对药代动力学实验的两点改进

通过具体的实验数据，讨论在药代动力学实验中如何加入曲线下面积（AUC）的计算，以帮助学生理解并掌握这一概念；在实验室设置不同剂量组，给药后比较剂量与浓度是否呈等比例关系，以此加深学生对于一级动力学消除及其临床意义的理解。     

按AUC与体表面积计算伯尔定用量化疗毒性和疗效比较

【目的】为比较按药物血浆浓度 时间曲线下面积 (AUC)与体表面积计算伯尔定用量的两种方法在化疗中对血液毒性和疗效的影响。【方法】按接收的先后顺序 ,42例病人单盲随机分为按体表面积计算组和按AUC计算两组 ,总结两组的血液性毒性和疗效 ,并进行了对比研究。【结果】 42例病人均可评价疗效 ,AUC和体表面积两组有效率分别为 47 6 1%和 19 0 5 %。AUC组明显高于体表面积组 ,但P =0 0 5 ;AUC组和体表面积组白细胞 (WBC)减少发生率分别为 38 10 %和 76 2 0 % ,Ⅲ和Ⅳ度为 4 76 %和 2 8 5 7% (P >0 0 5 ) ;血小板 (Pt)减少发生率分别为 4 76 %和 19 0 5 % (P >0 0 5 ) ;血红蛋白 (Hb)减少发生率均为 47 6 2 % ,Ⅲ和Ⅳ度为 9 5 2 %和 4 76 % (P >0 0 5 )。【结论】应用伯尔定行全身化疗 ,按AUC计算用药剂量在提高疗效和降低血液毒性反应方面优于按体表面积计算。     

Effect of Combined Norethisterone Enantate 50mg Monthly InjectableContraceptive on Carbohydrate Metabolism

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Predicting development of sustained unresponsiveness to milk oral immunotherapy using epitope-specific antibody binding profiles

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利用CT表现鉴别肾上腺隐匿型嗜铬细胞瘤和皮质腺瘤

目的探讨如何利用CT表现对肾上腺隐匿型嗜铬细胞瘤和肾上腺皮质腺瘤进行鉴别诊断。方法收集2010年1月至2020年1月期间中国医科大学附属第一医院收治的177例肾上腺肿瘤患者进行回顾性分析,对比各组患者之间的一般临床资料和CT表现。结果隐匿型嗜铬细胞瘤56例、皮质醇腺瘤32例、醛固酮腺瘤44例、无功能腺瘤45例,隐匿型嗜铬细胞瘤组在肿瘤侧别上与无功能腺瘤组差异有统计学意义,在肿瘤最大直径、平扫CT值、动脉期和延迟期增强CT值上均显著大于三组肾上腺腺瘤组。以肿瘤直径≥2.95 cm诊断隐匿型嗜铬细胞瘤,曲线下面积(AUC)为0.872,敏感度为87.5%,特异性为76.0%;当平扫CT值≥24.5 Hu时,AUC为0.929,敏感度为94.0%,特异性为82.5%;当动脉期增强CT值≥89.5 Hu时,AUC为0.886,敏感度为72.7%,特异性为90.6%;当延迟期增强CT值≥82.5 Hu时,AUC为0.937,敏感度为84.6%,特异性为95.3%;联合以上四个指标时,AUC为0.981,阈值为≥0.118,敏感度为100%,特异性为90.6%。结论以肿瘤直径2.95 cm、平扫CT值24.5 Hu、动脉期增强CT值89.5 Hu和延迟期增强CT值82.5 Hu为阈值对肾上腺隐匿型嗜铬细胞瘤有较好的鉴别诊断价值。     

The application of Ion chromatographic method for bioavailability and stability test of iron preparations

Postabsorptive serum iron level was determined after oral administration of the compounds to human. In serum and whole blood, Fe3+ was measured by ion chromatography (IC) using a pyridine-2,6-dicarboxylic acid (PDCA) as an eluent. The serum sample solutions were pretreated with I N HCI and 50% TCA. The whole blood sample solutions were treated with 3 N HCI for 30 min at 125 degrees C. The limit of detection (LOD) of the IC technique is 0.2 microM for Fe2- and 0.1 microM for Fe3+. The area under concentration (AUC) can be obtained by the above analytical condition. In addition, to compare the stability of Fe2+ to that of Fe3+ in pharmaceutical preparations, accelerated stability test was carried out. After storing the samples under 40 degrees C, 75%RH in light-resistant container for various time intervals, the contents of iron of different valencies were determined separately by the IC technique and the change and/or the interchange of among those iron species in preparations was investigated. Iron raw materials are stable, but Fe2+ in Fe3+ source materials was slightly converted to Fe3+ by oxidation. Fe2+ in Fe3+ source raw materials and Fe3+ in Fe2+ raw materials are determined as impurities. Therefore, IC technique is found to be an appropriate method for comparative evaluation of dissimilar bioavailability of Fe2+ and Fe3+, stability of Fe2+ and Fe3+ raw materials and preparations.     

A limited-sampling method for evaluation of the area under the curve of ultrafilterable carboplatin in children

Carboplatin is a widely used cytotoxic agent in numerous solid tumors of children. Since there is a large degree of interpatient variability in the area under the curve of free carboplatin for a given dose of the drug, the current tendency is to adjust the carboplatin dose so as to reach a target area under the curve rather than to determine a carboplatin dose on the basis of the body surface area. A limited-sampling method was developed for estimation of the ultrafilterable carboplatin area under the curve and for adjustment of the carboplatin dose on subsequent treatments. Population parameters were obtained from 16 children (reference group). We used the maximum a posteriori (MAP) Bayesian approach on 15 children with complete carboplatin pharmacokinetic data (test group). Two blood samples were sufficient to obtain reliable prediction of the area under the curve. The best sampling times were: (a) 30 min after the end of the infusion and (b) 5 h after the end of the infusion. On the basis of these data it is possible to prescribe prospectively a target area under the curve for free carboplatin given in a fractionated daily infusion and to adapt the carboplatin dose directly to ultrafilterable carboplatin measurements. Received: 8 June 1997 / Accepted: 9 January 1998     

Prediction of carboplatin clearance calculated by patient characteristics or 24-hour creatinine clearance: a comparison of the performance of three formulae

Purpose: Carboplatin doses can be individualized using the formula of Calvert et al. (Calvert formula) dose (mg) = area under the plasma concentration versus time curve (AUC) · [glomerular filtration rate (GFR) + 25]. Creatinine clearance (Ccr), either measured by the 24-h method or calculated by the formula of Cockcroft and Gault [Cockcroft-Gault (CG) formula], is often substituted for the GFR. The CG formula is based on patient weight, age and sex, and the serum creatinine (Cr) concentration. Another method for predicting carboplatin clearance (CL) using patient characteristics has also been proposed by Chatelut et al. (Chatelut formula). This study was undertaken to evaluate the performance of the three formulae in predicting standard- and low-dose carboplatin pharmacokinetics. Methods: A total of 52 patients with advanced lung cancer were enrolled in this pharmacokinetic study; 37 received standard-dose carboplatin and 25 received low-dose carboplatin. The Cr concentration was measured using an enzymatic assay. The three formulae were used to predict carboplatin CL. The median absolute percent error (MAPE) for each formula was evaluated by comparing the calculated and observed CL. For comparison of AUCs, free platinum plasma concentrations were measured at intervals up to 24 h after carboplatin administration. AUCs were determined and compared with predicted values. Results: In the standard-dose carboplatin group, the MAPEs for the prediction of carboplatin CL from the 24-h Calvert, CG-Calvert and Chatelut formulae were 13%, 12% and 23%, respectively. In the low-dose carboplatin group, the corresponding MAPEs were 27%, 18% and 44%, respectively. Observed standard-dose carboplatin AUCs after aiming for target AUCs of 5 and 6 mg · min/ml using the Calvert formula based upon the 24-h Ccr were 5.3 ± 0.8 and 5.9 ± 0.8, respectively, indicating a small and acceptable bias compared with that predicted from the dosing formula. Conclusions: The pharmacokinetics of standard-dose carboplatin were accurately predicted by the Calvert formula based upon either 24-h or CG-calculated Ccr, but not by the Chatelut formula. Either CG-calculated or 24-h Ccr can be substituted for the GFR in the Calvert formula for the determination of individual doses. The poor predictability of the Chatelut formula found in this study might be the result of a differences in either the Cr assay or the patient population. Therefore, formulae which attempt to estimate GFR are not necessarily valid if either the Cr assay or the patient population is changed. Received: 23 July 1997 / Accepted: 16 December 1997     

Prediction impact curve is a new measure integrating intervention effects in the evaluation of risk models

ObjectiveWe propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population.Study Design and SettingUsing simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease.ResultsWe estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval.ConclusionWe conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance.