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Investments in drug development for Alzheimer's disease (AD) have not led to the availability of a treatment to cure or halt the progression of the disease. This study aimed to provide insights into the current lack of an effective therapy against AD by exploring the evolution of research paths in the scientific domains corresponding to fundamental, preclinical and clinical research from the identification of the disease in 1906 up to 2016. More specifically, the influence of the amyloid cascade hypothesis and use of animal models in the evolution of drug development for AD were explored. We used bibliometric analysis for the identification of research paths taken over time, including main path analysis, direct citation analysis and co-word analysis. The results show that the amyloid cascade hypothesis has played an important role in the evolution of research paths in the drug development process of AD. The preclinical domain and to a lesser extent the clinical domain, were found to be increasingly involved in the study of interventions modulating amyloid-beta related neurotoxicity over time in line with the fundamental domain predominantly focussing on amyloid-beta as the primary cause of AD. The results open up a discussion about lock-in, i.e. that decreasing options in the fundamental domain results in less room for manoeuvre in the preclinical and clinical domain.

Simple Summary The value of animal research has been increasingly debated, owing to high failure rates in drug development. A potential contributor to this situation is the lack of predictivity of animal models of disease. Until recently, most initiatives focused on well-known problems related to the lack of reproducibility and poor reporting standards of animal studies. Only now, more methodologies are becoming available to evaluate how translatable the data from animal models of disease are. We discuss the use and implications of several methods and tools to assess the generalisability of animal data to humans. To analyse the relevance of animal research objectively, we must (1) guarantee the experiments are conducted and reported according to best practices; (2) ensure the selection of animal models is made with a clear and detailed translational rationale behind it. Once these conditions are met, the true value of the use of animals in drug development can be finally attested. Abstract Reports of a reproducibility crisis combined with a high attrition rate in the pharmaceutical industry have put animal research increasingly under scrutiny in the past decade. Many researchers and the general public now question whether there is still a justification for conducting animal studies. While criticism of the current modus operandi in preclinical research is certainly warranted, the data on which these discussions are based are often unreliable. Several initiatives to address the internal validity and reporting quality of animal studies (e.g., Animals in Research: Reporting In Vivo Experiments (ARRIVE) and Planning Research and Experimental Procedures on Animals: Recommendations for Excellence (PREPARE) guidelines) have been introduced but seldom implemented. As for external validity, progress has been virtually absent. Nonetheless, the selection of optimal animal models of disease may prevent the conducting of clinical trials, based on unreliable preclinical data. Here, we discuss three contributions to tackle the evaluation of the predictive value of animal models of disease themselves. First, we developed the Framework to Identify Models of Disease (FIMD), the first step to standardise the assessment, validation and comparison of disease models. FIMD allows the identification of which aspects of the human disease are replicated in the animals, facilitating the selection of disease models more likely to predict human response. Second, we show an example of how systematic reviews and meta-analyses can provide another strategy to discriminate between disease models quantitatively. Third, we explore whether external validity is a factor in animal model selection in the Investigator’s Brochure (IB), and we use the IB-derisk tool to integrate preclinical pharmacokinetic and pharmacodynamic data in early clinical development. Through these contributions, we show how we can address external validity to evaluate the translatability and scientific value of animal models in drug development. However, while these methods have potential, it is the extent of their adoption by the scientific community that will define their impact. By promoting and adopting high quality study design and reporting, as well as a thorough assessment of the translatability of drug efficacy of animal models of disease, we will have robust data to challenge and improve the current animal research paradigm.

National and international laws and regulations exist to protect animals used for scientific purposes in translational and applied research, which includes drug development. However, multiple animal models are available for each disease. We evaluated the argumentation behind the selection of a specific animal model using thematic content analysis in project applications issued in 2017-2019 in the Netherlands. In total, 125 animal models for translational and applied research from 110 project applications were assessed. Explanations to select a specific model included: the model’s availability (79%); the availability of expertise (62%); and the model showing similar disease pathology/symptoms (59%) to humans. Therefore, current selection of a specific animal model seems to be based on tradition rather than its potential predictive value for clinical outcome. The applicants’ explanations for the implementation of the 3R prin­ciples (replacement, reduction and refinement) as to the animal model were unspecific. Replacement was achieved by using data from prior in vitro studies, reduction by optimal experimental design and statistics, and refinement by reducing discomfort. Additionally, due to the stated need for a test model with high complexity (47%) and intactness (30%), the full replacement of animal models with alternative (non-live animal) approaches was thought unachievable. Without a clear, systematic and transparent justification for the selection of a specific animal model, the likelihood of poorly trans­latable research remains. It is not only up to the researcher to demonstrate this, as ethical committees and funding bodies can provide positive stimuli to drive this change.

Poor translation of efficacy data derived from animal models can lead to clinical trials unlikely to benefit patients-or even put them at risk-and is a potential contributor to costly and unnecessary attrition in drug development.To develop a tool to assess, validate and compare the clinical translatability of animal models used for the preliminary assessment of efficacy.We performed a scoping review to identify the key aspects used to validate animal models. Eight domains (Epidemiology, Symptomatology and Natural History-SNH, Genetic, Biochemistry, Aetiology, Histology, Pharmacology and Endpoints) were identified. We drafted questions to evaluate the different facets of human disease simulation. We designed the Framework to Identify Models of Disease (FIMD) to include standardised instructions, a weighting and scoring system to compare models as well as factors to help interpret model similarity and evidence uncertainty. We also added a reporting quality and risk of bias assessment of drug intervention studies in the Pharmacological Validation domain. A web-based survey was conducted with experts from different stakeholders to gather input on the framework. We conducted a pilot study of the validation in two models for Type 2 Diabetes (T2D)-the ZDF rat and db/db mouse. Finally, we present a full validation and comparison of two animal models for Duchenne Muscular Dystrophy (DMD): the mdx mouse and GRMD dog. We show that there are significant differences between the mdx mouse and the GRMD dog, the latter mimicking the human epidemiological, SNH, and histological aspects to a greater extent than the mouse despite the overall lack of published data.FIMD facilitates drug development by serving as the basis to select the most relevant model that can provide meaningful data and is more likely to generate translatable results to progress drug candidates to the clinic.

[This corrects the article DOI: 10.1371/journal.pone.0218014.].