对老年2型糖尿病住院患者多重用药相互作用的分析及干预研究

目的对内分泌科老年2型糖尿病住院患者多重用药的药物相互作用(DDI)进行分析和干预,提高临床用药的有效性和安全性。方法对2017年11月1日至2018年4月30日内分泌科老年2型糖尿病住院患者入院第2天的用药医嘱进行筛查,针对多重用药(全身用药品种≥5种)患者,采用Micromedex和PASS软件对用药医嘱进行药物相互作用的检索,结合患者具体情况对检索结果进行分析,采取相应的干预措施。结果研究共纳入247例住院的老年2型糖尿病多重用药患者(多重用药发生率为94.3%),合并疾病2～33(9±5)种,住院期间服用化学药品5～18(6±3)种,利用Micromedex和PASS软件分别筛出706条和789条药物相互作用(DDI),匹配的DDI总数为262条,匹配率分别为37.1%和33.2%。其中发生DDI频次较高的药物类别有:内分泌系统药物、循环系统药物、血液及造血系统药物等,发生DDI几率较高的药物有比索洛尔、阿司匹林、氢氯噻嗪等。根据患者用药DDI及其他具体情况,对4例患者进行干预,修改给药方案,对150例患者实施密切监护,对93例患者实施常规监护。结论老年2型糖尿病住院患者多重用药发生率高,DDI发生率高。Micromedex和PASS软件在DDI筛查方面各有特点,临床药师应结合使用,并综合患者其他具体情况,综合评估判断,协助医生制定合理的个体化给药方案。     

基于APOSTEL系列研究早产保胎药物治疗临床试验方法学的分析与探讨

目的：通过对早产保胎治疗结局评估(APOSTEL)系列研究进行分析,对早产保胎治疗临床试验方法学进行分析及探讨,为我国进一步开展保胎药物相关研究提供借鉴与参考。方法：针对APOSTEL系列研究中研究方案所涉及的研究类型、纳入与排除标准、结局指标、样本量设定和药物方案等因素进行分析。同时结合APOSTEL研究成果的主要结果及结论,针对保胎药物临床研究中的方法学设计进行分析及探讨。结果：APOSTEL系列研究多采用前瞻性及随机对照研究,试验设计在妊娠周期、宫缩强度和宫颈长度的诊断应用方面与我国指南有一定区别;中介范围宫颈长度的患者应用纤连蛋白实验进行诊断;排除标准包括急需分娩的临床指征、不可避免的胎儿不良结局、药物禁忌证;预计纳入样本量的计算方面,国家范围内的研究应用绝对比例的改善,而国际间研究以相对比例进行计算。试验组和对照组的药物治疗方案多与各国指南相似,其对于药物延迟治疗的方案设计可作为参考。结论：参考APOSTEL系列研究可进一步完善相关临床设计,依据本国国情开展高质量的早产药物保胎治疗临床研究的设计及实施。     

糖尿病综合管理中的药物相互作用调查与分析

目的 分析糖尿病综合管理中药物相互作用（DDI）的特点及影响因素。方法 使用Lexi-InteractTM数据库,筛选糖尿病综合管理中降血糖药、降血压药、调血脂药、减重药的DDI,根据严重程度分为X级（避免联用）、D级（考虑调整治疗）、C级（需监控治疗）、B级（无须调整）、A级（无DDI）;收集上海某三级甲等医院2型糖尿病并心肌梗死患者的病例资料,并根据患者的DDI数量分为低DDI组（<5组DDI）和高DDI组（≥5组DDI）,分析DDI的特点及影响因素。结果 共纳入109种药物,其中降血压药、降血糖药、调血脂药、减重药分别有57种、32种、19种、1种,DDI分别有5 091组、1 086组、723组、15组。除减重药外,其余3种药物DDI总数最多的分别为钙通道阻滞剂（CCB）、磺脲类药物、他汀类药物;X级药物DDI数量排名前3的药物分别为他汀类药物、CCB类药物及β受体拮抗药。共纳入患者69例,其中低DDI组和高DDI组分别有26例、43例,影响患者DDI总数的危险因素包括患者合并用药数量、慢性肾脏病（CKD）1-3期,以及糖化血红蛋白、糖化白蛋白和空腹血糖水平（P <...     

创新药物临床试验中暴露量-效应关系研究的探讨

创新药物临床试验旨在为药物上市提供安全有效的依据,涵盖了临床药理学研究、剂量探索性临床治疗试验和确证性临床治疗试验,其中暴露量-效应关系研究已经成为临床研究的核心。本文在梳理了创新药物临床试验的前后过程以及逻辑关系的基础上,对暴露量-效应关系研究的相关内容、暴露量-效应关系研究在审评、提高临床研究质量、降低临床失败风险以及加速新药上市方面的重要性方面进行了探讨。     

生理药代动力学模型及其在药物相互作用研究中的应用

生理药代动力学模型(PBPK model)在毒理学和药理学领域得到越来越多的关注和应用,如用于药物-药物相互作用(DDI)等研究.DDI会影响药物的安全性、有效性、药物标识及选择药物联用的合理性,其在药物研发和上市后研究中已成为临床药理研究的重要组成部分.基于模型的分析方法被证明是评价DDI作用的有力工具.本文对PBPK模型的特征及其在DDI研究中的应用现状进行阐述.     

中国附条件批准政策梳理及抗肿瘤创新药审评标准浅析

为了鼓励以临床价值为导向的药物创新,现行《药品注册管理办法》明确提出了4种加快上市注册的程序。对于早期临床试验数据可证实药物疗效并能预测其临床价值的创新药,经评价后可以通过附条件批准工作程序,在完成确证性临床研究之前上市,让有严重危及生命且尚无治疗手段的患者获得更多延长其生命或改善其生活质量的治疗机会。附条件批准工作程序可以缩短药品上市前的临床研发时间,因此其配套政策和审评标准备受关注。本文以肿瘤创新药为切入点,对我国附条件批准的政策要求和审评过程中已经形成的标准进行系统的梳理,并尝试对附条件批准实施过程中遇到的问题进行分析并提出建议,供业界参考。     

信息化管理系统在IIT研究的应用探讨

研究者或学术机构发起的临床研究(IIT)是促进对药物和治疗方法新认识的重要手段,我院采用临床试验管理系统对在我院开展的临床试验包括IIT临床研究进行信息化管理,通过对临床试验信息系统应用在IIT研究管理过程中的各个环节的管控进行分析,分别涉及项目立项管理、伦理审查管理、受试者随访管理、质量控制和药物管理各方面。并将我院信息化管理与国内外药物临床试验管理经验现状进行探讨,旨在进一步完善我院的药物临床试验的信息化管理,促进我院临床研究的规范化实施,提高IIT临床研究的效率和质量,并为试验项目的管理和决策提供建议。     

创新药物的零期临床试验

为了引导创新药物的快速开发、控制新药研发过程中的临床风险,美国食品和药物管理局于2006年颁布了“探索性新药研究”指导原则,提出在进行传统的Ⅰ期临床试验之前开展零期临床试验的概念,并取得了一系列有意义的结果。随着我国自主创新药物的研发体系的建立和发展,能够快速筛选、降低成本、减少风险、提高新药开发效率的探索性新药临床研究方法愈来愈受到关注。但在我国零期临床研究尚处于起步阶段,没有相应的法规和指导原则,缺乏合理的研究设计和专业的研究人员,零期临床研究本身也存在一些局限。本文就创新药物的零期临床试验的概念、研究方法、检测方法、适用药物、优势与不足及与传统临床试验的区别等做一综述。     

抗肿瘤药物Ⅱ期临床研究设计考虑要点

Ⅱ期临床试验在抗肿瘤药物研发中有重要作用,良好的Ⅱ期临床研究设计和开发策略将有助于及早淘汰无效瘤种和无效药物;从而选择有潜力的候选药物进入大规模的Ⅲ期试验.本文对抗肿瘤药物Ⅱ期临床研究设计中需关注的问题进行探讨,包括受试人群、剂量、对照、终点指标等;同时对靶向治疗药物的一些新的设计方法,以及联合用药的研究和评价策略进行介绍,期望对我国从事抗肿瘤新药研发人员有所帮助.     

大蒜素用于防治肺部感染的文献质量评价

目的:对大蒜素用于防治肺部感染的文献质量进行分析,为进一步的临床试验提供参考.方法:检索近20年大蒜素预防及治疗肺部感染的临床研究文献,包括随机对照研究及观察性研究,采用Jadad量表及NOS评分量表对纳入文献进行评分,同时结合循证医学的原则及评价方法对其进行分析和评价.结果:检索到符合纳入标准的临床研究5篇,在研究方法设计、疾病诊断标准、疗效判定标准以及不良反应报告等方面存在较多问题.结论:大蒜素用于防治肺部感染的临床研究文献存在样本量较少、研究方法不严谨、结局指标选取不统一的缺点,尚不能得出大蒜素可成为替代治疗肺部感染药物的结论,故有必要进一步实施多中心、大样本及高质量的临床试验研究.     

A Bayesian Meta-Analysis on Published Sample Mean and Variance Pharmacokinetic Data with Application to Drug–Drug Interaction Prediction

In drug-drug interaction (DDI) research, a two-drug interaction is usually predicted by individual drug pharmacokinetics (PK). Although subject-specific drug concentration data from clinical PK studies on inhibitor or inducer and substrate PK are not usually published, sample mean plasma drug concentrations and their standard deviations have been routinely reported. Hence there is a great need for meta-analysis and DDI prediction using such summarized PK data. In this study, an innovative DDI prediction method based on a three-level hierarchical Bayesian meta-analysis model is developed. The three levels model sample means and variances, between-study variances, and prior distributions. Through a ketoconazle-midazolam example and simulations, we demonstrate that our meta-analysis model can not only estimate PK parameters with small bias but also recover their between-study and between-subject variances well. More importantly, the posterior distributions of PK parameters and their variance components allow us to predict DDI at both population-average and study-specific levels. We are also able to predict the DDI between-subject/study variance. These statistical predictions have never been investigated in DDI research. Our simulation studies show that our meta-analysis approach has small bias in PK parameter estimates and DDI predictions. Sensitivity analysis was conducted to investigate the influences of interaction PK parameters, such as the inhibition constant Ki, on the DDI prediction.     

A Bayesian meta-analysis on published sample mean and variance pharmacokinetic data with application to drug-drug interaction prediction

In drug-drug interaction (DDI) research, a two-drug interaction is usually predicted by individual drug pharmacokinetics (PK). Although subject-specific drug concentration data from clinical PK studies on inhibitor or inducer and substrate PK are not usually published, sample mean plasma drug concentrations and their standard deviations have been routinely reported. Hence there is a great need for meta-analysis and DDI prediction using such summarized PK data. In this study, an innovative DDI prediction method based on a three-level hierarchical Bayesian meta-analysis model is developed. The three levels model sample means and variances, between-study variances, and prior distributions. Through a ketoconazle-midazolam example and simulations, we demonstrate that our meta-analysis model can not only estimate PK parameters with small bias but also recover their between-study and between-subject variances well. More importantly, the posterior distributions of PK parameters and their variance components allow us to predict DDI at both population-average and study-specific levels. We are also able to predict the DDI between-subject/study variance. These statistical predictions have never been investigated in DDI research. Our simulation studies show that our meta-analysis approach has small bias in PK parameter estimates and DDI predictions. Sensitivity analysis was conducted to investigate the influences of interaction PK parameters, such as the inhibition constant Ki, on the DDI prediction.     

Pharmacological approaches to methamphetamine dependence: a focused review

Methamphetamine dependence is a serious worldwide public health problem with major medical, psychiatric, socioeconomic and legal consequences. Various neuronal mechanisms implicated in methamphetamine dependence have suggested several pharmacological approaches. A literature search from a range of electronic databases (PubMed, EMBASE, PsycInfo, the NIDA research monograph index and the reference list of clinicaltrials.gov) was conducted for the period from January 1985 to October 2009. There were no restrictions on the identification or inclusion of studies in terms of publication status, language and design type. A variety of medications have failed to show efficacy in clinical trials, including a dopamine partial agonist (aripiprazole), GABAergic agents (gabapentin) and serotonergic agents (SSRI, ondansetron, mirtazapine). Three double-blind placebo-controlled trials using modafinil, bupropion and naltrexone have shown positive results in reducing amphetamine or methamphetamine use. Two studies employing agonist replacement medications, one with d-amphetamine and the other with methylphenidate, have also shown promise. Despite the lack of success in most studies to date, increasing efforts are being made to develop medications for the treatment of methamphetamine dependence and several promising agents are targets of further research.     

新型降糖药达格列净的临床研究进展

达格列净是一种新型降糖药,通过抑制近端肾小管钠-葡萄糖协同转运蛋白2介导的葡萄糖重吸收,促进尿糖排泄而发挥降糖作用。临床上可单药或者与其他降糖药联用治疗2型糖尿病,疗效显著,安全性高。本文对达格列净的降糖机制、临床应用、安全性等进行综述,为其临床合理应用及降糖药研发提供参考。     

老年糖尿病住院患者药疗方案的成本-效果分析

目的：评价老年糖尿病住院患者不同药物治疗方式中不同用药方案所产生的经济结果。方法：运用成本-效果分析法分别对口服降糖药物治疗和应用胰岛素治疗两种治疗方式中各3种主要的用药方案进行分析评价。结果：格列齐特、二甲双胍联合用药是口服抗糖尿病药物治疗方式中的较优方案；格列齐特、阿卡波糖、胰岛素联合用药是应用胰岛素的药物治疗方式中的较优方案。结论：运用药物经济学理论指导临床合理用药，可使有限的资源得到合理配置。     

Critical review of GABA-ergic drugs in the treatment of schizophrenia.

GABA-ergic medications may have a potential role in the treatment of schizophrenia. Laboratory evidence has generally supported the ability of gamma-aminobutyric acid (GABA) to reduce dopaminergic activity and has suggested that GABA may be effective in combating hypofrontality by acting on mesoprefrontocortical tracts in patients resistant to treatment with antipsychotic drugs. Although the results of clinical trials of several GABA-ergic compounds have been inconclusive because of methodologic limitations and drug toxicity, benzodiazepines and valproate seem to be associated with favorable treatment outcomes, especially when combined with typical antipsychotic agents. This study concludes that further investigation of the use of GABA in schizophrenia is likely to improve the understanding of the psychopathology of this illness and to expand our treatment alternatives. Also provided are suggestions to enhance the design of future studies, improve the potential for favorable treatment outcomes, and assist in predicting patients' responses to GABA-ergic medications.     

A new probabilistic rule for drug–dug interaction prediction

An innovative probabilistic rule is proposed to predict the clinical significance or clinical insignificance of DDI. This rule is coupled with a hierarchical Bayesian model approach to summarize substrate/inhibitor’s PK models from multiple published resources. This approach incorporates between-subject and between-study variances into DDI prediction. Hence, it can predict both population-average and subject-specific AUCR. The clinically significant DDI, weak DDI, and clinically insignificant inhibitions are decided by the probabilities of predicted AUCR falling into three intervals, (−∞, 1.25), (1.25, 2), and (2, ∞). The main advantage of this probabilistic rule to predict clinical significance of DDI over the deterministic rule is that the probabilistic rule considers the sample variability, and the decision is independent of sampling variation; while deterministic rule based decision will vary from sample to sample. The probabilistic rule proposed in this paper is best suited for the situation when in vivo PK studies and models are available for both the inhibitor and substrate. An early decision on clinically significant or clinically insignificant inhibition can avoid additional DDI studies. Ketoconazole and midazolam are used as an interaction pair to illustrate our idea. AUCR predictions incorporating between-subject variability always have greater variances than population-average AUCR predictions. A clinically insignificant AUCR at population-average level is not necessarily true when considering between-subject variability. Additional simulation studies suggest that predicted AUCRs highly depend on the interaction constant K _i and dose combinations.     

从药品技术指导原则的变迁看调脂药临床研究评价策略的进展

心血管疾病已居我国人群死亡原因的第一位。血脂异常是重要的心血管风险因素。新调脂药是近年来国内外心血管药物的开发热点之一。在新药的开发中,临床研究是证明新药人体有效性和安全性的重要环节,其设计和实施是否规范直接影响试验结果的可评价性。因而,药品管理机构常通过发布指导原则或者建议的方式,给企业提供临床研究方法的技术性建议。本文回顾了国内外药品管理机构不同时期的调脂药临床研究相关的指导原则,从中可以看出调脂药临床研究评价策略的进展,并可给我国的调脂药临床评价提供参考。     

Utilizing <Emphasis Type="Italic">In Vitro</Emphasis> Dissolution-Permeation Chamber for the Quantitative Prediction of pH-Dependent Drug-Drug Interactions with Acid-Reducing Agents: a Comparison with Physiologically Based Pharmacokinetic Modeling

For many orally administered basic drugs with pH-dependent solubility, concurrent administration with acid-reducing agents (ARAs) can significantly impair their absorption and exposure. In this study, pH-dependent drug-drug interaction (DDI) prediction methods, including in vitro dissolution-permeation chamber (IVDP) and physiologically based pharmacokinetic (PBPK) modeling, were evaluated for their ability to quantitatively predict the clinical DDI observations using 11 drugs with known clinical pH-dependent DDI data. The data generated by IVDP, which consists of a gastrointestinal compartment and a systemic compartment separated by a biomimic membrane, significantly correlated with the clinical DDI observations. The gastrointestinal compartment AUC ratio showed strong correlation with clinical AUC ratio (R=0.72 and P=0.0056), and systemic compartment AUC ratio showed strong correlation with clinical C_max ratio (R=0.91 and P=0.0003). PBPK models were also developed for the 11 test compounds. The simulations showed that the predictions from PBPK model with experimentally measured parameters significantly correlated with the clinical DDI observations. Future studies are needed to evaluate predictability of Z-factor-based PBPK models for pH-dependent DDI. Overall, these data suggested that the severity of pH-dependent DDI can be predicted by in vitro and in silico methods. Proper utilization of these methods before clinical DDI studies could allow adequate anticipation of pH-dependent DDI, which helps with minimizing pharmacokinetic variation in clinical studies and ensuring every patient with life-threatening diseases receives full benefit of the therapy.