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OBJECTIVE To explore the appropriate dosing regimen of meropenem in the elderly with renal insufficiency. METHODS The meropenem population pharmacokinetics of the two-compartment model of elderly patients were applied for Monte Carlo simulation. The model included the effect of renal function on the parameters. The designed dosages were 0.5， 1， 2 g； the administration modes included intravenous injection （lasting for 6 min） and intravenous drip （0.5， 3 h）； the administration frequencies were q12 h， q8 h. A total of 18 dosing regimens were designed. The probability of target attainment of %fT＞4MIC≥40% and Cmin≤27.5 mg/L were calculated respectively to optimize the dosing regimen. RESULTS For elderly patients with creatinine clearance （CLcr） ≤40 mL/min， when the minimum inhibitory concentration （MIC） was equaled to 1 mg/L， the suggested dosing regimens were “0.5 g， intravenous drip 0.5 h， q12 h”“ 1 g， intravenous injection， q12 h”. When the MIC was equaled to 2 mg/L， the suggested dosing regimens were “0.5 g， intravenous injection， q8 h”“ 1 g， intravenous drip 0.5 h， q12 h”. When the MIC was equaled to 4， 8 mg/L， the suggested dosing regimens were “1 g （or 2 g）， intravenous injection， q8 h”. For elderly patients with CLcr equal to 50 mL/min， when the MIC was equaled to 1 mg/L， the suggested dosing regimens were “0.5 g， intravenous injection， q8 h“”1 g， intravenous injection， q12 h”. When the MIC was equal to 2， 4， 8 mg/L，the suggested dosing regimens were“0.5 g （or 1 g， or 2 g）， intravenous drip for 0.5 h， q8 h”. The appropriate dosing regimens of all the above protocols were above 96.6%. In the dosing regimen of “2 g，intravenous injection or intravenous drip 0.5 h， q8 h”， Cmin＞27.5 mg/L occurred in 40 times among the 1 000 times of simulation， indicating adverse reactions of the nervous system may occur. CONCLUSIONS For the elderly patients with renal insufficiency， the dosing regimen of meropenem should be adjusted accordingly with CLcr＝40 mL/min as the boundary， and the toxicity of nervous system should be considered at the same time.
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AIM: To build a meropenem population pharmacokinetic model for Chinese elderly through model-based meta-analysis. METHODS: Informations including dosing regimen, sampling times, concentrations, sample size, age, gender, body weight (BW) and creatinine clearance were extracted after the literature were retrieved. The model was built by NONMEM. Stepwise covariate modeling strategy was used for covariates analysis. RESULTS: A two-compartment model was applied to describe meropenem pharmacokinetics. After stepwise covariate modeling, covariates that remained significant in the final model were creatinine clearance (CLcr) on CL and the BW on V
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OBJECTIVE To isolate and ide ntify the chemical constituents of the root of Ardisia virens and preliminarily evaluate the in vitro anti-inflammatory activity of the compounds. METHODS The ethyl acetate extraction part from 70％ ethanol extract of the root of A. virens were separated and purified by silica gel column chromatography ，ODS column chromatography ， etc. The structures of the compounds were identified according to physical and chemical properties and spectral data. The inflammation model of RAW 264.7 cells was induced by lipopolysaccharide ，and anti-inflammatory activity of the compound was investigated by MTT assay. RESULTS A total of 11 compounds were isolated from the ethyl acetate extraction part ，and were identified as cyclamiretin A （1），α-spinasterol （2），（3S，5R，6S，7E）-3，5，6-trihydroxy-7-megastigmen-9-one （3）， （+）-angelicoidenol（4），octadeca-dienoic acid- 2，3-dihydroxypropyl ester （5），α-linolenic acid （6），glycerol monooleate （7），5， 5′-（4，7-hexadecadlene-1，16-diyl）bisresorcinol（8），1-（3，5-dihydroxyphenyl）heptan-1-one（9），5-heptylresorcinol and （10） 5-n-nonylresorcinol（11）. The in vitro anti-inflammatory results showed that 80，40，20，10，5 μg/mL of compounds 2，8，9 and 10 could reduce the cell survival rate in different degrees. CONCLUSIONS Compounds 1-11 are isolated from this plant for the first time，and compound 8 is a new natural product. Compound 2，8，9 and 10 show certain anti-inflammatory activity in vitro .
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Objective To analysis the effect of metformin on the expression of heat shock protein and VEGF in 5637 cell line of bladder cancer. Methods 5637 cell line of bladder cancer were selected, divided into blank control group, metformin 2 mM group, 5 mM group, 10 mM group and 20 mM group, 5637 cell line of bladder cancer were treated in each group.The cell proliferation inhibition rate was detected by MTT colorimetric assay, cell clone formation rate was detected by plate clone formation test, flow cytometry was used to detect cell cycle change, the expression levels of HSP-70, HSP-90α, VEGF were detected by Western blot method.Results Compared with control group, the inhibition rate of 5637 cell line of bladder cancer after 24, 48, 72 h were higher in all concentrations of metformin group(P<0.05), the higher the concentration and the longer the action time, the stronger inhibitory effect on cell proliferation.Compared with control group, the clone formation rate of 5637 cell line of bladder cancer in all concentrations of metformin group was lower, the difference was statistically significant(P<0.05).Compared with control group, the proportion of G1 phase cells were higher, the proportion of S phase cells were lower in in all concentrations of metformin group (P<0.05), the proportion of G2 phase cells in metformin 2 mM group, 5 mM group were lower(P<0.05).Compared with control group, the expression levels of HSP-70, HSP-90α, VEGF protein were lower in all concentrations of metformin group, the difference was statistically significant(P <0.05).Conclusion Metformin has obvious inhibitory effects on the expression of HSP-70, HSP-90α, VEGF in 5637 cell line of bladder cancer, can inhibit cell proliferation and cloning, promote the occurrence of G1 block, cause cell apoptosis, the effect was dose dependent.
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In order to successfully develop the effective population pharmacokinetic model to predict the concentration of propofol administrated intravenously, the data including the concentrations across both distribution and elimination phases from five hospitals were analyzed using nonlinear mixed effect model (NONMEM). Three-compartment pharmacokinetic model was applied while the exponential model was used to describe the inter-individual variability and constant coefficient model to the intra-individual variability, accordingly. Covariate effect including the body weight on the parameter CL, V1, Q2, V2, Q3 and V3 were investigated. The performance of final model was assessed by Bootstrapping, goodness-of-fit and visual predictive checking (VPC). The context-sensitive half-times and the infusion rates necessary to maintain the concentration of 1 microg x mL(-1) were simulated to six subpopulations. The results were as follows: the typical value of CL, V1, Q2, V2, Q3 and V3 were 0.965 x (1 + 0.401 x VESS) x (BW/59)(0.578) L x min(-1), 13.4 x (AGE/45)(-0.317) L, 0.659 x (1 + GENDER x 0.385) L x min(-1), 28.8 L, 0.575 x (1 + GENDER x 0.367) x (1 - 0.369 x VESS) L x min(-1) and 196 L respectively. Coefficients of the inter-individual variability of CL, V1, Q2, V2, Q3 and V3 were 29.2%, 46.9%, 35.2%, 40.4%, 67.0% and 49.9% respectively, and the coefficients of residual variability were 24.7%, 16.1% and 22.5%, the final model indicated a positive influence of a body weight on CL, and also that a negative correlation of age with V1. Q2 and Q3 in males were higher than those in females at 38.5% and 36.7%. The CL and Q3 were 40.1% increased and 36.9% decreased in arterial samples compared to those in venous samples. The determination coefficient of observations (DV)-individual predicted value (IPRED) by the final model was 0.91 which could predict the propofol concentration fairly well. The stability and the predictive performance were accepted by Bootstrapping, the goodness-of-fit and VPC. The context-sensitive half-times and infusion rates necessary to maintain the concentration of 1 microg x mL(-1) were different obviously among the 6 sub-populations obviously. The three-compartment model with first-order elimination could describe the pharmacokinetics of propofol fairly well. The involved fixed effects are age, body weight, gender and sampling site. The simulations in 6 subpopulations were available in clinical anesthesia. The propofol anesthesia monitor care could be improved by individualization of pharmacokinetic parameter estimated from the final model.