Publisher Correction: Optimization of large animal MI models; a systematic analysis of control groups from preclinical studies.

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Optimization of large animal MI models; a systematic analysis of control groups from preclinical studies.

Large animal models are essential for the development of novel therapeutics for myocardial infarction. To optimize translation, we need to assess the effect of experimental design on disease outcome and model experimental design to resemble the clinical course of MI. The aim of this study is therefore to systematically investigate how experimental decisions affect outcome measurements in large animal MI models. We used control animal-data from two independent meta-analyses of large animal MI models. All variables of interest were pre-defined. We performed univariable and multivariable meta-regression to analyze whether these variables influenced infarct size and ejection fraction. Our analyses incorporated 246 relevant studies. Multivariable meta-regression revealed that infarct size and cardiac function were influenced independently by choice of species, sex, co-medication, occlusion type, occluded vessel, quantification method, ischemia duration and follow-up duration. We provide strong systematic evidence that commonly used endpoints significantly depend on study design and biological variation. This makes direct comparison of different study-results difficult and calls for standardized models. Researchers should take this into account when designing large animal studies to most closely mimic the clinical course of MI and enable translational success.

From a mouse: systematic analysis reveals limitations of experiments testing interventions in Alzheimer's disease mouse models.

The increasing prevalence of Alzheimer's disease (AD) poses a considerable socio-economic challenge. Decades of experimental research have not led to the development of effective disease modifying interventions. A deeper understanding of in vivo research might provide insights to inform future in vivo research and clinical trial design. We therefore performed a systematic review and meta-analysis of interventions tested in transgenic mouse models of AD. We searched electronically for publications testing interventions in transgenic models of AD. We extracted data for outcome, study characteristics and reported study quality and calculated summary estimates of efficacy using random effects meta-analysis. We identified 427 publications describing 357 interventions in 55 transgenic models, involving 11,118 animals in 838 experiments. Of concern, reported study quality was relatively low; fewer than one in four publications reported the blinded assessment of outcome or random allocation to group and no study reported a sample size calculation. Additionally, there were few data for any individual intervention-only 16 interventions had outcomes described in 5 or more publications. Finally, "trim and fill" analyses suggested one in seven pathological and neurobehavioural experiments remain unpublished. Given these historical weaknesses in the in vivo modelling of AD in transgenic animals and the identified risks of bias, clinical trials that are based on claims of efficacy in animals should only proceed after a detailed critical appraisal of those animal data.

The development of an online database for interventions tested in transgenic mouse models of Alzheimer's disease.

Despite many efforts by the research community, Alzheimer's disease (AD) is still an incurable neurodegenerative condition that affects an estimated 44 million individuals worldwide and this figure is expected to increase to 135 million by the year 2050. As the research community currently reflects on previous endeavours, it is essential that we maximize the use of existing knowledge to inform future trials in the field. This article describes the development of a systematically identified data set relating to over 300 interventions tested in over 10,000 animals. The data set includes cohort-level information for six structural outcomes and six behavioural assessments. We encourage others to use this dataset to inform the design of future animal experiments modelling AD and to promote effective translation to human health.

Meta-analysis of data from animal studies: a practical guide.

Meta-analyses of data from human studies are invaluable resources in the life sciences and the methods to conduct these are well documented. Similarly there are a number of benefits in conducting meta-analyses on data from animal studies; they can be used to inform clinical trial design, or to try and explain discrepancies between preclinical and clinical trial results. However there are inherit differences between animal and human studies and so applying the same techniques for the meta-analysis of preclinical data is not straightforward. For example preclinical studies are frequently small and there is often substantial heterogeneity between studies. This may have an impact on both the method of calculating an effect size and the method of pooling data. Here we describe a practical guide for the meta-analysis of data from animal studies including methods used to explore sources of heterogeneity.

A systematic review and meta-analysis of gene therapy in animal models of cerebral glioma: why did promise not translate to human therapy?

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.

Systematic review and meta-analysis of temozolomide in animal models of glioma: was clinical efficacy predicted?

BACKGROUND: Malignant glioma is an aggressive tumour commonly associated with a dismal outcome despite optimal surgical and radio-chemotherapy. Since 2005 temozolomide has been established as first-line chemotherapy. We investigate the role of in vivo glioma models in predicting clinical efficacy. METHODS: We searched three online databases to systematically identify publications testing temozolomide in animal models of glioma. Median survival and number of animals treated were extracted and quality was assessed using a 12-point scale; random effects meta-analysis was used to estimate efficacy. We analysed the impact of study design and quality and looked for evidence of publication bias. RESULTS: We identified 60 publications using temozolomide in models of glioma, comprising 2443 animals. Temozolomide prolonged survival by a factor of 1.88 (95% CI 1.74-2.03) and reduced tumour volume by 50.4% (41.8-58.9) compared with untreated controls. Study design characteristics accounted for a significant proportion of between-study heterogeneity, and there was evidence of a significant publication bias. CONCLUSION: These data reflect those from clinical trials in that temozolomide improves survival and reduces tumour volume, even after accounting for publication bias. Experimental in vivo glioma studies of temozolomide differ from those of other glioma therapies in their consistent efficacy and successful translation into clinical medicine.