Ferreira Guilherme S, Veening-Griffioen Désirée H, Boon Wouter P C, Moors Ellen H M, van Meer Peter J K
Department of Pharmaceutics, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3512 JE Utrecht, The Netherlands.
Copernicus Institute of Sustainable Development, Innovation Studies, Utrecht University, 3512 JE Utrecht, The Netherlands.
Animals (Basel). 2020 Jul 15;10(7):1199. doi: 10.3390/ani10071199.
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.
在过去十年中，可重复性危机的报道以及制药行业的高损耗率使得动物研究越来越受到审视。现在，许多研究人员和普通公众都在质疑进行动物研究是否仍然合理。虽然对临床前研究当前操作方式的批评确实有道理，但这些讨论所依据的数据往往不可靠。已经出台了多项旨在解决动物研究内部有效性和报告质量问题的倡议（例如，《研究中的动物：体内实验报告》（ARRIVE）以及《动物研究与实验程序规划：卓越建议》（PREPARE）指南），但很少得到实施。至于外部有效性，几乎没有取得进展。尽管如此，基于不可靠的临床前数据选择最佳疾病动物模型可能会阻碍临床试验的开展。在此，我们讨论三项有助于解决疾病动物模型预测价值评估问题的举措。首先，我们开发了疾病模型识别框架（FIMD），这是对疾病模型进行评估、验证和比较标准化的第一步。FIMD能够确定人类疾病的哪些方面在动物身上得到了复制，有助于选择更有可能预测人类反应的疾病模型。其次，我们展示了一个系统评价和荟萃分析如何能够提供另一种定量区分疾病模型的策略的例子。第三，我们探讨外部有效性是否是研究者手册（IB）中动物模型选择的一个因素，并使用IB风险降低工具在早期临床开发中整合临床前药代动力学和药效学数据。通过这些举措，我们展示了如何解决外部有效性问题，以评估药物开发中动物模型的可转化性和科学价值。然而，虽然这些方法具有潜力，但科学界对它们的采用程度将决定其影响。通过推广和采用高质量的研究设计与报告，以及对疾病动物模型药物疗效的可转化性进行全面评估，我们将获得有力的数据来挑战和改进当前的动物研究范式。
