Evidence-based preclinical medicine最新文献

英文中文

The relationship between risk of bias criteria, research outcomes, and study sponsorship in a cohort of preclinical thiazolidinedione animal studies: a meta-analysis 临床前噻唑烷二酮动物研究队列中偏倚风险标准、研究结果和研究赞助之间的关系:一项荟萃分析

Evidence-based preclinical medicine

Pub Date : 2015-01-20 DOI: 10.1002/ebm2.5

M. Abdel-Sattar, D. Krauth, A. Anglemyer, L. Bero

Introduction

There is little evidence regarding the influence of conflicts of interest on preclinical research. This study examines whether industry sponsorship is associated with increased risks of bias and/or effect sizes of outcomes in published preclinical thiazolidinedione (TZD) studies.

Methods

We identified preclinical TZD studies published between January 1, 1965, and November 14, 2012. Coders independently extracted information on study design criteria aimed at reducing bias, results for all relevant outcomes, sponsorship source and investigator financial ties from the 112 studies meeting the inclusion criteria. The average standardized mean difference (SMD) across studies was calculated for plasma glucose (efficacy outcome) and weight gain (harm outcome). In subgroup analyses, TZD outcomes were assessed by sponsorship source and risk of bias criteria.

Results

Seven studies were funded by industry alone, 17 studies funded by both industry and non-industry, 49 studies funded by non-industry alone and 39 studies had no disclosures. None of the studies used sample size calculations, intention-to-treat analyses, blinding of investigators or concealment of allocation. Most studies reported favourable results (88 of 112) and conclusions (95 of 112) supporting TZD use. Efficacy estimates were significantly larger in six studies sponsored by industry alone (−3.41; 95% CI −5.21, −1.53; I² = 93%) versus 42 studies sponsored by non-industry sources (−0.97; 95% CI −1.37, −0.56; I² = 81%; p-value = 0.01). Harms estimates were significantly larger in four studies sponsored by industry alone (5.00; 95% CI 1.22, 8.77; I² = 93%) versus 38 studies sponsored by non-industry sources (0.30; 95% CI −0.08, 0.68; I² = 79%; p-value = 0.02). TZD efficacy and harms did not differ by disclosure of financial COIs or risks of bias.

Conclusions

Industry-sponsored TZD animal studies have exaggerated efficacy and harms outcomes compared with studies funded by non-industry sources. There was poor reporting of COIs.

很少有证据表明利益冲突对临床前研究的影响。本研究探讨了在已发表的临床前噻唑烷二酮(TZD)研究中，行业赞助是否与偏倚风险增加和/或结果效应大小有关。方法选取1965年1月1日至2012年11月14日期间发表的临床前TZD研究。编码器独立地从符合纳入标准的112项研究中提取有关旨在减少偏倚的研究设计标准、所有相关结果的结果、赞助来源和研究者经济关系的信息。计算各研究的平均标准化平均差(SMD)对血糖(疗效结果)和体重增加(危害结果)的影响。在亚组分析中，通过赞助来源和偏倚风险标准评估TZD结果。结果7项研究由工业界单独资助，17项研究由工业界和非工业界共同资助，49项研究由非工业界单独资助，39项研究没有披露。没有一项研究使用了样本量计算、意向治疗分析、调查人员的盲法或隐瞒分配。大多数研究报告了有利的结果(112项中的88项)和支持使用TZD的结论(112项中的95项)。在由工业界单独赞助的6项研究中，疗效估计明显更大(- 3.41;95% ci为−5.21，−1.53;I2 = 93%)，而非工业来源赞助的研究为42项(- 0.97;95% ci为−1.37，−0.56;I2 = 81%;p值= 0.01)。在仅由工业界赞助的四项研究中，危害估计要大得多(5.00;95% ci 1.22, 8.77;I2 = 93%)，而非工业来源赞助的研究有38项(0.30;95% ci−0.08,0.68;I2 = 79%;p值= 0.02)。TZD的疗效和危害没有因财务coi或偏倚风险的披露而不同。与非工业资助的研究相比，工业资助的TZD动物研究夸大了疗效和危害结果。coi报告不完善。

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引用次数: 14

A systematic review and meta-analysis of gene therapy in animal models of cerebral glioma: why did promise not translate to human therapy? 脑胶质瘤动物模型基因治疗的系统综述和荟萃分析：为什么promise没有转化为人类治疗？

Evidence-based preclinical medicine

Pub Date : 2015-01-20 DOI: 10.1002/ebm2.6

T. C. Hirst, H. M. Vesterinen, S. Conlin, K. J. Egan, A. Antonic, A. Lawson McLean, M. R. Macleod, R. Grant, P. M. Brennan, E. S. Sena, I. R. Whittle

Background

The development of therapeutics is often characterized by promising animal research that fails to translate into clinical efficacy; this holds for the development of gene therapy in glioma. We tested the hypothesis that this is because of limitations in the internal and external validity of studies reporting the use of gene therapy in experimental glioma.

Method

We systematically identified studies testing gene therapy in rodent glioma models by searching three online databases. The number of animals treated and median survival were extracted and studies graded using a quality checklist. We calculated median survival ratios and used random effects meta-analysis to estimate efficacy. We explored effects of study design and quality and searched for evidence of publication bias.

Results

We identified 193 publications using gene therapy in experimental glioma, including 6,366 animals. Overall, gene therapy improved median survival by a factor of 1.60 (95% CI 1.53–1.67). Study quality was low and the type of gene therapy did not account for differences in outcome. Study design characteristics accounted for a significant proportion of between-study heterogeneity. We observed similar findings in a data subset limited to the most common gene therapy.

Conclusion

As the dysregulation of key molecular pathways is characteristic of gliomas, gene therapy remains a promising treatment for glioma. Nevertheless, we have identified areas for improvement in conduct and reporting of studies, and we provide a basis for sample size calculations. Further work should focus on genes of interest in paradigms recapitulating human disease. This might improve the translation of such therapies into the clinic.

背景治疗学的发展往往以有前景的动物研究为特征，但未能转化为临床疗效；这为神经胶质瘤基因治疗的发展奠定了基础。我们检验了这一假设，即这是因为报告在实验性神经胶质瘤中使用基因治疗的研究的内部和外部有效性存在局限性。方法通过检索三个在线数据库，系统地确定了在啮齿类神经胶质瘤模型中测试基因治疗的研究。提取接受治疗的动物数量和中位生存率，并使用质量检查表对研究进行评分。我们计算了中位生存率，并使用随机效应荟萃分析来评估疗效。我们探讨了研究设计和质量的影响，并寻找了发表偏倚的证据。结果我们鉴定了193篇在实验性胶质瘤中使用基因治疗的出版物，包括6366只动物。总的来说，基因治疗将中位生存率提高了1.60倍（95%CI 1.53-1.67）。研究质量较低，基因治疗的类型没有解释结果的差异。研究设计特征在研究间异质性中占很大比例。我们在一个仅限于最常见基因治疗的数据子集中观察到了类似的发现。结论由于关键分子通路的失调是胶质瘤的特征，基因治疗仍是胶质瘤的一种有前景的治疗方法。尽管如此，我们已经确定了研究实施和报告方面需要改进的领域，并为样本量计算提供了基础。进一步的工作应该集中在概括人类疾病的范式中感兴趣的基因上。这可能会改善这种疗法在临床上的应用。

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引用次数: 6

Anti-inflammatory compounds to reduce infarct size in large-animal models of myocardial infarction: A meta-analysis 抗炎化合物减少心肌梗死大动物模型梗死面积的荟萃分析

Evidence-based preclinical medicine

Pub Date : 2015-01-20 DOI: 10.1002/ebm2.4

G.P.J. van Hout, S.J. Jansen of Lorkeers, K.E. Wever, E.S. Sena, W.W. van Solinge, P.A. Doevendans, G. Pasterkamp, S.A.J. Chamuleau, I.E. Hoefer

Targeting the inflammatory response after myocardial infarction (MI) could potentially prevent infarct expansion, resulting in a preservation of cardiac function. Despite extensive testing in large-animal models of MI, anti-inflammatory therapeutics are not incorporated in daily clinical practice. Methodological review of the literature describing the effects of anti-inflammatory compounds in large-animal models of MI may provide useful insights into the reasons for the translational failure from preclinical to clinical studies. Moreover, systematic review of these preclinical studies may allow us to determine which anti-inflammatory agents have the greatest potential to successfully treat MI in the clinic and guide which preclinical setting appears most appropriate to test these future treatment strategies in. The current systematic review protocol provides a detailed description of the design of this systematic review of studies investigating the effects of anti-inflammatory compounds in large-animal models of MI.

靶向心肌梗死（MI）后的炎症反应可以潜在地防止梗死扩大，从而保护心脏功能。尽管在MI的大型动物模型中进行了广泛的测试，但抗炎疗法并未纳入日常临床实践。对描述抗炎化合物在MI大型动物模型中的作用的文献进行方法学综述，可能会为从临床前研究到临床研究的转化失败的原因提供有用的见解。此外，对这些临床前研究的系统回顾可能使我们能够确定哪些抗炎药最有可能在临床上成功治疗MI，并指导哪种临床前环境最适合测试这些未来的治疗策略。目前的系统综述方案对研究抗炎化合物在MI大型动物模型中的作用的系统综述的设计进行了详细描述。

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引用次数: 2

Raising standards for preclinical research 提高临床前研究标准

Evidence-based preclinical medicine

Pub Date : 2014-04-30 DOI: 10.1111/ebm2.3

Systematic review and meta-analysis are powerful analytical tools. The Cochrane Collaboration, formed in 1993, provides an excellent example of the use of these tools to gather the best evidence regarding the efficacy of interventions in clinical medicine. The use of these tools however is not widespread in preclinical science. Thus Evidence-Based Preclinical Medicine (EBPM) is a new online peer-reviewed open access journal designed to provide a vehicle which fosters the systematic capture and rigorous analysis of all available basic science data on questions relevant to human health. By doing so we aim to raise the standards of preclinical research and improve the efficiency with which preclinical data is translated into improvements in human health.

The analysis of industrial, agricultural or environmental toxicology, processes of drug discovery and evaluation, disease risk factor modelling and pre- and post-disease behavioural modification as well as early discovery science are all areas where systematic capture of all available data will accelerate our ability to improve human health. The application of rigorous analytical techniques which can give a realistic appreciation of the quality, breadth and potential importance of the available evidence will help researchers decide which hypotheses should be explored further, identify the presence and likely impact of confounding biases and will help health professionals decide which will have an impact on people.

Most scientists would like to believe that the systems required for these aims are already in place. However, the explosion in the volume of available data makes reliance on traditional systems untenable.

The problems start with the way we portray science and the aspirations this engenders. In the mass media, text books and popular histories of science and medicine the process of discovery is commonly portrayed a series of Eureka moments. Giant leaps forward made by the greatest minds of an era. But this is not the process. Around the world teams of scientists nibble away at a problem, new ideas are circulated and considered and experiments designed and performed. Many ideas and experiments are dead ends and lead nowhere. But since we learn by our mistakes, knowing how things don't happen refines our knowledge base and nudges us ever closer to the truth by allowing more scientists to focus on the threads that do reveal the true pattern of life.

Two of the most famous quotes in science speak directly to these issues. Louis Pasteur's “Chance favours only the prepared mind” makes it clear that you have to understand a field if you are to contribute to it. Isaac Newton's “If I have seen further it is by standing on the shoulders of giants” is perhaps more important because it also acknowledges that science is an incremental process. Only a fortunate few are in the right place at the right time and with the right education and knowledge base to finally understand a la

不完整的知识不仅会导致财务风险，还会导致志愿者和患者接触到新的但鲜为人知的化学物质时受伤或死亡的真实风险。如果只有一种新的候选药物的阳性实验被公布，而中性或阴性结果仍然被隐藏，那么该领域将相信这些药物有效，而事实上它们并不有效。临床试验的进展将是浪费，使患者面临无法预见的副作用的风险，并使找到有效药物的可能性降低，因为现在人力和财力资源不足。个人或一小群作家的传统叙事评论并不能很好地帮助科学家处理大量可用数据并了解这些风险。无论评论者多么诚实、博览群书、用心良苦，读者都不知道遗漏了什么或遗漏的原因。传统的叙事评论者和其他评论者一样受制于该领域的时尚，对出版的影响和数据集中的其他偏见视而不见。此外，作为一个物种，我们受到新奇事物的刺激，因此很少有叙事评论将专栏空间用于不起作用的内容。然而，如果我们要防止不断增加的浪费性、不知情的错误开始，这些信息至关重要。系统综述为大量数据的整理和解释提供了一种科学的方法。只需详细说明所使用的搜索策略并定义包含和排除标准，读者就可以自行判断作者是否采取了严格的方法来寻找相关数据，并提供了科学的关键元素，一种定义的方法，允许其他人确认和扩展结果。数据的电子传播意味着系统审查的结果现在可以而且将越来越多地超越隐喻性的连接点。荟萃分析允许对系统综述中的数据进行汇总和重新分析，并允许研究人员发现在单个已发表的数据集或这些数据集的叙述性综述中很少明显的新趋势。在疾病研究中，一个动物模型的结果是否表明参与了一种可以靶向的特定机制？毒物暴露与健康不良之间是否存在明确的剂量反应关系？如果在移植实验中，所用动物模型的选择比干细胞生物学的变化对结果的影响更大，我们下一步该怎么办？一项研究是否经常在动物身上复制，以至于结果毫无疑问，不需要进一步复制，还是还需要更多的数据？对实验室实验的同质性的错误信任和对所需额外成本的真实理解导致许多研究人员进行的实验太小，并且没有受到随机性和盲目性的保护，无法对抗人性的反常因素和不可见的实验变量。没有一项单独的研究或证据是完美的，总的来说，我们认为我们理解科学过程中可能出现的问题。随着数据的增长，诚实的误解是假设检验迭代过程中固有的。然而，我们在追求新颖性的过程中确实引入了一系列偏见。我们倾向于只进行最有可能得到积极结果的实验。在临床前医学中，这意味着对那些可能会降低假说“可销售性”的关键实验视而不见，但如果一种新药要在临床上发现的现实世界中生存下来，这些实验是至关重要的。我们也很少询问出版研究人员是否做出了合理的努力，如随机化和盲法，以避免引入系统性偏见。如果不是所有人都报告这样做，那么对数据进行分层可以揭示这种偏见的程度，并可能阻碍或支持进一步的努力。功率不足的小实验也更容易进行，而且由于偶然性的发挥以及对假设检验统计数据的理解和应用不力，可能会返回高比例的假阳性结果。尽管证实其他研究结果的复制研究仍然被整个研究界视为衍生物和非原创研究，并被一些伦理审查委员会和资助机构积极劝阻甚至禁止，但假阳性仍将继续未被发现。系统综述和荟萃分析可用于汇总个体研究数据，以确定整体数据集是否支持存在真正的效果。评审员、编辑、拨款和晋升小组的这些小决定和类似决定都有能力扭曲科学的报道和行为。由于实验结果对真实结果有统计定义的分布，当有足够的数据可用时，对系统收集的数据进行荟萃分析可以检测和量化任何发表偏倚的影响。虽然这些数据在生物学上并不有趣，但对研究人员来说很重要。一个几乎完整的数据分布可能表明一个分子，例如一种新的候选药物，正如广告中所说的那样。高度偏斜的数据分布可能表明阳性结果的统计异常发布，而大多数真正中性或阴性的数据仍保留在研究人员的文件柜中。虽然大多数研究人员在理智上理解出版偏见的存在，但他们并不认为这是一个高度优先事项。当EBPM强调它可能对他们研究世界的特定领域产生的影响时，这种情况可能会改变。如果科学咨询委员会及其同类要求在批准投资决策之前查看所有可用数据的分布情况，那么由于数据的发布可能会损害既得利益而对数据的积极压制也可能减少。EBPM是朝着了解我们用于做出重要决策的数据的优势和劣势以及确保我们在做出这些决策时使用最佳可用数据的目标迈出的重要一步。

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