基于药理学网络模型的抗肿瘤药物不良事件预测

摘 要 目的：针对抗肿瘤药物引起的不良事件,为提高患者的生活质量,提出了一种抗肿瘤药物不良事件的预测方法,从而减少药品不良事件的发生。方法：该方法选择了药理学网络模型（pharmacological network models,PNM）,在充分考虑时间顺序的基础之上,由特定药物和不良事件信息的关联构建二分网络,定义3类协变量,采用逻辑回归实现预测。文中选择美国食品药品监督管理局不良事件报告系统（FAERS）数据库2010年的数据,构建了由151种抗肿瘤药物和625种不良事件组成的网络,通过训练逻辑回归模型对2011~2015年FAERS数据库中的新抗肿瘤药物 不良事件关联组合进行预测。结果：PNM实现了受试者工作特征曲线下面积（AUROC）为0.824,具有良好的预测结果。结论：PNM对抗肿瘤药物的不良事件有良好的预测性能,可以为临床的合理用药以实际指导意义。     

肺损伤预测评分对重症患者急性呼吸衰竭风险的预测价值

目的 探讨肺损伤预测评分（LIPS）对重症患者急性呼吸衰竭风险的预测价值。方法 选择2016年11月至2018年12月安徽省阜阳市人民医院63例收入ICU时未出现急性呼吸衰竭的患者,依据入ICU后患者是否发生急性呼吸衰竭分为急性呼吸衰竭组40例与非急性呼吸衰竭组23例。采用多因素logistic回归模型筛选发生急性呼吸衰竭的影响因素,受试者工作特征曲线（ROC曲线）评价不同指标的预测价值。结果 多因素logistic回归分析显示,LIPS、快速序贯性脏器功能衰竭评分（qSOFA）和急性生理和慢性健康评分（APACHEⅡ）均可作为急性呼吸衰竭的预测评分指标,其中以LIPS的OR值最高。LIPS的ROC曲线下面积（AUC）为0.754（P<0.001）,LIPS的最佳截断点为4分,其灵敏度为83.24%,特异度为64.21%。LIPS≥4分组患者急性呼吸衰竭发生率、住ICU病死率、住院病死率等均高于LIPS<4分组,差异均有统计学意义（P<0.05）。结论 LIPS有助于早期识别发生急性呼吸衰竭高风险重症患者,从而降低重症患者机械通气使用率和ICU病死率。     

注射用甲磺酸培氟沙星近红外定量分析通用性模型的建立

摘 要 目的：利用近红外光谱法,建立注射用甲磺酸培氟沙星含量快速测定方法。方法: 采集注射用甲磺酸培氟沙星的近红外（NIR）光谱,以矢量归一化法对光谱进行预处理,选择谱段范围为9 176.2~8 169.5 cm^-1、6 051.9~5 716.3 cm^-1和4 509~3 999.9 cm^-1,回归方法为偏最小二乘法（PLS）,建立近红外定量模型。 结果: 经内部交叉验证建立预测模型,浓度范围为7.55%~77.69%,交叉验证均方根（RMSECV）为1.61%,相关系数为0.992 4。结论:建立的近红外定量分析模型可用于注射用甲磺酸培氟沙星快速定量分析。     

慢性鼻-鼻窦炎伴鼻息肉术后复发的预测模型建立

目的 研究慢性鼻-鼻窦炎伴鼻息肉术后复发的预测模型建立。方法 选择2014年9月至2018年6月在南京大学医学院附属鼓楼医院高淳分院治疗的258例鼻-鼻窦炎伴鼻息肉患者作为模型建立组,建立术后复发的预测模型,选择同期本院收治的134例患者验证模型的区分度和校准度。结果 logistic回归分析结果显示,吸烟史（OR=2.998,95% CI：2.095~4.292）、鼻窦总积分（OR=1.489,95% CI：1.129~1.963）、支气管哮喘（OR=2.186,95% CI：1.688~2.831）、变应性鼻炎（OR=1.740,95% CI：1.403~2.159）、头/面疼痛评分（OR=2.083,95% CI：1.637~2.651）及嗅觉损伤评分（OR=1.879,95% CI：1.509~2.341）是患者术后复发的影响因素（P<0.05）。结论 吸烟史、鼻窦总积分、支气管哮喘、变应性鼻炎、头/面疼痛评分及嗅觉损伤评分等影响因素建立预测模型可有效预测患者术后复发。     

正交试验法与多元非线性回归法优选注射用炎琥宁配伍条件

摘 要 目的：通过正交试验与多元非线性回归法,优选注射用炎琥宁的配伍条件。方法: 选择放置时间、溶媒种类、溶媒用量和温度4个影响因素,以不溶性微粒数和炎琥宁含量为指标,运用正交试验及多元非线性回归分析法优化配伍条件。结果: 注射用炎琥宁最佳配伍条件为在20℃条件下与100 ml的0.9%氯化钠注射配伍,即配即用。结论:正交试验结合多元非线性回归法优选注射剂的配伍条件是一种简便、可行的实验设计方法。     

近红外光谱法快速鉴别不同产地的前胡药材

目的 应用近红外光谱技术结合化学计量学方法建立一种快速鉴别前胡药材产地的方法。方法 首先收集6个产地的90个前胡样本，采集各样本的近红外光谱，并划分为校正集（72个样本）和预测集（18个样本），然后利用校正集，采用化学计量学方法建立前胡药材产地的判别分析模型，最后利用预测集，对判别分析模型进行性能评价。结果 优选的判别分析模型参数如下：光谱预处理方法为多元散射校正（MSC）+Savitzky-Golay卷积二阶求导算法（SG）（窗口参数为51，拟合次数为1），光谱波段为8 400~4 200 cm^-1，判别分析模型的主成分数为18。预测集的鉴别结果表明该判别分析模型的正确识别率为100%，6个产地前胡药材之间存在明显的界限。结论 研究表明，近红外光谱法能简便、准确地实现前胡药材产地的快速鉴别，为前胡药材产地快速鉴别研究提供了理论支持和实用方法。     

基于药物代谢相关基因构建肺癌预后风险预测模型★

目的 研究基于药物代谢相关基因构建的风险模型预测肺癌患者预后的应用价值。方法 基于Pharma ADME Consortium鉴定的298个药代动力学（ADME）相关基因，通过GEO芯片GSE7670、GSE32863获得ADME相关差异基因（ADME-DEG）；将ADME-DEGs进行基因本体（GO）功能富集及蛋白质-蛋白质相互作用（PPI）网络分析；利用一致性聚类、PCA主成分分析，基于18个ADME-DEGs将肺癌患者分为两组：Cluster1（n=315）、Cluster2（n=178）；通过LASSO算法获得由10个ADME-DEGs组成的风险模型；通过Kaplan-Meier生存分析、ROC分析及多因素回归分析，基于风险得分建立一个肺癌患者预测Nomogram图，分析风险得分对肺癌患者生存期的预测能力。结果 成功建立由10个ADME-DEGs（SLC22A18、AOX1、CAT、ADH1B、SULF1、PPARG、CYP4B1、FMO2、GPX3、ABCA4）构建的肺癌风险预测模型。Kaplan-Meier生存分析显示，风险得分较高的患者总生存期（OS）较差；ROC分析显示，风险得分能较好地预测肺癌患者的生存期，年龄、性别、风险得分均与肺癌患者OS显著相关；Nomogram图显示，风险得分对于肺癌患者10年内的OS具有良好的预测能力（C-index： 0.688）。结论 基于10个ADME-DEGs构建的风险模型具有预测肺癌患者用药后预后情况的潜在作用，为改善患者预后提供了新方法。     

近红外光谱技术快速测定三七水分和醇溶性浸出物

目的 利用近红外光谱技术（near infrared spectroscopy,NIRS）建立三七药材水分和醇溶性浸出物定量分析的快速测定方法。方法 参照《中国药典》2015年版三七水分和醇溶性浸出物含量测定方法对53批药材分别测定水分和醇溶性浸出物含量,采用偏最小二乘法（PLS）分别建立水分和醇溶性浸出物的近红外定量分析模型,并利用内部交叉验证和外部验证的方法对模型进行优化。结果 药材样品中水分和醇溶性浸出物预测最佳波段分别为4 450.32～7 350.01 cm^-1和6 163.92～3 984.71 cm^-1。定量模型校正集相关系数分别为0.997 2和0.962 4,校正均方差分别为0.039 6和0.776 0;验证集的相关系数为0.962 4和0.988 4,验证均方差分别为0.173和0.595。结论 该方法准确、快速、无污染,可用于三七药材中水分和醇溶性浸出物含量的快速测定。     

一测多评法测定左归丸中3种环烯醚萜苷类成分的含量

目的：建立以马钱苷为内参物,同时测定左归丸中莫诺苷、马钱苷和山茱萸新苷含量的一测多评法（QAMS）。方法：采用高效液相色谱法,Phenomenex C18（250 mm×4.6 mm,5 μm）硅胶色谱柱;流动相：乙腈（A）-0.3%磷酸水溶液（B）梯度洗脱;柱温35℃;检测波长（0~65 min,240 nm,检测莫诺苷和马钱苷;66~80 min,218 nm,检测山茱萸新苷）。以马钱苷为内参物,建立其与莫诺苷、山茱萸新苷的相对校正因子（RCFS）,并进行含量计算,实现一测多评;同时采用外标法（ESM）测定左归丸中3种有效成分的含量,比较一测多评法计算值与外标法实测值的差异。结果：在一定线性范围内,马钱苷与莫诺苷、山茱萸新苷的相对校正因子分别为1.048、1.390。建立的相对校正因子重现性良好,7批左归丸中3种成分的计算值与实测值间无显著差异。结论：采用本研究建立的“一测多评”法控制左归丸的质量是可行的。     

地稔水提液中6种活性成分含量的近红外光谱快速预测方法研究

目的 研究基于近红外光谱的地稔水提液中6种活性成分含量的快速预测方法。方法 采用HPLC测定地稔水提液中没食子酸、阿魏酸、芦丁、槲皮素、木犀草素、山奈酚的含量；采集4 000~10 000 cm^-1的近红外光谱；分别优化光谱预处理方法和波数范围，建立6种活性成分含量与近红外光谱的偏最小二乘回归模型。采用Visual Basic开发应用软件，将所建模型嵌套入软件，为后续待测溶液中6种活性成分含量的快速预测提供工具。结果 基于所开发的应用软件，采集待测溶液的近红外光谱后点击软件中的"预测"按钮，可在20 s内自动计算得到6种活性成分含量的预测结果。验证集中6种活性成分近红外光谱预测含量和HPLC测定含量的相关系数分别为0.966，0.983，0.850，0.946，0.977和0.979，预测均方根误差分别为9.23，0.496，4.07，0.059 6，0.023 4，0.039 9 μg·mL^-1。结论 近红外光谱结合偏最小二乘回归算法可用于准确、快速分析地稔水提液中没食子酸、阿魏酸、芦丁、槲皮素、木犀草素、山奈酚的含量，可为地稔的快速质量评价提供依据。     

Experimental and Computational Screening Models for Prediction of Aqueous Drug Solubility

Purpose. To devise experimental and computational models to predict aqueous drug solubility. Methods. A simple and reliable modification of the shake flask method to a small-scale format was devised, and the intrinsic solubilities of 17 structurally diverse drugs were determined. The experimental solubility data were used to investigate the accuracy of commonly used theoretical and semiexperimental models for prediction of aqueous drug solubility. Computational models for prediction of intrinsic solubility, based on lipophilicity and molecular surface areas, were developed. Results. The intrinsic solubilities ranged from 0.7 ng/mL to 6.0 mg/mL, covering a range of almost seven log₁₀ units, and the values determined with the new small-scale shake flask method agreed well with published solubility data. Solubility data computed with established theoretical models agreed poorly with the experimentally determined solubilities, but the correlations improved when experimentally determined melting points were included in the models. A new, fast computational model based on lipophilicity and partitioned molecular surface areas, which predicted intrinsic drug solubility with a good accuracy (R ²of 0.91 and RMSE_tr of 0.61) was devised. Conclusions. A small-scale shake flask method for determination of intrinsic drug solubility was developed, and a promising alternative computational model for the theoretical prediction of aqueous drug solubility was proposed.     

自回归整合移动平均模型在医院药库采购预测中的应用

目的:探讨利用自回归整合移动平均模型(ARIMA)预测的采购新模式提高医院药库工作质量和效率。方法:采集2008年01～47周药品消耗数据,根据ABC分类法确定A类品种,并从中随机抽样10个品种,基于2008年01～44周的数据,应用SPSS13软件作ARIMA模型建模拟合,用所得到的模型作45～47周预测,并比较实际消耗数据。结果:利用ARIMA模型预测的采购件数与实际消耗数基本相符,数量预测准确率为89.19%,整件预测准确率为97.56%。结论:ARIMA模型能够很好地拟合并可获得较高的中短期预测精度,能够为采购提供科学合理的决策支持,做到既不断货也不积压,合理控制药品库存量。     

Development and Internal Validation of an In Vitro-in Vivo Correlation for a Hydrophilic Metoprolol Tartrate Extended Release Tablet Formulation

Purpose. To develop and validate internally an in vitro-in vivo correlation (IVIVC) for a hydrophilic matrix extended release metoprolol tablet. Methods. In vitro dissolution of the metoprolol tablets was examined using the following methods: Apparatus II, pH 1.2 & 6.8 at 50 rpm and Apparatus I, pH 6.8, at 100 and 150 rpm. Seven healthy subjects received three metoprolol formulations (100 mg): slow, moderate, fast releasing and an oral solution (50 mg). Serial blood samples were collected over 48 hours and analyzed by a validated HPLC assay using fluorescence detection. The f ₂ metric (similarity factor) was used to analyze the dissolution data. Correlation models were developed using pooled fraction dissolved (FRD) and fraction absorbed (FRA) data from various combinations of the formulations. Predicted metoprolol concentrations were obtained by convolution of the in vivo dissolution rates. Prediction errors were estimated for C_max and AUC to determine the validity of the correlation. Results. Apparatus I operated at 150 rpm, and pH of 6.8 was found to be the most discriminating dissolution method. There was a significant linear relationship between FRD and FRA when using either two or three of the formulations. An average percent prediction error for C_max and AUC for all formulations of less than 10% was found for all IVIVC models. Conclusions. The relatively low prediction errors for C_max and AUC observed strongly suggest that the metoprolol IVIVC models are valid. The average percent prediction error of less than 10% indicates that the correlation is predictive and allows the associated dissolution data to be used as a surrogate for bioavailability studies.     

基于多元回归-灰色模型的非小细胞肺癌靶向药物用量预测分析

目的 建立非小细胞肺癌(NSCLC)常用靶向药物的用量预测模型,为指导医疗机构抗肿瘤靶向药物的采购和库存管理提供数据支撑.方法 参考天津市肿瘤医院2019年各月的靶向药物用量数据,以4种临床常用的NSCLC靶向药物(吉非替尼、埃克替尼、奥希替尼和克唑替尼)为例,建立多元回归模型、GM(1,1)灰色模型以及多元回归-灰色...     

消炎汤调节肠道微生态改善大鼠慢性盆腔炎的研究

目的探讨消炎汤通过调节肠道微生态的变化对慢性盆腔炎大鼠的治疗作用。方法采用子宫内注射苯酚胶浆建立大鼠慢性盆腔炎模型,造模2周后ig消炎汤5、10、20 g/kg,连续14 d,观察大鼠子宫内膜形态,qPCR检测子宫组织中细胞间黏附分子(ICAM-1mRNA)表达情况,并采用16SrRNA高通道测序观察慢性盆腔炎大鼠肠道菌群的构成和比例。结果各给药组中充血水肿现象少见,且炎症浸润程度好于妇科千金片组。与模型组相比,消炎汤各剂量组子宫组织中ICAM-1mRNA的表达均有下调,尤以消炎汤20 g/kg组为甚(P0.01)。与模型组相比,消炎汤20 g/kg组Simpson指数显著上升(P0.05)。给予消炎汤治疗后肠道菌群多样性、丰度均得到一定程度改善,但未见统计学差异。与模型组相比,消炎汤10、20g/kg组乳杆菌、梭状芽孢杆菌OUT比例有不同程度升高。治疗后,消炎汤20、10g/kg组OUT比例较模型组明显下降。结论消炎汤能够明显改善慢性盆腔炎大鼠子宫的炎症程度,有效减少黏连的发生,作用机制与其调节肠道微生态影响雌激素代谢有关。     

Econometric ARIMA methodology to elucidate the evolution of trends in nosocomial antimicrobial resistance rates in the European Union

BackgroundInfections with bacteria harbouring resistance to cephalosporins or fluoroquinolones (FQ) constitute a serious hazard to human health.ObjectivesTo establish a methodology based on econometric analysis and the largest European Union (EU) resistance database (EARS-Net), to model nosocomial antimicrobial resistance (AMR) in the EU and to detect tendency changes, steps or peaks. The contribution of legislation based on third-generation cephalosporin (3GC) and FQ class referrals to resistance rate patterns is evaluated.MethodsResistance to 3GC and FQ was examined in nosocomial Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa in at least 25 out of 30 EU countries (> 94% population coverage), weighted by their mean annual population, between 2006 and 2016. Autoregressive integrated moving average (ARIMA) model analysis, inspired by Box–Jenkins methodology, was prepared to adjust series to a mathematical model to detect hypothetical changes in the general behaviour. To the best of the authors’ knowledge, this is the first study to use ARIMA with interventions to model overall nosocomial AMR data compiled in EARS-Net.Results and conclusionsEconometric ARIMA models statistically prove the occurence of slowdowns and reversions in the increasing trend of AMR prevalence in nosocomial E. coli and K. pneumoniae to 3GC and FQ, as well as resistance of P. aeruginosa to 3GC. The resistance of P. aeruginosa to FQ exhibited a descending slope. The presented decreasing trends constitute noteworthy milestones in tackling AMR in Europe.     

Predicting Oral Absorption of Drugs: A Case Study with a Novel Class of Antimicrobial Agents

Purpose. The purpose of this work was to evaluate an oral absorption prediction model, maximum absorbable dose (MAD), which predicts a theoretical dose of drug that could be absorbed across rat intestine based on consideration of intestinal permeability, solute solubility, intestinal volume, and residence time. Methods. In the present study, Caco-2 cell permeability, as a surrogate for rat intestinal permeability, and aqueous solubility were measured for 27 oxazolidinones. The oxazolidinones are a novel class of potential antibacterial agents currently under investigation. These values were used to estimate MAD for each of the compounds. Finally, these predicted values were compared to previously measured bioavailability data in the rat in order to estimate oral absorption properties. Results. A reasonably good correlation between predicted dose absorbed and bioavailability was observed for most of the compounds. In a few cases involving relatively insoluble compounds, absorption was underestimated. For these compounds while aqueous solubility was low, solubility in 5% polysorbate 80 was significantly higher, a solvent possibly more representative of the small intestinal lumen. Conclusions. These results suggest that MAD may be useful for prioritizing early discovery candidates with respect to oral absorption potential. In the case of compounds with poor aqueous solubility, additional factors may have to be considered such as solubility in the intestinal lumen.     

Bootstrapping for Pharmacokinetic Models: Visualization of Predictive and Parameter Uncertainty

Purpose. We explore use of "bootstrapping methods to obtain a measure of reliability of predictions made in part from fits of individual drug level data with a pharmacokinetic (PK) model, and to help clarify parameter identifiability for such models. Methods. Simulation studies use four sets (A-D) of drug concentration data obtained following a single oral dose. Each set is fit with a two compartment PK model, and the "bootstrap is employed to examine the potential predictive variation in estimates of parameter sets. This yields an empirical distribution of plausible steady state (SS) drug concentration predictions that can be used to form a confidence interval for a prediction. Results. A distinct, narrow confidence region in parameter space is identified for subjects A and B. The bootstrapped sets have a relatively large coefficient of variation (CV) (35-90% for A), yet the corresponding SS drug levels are tightly clustered (CVs only 2-9%). The results for C and D are dramatically different. The CVs for both the parameters and predicted drug levels are larger by a factor of 5 and more. The results reveal that the original data for C and D, but not A and B, can be represented by at least two different PK model manifestations, yet only one provides reliable predictions. Conclusions. The insights gained can facilitate making decisions about parameter identifiability. In particular, the results for C and D have important implications for the degree of implicit overparameterization that may exist in the PK model. In cases where the data support only a single model manifestation, the "bootstrap method provides information needed to form a confidence interval for a prediction.     

基于近红外光谱技术对山茱萸炮制程度的质量评价

目的 采用近红外光谱技术建立山茱萸饮片中5-羟甲基糠醛含量的快速无损检测方法,并用于快速判别山茱萸饮片的炮制程度。方法 以54个样本作为校正集,运用偏最小二乘法建立定量校正模型并进行优化。以9个样本作为验证集,进行含量预测。结果 校正模型决定系数为0.925 6,相对分析误差为5.06,交叉验证均方根误差为0.022 0,外部验证均方差为0.019 3,光谱预测值与真值之间的相关性较好。结论 该法可直接用于中药山茱萸中5-羟甲基糠醛含量的快速预测,为快速判断山茱萸炮制程度提供可能。该方法具有操作简便、快速无损、准确可靠等优点。     

基于化合物结构预测人体ADME/PK性质的效能评价

目的：整合定量结构性质关系（QSPR）模型预测化合物在人体的吸收、分布、代谢、排泄 （ADME）性质参数和基于生理药代动力学（PBPK）模型预测人体药代动力学（PK）曲线的方法,并评价该方法的预测能力。方法：以文献报道的具有体外实测理化、生物药剂学性质和临床观测PK性质的 14个化合物作为模型药物。采用ADMET Predictor软件的QSPR模型预测各个化合物的理化与生物药剂学参数,将上述预测的参数加载到GastroPlus软件的PBPK模型中预测各个化合物经口服给药后在人体的PK 曲线以及主要PK参数。对比预测与实测ADME/PK参数间的差异,以评估所用模型的预测效能。结果： QSPR模型预测的理化与生物药剂学性质参数与观测值间的绝对值较为接近,两者具有较好的线性关系（大部分参数的相关系数均接近或超过0.7）;14个化合物中,有6个化合物（43%）的最大血药浓度 （C_max）预测值落在观测值的2倍误差范围内,9个化合物（64%）的C_max落在观测值的3倍误差范围内; 有7个化合物（50%）的血药浓度-时间曲线下的面积（AUC）预测值落在观测值的2倍误差范围内,8个化合物（57%）的AUC落在观测值的3倍误差范围内。结论：联合QSPR和PBPK模型可用于评估化合物的ADME性质并进一步预测人体PK特征。经过当前工作的验证,表明该方法具有较高的预测能力。