Translatability of preclinical to early clinical tolerable and pharmacologically active dose ranges for central nervous system active drugs.

The primary purpose of this study was to assess the translatability of preclinical to early clinical tolerable and pharmacologically active dose ranges for central nervous system (CNS) active drugs. As a part of this, IBs were reviewed on reporting quality. Investigator's Brochures (IBs) of studies performed at the Centre for Human Drug Research (CHDR) reporting statistically significant results of CNS activity related to the drug's mechanism of action were included. The quality of IBs was assessed based on the presence of a rationale for the chosen animal model, completeness of pharmacokinetic (PK) results in reporting and internal validity information of the preclinical evidence. The IB-derisk tool was used to generate preclinical and early clinical data overviews data. For each compound, the overlap between pharmacologically active dose ranges and well-tolerated levels was calculated for three pharmacokinetic (PK) parameters: human equivalent dose (HED), maximum plasma concentration (Cmax) and area under the curve (AUC). Twenty-five IBs were included. In general, the quality of reporting in IBs was assessed as poor. About a third of studies did not explore the entire concentration-effect curve (pre)clinically. Single dose tolerability ranges were most accurately predicted by Cmax. Human equivalent dose and AUC were the best predictors of pharmacologically active ranges. Tolerable and pharmacologically active dose ranges in healthy volunteers can be reasonably well predicted from preclinical data with the IB-derisk tool. The translatability of preclinical studies can be improved by applying a higher reporting standard in IBs including comparable PK measurements across all preclinical and clinical studies.

Tradition, not science, is the basis of animal model selection in translational and applied research.

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.

Levelling the Translational Gap for Animal to Human Efficacy Data.

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.

Comparison of drug efficacy in two animal models of type 2 diabetes: A systematic review and meta-analysis.

Previous qualitative research has suggested there are only minor differences between the db/db mouse and the Zucker Diabetic Fatty (ZDF) rat, both animal models of type 2 diabetes. However, it is not known whether these models are also comparable regarding drug response in quantitative terms (effect size). To investigate the extent of these differences, we conducted a systematic review and meta-analysis of approved drugs in these models. We searched on PubMed and Embase on July 3, 2019 for studies including either model, a monotherapy arm with an EMA/FDA approved drug for the treatment of type 2 diabetes, HbA1c assessment and a control group. Studies aimed at diabetes prevention or with surgical interventions were excluded. We calculated the Standardised Mean Difference (SMD) to compare effect sizes (HbA1c reduction) per drug and drug class across models. We included a risk of bias assessment for all included publications. A total of 121 publications met our inclusion criteria. For drugs with more than two comparisons, both models predicted the direction of the effect regarding HbA1c levels. There were no differences between the db/db mouse and ZDF rat, except for exenatide (P = 0.02) and GLP-1 agonists (P = 0.03) in which a larger effect size was calculated in the ZDF rat. Our results indicate the differences between the db/db mouse and ZDF rat are not relevant for preliminary efficacy testing. This methodology can be used to further differentiate between animal models used for the same indication, facilitating the selection of models more likely to predict human response.

Decreased Histone Acetylation Levels at Th1 and Regulatory Loci after Induction of Food Allergy.

Immunoglobulin E (IgE)-mediated allergy against cow's milk protein fractions such as whey is one of the most common food-related allergic disorders of early childhood. Histone acetylation is an important epigenetic mechanism, shown to be involved in the pathogenesis of allergies. However, its role in food allergy remains unknown. IgE-mediated cow's milk allergy was successfully induced in a mouse model, as demonstrated by acute allergic symptoms, whey-specific IgE in serum, and the activation of mast cells upon a challenge with whey protein. The elicited allergic response coincided with reduced percentages of regulatory T (Treg) and T helper 17 (Th17) cells, matching decreased levels of H3 and/or H4 histone acetylation at pivotal Treg and Th17 loci, an epigenetic status favoring lower gene expression. In addition, histone acetylation levels at the crucial T helper 1 (Th1) loci were decreased, most probably preceding the expected reduction in Th1 cells after inducing an allergic response. No changes were observed for T helper 2 cells. However, increased histone acetylation levels, promoting gene expression, were observed at the signal transducer and activator of transcription 6 (Stat6) gene, a proallergic B cell locus, which was in line with the presence of whey-specific IgE. In conclusion, the observed histone acetylation changes are pathobiologically in line with the successful induction of cow's milk allergy, to which they might have also contributed mechanistically.

Correction: A standardised framework to identify optimal animal models for efficacy assessment in drug development.

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

Are some animal models more equal than others? A case study on the translational value of animal models of efficacy for Alzheimer's disease.

Clinical trial failures (>99%) in Alzheimer's disease are in stark contrast to positive efficacy data in animals. We evaluated the correlation between animal and clinical efficacy outcomes (cognition) in Alzheimer's disease using data from registered drugs as well as interventions tested in phase II or III clinical trials for Alzheimer's disease. We identified 20 interventions, which were tested in 208 animal studies in 63 different animal models. Clinical outcome was correlated with animal results in 58% of cases. But, individual animal models showed divergent results across interventions, individual interventions showed divergent results across animal models, and animal model outcomes were determined with 16 different methods. This result is unsurprising due to poor external validity (what do we model) of the animal models. Although the animal models all share Alzheimer's disease symptoms, none represents the whole syndrome. Investigators did not motivate why one model was chosen over another, and did not consider the ways the disease phenomena were generated (spontaneous, (experimentally) induced or by genetic modification), or the species characteristics, which determine the outcomes. The explanation for the lack of correlation between animal and human outcomes can be manifold: the pathogenesis of Alzheimer's disease is not reflected in the animal model or the outcomes are not comparable. Our conclusion is that currently no animal models exist which are predictive for the efficacy of interventions for Alzheimer's disease.

A standardised framework to identify optimal animal models for efficacy assessment in drug development.

INTRODUCTION: 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. OBJECTIVES: To develop a tool to assess, validate and compare the clinical translatability of animal models used for the preliminary assessment of efficacy. DESIGN AND RESULTS: 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. CONCLUSIONS: 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.