• Journal of neurotrauma · Jun 2019

    Review

    How to Translate Time; the Temporal Aspects of Rodent and Human Pathobiological Processes in Traumatic Brain Injury.

    • Denes V Agoston, Robert Vink, Adel Helmy, Mårten Risling, David Nelson, and Mayumi Prins.
    • 1 Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, Maryland.
    • J. Neurotrauma. 2019 Jun 1; 36 (11): 172417371724-1737.

    AbstractTraumatic brain injury (TBI) triggers multiple pathobiological responses with differing onsets, magnitudes, and durations. Identifying the therapeutic window of individual pathologies is critical for successful pharmacological treatment. Dozens of experimental pharmacotherapies have been successfully tested in rodent models, yet all of them (to date) have failed in clinical trials. The differing time scales of rodent and human biological and pathological processes may have contributed to these failures. We compared rodent versus human time scales of TBI-induced changes in cerebral glucose metabolism, inflammatory processes, axonal integrity, and water homeostasis based on published data. We found that the trajectories of these pathologies run on different timescales in the two species, and it appears that there is no universal "conversion rate" between rodent and human pathophysiological processes. For example, the inflammatory process appears to have an abbreviated time scale in rodents versus humans relative to cerebral glucose metabolism or axonal pathologies. Limitations toward determining conversion rates for various pathobiological processes include the use of differing outcome measures in experimental and clinical TBI studies and the rarity of longitudinal studies. In order to better translate time and close the translational gap, we suggest 1) using clinically relevant outcome measures, primarily in vivo imaging and blood-based proteomics, in experimental TBI studies and 2) collecting data at multiple post-injury time points with a frequency exceeding the expected information content by two or three times. Combined with a big data approach, we believe these measures will facilitate the translation of promising experimental treatments into clinical use.

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