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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.

IntroductionPoor translation of efficacy data derived from animal models is a potential contributor to costly and unnecessary attrition in clinical trials.ObjectivesTo develop a tool to assess, validate and compare the clinical translatability of animal models used for the preliminary assessment of efficacy.Design and ResultsWe conducted an exploratory literature search to identify the key aspects to validate animal models. Eight aspects (Epidemiology, Pathophysiology, Genetic, Biochemistry, Aetiology, Histology, Pharmacology and Endpoints) were identified for which questions were drafted to evaluate the different faces of the human disease simulation. Features of the framework include standardised instructions, a weighting and scoring system to compare models as well as contextualising factors regarding model similarity and evidence uncertainty. We included a quality assessment of the internal validity of drug intervention studies included in the Pharmacological validation section for both effective and ineffective drugs in humans. A web-based survey was conducted with experts from different stakeholders to gather input on the framework. Finally, we present a case study of a preliminary validation and comparison of two animal models for Duchenne Muscular Dystrophy (mdx mouse and GRMD dog) and Diabetes Type 2 (ZDF rat and db/db mouse). We show that there are significant differences between the mdx mouse and the GRMD dog, the latter mimicking the human condition to a greater extent than the mouse despite the considerable lack of published data. In DT2, both the ZDF rat and the db/db mouse are comparable with minor differences in pathophysiology.ConclusionsFIMD facilitates drug development by serving as the basis to select the most relevant model that can provide meaningful and translatable results to progress drug candidates to the clinic.

OBJECTIVE: To investigate whether early nonresponse to antipsychotic treatment of acute mania predicts treatment failure and, if so, to establish the best definition or criterion of an early nonresponse. DATA SOURCES: Short-term efficacy studies assessing antipsychotics that were submitted to the Dutch Medicines Evaluation Board during an 11-year period as part of the marketing authorization application for the indication of acute manic episode of bipolar disorder. Pharmaceutical companies provided their raw patient data, which enabled us to perform an individual patient data meta-analysis. STUDY SELECTION: All double-blind, randomized, placebo-controlled trials assessing the efficacy of antipsychotics for acute manic episode of bipolar disorder were included (10 trials). DATA EXTRACTION: All patients with data available for completer analysis (N = 1,243), symptom severity scores on the Young Mania Rating Scale (YMRS) at weeks 0, 1, and 2 and at study end point (week 3 or 4). RESULTS: The a priori chances of nonresponse and nonremission at study end point were 40.9% (95% CI, 38.2%-43.6%) and 65.3% (95% CI, 62.0%-68.6%), respectively. Early nonresponse in weeks 1 and 2, defined by cutoff scores ranging from a ≤ 10% to a ≤ 50% reduction in symptoms compared to baseline on the YMRS, significantly predicted nonresponse (≤ 0% symptom reduction) and nonremission (YMRS score higher than 8) in week 3. The predictive value of early nonresponse (PVnr_se) at week 1 for both nonresponse and nonremission at study end point declined linearly with increasing cutoff scores of early nonresponse; nonresponse: 76.0% (95% CI, 69.7%-82.3%) for a ≤ 10% response to 48.7% (95% CI, 45.5%-51.9%) for a ≤ 50% response; nonremission: 92.2% (95% CI, 88.3%-96.1%) for a ≤ 10% response to 76.8% (95% CI, 74.4%-79.5%) for a ≤ 50% response. A similar linear decline was observed for increasing cutoff scores of early nonresponse at week 2 for nonresponse, but not for nonremission at end point: nonresponse 90.3% (95% CI, 84.6%-96.0%) for a ≤ 10% response to 65.0% (95% CI, 61.4%-68.6%) for a ≤ 50% response; nonremission: 94.2% (95% CI, 89.7%-98.7%) for a ≤ 10% response and 93.2% (95% CI, 93.1%-95.1%) for a ≤ 50% response. Specific antipsychotic characteristics did not modify these findings at either time point (week 1: P = .127; week 2: P = .213). CONCLUSIONS: When patients fail to respond early (1-2 weeks) after the initiation of antipsychotic treatment for acute mania, clinicians should reconsider their treatment choice using a 2-stage strategy.