Current Status of Animal Models of Posttraumatic Stress Disorder: Behavioral and Biological Phenotypes, and Future Challenges in Improving Translation

Increasing predictability of animal models of posttraumatic stress disorder (PTSD) has required active collaboration between clinical and preclinical scientists. Modeling PTSD is challenging, as it is a heterogeneous disorder with >= 20 symptoms. Clinical research increasingly utilizes objective biological measures (e.g., imaging, peripheral biomarkers) or nonverbal behaviors and/or physiological responses to complement verbally reported symptoms. This shift toward more-objectively measurable phenotypes enables refinement of current animal models of PTSD, and it supports the incorporation of homologous measures across species. We reviewed.600 articles to examine the ability of current rodent models to probe biological phenotypes of PTSD (e.g., sleep disturbances, hippocampal and fear-circuit dysfunction, inflammation, glucocorticoid receptor hypersensitivity) in addition to behavioral phenotypes. Most models reliably produced enduring generalized anxiety-like or depression-like behaviors, as well as hyperactive fear circuits, glucocorticoid receptor hypersensitivity, and response to long-term selective serotonin reuptake inhibitors. Although a few paradigms probed fear conditioning/extinction or utilized peripheral immune, sleep, and noninvasive imaging measures, we argue that these should be incorporated more to enhance translation. Data on female subjects, on subjects at different ages across the life span, or on temporal trajectories of phenotypes after stress that can inform model validity and treatment study design are needed. Overall, preclinical (and clinical) PTSD researchers are increasingly incorporating homologous biological measures to assess markers of risk, response, and treatment outcome. This shift is exciting, as we and many others hope it not only will support translation of drug efficacy from animal models to clinical trials but also will potentially improve predictability of stage II for stage III clinical trials.