Our Mission

The landscape of preclinical translational research is currently facing a critical juncture, with a striking disconnect between the results of animal experimentation and clinical trial success. The reproducibility crisis is affecting both the scientific community and public perception. Implicit and explicit bias in underpowered single-lab studies contribute to poor rigor, reliability, and reproducibility. Recent clinical trial failures, despite promising mechanistic preclinical investigations, have garnered significant public attention. In a climate where public skepticism towards science and medical research is already prevalent, these widely reported failures contribute to a declining opinion of basic and translational science.

​

Ischemic stroke is a leading cause of death and disability worldwide. Numerous experimental stroke treatments advanced to clinical trials based on ostensibly supportive preclinical data. Yet, all except recanalization therapies failed. This preclinical-clinical disconnect is attributed to several factors compromising internal and external validity in animal studies. Internal validity issues include insufficient attention to study quality aspects such as sample size calculation, eligibility criteria, treatment allocation, allocation concealment, blinding, and control of physiological variables. Common causes of reduced external validity involve inducing diseases in young and healthy animals instead of aged subjects with co-morbidities, assessing treatments in homogeneous animal groups compared to heterogeneous patient populations, and administering interventions in preclinical studies at early time points after stroke that are unrealistic to achieve in the clinical setting. In addition, there is increasing evidence that employing causal methods in animal experiments, such as Directed Acyclic Graphs, offers a robust solution to overcome bias that leads to incorrect conclusions. Developing a reliable and valid experimental stroke model and trial platform predictive of clinical success is crucial to test interventions with strong theoretical and experimental foundations.