6 research outputs found
Improving our understanding of the in vivo modelling of psychotic disorders: a systematic review and meta-analysis
Psychotic disorders represent a severe category of mental disorders affecting about one
percent of the population. Individuals experience a loss or distortion of contact with reality
alongside other symptoms, many of which are still not adequately managed using existing
treatments. While animal models of these disorders could offer insights into these disorders
and potential new treatments, translation of this knowledge has so far been poor in terms of
informing clinical trials and practice. The aim of this project was to improve our
understanding of these pre-clinical studies and identify potential weaknesses underlying
translational failure.
I carried out a systematic search of the literature to provide an unbiased summary of
publications reporting animal models of schizophrenia and other psychotic disorders. From
these publications, data were extracted to quantify aspects of the field including reported
quality of studies, study characteristics and behavioural outcome data. The latter of these
data were then used to calculate estimates of efficacy using random-effects meta-analysis.
Having identified 3847 publications of relevance, including 852 different methods used to
induce the model, over 359 different outcomes tested in them and almost 946 different
treatments reported to be administered. I show that a large proportion of studies use simple
pharmacological interventions to induce their models of these disorders, despite the
availability of models using other interventions that are arguably of higher translational
relevance. I also show that the reported quality of these studies is low, and only 22% of
studies report taking measures to reduce the risk of biases such as randomisation and
blinding, which has been shown to affect the reliability of results drawn.
Through this work it becomes apparent that the literature is incredibly vast for studies looking
at animal models of psychotic disorders and that some of the relevant work potentially
overlaps with studies describing other conditions. This means that drawing reliable
conclusions from these data is affected by what is made available in the literature, how it is
reported and identified in a search and the time that it takes to reach these conclusions. I
introduce the idea of using computer-assisted tools to overcome one of these problems in
the long term.
Translation of results from studies looking at animals modelling uniquely-human psychotic
disorders to clinical successes might be improved by better reporting of studies including
publishing of all work carried out, labelling of studies more uniformly so that it is identifiable,
better reporting of study design including improving on reporting of measures taken to
reduce the risk of bias and focusing on models with greater validity to the human condition
Data from: Risk of bias in reports of in vivo research: a focus for improvement
The reliability of experimental findings depends on the rigour of experimental design. Here we show limited reporting of measures to reduce the risk of bias in a random sample of life sciences publications, significantly lower reporting of randomisation in work published in journals of high impact, and very limited reporting of measures to reduce the risk of bias in publications from leading United Kingdom institutions. Ascertainment of differences between institutions might serve both as a measure of research quality and as a tool for institutional efforts to improve research quality
PLoS biology Figure 3 data
Reporting of randomisation, blinded assessment of outcome, sample size calculations and conflict of interest reporting and journal impact factor for 2671 publications describing the efficacy of interventions in animal models of eight different diseases identified in the context of systematic reviews
PLoS biology Figure 5 data
Reporting of randomisation, blinded assessment of outcome, sample size calculation and of inclusion or exclusion criteria in 1173 publications describing in vivo research published from 5 leading UK institutions (labelled A through E)
PLoS biology Figure 1 data
Prevalence of reporting of randomisation, blinded assessment of outcome, sample size calculation and conflict of interest in 146 publications describing in vivo research identified through random sampling from PubMed