Efficacy and tumor shrinkage pattern of neoadjuvant abemaciclib combined with AI in HR+/HER2- breast cancer with Ki-67 > 10% after 2 weeks preoperative endocrine therapy

开始日期2024-03-20

申办/合作机构

Liaoning People's Hospital

ChiCTR2300078928 / Recruiting临床4期IIT

A randomized, controlled, multicenter clinical study of the effect of pyrotinib maleate on the treatment of the curative effect is SD patients with HR+ HER2 low expression of advanced first-line breast cancer treated with dalpiciclib combined and AI

开始日期2023-12-31

申办/合作机构-

100 项与氨曲南赖氨酸相关的临床结果

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100 项与氨曲南赖氨酸相关的转化医学

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100 项与氨曲南赖氨酸相关的专利（医药）

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项与氨曲南赖氨酸相关的文献（医药）

2024-12-01·Current Computer-Aided Drug Design

Efficacy and Safety of PARP Inhibitor Therapy in Advanced Ovarian Cancer: A Systematic Review and Network Meta-analysis of Randomized Controlled Trials

Review

作者: Zhan, Xiangbo ; Chen, Juying ; Wang, Hongzhe ; Lian, Xiaoshan ; Wu, Xiaozhe ; Li, Bing

Aims:This study aims to evaluate the efficacy and safety of PARP inhibitor therapy in advanced ovarian cancer and identify the optimal treatment for the survival of patients.Background:The diversity of PARP inhibitors makes clinicians confused about the optimal strategy and the most effective BRCAm mutation-based regimen for the survival of patients with advanced ovarian cancer.Objectives:The objective of this study is to compare the effects of various PARP inhibitors alone or in combination with other agents in advanced ovarian cancer.Methods:PubMed, Embase, Cochrane Library, and Web of Science were searched for relevant studies on PARP inhibitors for ovarian cancer. Bayesian network meta-analysis was performed using Stata 15.0 and R 4.0.4. The primary outcome was the overall PFS, and the secondary outcomes included OS, AE3, DISAE, and TFST.Results:Fifteen studies involving 5,788 participants were included. The Bayesian network metaanalysis results showed that olaparibANDAI was the most beneficial in prolonging overall PFS and non-BRCAm PFS, followed by niraparibANDAI. However, for BRCAm patients, olaparibTR might be the most effective, followed by niraparibANDAI. Olaparib was the most effective for the OS of BRCAm patients. AI, olaparibANDAI, and veliparibTR were more likely to induce grade 3 or higher adverse events. AI and olaparibANDAI were more likely to cause DISAE.Conclusion:PARP inhibitors are beneficial to the survival of patients with advanced ovarian cancer. The olaparibTR is the most effective for BRCAm patients, whereas olaparibANDAI and niraparibANDAI are preferable for non-BRCAm patients.Other:More high-quality studies are desired to investigate the efficacy and safety of PARP inhibitors in patients with other genetic performances.

2024-12-01·Journal of Medical Imaging and Radiation Sciences

The future of radiology and radiologists: AI is pivotal but not the only change afoot

Article

作者: Weisberg, Edmund M ; Fishman, Elliot K

Uncertainty regarding the future of radiologists is largely driven by the emergence of artificial intelligence (AI). If AI succeeds, will radiologists continue to monopolize imaging services? As AI accuracy progresses with alacrity, radiology reads will be excellent. Some articles show that AI can make non-radiologists experts. However, eminent figures within AI development have expressed concerns over its possible adverse uses. Bad actors, not bad AI, may account for a future in which AI is not as successful as we might hope and, as some fear, even pernicious. More relevant to current predictions over the course of AI in medicine, and radiology in particular, is how the evolution of AI is often seen in a vacuum. We cannot predict the future with certainty. But as we contemplate the potential impact of AI in radiology, we should remember that radiology does not exist in a vacuum; while AI is changing, so is everything else. The medical system, not to mention the world's population, has been severely impacted by the global COVID-19 pandemic and numerous experts expect future worldwide pandemics. We cannot predict the condition of the healthcare system in two decades but may assume that radiology will likely remain critical in any future medical practice. For now, we should responsibly use all tools at our disposal (including AI) to make ourselves as indispensable as possible. Our best chances of remaining relevant and instrumental to patient care will likely hinge on our ability to lead the changes rather than be passively impacted by them.

2024-11-01·BMJ Supportive & Palliative Care3区 · 医学

Cognitive complaints during breast cancer endocrine therapy: aromatase inhibitors versus tamoxifen

3区 · 医学

Article

作者: Berrazega, Yosra ; Rachdi, Haifa ; Mejri, Nesrine ; Laabidi, Soumaya ; Boussen, Hamouda ; Abdallah, Ichrak Ben

ObjectivePrevious data hypothesise that women receiving aromatase inhibitors (AIs) exhibit worse cognitive functioning than patients on tamoxifen (TAM) since their oestrogen levels are lower. We aimed to compare cognitive complaints in both groups.MethodsFrom September 2020 to January 2021, we conducted a cross-sectional study on patients with stage I–III breast cancer undergoing adjuvant endocrine therapy for at least 6 months. Cognitive complaints were assessed using the Functional Assessment of Cancer Therapy–Cognitive V.3 questionnaire with higher scores indicating better outcomes.ResultsWe included 108 female patients, 60 on AI and 48 on TAM. Mean age at diagnosis was 52 (44 in the TAM group vs 58 in the AI group, p<0.001). Assessment of ‘perceived cognitive impairment-20 subscale’ did not identify a significant difference between the two groups (mean score: patients on AI=63/80 vs patients on TAM=58/80, p=0.198). Patients on TAM scored significantly worse than patients on AI (p<0.001) on the concentration complaints, while for the verbal domain, memory, multitasking, speed and functional interference, no significant difference between the two groups was observed. The difference in concentration complaints was maintained after adjustment to age, educational level, physical activity, prior exposure to chemotherapy, and living alone or with others. Finally, a favourable impact of regular physical activity on concentration scores was observed in both groups (p<0.001).ConclusionDespite age difference, patients on AI did not demonstrate worse complaints than patients on TAM. Patients on TAM exhibited significantly increased concentration complaints. Oncologists should carefully screen their patients for mental fog and educate them on the importance of regular exercise.

项与氨曲南赖氨酸相关的新闻（医药）

2024-07-10

·药通社

文末领资料 | 肺部感染性疾病用吸入制剂的上市与临床研究现状

扫描二维码抢免费门票！目前，临床常采用口服或静脉注射的给药方式治疗肺部感染类疾病，但需要较高的给药剂量，否则无法使得肺部的药物浓度达到最低抑菌浓度(minimum inhibitory concentration,MIC)以上，由于较多的全身暴露，不良反应的发生率较高，且长期过量使用抗菌药物，会使得细菌产生耐药突变。而据有关研究报道，抗菌药耐药相关死亡是仅次于缺血性心脏病和中风的全球第三大死亡病因，其中以下呼吸道感染(如肺炎)引起的抗菌药耐药(anti-microbial resistance,AMR)的影响较大，直接导致40万人死亡、间接导致150万人死亡。因此，在减少全身药物暴露和降低给药剂量的情况下，靶向递送药物到肺部感染部位是治疗肺部感染性疾病的研究重点。本研究将对抗感染药物吸入制剂的上市及临床情况进行简要介绍，并概述抗感染药物的吸入粉雾剂与雾化吸入溶液的研发进展，以期为肺部吸入用抗感染药物的研发提供参考。肺部感染性疾病用吸入制剂的上市与临床研究现状 1997年，美国FDA批准妥布霉素雾化吸入溶液用于铜绿假单胞菌(Pseudomonas aeruginosa,PA)感染的囊性纤维化(cystic fibrosis,CF)的治疗，使其成为该领域的首个上市产品。多黏菌素E甲磺酸钠、左氧氟沙星、两性霉素B等药物的吸入制剂也陆续上市或进入临床试验阶段，如表1所示。妥布霉素妥布霉素为氨基糖苷类抗菌药，易溶于水，在胃肠道不易被吸收，难以透过细胞膜，通过口服与静脉给药方式治疗肺部感染性疾病的生物利用度低。目前，妥布霉素吸入制剂已有吸入溶液与吸入粉雾剂2种剂型获批上市，用于治疗由PA引起的CF所致的慢性肺部感染与NCFB。 01 妥布霉素吸入溶液已有多款妥布霉素吸入溶液上市，如Tobi®、Bramitob®和Vantobra®等。美国Mylan公司推出的Tobi® ，规格为5 m L∶300 mg，为首个获批上市的妥布霉素吸入溶液，搭配使用Pari LC PLUS射流雾化杯给药。KONSTAN等研究显示，Tobi®雾化给药时间约为15 min，递送药量约为101.5 mg，且其质量中值空气动力学粒径(median mass aerodynamic diameter,MMAD)为3.6μm，几何标准偏差(geometric standard deviation,GSD)为2.3。而由德国Pari公司推出的Vantobra®，规格为1.7 m L∶170 mg，通过振动网式雾化器Tolero®进行给药，雾化给药时间约为4 min，递送药量为96.4 mg，MMAD为3.8μm，GSD为1.5。振动网式雾化器是利用压电陶瓷片的高频振动，使药液通过固定直径的微小筛孔，产生可直达肺深部的药物液滴。该类雾化器产生的气溶胶微粒大小较均匀、微细粒子分数(fine particle fraction,FPF)高、药液残留少，并且装置轻巧、方便易携带、噪音小。相较于Tobi®，Vantobra®具有递送比例高、给药时间短、患者满意度高的优点。 CF为欧洲人常见的染色体显性遗传病，在亚洲人中发病率较低，而我国由PA引起的肺部感染多为NCFB，主要通过长期给予妥布霉素吸入溶液进行治疗。由健康元公司推出的妥布霉素吸入溶液健可妥®于2022年10月获得上市许可，为国内首个吸入式抗菌药产品。研究显示，以中国支气管扩张患者为目标人群，与安慰剂组相比，雾化吸入健可妥®(以28 d为1个治疗周期)的PA菌落数显著降低，具有较好的疗效。 02 妥布霉素吸入粉雾剂 Tobi PodhalerTM妥布霉素吸入粉雾剂为美国Mylan公司采用(Pulmo SphereTM)技术制备而成的吸入用脂质颗粒，为国内外唯一一款被批准上市的妥布霉素吸入粉雾剂。 Tobi PodhalerTM采用 T-326胶囊型干法吸入(DPI)装置给药，单粒胶囊含药28 mg，每次给药4粒胶囊，给药剂量为112 mg，与妥布霉素吸入溶液相比，其给药剂量大大减少。 FLUME等将CF患者分为对照组与试验组，分别给予妥布霉素吸入溶液(Tobi®)与吸入粉雾剂(Tobi PodhalerTM)。结果显示，吸入粉雾剂治疗组患者一秒率 (FEV1%，即FEV1/FVC比值；式中：FEV1为最大吸气后用力快速呼气1 s所呼出的最大气量，FVC为尽力最大吸气后，尽力、尽快呼气时所能呼出的最大气量) 的基线与第3个治疗周期d28时相比，其增大的相对比例为5.8%，而吸入溶液治疗组的这一比例为4.7%，表明吸入粉雾剂治疗组患者的FEV1%得到更大改善，治疗效果更好。此外，Tobi PodhalerTM单次给药仅需3 min，耗时较短，且胶囊型DPI装置便于携带与使用，减少了反复清洗装置的步骤，患者满意度更高。多黏菌素E甲磺酸钠多黏菌素E甲磺酸钠为前药形式，本身无活性，在体内水解形成活性成分多黏菌素E后发挥抗菌作用。 EMA已批准多黏菌素E甲磺酸钠(Colobreathe®)用于治疗由PA引起的慢性肺部感染。以色列Teva公司生产的Colobreathe®是采用喷雾干燥法形成的纯药物微粉型无载体吸入粉雾剂，有效期为3年，产品粒径较小，其中位径[d(0.5)]约为1.5μm；且在相对湿度(RH)60%、25℃条件下可保持其无定形粉末状态，具有稳定的FPF值。 Colobreathe®单粒胶囊含多黏菌素E甲磺酸钠125 mg，单次给药仅需要1粒胶囊，减少了给药步骤，患者满意度高。阿米卡星阿米卡星主要用于治疗由 MAC引起的难治性非结核分枝杆菌 (nontuberculous mycobacteria,NTM)肺病。通过静脉注射的阿米卡星主要分布于细胞外液，并会在肾脏皮质细胞和内耳液中积蓄，产生较大的肾毒性和耳毒性。采用脂质体包裹的阿米卡星通过吸入方式给药可靶向递送至肺部，不仅可以减少不良反应的发生，而且可延长药物在肺部的滞留时间，起到长效、缓释的作用。由美国Insmed公司研发的Arikayce Kit® (阿米卡星脂质体吸入混悬液)，通过振动网式雾化器Lamira递送给药。在成人呼吸模式的体外测试中，其平均给药剂量约为312 mg(药物总含量为590 mg，递送比例约为53%),MMAD为4.9μm，可有效递送药物至肺部。将由MAC引起的难治性NTM肺病患者分为2组，对照组仅给予传统支持性治疗，试验组则在给予传统支持性治疗的基础上增加阿米卡星脂质体吸入混悬液的治疗；6个月后，对照组患者的痰液检测结果由阳性转为阴性的比例为8.9%，而试验组为29%，这表明阿米卡星脂质体吸入混悬液可有效治疗由MAC引起的难治性NTM肺病。两性霉素B 两性霉素B为多烯类抗真菌药，主要用于慢性肺曲霉病(chronic pulmonary aspergillosis,CPA)的治疗。目前，两性霉素B仅有静脉注射剂型获批上市，主要分为脱氧胆酸盐和含脂制剂。临床常采用雾化吸入两性霉素B脱氧胆酸盐注射液的方式治疗支气管肺真菌病；两性霉素B脱氧胆酸盐注射液说明书中也介绍了雾化吸入治疗的具体使用方法。张敏等报道，呼吸道真菌感染患者分别静脉注射与雾化吸入两性霉素B进行治疗的有效率为 73.6%和66.6%，但雾化吸入组患者的不良反应发生率明显低于静脉给药组，这说明雾化吸入两性霉素B可有效治疗由真菌感染引起的肺曲霉病，且具有较好的安全性。美国感染病协会推出的 “2016IDSA指南：曲霉病的诊断与管理”建议采用雾化吸入两性霉素B的方式预防侵袭性肺曲霉病(invasive pulmonary aspergillosis,IPA)。虽然两性霉素B静脉注射剂说明书中有雾化吸入用法，但该产品本身含有防腐剂、助溶剂等辅料，这些辅料雾化吸入后会对人体产生何种影响目前还不明确，存在一定的隐患，因此研发吸入专用的两性霉素B雾化吸入溶液可使得肺部真菌感染患者受益。伊维菌素伊维菌素为一种广谱的抗寄生虫药物。近期研究显示，伊维菌素可通过抑制与严重急性呼吸综合征冠状病毒2(SARS-Co V-2)复制相关的关键蛋白酶(3-胰凝乳蛋白酶样蛋白酶)的活性而治疗SARS-Co V-2引发的感染。印度已使用伊维菌素作为德尔塔毒株感染的治疗药物。将伊维菌素用于SARS-Co V-2感染患者的治疗时要达到半抑制浓度(IC50)水平。体外试验测得伊维菌素对SARS-Co V-2的IC50为2.5μmol/L。COVID-19患者每日口服伊维菌素0.6 mg/kg后，药物经胃肠道吸收到达肺部感染部位而起效，能使病毒载量降低72%，但给药剂量较大。通过吸入方式直接将药物靶向递送至肺部(SARS-Co V-2的主要聚集部位)，可减少给药剂量，试验证明每日只需吸入伊维菌素0.1 mg/kg即可有效清除病毒，且耐受性好。埃及曼苏尔大学报道通过喷雾干燥法制备了以乳糖为载体的无定形伊维菌素吸入粉雾剂。体外试验测得其FPF为70%,MMAD为4.3μm，且稳定性试验表明该吸入粉雾剂中的伊维菌素在RH 58%的条件下仍可保持无定形状态。治疗肺部感染性疾病时要使药物浓度达到MIC以上，通常给药剂量较高，而以吸入方式给予吸入用液体制剂或吸入粉雾剂可满足大剂量给药的需求。目前已上市的治疗肺部感染性疾病的吸入制剂均为上述2种剂型。以下将概述不同吸入制剂在肺部感染性疾病中的应用。吸入用液体制剂在肺部感染性疾病中的应用雾化吸入是指利用雾化器将药物溶液或混悬液撞击成微小的气溶胶颗粒，通过患者呼吸气流驱动而使药物到达呼吸道与肺部，发挥局部或全身作用的给药方式。吸入溶液吸入溶液是目前应用较多的吸入用液体制剂，主要由API、注射用水、p H调节剂和渗透压调节剂组成。其在肺部感染性疾病治疗领域中的应用具有较长的历史，许多抗感染药物均可以溶液的形式雾化吸入给药，疗效良好且不良反应较少。与注射用左氧氟沙星相比，左氧氟沙星吸入溶液Aeroquin®采用振动网式雾化器e Flow®PARI进行给药，生物利用度更高、起效更快、在肺内停留时间更长，且无严重不良反应。吸入冻干粉将药液在无菌环境下冷冻干燥形成的粉末即为吸入用冻干粉，使用时用溶剂复溶，可快速溶解成为吸入溶液。前述多黏菌素E甲磺酸钠(Promixin®)和赖氨酸氨曲南(Cayston®)均为吸入用冻干粉。例如，赖氨酸氨曲南(Cayston®)吸入用冻干粉采用0.17%氯化钠无菌溶液1 m L作为溶剂，复溶后使用Altera®雾化器给药，仅需2～3 min就可给药完毕。经临床试验证实，Cayston®可显著改善NCFB患者的呼吸道症状。吸入溶液一般需在2～8℃条件下贮存与运输，否则会发生变质，因此运输与贮存成本较高。相对而言，吸入用冻干粉含水量低、理化性质稳定、不易氧化变质，可在室温条件下贮存，便于运输与贮存。并且，对于易水解变质或失活的药物，如多黏菌素E甲磺酸钠，采用吸入用冻干粉形式，现配现用，可最大程度保留药效并降低不良反应发生率。吸入用脂质体混悬液脂质体的磷脂双分子层结构与人体细胞膜的结构类似，因此脂质体具有较好的生物相容性，可减少不良反应的发生。并且，脂质体可通过磷脂双分子层缓慢释放药物，使肺部感染部位的药物浓度较长时间维持在MIC以上，减少给药频率。环丙沙星脂质体Linhaliq®通过射流雾化器LC Sprint进行给药，目前处于临床试验状态。环丙沙星通过口服给药(80 mg/kg)的血清半衰期为4.2 h，而制成吸入用脂质体给药时的半衰期为7.4 h，作用时间显著延长。阿米卡星脂质体混悬液采用乙醇注入法制成，主要脂质成分为二棕榈酰磷脂酰胆碱(DPPC)和胆固醇(质量比2∶1)。在雾化过程中，约70%的阿米卡星被包裹在脂质体中，约30%的剂量以游离阿米卡星的形式释放，可提供足够剂量的阿米卡星进入肺部感染部位并维持其浓度。但脂质体混悬液的制备方法复杂、成本高，一般适用于毒性大、半衰期短的药物。除已上市的阿米卡星(Arikayce Kit®)脂质体吸入混悬液以外，目前国内外有多种治疗肺部感染性疾病的吸入用脂质体混悬液在研，如妥布霉素脂质体吸入混悬液、环丙沙星脂质体吸入混悬液、两性霉素B脂质体吸入混悬液等。肺部感染性疾病的治疗一般需递送高剂量药物，吸入用液体制剂给药通常存在装置中残留药液较多、给药时间长等问题，还需对雾化装置进行清洗和消毒，步骤较烦琐。因此，开发性质稳定、使用方便的吸入粉雾剂，在肺部感染性疾病的治疗上具有较好的应用前景。吸入粉雾剂在肺部感染性疾病中的应用 DPI系指微粉化API单独或与合适的载体混合后以胶囊、泡囊或多剂量贮库形式贮存，使用时采用特制的装置，由患者主动吸入雾化药物至肺部的制剂。DPI装置小巧、使用方便、易于携带，且药物以干粉(固体颗粒)形式存在，稳定性好，易于保存和运输。此外，吸入粉雾剂具有递送效率高和无创给药等优势。随着微粉化制备技术的不断进步以及新型给药装置的发展，吸入粉雾剂在肺部感染性疾病中的应用研究不断深入。乳糖载体型吸入粉雾剂此类吸入粉雾剂主要由粒径较大的乳糖载体颗粒和微粉化药物组成；微粉化药物通过吸附在乳糖表面而改善流动性和分散性、减少聚集。当患者使用DPI时，空气湍流产生的剪切力使药物微粉和乳糖分离，脱落下来的微粉化药物随气流进入气道，而大粒径的乳糖载体沉积于口腔和咽喉部位。直径为10～130µm的一水合α-乳糖颗粒较常用，具有安全、无毒、稳定、易溶于水、无刺激性和价格低廉等优点，其理化性质对气溶胶的分散和递送性能具有较大影响。扎那米韦吸入粉雾剂(Relenza®)采用乳糖为载体，通过粉碎法制备而成，每粒胶囊的载药量较低(仅含扎那米韦5 mg)。给药时采用英国GSK公司推出的Diskhaler®泡罩型DPI装置，单次给药需安装1个泡罩条(具有4个独立包装的含药小囊)，减少了患者反复填充药物的步骤。但Relenza®通过Diskhaler®DPI装置给药时FPF仅为5.4%，表明肺部沉积率较低。目前上市的常用吸入粉雾剂以乳糖型吸入粉雾剂为主，用于递送高活性、低剂量药物。由于处方中乳糖载体占比较高、药物比例较低，而肺部感染性疾病所需的给药剂量又较高，因此其在肺部感染性疾病治疗中的应用较少。颗粒工程粒子型吸入粉雾剂高剂量抗菌药一般通过颗粒工程粒子型吸入粉雾剂进行递送。这类颗粒具有多孔、低密度和大几何粒径的特征，因而载药量大、溶解性高和吸入性能好。目前，常用的颗粒工程造粒方法有喷雾干燥法、喷雾-冷冻干燥法、超临界流体法和薄膜冷冻干燥法等。 Pulmo SphereTM是指先将药物与辅料制成乳剂，再经喷雾干燥形成含药颗粒的方法。其中，油相中的二硬脂酰磷脂酰胆碱(DSPC)与全氟辛基溴作为“吹泡剂”，在喷雾干燥过程中通过延缓液滴的收缩，使其形成表面多孔及中空内核的结构。前述妥布霉素DPI(Tobi PodhalerTM)就是采用Pulmo Sphere技术制备而成吸入用脂质颗粒。工艺流程如下：以氯化钙和硫酸妥布霉素溶液为水相，DSPC与全氟辛基溴为油相，制备形成乳液；将液滴乳化、喷雾干燥，水分和全氟辛溴蒸发后可形成以DSPC和氯化钙为外壳的大多孔载体颗粒，妥布霉素则以无定形状态分布在其中。 YU等分别采用喷雾干燥法和喷雾-冷冻干燥法制备了环丙沙星可吸入微粉。试验表明，2种微粉的抗菌活性一致(MIC均为0.18μg/m L)，且均为大多孔球形颗粒；喷雾干燥组粒子的FPF为23%,MMAD为4.6μm，而喷雾-冷冻干燥组粒子的FPF为29%,MMAD为3μm。喷雾-冷冻干燥的制备过程中无高温程序，较适用于易热降解类药物的微粉化，但其设备和运行成本昂贵、技术要求高，限制了发展应用。通过颗粒工程造粒技术制成的粒子一般为无定形状态，具有较大的表面积，可促进难溶性药物在体内的溶解，提高生物利用度；但此类粒子具有高度吸潮性，在湿度较大的条件下易转化为活性较小或无活性的晶体，发生粒子间的聚集，因此需通过选择具有较好防潮效果的包装材料和合适的贮存环境，来维持药物的活性和分散性。无辅料型吸入粉雾剂无辅料型吸入粉雾剂中仅含有纯药物微粉，可提高单粒胶囊的药物剂量，并减少由于药物与载体吸附而导致的药物损失。因此，对于需大剂量给药的抗菌药物，无辅料型吸入粉雾剂可减少给药步骤、提高患者的依从性。多黏菌素E甲磺酸钠(Colobreathe®)为已上市的无载体吸入粉雾剂，其单粒胶囊含药125 mg。Colobreathe®是多黏菌素E甲磺酸钠的50%乙醇溶液通过喷雾干燥法制备而成的纯药物微粉，表面褶皱，呈球形，经试验证明喷雾干燥后药物的抗菌活性保持不变。该微粉的d(0.9)仅约2μm，雾化效果较好，FPF高达83%。 Colobreathe®使用单剂量胶囊型Turbospin®装置给药，可与美国Propeller Health公司研发的外接式智能系统联用，记录患者的用药信息如吸气流速、用药时间和用药剂量等，并通过互联网实现数据共享，帮助主治医生了解患者的用药情况以及时调整治疗方案。该系统还可通过发送信息提醒患者按时用药。据报道，CF患者每天花费在治疗上的时间高达100 min以上，治疗内容包括胸部理疗和使用抗菌药、维生素及黏液溶解剂等多种药物治疗等，因此长期坚持按时用药是保证吸入治疗效果的重要因素，而通过智能型吸入装置来记录与提醒患者用药，有助于提高吸入疗法的疗效和患者依从性。吸入疫苗疫苗是将特定疾病的病原体或与其相关的蛋白、多糖或核酸成分，经免疫接种进入人体，从而诱导机体产生特异性的体液免疫、细胞免疫和(或)黏膜免疫，使人体获得预防该疾病的免疫力。大多病毒和细菌通过飞沫传播，如SARS-Co V-2的主要感染途径为呼吸道、口腔黏膜和眼结膜表面。注射型疫苗诱导生成的Ig G抗体在这些部位的分布水平极低，难以抵抗病毒的侵入；而雾化吸入式疫苗不仅可诱导生成Ig G抗体而产生全身免疫反应，还能与黏膜表面的抗原识别细胞结合，诱导生成Ig A抗体，改善注射型疫苗的中和抗体在呼吸道黏膜等部位分布水平低的问题。此外，吸入疫苗还可诱导产生体液免疫和细胞免疫，为人体筑起多道防线，降低感染风险。目前处于临床试验阶段和已上市的吸入疫苗主要有溶液型吸入疫苗和吸入干粉疫苗。溶液型吸入疫苗疫苗以溶液形式通过雾化装置给药时，能被直接递送至肺部，从而在病毒的主要传染途径——呼吸道处诱导多种免疫反应，增强机体免疫能力。康希诺生物公司自主研发的克威莎®雾优®吸入用重组新型冠状病毒疫苗(5型腺病毒载体m RNA)，已获得国家药品监督管理局的批准作为加强针纳入紧急使用。克威莎®雾优®采用爱尔真振动网式雾化器(Aerogen®)给药，FPF高达70%以上；需在2～8℃下贮存与运输。该疫苗与Ad5-n Co V(已获批上市的重组腺病毒载体疫苗，商品名为克威莎®)的制剂工艺和处方完全相同，但每剂克威莎®雾优®的含量(1×1010个病毒颗粒)仅为肌注类疫苗克威莎®(5×1010个病毒颗粒)的1/5。结核病为全球十大死亡原因之一，主要通过飞沫形式传播，肺部为其原发感染部位。ID93+GLA-SE亚单位结核疫苗(其中，ID93为4种结核杆菌抗原重组融合蛋白，GLA-SE即吡喃葡萄糖脂质A-角鲨烯，作为佐剂系统)为一种纳米乳液，作为辅助治疗型疫苗(接种卡介苗后的加强保护疫苗)通过吸入方式被递送至肺部，可在人体中呈现较好的免疫原性和安全性，目前已进入Ⅱ期临床阶段。吸入干粉疫苗将疫苗制备成可吸入干粉形式，就能使用一次性干粉吸入器进行递送。可吸入干粉性质稳定，可常温保存，且使用时无需专业人员操作，可由患者自行使用。但疫苗作为一类生物制品，需对其制备过程的工艺参数进行严格控制，通过加入赋性剂来保持结构稳定性，避免蛋白失活。目前在研的肺部感染性疾病治疗用吸入粉雾剂有肺结核疫苗、流感疫苗和新冠疫苗等。 KANOJIA等以灭活的甲型流感病毒为活性成分、海藻糖为赋性剂，通过喷雾干燥法制备了可吸入微粉，所得颗粒的粒径为2.4μm，在60℃下密封放置90 d后，其活性保持在初始时的92%，具有较好的稳定性。为防止疫苗接种过程中出现交叉感染，吸入疫苗的接种均采用单剂量、一次性吸入装置。为保证疫苗在公众群体中的有效性，需保证其在宽流速范围内递送效率的均一性，通过外接递送反馈装置可判断是否实现了有效递送。总结与展望吸入制剂用于治疗肺部感染性疾病具有肺部生物利用度高、起效快和不良反应发生率低等独特优势，具有较好的应用前景。通过吸入途径递送生物制剂用于肺部感染性疾病的治疗也逐渐被更多的人关注。目前已有多款应用于肺部感染性疾病的吸入制剂被批准上市。此外，吸入用抗菌药新剂型的不断开发，有利于增强药物疗效、减轻不良反应和提高患者依从性。例如，脂质体作为常见的递送载体，具有制备工艺成熟、生物相容性好和长效缓释的特点，通过吸入方式递送抗菌药脂质体制剂用于治疗肺部感染性疾病具有安全性高、抗菌活性好和不良反应少等优势；无载体吸入粉雾剂的FPF值较高，对于需大剂量给药的吸入抗菌药，单次给药剂量高能减少给药频率、提高患者的依从性；吸入疫苗在保持有效性与安全性的基础上，作为无痛接种方式，有利于快速推进大规模疫苗接种进程，防治传染能力强的病原体感染。目前，我国已批准上市的、用于肺部感染性疾病的吸入制剂仅有健可妥®和克威莎®雾优®，国外已上市产品的种类也较少，仍需开发创新药或新的制剂形式来使得已产生耐药性和免疫力弱的人群有药可用。免费领CDE指导原则&参考文献扫码报名--保存报名截图--联系秘书 *以上内容来源：《中国医药工业杂志》作者：郭俊彦吴闻哲王健吸入制剂大会 *上下滑动查看完整版议程论坛议程 08/15 AM 百亿赛道—吸入制剂究竟该如何做？ 09:30-10:00 主题报告：对吸入给药创新的思考游一中常州市第一人民医院主任药师、副主任医师；中国包装联合会气雾剂专业委员会终身荣誉主任；中国颗粒学会吸入颗粒专委会名誉主任 10:00-10:30 主题报告：中药吸入制剂研发的挑战与应对策略廖永红中国医学科学院药用植物研究所研究员 10:30-11:00 主题报告：改良型吸入制剂开发策略与难点分析吴闻哲医药先进制造国家工程研究中心经鼻及吸入给药研究室负责人 11:00-11:30 主题报告：储库式吸入粉雾剂开发难点及策略史楷岐博士、苏州易合医药有限公司创始人&总经理 08/15 PM 乘风破浪—吸入制剂BE研究挑战性在哪？ 14:00-14:30 主题报告：吸入疗法在慢性气道疾病治疗中的地位与展望殷凯生南京医科大学第一附属医院呼吸与危重症医学科二级教授、主任医师、博导；中华医学会呼吸分会哮喘学组顾问；中国哮喘联盟总负责人（之一） 14:30-15:00 主题报告：吸入制剂人体生物等效评价研究郑恒国家药品审评中心专家、湖北省药监局核查专家 15:00-15:45 主题报告：经口鼻吸入制剂关键质量属性研究以及吸入仿制药体外生等效APSD评价方法及重点 EMA FDA 指导原则的更新沈丹蕾全国吸入给药联盟主席、南京白令信息科技有限公司总经理 15:45-16:15 主题报告：吸入制剂临床研究的挑战与经验 08/16 AM 未来已来—吸入制剂产业化壁垒？ 09:30-10:00 主题报告：高壁垒吸入剂开发，如何占据竞争优势佟振博东南大学教授 10:00-10:30 主题报告：鼻喷雾剂的开发及质量研究谷丽南京樟益医药科技有限公司创始人&总经理 10:30-11:00 主题报告：PulmoSol 与 PulmoSphere：干粉吸入剂制剂技术及研发难点刘恒利凯信远达医药有限公司研发中心负责人 11:00-11:30 主题报告：吸入制剂包装相容性研究与实施蔡荣高级工程师（教授级）、原上海市食品药品包装材料测试所 08/16 PM 道阻且长—吸入制剂如何翻越审批大山？ 14:00-14:30 主题报告：简评欧美吸入制剂审评及指导原则张星一前CDE高级审评员 14:30-15:00 主题报告：吸入制剂注册申报关键要点及案例分析 15:00-15:30 主题报告：干粉吸入制剂的中欧审评评价及技术难点陈永奇珠海瑞思普利医药科技有限公司董事长兼首席科学家

上市批准临床研究微生物疗法

2023-12-15

·MedCity News

Building Trust in AI: Why All Health Organizations Need a Plan To Address AI Bias

Health inequities, racial disparities, and access barriers have long plagued the healthcare system. While digital solutions hold the potential to mitigate these challenges, the unintentional improper use of these technologies can actually have the opposite effect: widening the gap in healthcare access and exacerbating disparities among vulnerable populations. Nowhere is that concern more critical than with artificial intelligence (AI). AI advancements are revolutionizing the healthcare landscape and opening up new possibilities to enhance patient care and health outcomes, provide more personalized and meaningful experiences, and respond better to consumer needs. However, AI also introduces the potential for bias, which in turn creates complex ethical concerns and high levels of consumer distrust. If organizations aren’t careful in their approach — and neglect critical concerns about ethical standards and safeguards — the risks of AI could outweigh the benefits. The root causes of AI bias AI bias often originates from two key sources: data and algorithms. AI bias is often created as a result of hypotheses and objectives of the creators, and may be unintended. Data curation and algorithm development are both human activities, and the frame of mind of the developers matters greatly in increasing or reducing bias. AI technologies are only as good as the data that feeds them — and from data selection to representation, several factors can impact data quality, accuracy, and representation. Historical disparities and inequalities have resulted in vast data gaps and inaccuracies related to symptoms, treatment, and the experiences of marginalized communities. These issues can significantly affect AI’s performance and lead to erroneous conclusions. On the algorithm side, developers often have specific goals in mind when creating AI products that influence how algorithms are designed, how they function, and the outcomes they produce. Design and programming choices made during AI development can inject personal or institutional biases into the algorithm’s decision-making process. In one highly publicized case, a widely used AI algorithm designed to gauge which patients needed extra medical care was found to be biased against Black patients, underestimating their needs compared to White patients and leading to fewer referrals for vital medical interventions. When AI systems are trained on data that reflects these biases (or algorithms are flawed from the start), they can inadvertently learn and propagate them. For instance, AI-powered tools fail to take into account the fact that medical research has historically undersampled marginalized populations. This oversight can easily produce inaccurate or incomplete diagnosis and treatment recommendations for racial minorities, women, low-income populations, and other groups. These instances of biases negatively impact care, perpetuate existing disparities, and undermine progress on health equity. But they have another side effect — one that’s perhaps less overt, yet equally debilitating: They erode trust in the healthcare system among populations that are most vulnerable. From early detection and diagnosis tools to personalized consumer messaging and information, AI provides organizations with opportunities to improve care, streamline operations, and innovate into the future. It’s no wonder nine in 10 healthcare leaders believe AI will assist in improving patients’ experiences. But when consumers, providers, or health organizations perceive AI as unreliable or biased, they are less likely to trust and use AI-driven solutions, and less likely to experience its vast benefits. How organizations can build trust in AI The vast majority of health organizations recognize the competitive importance of AI initiatives and most are confident that their organizations are prepared to handle potential risks. However, research shows that AI bias is often more prevalent than executives are aware of — and your organization can’t afford to maintain a false sense of security when the stakes are so high. The following areas of improvement are critical to ensure your organization can benefit from AI without adding to inequities. Set standards and safeguards To prevent bias and minimize other negative effects, it’s critical to adhere to high ethical standards and implement rigorous safeguards in the adoption of digital tools. Implement best practices established by trusted entities, like the ones established by the Coalition for Health AI. Best practices may include, but are not limited to: Data quality: Adopting robust data quality, collection, and curation practices that ensure data used for AI is diverse, complete, accurate, and relevant Governance: Implementing algorithm governance structures to monitor AI outcomes and detect biases Audits: Conducting regular audits to identify and rectify bias in outcomes. Pattern matching: Investing in pattern-matching capabilities that can recognize bias patterns in AI outcomes to aid in early detection and mitigation. Manual expertise: Deploying trained experts who can manually oversee AI results to ensure they align with ethical standards. Assistive technology: Using AI as assistive technology, analyzing its effectiveness, identifying areas of improvement, and then scaling tools up before AI technology interfaces with consumers Most importantly, it is vital to verify the impact of using AI on patient outcomes at frequent intervals, seeking evidence of bias through analysis, and correcting data curation or algorithms to reduce the effects of bias. Build trust and transparency. Successful AI adoption requires building a strong foundation of trust and transparency with consumers. These efforts ensure your organization acts responsibly and takes the necessary steps to mitigate potential bias while enabling consumers to understand how your organization uses AI tools. To start, foster greater transparency and openness about how data is used in AI tools, how it’s collected, and the purpose behind such practices. When consumers understand the reasoning behind your decisions, they are more likely to trust and follow them. Likewise, do your diligence to ensure that all outputs from AI systems come from known and trusted sources. The behavior science principle known as authority bias underscores the notion that when messages come from trusted experts or sources, consumers are more likely to trust and act on the guidance provided. Add value and personalization. Healthcare happens in the context of a relationship — and the best way your digital operations can build strong, trusting relationships with consumers is by offering meaningful, personalized experiences. It’s an area in which most organizations could use some help: Three-quarters of consumers wish their healthcare experiences were more personalized. Fortunately, AI can help organizations achieve this at scale. By analyzing large data sets and recognizing patterns, AI can create personalized experiences, provide valuable information, and offer helpful recommendations. For instance, AI-powered solutions can analyze a consumer’s data and health history to recommend appropriate actions and resources, such as providing relevant education resources on heart health, detailing a customized diabetes management plan, or helping someone locate and book an appointment with a specialist. By meeting consumer needs and providing tangible value, AI tools can help alleviate the very concerns consumers may have about the technology and demonstrate the benefits it offers for their care. Ethical AI starts with a plan AI puts a vast amount of power in the hands of healthcare organizations. Like any digital tool, it has the potential to improve healthcare, as well as introduce risks that could prove detrimental to patient outcomes and the overall integrity of the healthcare system. To harness the best parts of AI — and avoid its worst possible outcomes — you need an AI strategy that not only includes technical implementation tactics but also prioritizes efforts to minimize bias, address ethical considerations, and build consumer trust and confidence. AI is here to stay, and offers great promise to accelerate innovation in healthcare. By prioritizing these responsibilities, you can achieve the full promise of healthcare’s digital transformation: a healthier, more equitable future.

2023-10-31

·Science Daily

Human input boosts citizens' acceptance of AI and perceptions of fairness, study shows

Increasing human input when AI is used for public services boosts acceptance of the technology, a new study shows. Increasing human input when AI is used for public services boosts acceptance of the technology, a new study shows. The research shows citizens are not only concerned about AI fairness but also about potential human biases. They are in favour of AI being used in cases when administrative discretion is perceived as too large. Researchers found citizens' knowledge about AI does not alter their acceptance of the technology. More accurate systems and lower cost systems also increased their acceptance. Cost and accuracy of technology mattered more to them than human involvement. The study, by Laszlo Horvath from Birkbeck, University of London and Oliver James, Susan Banducci and Ana Beduschi from the University of Exeter, is published in the journal Government Information Quarterly. Academics carried out an experiment with 2,143 people in the UK. Respondents were asked to select if they would prefer more or less AI in systems to process immigration visas and parking permits. Researchers found more human involvement tended to increase acceptance of AI. Yet, when substantial human discretion was introduced in parking permit scenarios, respondents preferred more limited human input. System-level factors such as high accuracy, the presence of an appeals system, increased transparency, reduced cost, non-sharing of data, and the absence of private company involvement all boosted both acceptance and perceived procedural fairness. Dr Horvath said: "Our results suggest resistance to the accumulation and sharing of citizens' data -- but we also show, in the context of other system-level characteristics, that citizens want working technology in which case are willing to forgo heavy human supervision." Professor Banducci said "Our results contribute to the understanding of technology acceptance in digital government and AI. Citizens who have a baseline resistance to new technologies in other contexts will prefer greater human administrative involvement." Professor James said: "Many routine interactions with the government involve permit applications similar to the kinds we examined such that the findings are of broad relevance to government services using AI. "Respondents appeared to be more strongly influenced by the costs and accuracy of the technology than by concerns about "humans in the loop," transparency, or even data sharing. This suggests citizens may perceive legitimacy more profoundly in terms of the system's efficiency and its ability to deliver accurate and cost-effective results."

100 项与氨曲南赖氨酸相关的药物交易

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外链

KEGG	Wiki	ATC	Drug Bank
D06558	-	J01DF01	DB00355

研发状态

批准上市

10 条最早获批的记录,

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适应症	国家/地区	公司	日期
囊性纤维化	欧盟	Gilead Sciences Ireland UC	2009-09-21
囊性纤维化	挪威	Gilead Sciences Ireland UC	2009-09-21
囊性纤维化	冰岛	Gilead Sciences Ireland UC	2009-09-21
囊性纤维化	列支敦士登	Gilead Sciences Ireland UC	2009-09-21
肺部感染	欧盟	Gilead Sciences Ireland UC	2009-09-21
肺部感染	列支敦士登	Gilead Sciences Ireland UC	2009-09-21
肺部感染	冰岛	Gilead Sciences Ireland UC	2009-09-21
肺部感染	挪威	Gilead Sciences Ireland UC	2009-09-21

未上市

10 条进展最快的记录,

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适应症	最高研发状态	国家/地区	公司	日期
囊性纤维化	临床1期	美国	Gilead Sciences, Inc.	2005-02-01
伯克霍尔德氏菌属感染	临床前	加拿大	Gilead Sciences, Inc.	2010-02-01
伯克霍尔德氏菌属感染	临床前	美国	Gilead Sciences, Inc.	2010-02-01
肺部感染	临床前	加拿大	Gilead Sciences, Inc.	2008-05-01
肺部感染	临床前	澳大利亚	Gilead Sciences, Inc.	2008-05-01
假单胞菌感染	临床前	澳大利亚	Gilead Sciences, Inc.	2008-05-01
假单胞菌感染	临床前	美国	Gilead Sciences, Inc.	2008-05-01
假单胞菌感染	临床前	加拿大	Gilead Sciences, Inc.	2008-05-01
肺部感染	药物发现	美国	Gilead Sciences, Inc.	2008-05-01
囊性纤维化	药物发现	美国	Gilead Sciences, Inc.	2005-02-01

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研究	分期	人群特征	评价人数	分组	结果	评价	发布日期


CLEE011ADE03 \| RIBANNA (ESMO2023) 人工标引	N/A	HR阳性HER2阴性淋巴结阳性乳腺癌 HR positive \| HER2 negative	2,581	AI or fulvestrant +ribociclib	(憲觸齋鑰網網顧蓋鏇憲) = 膚積構獵顧鬱壓糧獵鹹齋願蓋願範觸廠積繭齋 (鏇觸壓鹹廠蓋構選獵廠, 29.3 ~ 34.8)	积极	2023-10-21
NCT03219164 (ALPINE2 \| ALPINE 2, CTgov)	临床3期	铜绿假单胞菌感染 \| 囊性纤维化	149	Placebo+AZLI (AZLI 14 Days + Placebo 14 Days)	衊簾齋蓋獵糧艱遞獵蓋(觸糧繭鑰網廠顧糧憲憲) = 壓簾簾獵鏇鬱糧築鏇簾壓艱襯蓋餘壓願夢壓觸 (蓋餘衊襯糧衊鏇襯鏇襯, 鹽壓願壓鑰衊夢簾積範 ~ 膚鬱製鏇憲鏇網鹹艱淵) 更多	-	2022-05-16
				aztreonam (AZLI 28 Days)	衊簾齋蓋獵糧艱遞獵蓋(觸糧繭鑰網廠顧糧憲憲) = 構範製餘壓憲餘鬱繭壓壓艱襯蓋餘壓願夢壓觸 (蓋餘衊襯糧衊鏇襯鏇襯, 齋衊選遞範憲膚壓醖積 ~ 糧範鏇窪鹹積顧選窪鹽) 更多
CSCO2021	N/A	软组织肉瘤	28	安罗替尼+AI	觸觸憲選鹽選選壓構選(衊顧選願夢築繭蓋遞願) = 67.86% 顧襯範鹽膚醖鏇觸繭衊 (衊壓衊醖蓋憲窪艱鹹顧 ) 更多	-	2021-09-25
NCT01641822 (AZLI CAT, CTgov)	临床3期	假单胞菌感染 \| 囊性纤维化 \| 肺部感染	107	Tobramycin inhalation solution+AZLI (AZLI)	夢構衊獵鹽鹽顧廠獵構(衊遞構積選範範壓製選) = 夢蓋範遞鹹顧壓簾觸遞鏇淵鏇齋襯顧網蓋遞範 (構襯顧齋遞鹽醖糧齋構, 淵築製製顧襯淵艱繭繭 ~ 膚膚鏇窪醖齋醖範壓構) 更多	-	2016-05-09
				Placebo to match AZLI+Tobramycin inhalation solution (Placebo)	夢構衊獵鹽鹽顧廠獵構(衊遞構積選範範壓製選) = 積觸網鑰構夢淵鏇顧鬱鏇淵鏇齋襯顧網蓋遞範 (構襯顧齋遞鹽醖糧齋構, 製糧構獵簾襯簾獵繭醖 ~ 積鹽選構遞艱製積衊糧) 更多
NCT01404234 (PALS, CTgov)	临床3期	假单胞菌感染 \| 囊性纤维化	61	AZLI	壓選鏇餘衊選憲繭築網(鏇鏇鏇膚顧蓋窪遞鑰願) = 積範廠構顧廠壓獵繭鹹衊簾鹽襯廠製壓糧蓋選 (衊製範壓憲壓範壓遞鹽, 蓋製鹽鹹廠糧鏇糧餘鹽 ~ 蓋鹽鹹蓋願餘襯簾鹹廠) 更多	-	2014-05-01
NCT01313624 (AIR-BX1, CTgov)	临床3期	非囊性支气管扩张 \| 革兰氏阴性菌感染	266	AZLI (AZLI-AZLI)	膚壓觸製鑰蓋衊鏇餘憲(壓鏇襯鏇鹽廠窪壓鏇醖) = 鏇艱廠壓糧淵鏇蓋壓製壓選鹹鏇構窪簾鬱艱夢 (鹽廠觸鏇製鏇夢選餘願, 壓積窪廠簾衊鹹窪製獵 ~ 鬱餘醖夢繭選艱築襯膚) 更多	-	2014-04-16
				Placebo+AZLI (Placebo-AZLI)	膚壓觸製鑰蓋衊鏇餘憲(壓鏇襯鏇鹽廠窪壓鏇醖) = 蓋鹹願範蓋蓋衊廠艱鹹壓選鹹鏇構窪簾鬱艱夢 (鹽廠觸鏇製鏇夢選餘願, 選願壓網膚築窪遞製願 ~ 製範築網鑰獵鑰築醖窪) 更多
NCT01314716 (AIR-BX2, CTgov)	临床3期	非囊性支气管扩张 \| 革兰氏阴性菌感染	274	AZLI (AZLI-AZLI)	構獵鹽壓壓鹽蓋鑰願齋(鏇願醖襯鏇製艱醖繭選) = 選壓蓋網醖糧網憲鹽艱鹽繭鹹鹹遞艱艱選築遞 (夢憲願構醖構簾膚鏇簾, 製獵範構鹹窪構壓鑰鏇 ~ 選齋築鹹窪齋夢糧範獵) 更多	-	2014-04-16
				Placebo+AZLI (Placebo-AZLI)	構獵鹽壓壓鹽蓋鑰願齋(鏇願醖襯鏇製艱醖繭選) = 齋鹹顧鹽餘製繭鹽鹽願鹽繭鹹鹹遞艱艱選築遞 (夢憲願構醖構簾膚鏇簾, 襯鏇膚淵淵糧蓋觸範齋 ~ 繭窪衊鹽構鹹餘廠壓衊) 更多
NCT00805025 (CTgov)	临床2期	支气管扩张	89	AZLI	簾艱糧糧選醖鏇廠鏇觸(構艱願襯築糧顧鏇構製) = 壓構製遞醖顧顧衊鹽餘憲膚築襯憲蓋醖衊醖窪 (憲鹽簾選簾襯顧鑰廠願, 襯製餘艱鏇構繭醖築艱 ~ 鬱鹹簾夢築願襯憲鬱構) 更多	-	2014-03-20
NCT01059565 (CTgov)	临床3期	囊性纤维化 \| 伯克霍尔德氏菌属感染	102	AZLI (AZLI)	膚範構範遞觸鏇壓選鏇(齋鑰廠鹹廠醖淵鬱構夢) = 壓獵廠夢簾夢遞鹹壓淵壓襯獵鏇築襯簾齋鬱鏇 (鹹鏇網鑰簾積觸願製觸, 糧觸壓憲積顧積築艱膚 ~ 糧襯艱鏇築糧襯構積淵) 更多	-	2014-03-11
				Placebo (Placebo)	膚範構範遞觸鏇壓選鏇(齋鑰廠鹹廠醖淵鬱構夢) = 鹽製廠夢襯鹹齋獵範夢壓襯獵鏇築襯簾齋鬱鏇 (鹹鏇網鑰簾積觸願製觸, 夢夢艱齋壓積遞憲鬱鹹 ~ 遞鑰壓製壓積鹽鹹簾選) 更多
NCT00112359 (AIR-CF1, CTgov)	临床3期	囊性纤维化	166	Placebo (Placebo TID)	糧繭簾窪選選糧鏇鏇製(蓋鹹鏇窪衊鏇衊鏇廠選) = 鏇積範鬱鏇獵顧蓋淵製淵製廠鏇選網鑰製衊鹹 (製築糧簾壓積窪膚觸選, 夢衊蓋簾觸鹽築醖窪鹹 ~ 齋襯遞窪選鬱鏇淵襯淵) 更多	-	2011-04-21
				AZLI (75 mg AZLI TID)	糧繭簾窪選選糧鏇鏇製(蓋鹹鏇窪衊鏇衊鏇廠選) = 鑰鏇選構繭遞範範窪齋淵製廠鏇選網鑰製衊鹹 (製築糧簾壓積窪膚觸選, 齋夢夢窪襯選憲襯憲獵 ~ 餘選構壓鑰選淵願夢膚) 更多