Pharmacokinetic modeling and simulation of etodolac following single oral administration in dogs

1. Etodolac is a nonsteroidal anti-inflammatory drug with selective cyclooxygenase-2 inhibition to treat pain and inflammation associated with osteoarthritis in humans and dogs. The aim of the study was to investigate the pharmacokinetics of etodolac following single oral administration of 200?mg to 10 healthy beagle dogs.

2. The plasma concentrations of etodolac were detected using liquid chromatography-tandem mass spectrometry. Pharmacokinetic analysis was conducted using the noncompartmental method and modeling approaches.

3. Etodolac was rapidly absorbed (T_max?=?0.85?h, K_a?=?1.49?h^?1) and slowly eliminated (T_1/2?=?39.55?h) following oral administration to the dogs. A two-compartment pharmacokinetic model with first-order absorption and elimination rate constants was successfully explained for the pharmacokinetic aspects of etodolac in dogs. From a Monte Carlo simulation (1000 repetitions), the accumulation index and AUC_τ at steady state were predicted as 1.60 [90% confidence intervals (CI), 1.24–2.81] and 408.18?ng·hr/mL [90% CI, 271.26–590.58?ng·hr/mL], respectively.

4. This study will help to enact a more accurate optimal dosing regimen of etodolac in dogs with osteoarthritis, and may be useful in developing a novel formulation of etodolac for human in the future.

     

基于自动勾画和快速蒙特卡罗计算的放射治疗全身器官剂量数据库平台的可行性研究

目的:探讨建立一种放射治疗全身器官剂量数据库平台的可行性。方法:使用基于深度学习的自动勾画软件DeepViewer?1例食管癌患者的全身CT上勾画全身器官，然后利用基于GPU加速的蒙特卡罗软件ARCHER计算相应的器官剂量分布，最后利用Lyman-Kutcher-Burman（LKB）模型评估放疗患者正常组织并发症概率（NTCP）。结果:针对该病例，成功建立基于DeepViewer?ARCHER和LKB模型的全身器官剂量数据库，发现距离靶区越近的器官剂量越大，其中心脏与靶区间距离最小，剂量为14.11 Gy，但因其模型参数特殊，通过LKB模型计算的NTCP为0.00%；左、右肺的剂量分别为3.19和1.16 Gy，但是NTCP值却很大，分别为2.13%和1.60%。对于距离靶区较远的头颈部器官（视交叉、视神经和眼）和腹部器官（直肠、膀胱和股骨头）剂量分别约为9和2 mGy，并且NTCP均近似为0.00%。结论:研究结果证明通过自动勾画软件DeepViewer?蒙特卡罗软件ARCHER和LKB模型建立全身器官剂量数据库的可行性。     

替考拉宁在中国成人患者中的群体药动学研究

目的建立中国成年患者的替考拉宁(teicoplanin,TEC)群体药动学(population pharmacokinetics,PPK)模型,考察TEC药动学参数的影响因素。方法前瞻性收集139例革兰阳性球菌感染患者静脉注射TEC后的222份常规监测血药浓度和相关信息,采用一级消除的一室模型进行数据拟合,并应用非线性混合效应模型(nonlinear mixed effect model,NONMEM)程序建立PPK模型。采用Bootstrap、正态预测分布误差法(normalized predictive distribution error,NPDE)进行最终模型评价。利用蒙特卡洛模拟法对给药方案进行优化。结果确定了肌酐清除率(creatinine clearance,CLcr)、白蛋白(albumin,ALB)为影响TEC清除率的主要因素。最终模型为:CL(L·h^-1)=1.24×(CLcr/77)0.564×31/ALB;V(L)=69.2。验证表明,模型稳定、有效,且有较好的预测效能。对于不同ALB和CLcr的多数患者起始负荷剂量400 mg/q12h,iv,3次,维持剂量400~800 mg·d^-1的给药方案可达有效治疗谷浓度。严重感染者需调整负荷剂量至800 mg/q12h,iv,3次,维持剂量400~800 mg·d^-1的给药方案来确保血药浓度达到15 mg·L^-1以上。结论本实验报道了CLcr、ALB对TEC清除率有显著影响,所建模型对TEC在中国成人患者中实现个体化给药具有重要应用价值。     

Preventing heart disease: is treating the high risk sufficient?

Monte Carlo simulation was used to assess the effects of several intervention strategies on coronary heart disease mortality rates in a Finnish and a North American cohort. Lowering total serum cholesterol by 4%, smoking by 15%, and diastolic blood pressure by 3% for the whole cohort would be expected to reduce the incidence of non-fatal myocardial infarction by at least 13% and coronary heart disease deaths by at least 18%. Lowering serum cholesterol by 34%, diastolic blood pressure to 90 mmHg, and reducing smoking by 20% in the subset of the population with all three risk factors in the highest quartile would result in a 6-8% reduction in non-fatal myocardial infarction and a 2-9% reduction in deaths from coronary heart disease in these cohorts. These data demonstrate that in populations with a relatively high incidence of heart disease, treating the entire population will produce larger effects than focusing only on high-risk populations.     

Bayesian estimation of a random effects heteroscedastic probit model

Summary Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross‐validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison.     

金属换热器传热过程的数学模型及应用

应用蒙特卡洛法仿真辐射式金属换热器内的辐射传热过程，建立了换热器综合传热的数学模型，利用概率论原理，把辐射能迁移过程仿真为一定累积概率分面下的随机过程，该模型能较好地反映换热器内传热过程的真实情况，可以计算出换热器内烟气、筒壁、空气的温度和壁面热流分布，以及研究烟气进口温度、换热器筒径和狭逢宽度对换热效果的影响。     

Weighted Markov chains for forecasting and analysis Incidence of infectious diseases in jiangsu Province,China

This paper first applies the sequential cluster method to set up the classification standard of infectious disease incidence state based on the fact that there are many uncertainty characteristics in the incidence course.Then the paper presents a weighted Markov chain,a method which is used to predict the future incidence state.This method assumes the standardized self-coefficients as weights based on the special characteristics of infectious disease incidence being a dependent stochastic variable.It also analyzes the characteristics of infectious diseases incidence via the Markov chain Monte Carlo method to make the long-term benefit of decision optimal.Our method is successfully validated using existing incidents data of infectious diseases in Jiangsu Province.In summation,this paper proposes ways to improve the accuracy of the weighted Markov chain,specifically in the field of infection epidemiology.     

丹皮多糖对正常及高血糖小鼠的降血糖作用

观察了丹皮多糖对正常小鼠血糖及葡萄糖致小鼠血糖的影响。结果表明，丹皮多糖粗品１００ｍｇ／ｋｇ，２００ｍｇ／ｋｇ灌胃给药可使正常小鼠血糖显著降低，２００ｍｇ／ｋｇ，４００ｍｇ／ｋ灌胃给药对葡萄糖诱发的小鼠高血糖有显著降低作用。