Limited reproducibility of preclinical data is increasingly discussed in the literature. Failure of drug development programs due to lack of clinical efficacy is also of growing concern. The two phenomena may share an important root cause — a lack of robustness in preclinical research. Such a lack of robustness can be a relevant cause of failure in translating preclinical findings into clinical efficacy and hence attrition, and exaggerated cost in drug development. Apart from the study design and data analysis factors (e.g., insufficient sample sizes, failure to implement blinding, and randomization), heterogeneity among experimental models (e.g., animal strains) and the conditions of the study
used between different laboratories is a major contributor to the lacking of robustness of research findings. The flipside
of this coin is that the understanding of the causes of heterogeneity across experimental models may lead to the identification of relevant factors for defining the responder populations. Thus, this heterogeneity within preclinical findings could be an asset, rather than an obstacle, for precision medicine. To enable this paradigm shift, several steps need to be taken to identify conditions under which drugs do not work. An improved granularity in the reporting of preclinical studies is central among them (i.e., details about the study design, experimental conditions, quality of tools and reagents, validation of assay conditions, etc.). These actions need to be discussed jointly by the research communities interested in preclinical data robustness and precision medicine. Thus, we propose that a lack of robustness due to the heterogeneity across models and conditions of the study is not necessarily a liability for biomedical research but can be transformed into an asset of precision medicine.