Levelling the Translational Gap for Animal to Human Efficacy Data

Abstract

Simple SummaryThe 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.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.