Accelerating drug discovery for Alzheimer's disease: best practices for preclinical animal studies

Animal models have contributed significantly to our understanding of the underlying biological mechanisms of Alzheimer's disease (AD). As a result, over 300 interventions have been investigated and reported to mitigate pathological phenotypes or improve behavior in AD animal models or both. To date, however, very few of these findings have resulted in target validation in humans or successful translation to disease-modifying therapies. Challenges in translating preclinical studies to clinical trials include the inability of animal models to recapitulate the human disease, variations in breeding and colony maintenance, lack of standards in design, conduct and analysis of animal trials, and publication bias due to under-reporting of negative results in the scientific literature. The quality of animal model research on novel therapeutics can be improved by bringing the rigor of human clinical trials to animal studies. Research communities in several disease areas have developed recommendations for the conduct and reporting of preclinical studies in order to increase their validity, reproducibility, and predictive value. To address these issues in the AD community, the Alzheimer's Drug Discovery Foundation partnered with Charles River Discovery Services (Morrisville, NC, USA) and Cerebricon Ltd. (Kuopio, Finland) to convene an expert advisory panel of academic, industry, and government scientists to make recommendations on best practices for animal studies testing investigational AD therapies. The panel produced recommendations regarding the measurement, analysis, and reporting of relevant AD targets, th choice of animal model, quality control measures for breeding and colony maintenance, and preclinical animal study design. Major considerations to incorporate into preclinical study design include a priori hypotheses, pharmacokinetics-pharmacodynamics studies prior to proof-of-concept testing, biomarker measurements, sample size determination, and power analysis. The panel also recommended distinguishing between pilot 'exploratory' animal studies and more extensive 'therapeutic' studies to guide interpretation. Finally, the panel proposed infrastructure and resource development, such as the establishment of a public data repository in which both positive animal studies and negative ones could be reported. By promoting best practices, these recommendations can improve the methodological quality and predictive value of AD animal studies and make the translation to human clinical trials more efficient and reliable

A call for transparent reporting to optimize the predictive value of preclinical research

The US National Institute of Neurological Disorders and Stroke convened major stakeholders in June 2012 to discuss how to improve the methodological reporting of animal studies in grant applications and publications. The main workshop recommendation is that at a minimum studies should report on sample-size estimation, whether and how animals were randomized, whether investigators were blind to the treatment, and the handling of data. We recognize that achieving a meaningful improvement in the quality of reporting will require a concerted effort by investigators, reviewers, funding agencies and journal editors. Requiring better reporting of animal studies will raise awareness of the importance of rigorous study design to accelerate scientific progress

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

Dementia Prevention: optimizing the use of observational data for personal, clinical, and public health decision-making.

Worldwide, over 35 million people suffer from Alzheimer's disease and related dementias. This number is expected to triple over the next 40 years. How can we improve the evidence supporting strategies to reduce the rate of dementia in future generations? The risk of dementia is likely influenced by modifiable factors such as exercise, cognitive activity, and the clinical management of diabetes and hypertension. However, the quality of evidence is limited and it remains unclear whether specific interventions to reduce these modifiable risk factors can, in turn, reduce the risk of dementia. Although randomized controlled trials are the gold-standard for causality, the majority of evidence for long-term dementia prevention derives from, and will likely continue to derive from, observational studies. Although observational research has some unavoidable limitations, its utility for dementia prevention might be improved by, for example, better distinction between confirmatory and exploratory research, higher reporting standards, investment in effectiveness research enabled by increased data-pooling, and standardized exposure and outcome measures. Informed decision-making by the general public on low-risk health choices that could have broad potential benefits could be enabled by internet-based tools and decision-aids to communicate the evidence, its quality, and the estimated magnitude of effect

A call for comparative effectiveness research to learn whether routine clinical care decisions can protect from dementia and cognitive decline

Common diseases like diabetes, hypertension, and atrial fibrillation are probable risk factors for dementia, suggesting that their treatments may influence the risk and rate of cognitive and functional decline. Moreover, specific therapies and medications may affect long-term brain health through mechanisms that are independent of their primary indication. While surgery, benzodiazepines, and anti-cholinergic drugs may accelerate decline or even raise the risk of dementia, other medications act directly on the brain to potentially slow the pathology that underlies Alzheimer's and other dementia. In other words, the functional and cognitive decline in vulnerable patients may be influenced by the choice of treatments for other medical conditions. Despite the importance of these questions, very little research is available. The Alzheimer's Drug Discovery Foundation convened an advisory panel to discuss the existing evidence and to recommend strategies to accelerate the development of comparative effectiveness research on how choices in the clinical care of common chronic diseases may protect from cognitive decline and dementia

Potent, Long-Acting Cyclopentane-1,3-Dione Thromboxane (A2)-Receptor Antagonists

A series of derivatives of the known thromboxane A2 prostanoid (TP) receptor antagonists, 3-(6-((4-chlorophenyl)sulfonamido)-5,6,7,8-tetrahydronaphthalen-1-yl)propanoic acid and 3-(3-(2-((4-chlorophenyl)sulfonamido)ethyl)phenyl) propanoic acid, were synthesized in which the carboxylic acid functional group was replaced with substituted cyclopentane-1,3-dione (CPD) bioisosteres. Characterization of these molecules led to the discovery of remarkably potent new analogues, some of which were considerably more active than the corresponding parent carboxylic acid compounds. Depending on the choice of the C2 substituent of the CPD unit, these new derivatives can produce either a reversible or an apparent irreversible inhibition of the human TP receptor. Given the potency and the long-lasting inhibition of TP receptor signaling, these novel antagonists may comprise promising leads for the development of antithromboxane therapies