difference between the pairs of T>MIC calculated by the two models.Results and Conclusion Simple model of T>MIC can replace the two-compartment model.Meropenem regimens can be optimized based on this simple model of T>MIC conveniently and quickly.
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@#The aim of this study was to prepare and characterise docetaxel lipid emulsion injection and to conduct the characterization of its pharmacokinetics in rats after tail-vein injection. High pressure homogenization method was used to prepare docetaxel lipid emulsion. 12 Wistar rats were randomly divided into docetaxel lipid emulsion injection group and docetaxel injection group, and dosed at 6 mg/kg through tail-vein injection. Docetaxel concentration in plasma was determined by HPLC. The pharmacokinetic parameters of docetaxel in rats were obtained using the 3P97 program. Particle size, polydispersion index, Zeta potential of docetaxel lipid emulsion were found to be(221. 6±13. 4)nm, (0. 092±0. 003)and -30. 3 mV, respectively. t1/2(α) of docetaxel lipid emulsion injection and docetaxel injection were(0. 072±0. 014)and(0. 066±0. 015)h; t1/2(β) were(0. 573±0. 253)and(0. 432±0. 184)h; AUC0-12 h were(7. 98±1. 25)and(6. 26±1. 83)μg ·h/mL, respectively. Docetaxel lipid emulsion injection had similar pharmacokinetic characteristics to docetaxel injection. The pharmacokinetic data obtained for both preparations fitted a two-compartment model.
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Objective:To dynamically describe the drug concentration change in different time in vivo to understand the in vivo be-havior patterns and rules of drugs, provide reference for performing individual treatment and avoiding adverse drug reactions and lay foundation for dynamic description of physiological pharmacokinetic model. Methods:A two-compartment model was established using Simulink dynamic simulation and used in curve fitting and parameter estimation. The results from the model were compared with those from 3P97 software. Results:There was no significant difference between the results from the dynamic model and those from 3P97 soft-ware. Conclusion:The dynamic model can be used to dynamically simulate two-compartment model for oral administration. The re-sults from the dynamic simulation are more direct, and can correct the fitting error in 3P97 software.
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OBJECTIVE: To determine the reliable perfusion parameters in dynamic contrast-enhanced MRI (DCE-MRI) for the monitoring antiangiogenic treatment in mice. MATERIALS AND METHODS: Mice, with U-118 MG tumor, were treated with either saline (n = 3) or antiangiogenic agent (sunitinib, n = 8). Before (day 0) and after (days 2, 8, 15, 25) treatment, DCE examinations using correlations of perfusion parameters (Kep, Kel, and AH from two compartment model; time to peak, initial slope and % enhancement from time-intensity curve analysis) were evaluated. RESULTS: Tumor growth rate was found to be 129% +/- 28 in control group, -33% +/- 11 in four mice with sunitinib-treatment (tumor regression) and 47% +/- 15 in four with sunitinib-treatment (growth retardation). Kep (r = 0.80) and initial slope (r = 0.84) showed strong positive correlation to the initial tumor volume (p < 0.05). In control mice, tumor regression group and growth retardation group animals, Kep (r : 0.75, 0.78, 0.81, 0.69) and initial slope (r : 0.79, 0.65, 0.67, 0.84) showed significant correlation with tumor volume (p < 0.01). In four mice with tumor re-growth, Kep and initial slope increased 20% or greater at earlier (n = 2) than or same periods (n = 2) to when the tumor started to re-grow with 20% or greater growth rate. CONCLUSION: Kep and initial slope may a reliable parameters for monitoring the response of antiangiogenic treatment.
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Animals , Female , Mice , Angiogenesis Inhibitors/therapeutic use , Contrast Media , Heterografts , Indoles/therapeutic use , Longitudinal Studies , Magnetic Resonance Imaging/methods , Mice, Inbred BALB C , Neoplasm Transplantation , Neoplasms, Experimental/diagnosis , Pyrroles/therapeutic use , Reproducibility of Results , Tumor BurdenABSTRACT
PURPOSE: To determine appropriate sampling frequency and time of multiple blood sampling dual exponential method with 99mTc-DTPA for calculating glomerular filtration rate (GFR). MATERIALS AND METHODS: Thirty four patients were included in this study. Three mCi of 99mTc-DTPA was intravenously injected and blood sampling at 9 different times, 5ml each, were done. Using the radioactivity of serum, measured by gamma counter, the GFR was calculated using dual exponential method and corrected with the body surface area. Using spontaneously chosen 2 data points of serum radioactivity, 15 collections of 2-sample GFR were calculated. And 10 collections of 3-sample GFR and 12 collections of 4-sample GFR were also calculated. Using the 9-sample GFR as a reference value, degree of agreement was analyzed with Kendall's tau correlation coefficients, mean difference and standard deviation. RESULTS: Although some of the 2-sample GFR showed high correlation coefficient, over or underestimation had evolved as the renal function change. The 10-120-240 min 3-sample GFR showed a high correlation coefficient (tau=0.93), minimal difference (Mean+/-SD=-1.784+/-3.972), and no over or underestimation as the renal function changed. The 4-sample GFR showed no better accuracy than the 3-sample GFR. CONCLUSIONS: In the wide spectrum of renal function, the 10-120-240 min 3-sample GFR could be the best choice for estimating the patients' renal function.
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Humans , Body Surface Area , Glomerular Filtration Rate , Radioactivity , Reference ValuesABSTRACT
AIM: To establish a calculation of dosage regimen design on multiple dosing intravenous bolus injection administration of two compartment model. METHODS: Microsoft EXCEL was used to write a program by the pharmacokinetic parameters to show the dosage regimen. RESULTS: The e table displayed maximum dose (D max ) and minimum dose (D min ) of the administration after inputting parameters (?, ?, K 21 and V 1) ,dosing interval (T s), maximum toxic concentration(MTX) and minimum effective concentration(MEC). After inputting maintenance dose and cycle number (n), the e table displayed plasma concentration at any time after the administration, including C (n,s) max and C (n,s) min , fractional number of reaching plateau, loading dose and accumulating time when the plasma concentration was above MEC in Ts or a cycle interval. CONCLUSION: This design method can provide safe and effective dosage regimen for clinical administration.
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OBJECTIVE:To calculate pharmacokinetic parameters and compartment model's parameters by extravascular administration based on Excel function.METHODS:The parameters of pharmacokinetics and compartment model were calculated using residual method and Excel function.RESULTS:Excel can be used to directly demonstrate calculation process of parameters of pharmacokinetics and compartment model,with calculation results in line with those stated in textbooks.CONC-LUSION:The established method is simple,direct-viewing,reliable and easy to be extended,and it can be used to calculate pharmacokinetic parameters in clinical teaching or experiments.