Chemical Constituents of n-Butanol Extract Part of Akebia trifoliata Caulis / 中国实验方剂学杂志

<b>Objective::To study the chemical constituents from <italic>n</italic>-butanol extract of <italic>Akebia trifoliata</italic> caulis. <b>Method::The 100 kg caulis of <italic>A</italic>. <italic>trifoliata</italic> was extracted with 75% ethanol (EtOH) for three times by heating reflux. These 3 extracts were decompressed and concentrated, and then dissolved in water. The solvent was successively extracted with dichloromethane, ethyl acetate and <italic>n</italic>-butanol. The chemical constituents from the <italic>n</italic>-butanol fraction were isolated by macroporous, silica gel, sephadex LH-20 and ODS columns, and semi-preparative high performance liquid chromatography, and their chemical structures were determined through MS, NMR analysis (¹H and ¹³C-NMR) and spectroscopic data from literatures. <b>Result::Totally 14 compounds were isolated and identified as mutongsaponin B(<bold>1</bold>), mutongsaponin C(<bold>2</bold>), saponin PH(<bold>3</bold>), begoniifolide A(<bold>4</bold>), 2<italic>α</italic>, 3<italic>β</italic>, 23-trihydroxy-30-noroleana-12, 19-dien-28-oicacid-<italic>O</italic>-<italic>β</italic>-<italic>D</italic>-xylopyranosyl-(1→3)-<italic>α</italic>-<italic>L</italic>-rhamnopyranosyl-(1→4)-<italic>β</italic>-<italic>D</italic>-glucopyranosyl-(1→6)-<italic>β</italic>-<italic>D</italic>-glucopyranosyl ester(<bold>5</bold>), akemisaponins D(<bold>6</bold>), akemisaponins E(<bold>7</bold>), asiaticoside(<bold>8</bold>), saponin PJ1(<bold>9</bold>), scheffoleoside A(<bold>10</bold>), symplocosneolignan A(<bold>11</bold>), kalopanax-saponins D(<bold>12</bold>), leonticin E(<bold>13</bold>), ciwujianoside A₁(<bold>14</bold>). <b>Conclusion::Compounds <bold>1-4</bold>, <bold>11</bold>, <bold>13, 14</bold> were isolated from this plant for the first time. The discovery of these compounds further enriched the chemical constituents of <italic>A</italic>. <italic>trifoliata</italic>, and provided experimental and scientific basis for the comprehensive development and utilization of <italic>A</italic>. <italic>trifoliata</italic>.

Controllable extension of hairpin-structured flaps to allow low-background cascade invasive reaction for a sensitive DNA logic sensor for mutation detection.

A DNA logic sensor was constructed for gene mutation analysis based on a novel signal amplification cascade by controllably extending a hairpin-structured flap to bridge two invasive reactions. The detection limit was as low as 0.07 fM, and the analytical specificity is high enough to unambiguously pick up 0.02% mutants from a large amount of wild-type DNA. Gene mutations related to the personalized medicine of gefitinib, a typical tyrosine kinase inhibitor, were analyzed by the DNA logic sensor with only a 15 minute response time. Successful assay of tissue samples and cell-free plasma DNA indicates that the new concept we proposed here could benefit clinicians for straightforward prescription of a mutation-targeted drug.

Propofol pharmacokinetics in China: a multicentric study.

OBJECTIVE: A multicenter population pharmacokinetics study of propofol was performed to establish a new population model. MATERIALS AND METHODS: Three thousand two hundred and fifty-nine blood samples of 220 participants were measured by HPLC-UV or HPLC-FLU or GC-MS. Target-controlled infusion after single bolus or continuous infusion was applied for propofol anesthesia. The samples were taken from 2 to 1500 min. The concentration-time profiles were analyzed by nonlinear mixed effect model (NONMEM) with first order estimation method. The inter-individual variability and the residual variability were described by exponential model and constant coefficient variation model. The stepwise modeling strategy using PsN was applied for covariate modeling. The criteria of forward addition and backward elimination were (α = 0.01 and α = 0.005, χ(2), df = 1). The final model was evaluated by bootstrap using PDx and visual predictive check using PsN. 500 bootstraps and 1000 simulation were run. RESULT: The propofol population model was described by 3-compartment model with inter-individual variability of CL, V(1), Q(2,) and Q(3) describing by exponential model. The inter-individual variability of V(2), V(3) were not included because it is reported that the parameter was near its boundary. The typical value of CL, V1, Q2, V2, Q3 and V3 were 1.28 L · min(-1), 10.1 × (age/44)-0.465 × (1 + 0.352 × sex) L, 0.819 L · min(-1), 36.0 L, 0.405 × (bodyweight/60)1.58 L · min(-1) and 272 L, respectively. Coefficients of inter-individual variability of CL, V1, Q2 and Q3 were 30.5%, 35.6%, 43.7% and 66.9%, respectively, and the coefficients of variation of HPLC-UV, GC-MS and HPLC-FLU were 13.3%, 16.9% and 24.2%, respectively. The bootstrap evaluation showed that the final model parameter estimates were within ± 3.39% compared with bootstrap median. The curves of observations percentiles were distributed within the corresponding 95 prediction percentiles by the visual predictive check. CONCLUSION: The three-compartment model with first-order elimination could describe the pharmacokinetics of propofol fairly well. The involved fixed effects are age, body weight and sex. The population model was evaluated to be stable by bootstrap and visual predictive check.

Population pharmacokinetics and pharmacogenetics of tacrolimus in healthy Chinese volunteers.

AIM: The aim of this study was to establish population pharmacokinetic models of tacrolimus in healthy Chinese volunteers. METHODS: A total of 956 tacrolimus whole blood concentrations from 73 healthy volunteers were determined using ultraperformance liquid chromatography mass spectrometry/mass spectrometry. Population pharmacokinetic analyses were performed using NONMEM. The final population pharmacokinetic models were validated with bootstrap and visual predictive check. A number of covariates were analyzed, including CYP3A5 and ABCB1 polymorphism, demographic characteristics and hematological and biological indices. RESULTS: The structural model was a two-compartment model with first-order absorption, and a lag time was fitted to the data. The typical population values of tacrolimus for the pharmacokinetic parameters of apparent clearance (CL/F), apparent distribution volume of the central compartment (V(2)/F), intercompartmental clearance (Q/F), apparent distribution volume of the peripheral compartment (V(3)/F), absorption rate (ka) and lag time (ALAG) were 27.7 l/h, 37.5 liters, 34.4 l/h, 357 liters, 0.795 h(-1) and 0.226 h, respectively. The interindividual variabilities of these parameters were 63.3, 62.0, 50.8, 52.3, 32.9 and 4.45%, respectively, and the intraindividual variability of observed concentrations was 14.9%. The covariates that were retained in the final models were CYP3A5 genotype on CL/F, and body surface area and red blood count on V(3)/F. CONCLUSION: Population pharmacokinetic models of tacrolimus were developed in healthy volunteers. These results could provide a reference for individualized tacrolimus therapy in the clinical setting.

Pharmacokinetics and bioequivalence study of two digoxin formulations after single-dose administration in healthy Chinese male volunteers.

The pharmacokinetics and relative bioavailability/bioequivalence of two formulations of digoxin (CAS 20830-75-5) were assessed in this paper. The study was conducted in 20 healthy Chinese male volunteers according to an open, randomized, single-blind, 2-way crossover study design with a wash-out phase of 14 days. Blood samples for pharmacokinetic profiling were taken up to 72 h post-dose and digoxin plasma concentrations were determined by a validated liquid chromatography-tandem mass spectrometry (LCMS/MS) method. Based on the plasma concentration-time data of each individual during two periods, pharmacokinetic parameters, Cmax, AUC0-tau, AUC0-infinity and t1/2, were calculated by applying noncompartmental analysis. Pharmacokinetic data for test and reference formulations were analyzed statistically to evaluate bioequivalence of the two formulations. After oral administration, the values of Cmax Tmax, t1/2, AUC0-tau, AUC0-infinity for test and reference formulations were 2.61 +/- 0.98 and 2.68 +/- 1.09 ng/ mL, 1.0 +/- 0.4 and 1.0 +/- 0.4 h, 27.94 +/- 3.14 and 27.56 +/- 3.86 h, 28.57 +/- 4.99 and 28.77 +/- 6.53 ng x h/mL, 33.44 +/- 4.85 and 33.63 +/- 7.57 ng x h/mL, respectively. Both primary target parameters, AUC0-infinity and AUC0-tau, were tested parametrically by analysis of variance (ANOVA). Relative bioavailabilities were 102.5 +/- 19.2% for AUC0-infinity, 102.0 +/- 19.3% for AUC0-tau. Bioequivalence between test and reference formulations was demonstrated for both parameters, AUC0-infinity and AUC0-tau. The 90% confidence intervals of the T/R-ratios of logarithmically transformed data were in the generally accepted range of 80%-125%, which means that the test formulation is bioequivalent to the reference formulation of digoxin.

Population pharmacokinetic study of cyclosporine based on NONMEM in Chinese liver transplant recipients.

A population pharmacokinetic study of cyclosporine (CsA) was performed in liver transplant recipients. A total of 3731 retrospective drug monitoring data points at predose (C0) and 2 hours postdose (C2) were collected from 124 liver transplant recipients receiving CsA microemulsion. Population pharmacokinetic analysis was performed using the program NONMEM (nonlinear mixed-effect modeling). Various covariates potentially related to CsA pharmacokinetics were explored, and the final model was validated by a bootstrap method and by assessing the predictive performance using empiric Bayesian estimates. A one-compartment model with first-order absorption was considered. Population parameters of apparent clearance (CL/F) and volume of distribution were estimated as 23.1 L/h and 105 L, respectively. CL/F was influenced by four covariates: duration of CsA therapy (DT), hematocrit (HCT), and concurrent prednisone dose (PR). The final model for CL/F was fitted as follows: CL/F = 23.1 + 0.5 × (DT/200) - 0.07 × HCT + 0.04 × PR. The interindividual variability in CL/F, volume of distribution, and Ka calculated as coefficient of variation were 15.1%, 9.3%, and 66.0%, respectively. The intraindividual variability was 18.6%. The model fitted well with the observed data, and the bootstrap method guaranteed robustness of the population pharmacokinetic study model. Model validation was performed by a visual predictive check. Moreover, simulation was conducted to facilitate the individualized treatment based on patient information and the final model. The model to characterize population pharmacokinetic study of CsA provided better clinical individualization of CsA dosing in liver transplant recipients based on patient information and to assess patients' suitability for CsA therapy.

[Population pharmacokinetic modeling and evaluation of propofol from multiple centers].

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.

[Population pharmacokinetic study of tacrolimus in patients with hematopoietic stem cell transplant].

The present study is to establish the population pharmacokinetic (PPK) model of tacrolimus and to estimate PPK parameters of tacrolimus for the individualization of tacrolimus administration in patients with hematopoietic stem cell transplant. A total of 671 blood samples were collected from 68 hematopoietic stem cell transplant patients and clinical data were retrospectively collected from the medical records of these patients. Population pharmacokinetical analysis was performed using the nonlinear mixed-effect model (NONMEM) program. The Bootstrap and data splitting were used simultaneously to validate the final population pharmacokinetical models. The basic structural model was best described as one-compartment pharmacokinetical model with first-order absorption and elimination. A number of covariates including demographic characteristic, biochemical and hematological index, combined drugs, inter-occasion variability (IOV) and other variables, e.g. primary disease, post operation days (POD), the type of transplantation and the sources of donor, were screened for their influence on the pharmacokinetic parameters of tacrolimus. The population typical values of tacrolimus CL, V, F were 12.1 L x h(-1), 686 L, 42.2%; and the inter-individual variability of these parameters were 23.5%, 96.4%, 43.8%, respectively. The absorption rate constant was fixed 4.3 h(-1). The residual error between observed and model- predicted concentration was 3.03 ng x mL(-1). The CYP enzyme inhibitor (INHI), POD and age were identified to be the main covariates that influence tacrolimus CL, and hemoglobulin (HGB) was the main covariate that may explain the variability in tacrolimus V. The IOV of CL, V, F were 22.2%, 6.23%, 30.3%, respectively. The population pharmacokinetic data obtained in the present study have significant clinical value for the individualization of tacrolimus therapy in hematopoietic stem cell transplant patients.

[Rapid analysis of antiepileptic drugs in human plasma by micellar electrokinetic capillary chromatography].

AIM: To develop a rapid and feasible method based on micellar electrokinetic capillary chromatography (MECC) for the simultaneous determination of antiepileptic drugs (AEDs)--phenytoin (PHT), phenobarbital (PB), carbamazepine (CBZ), primidone (PRM) and clonazepam (CZP) in human plasma. METHODS: Several factors that impact the separation of AEDs with MECC were investigated, such as concentration of sodium dodecyl sulfate (SDS), buffer compositions, pH, organic modifier, internal diameter and temperature, and an optimized MECC running condition was obtained the running buffer consisted of 8 mmol x L(-1) phosphate, 3 mmol x L(-1) sodium tetraborate, and 50 mmol x L(-1) sodium dodecylsulfate (SDS) (pH 8.0), containing acetonitrile (ACN) (18%) as organic modifier. Detection at 210 nm, run at 25 kV at 30 degrees C in a untreated fused silica capillary (50/45.5 cm length, 50 microm ID). RESULTS: The reproducibility of both migration time and relative peak area with MECC analysis were appropriate for the intra- and inter-assay coefficients. The evaluated drugs concentration intervals of PRM 1.0-40.0 microg x mL(-1), PB 1.0-60.0 microg x mL(-1), PHT 1.0-40.0 microg x mL(-1), CBZ 1.0-40.0 microg x mL(-1), CZP 0.2-8.0 microg x mL(-1) were linear with correlation coefficients higher than 0.999 1, and coefficients of the variation of the points of the calibration curve lower than 10%. The recoveries of AEDs varied from 80.0% to 100.0%, depending on the drug, with coefficients of the variation lower than 10.0%. CONCLUSION: The MECC technique is showed to be rapid, simple, efficient and low cost when applied to monitoring therapeutic drugs in patient treated with a combination of PHT and other AEDs such as hepatic enzyme-inducing agents.

[Albumin kinetics in patients with severe sepsis].

OBJECTIVE: To explore the mechanism of hypoalbuminemia in patients with severe sepsis. METHODS: I(125)-labeled albumin was administered intravenously to 10 health volunteers and 10 patients with severe sepsis. Blood samples were taken at 0, 1, 2, 4, 8, 12, 24 hours and 2, 3, 4, 5, 6, 7, 9, 11, 13, 15, 18, 22, 25 days for the measurement of the dose of gamma-radiation and the curve of concentration and time. Then the half-life time (t(1/2)), apparent volume of distribution (V(d)) and transportation rate (K(12)) from center compartment to side compartment of albumin were calculated. RESULTS: The half-life time in septic group was obviously shorter than that in control group (8.2 +/- 1.4 vs. 12.5 +/- 1.7, P < 0.01). The transportation rate in the septic group was higher than that in the control group [(4.4 +/- 1.9) x 10(-2)/h vs. (2.4 +/- 0.6) x 10(-2)/h, P < 0.05]. There was no significant difference in apparent volume of distribution between the two groups. CONCLUSIONS: In patients with severe sepsis, the distribution rate of albumin from vessel to tissue was obviously increased and the decomposition rate of albumin was markedly improved.

Population pharmacokinetics of propofol in Chinese patients.

AIM: To analyze population pharmacokinetics of propofol in Chinese surgical patients using a nonlinear mixed-effect model (NONMEM) program and to quantitate the effects of covariance of gender, age, and body weight. METHODS: The population pharmacokinetics of propofol was investigated in 76 selective surgical patients (37 males and 39 females aged 19-77 a, weighing 39-86 kg). A total of 1439 blood samples were analyzed using NONMEM (NONMEM Project Group, University of California, San Francisco, CA). Interindividual variability was estimated for clearances and distribution volumes. The effects of age, body weight, and gender were investigated. RESULTS: The pharmacokinetics of propofol in Chinese patients was best described by a three-compartment pharmacokinetic model. Body weight was found to be a significant factor for the elimination clearance, the two inter-compartmental clearances, and the volume of the central compartment. The volumes of the shallow peripheral compartment and deep peripheral compartment remain constant for all individuals. The estimates of these parameters for a 60-kg adult were 1.56 L/min, 0.737 L/min, 0.360 L/min, 12.1 L, 43 L, and 213 L, respectively. For old patients, the elimination clearance and volume of the central compartment decreased. CONCLUSION: The pharmacokinetics of propofol in Chinese patients can be well described by a standard three-compartment pharmacokinetic model. Inclusion of age and body weight as covariances significantly improved the model. Adjusting pharmacokinetics to the individual patients should improve the precision of target-controlled infusion system.