The Evolution of Animal Models in Wound Healing Research: 1993-2017.

Significance: Wound healing is a complex and dynamic series of events influenced by a variety of intrinsic and extrinsic factors. Problematic wounds, particularly chronic wounds and pathologic scars, remain clinically significant burdens. Modeling physiologic and aberrant wound repair processes using in vitro or in vivo models have contributed to Advances in Wound Care (AWC); however, the fidelity of each model used, particularly with respect to its species-specific limitations, must be taken into account for extrapolation to human patients. Twenty-five years of wound healing models published in Wound Repair and Regeneration (1993-2017) and AWC (2012-2017) were collected and analyzed to determine trends in species utilization and models used. Recent Advances: In 25 years, 1,521 original research articles utilizing one or more wound models were published (total of 1,665 models). Although 20 different species were used over the course of 25 years, 5 species were most commonly utilized: human, mouse, rat, pig, and rabbit. In vivo modeling was used most frequently, followed by in vitro, ex vivo, and in silico modeling of wound healing processes. Critical Issues: A comparison of articles from 1993 to 1997 and 2013 to 2017 periods showed notable differences in model and species usage. Experiments utilizing mouse and human models increased, while the usage of pig models remained constant, rabbit and rat models declined in the more recent time period examined compared to the time period two decades before. Future Directions: This analysis shows notable changes in types of models and species used over time which may be attributed to new knowledge, techniques, technology, and/or reagents. Explorations into mechanisms of limb regeneration and wound healing of noncutaneous tissues have also contributed to a shift in modeling over time. Changes within the journals (i.e., page expansion and increased rejection rates), research funding, and model expense may also influence the observed shifts. Copyright 2019, Mary Ann Liebert, Inc., publishers.