Digging deeper into pain: an ethological behavior assay correlating well-being in mice with human pain experience.

Luke A Pattison, Alexander Cloake,Sampurna Chakrabarti,Helen Hilton, Rebecca H Rickman,James P Higham, Michelle Y Meng, Luke W Paine, Maya Dannawi, Lanhui Qiu, Anne Ritoux,David C Bulmer,Gerard Callejo,Ewan St John Smith

Pain(2024)

引用 0|浏览3
暂无评分
摘要
ABSTRACT:The pressing need for safer, more efficacious analgesics is felt worldwide. Preclinical tests in animal models of painful conditions represent one of the earliest checkpoints novel therapeutics must negotiate before consideration for human use. Traditionally, the pain status of laboratory animals has been inferred from evoked nociceptive assays that measure their responses to noxious stimuli. The disconnect between how pain is tested in laboratory animals and how it is experienced by humans may in part explain the shortcomings of current pain medications and highlights a need for refinement. Here, we survey human patients with chronic pain who assert that everyday aspects of life, such as cleaning and leaving the house, are affected by their ongoing level of pain. Accordingly, we test the impact of painful conditions on an ethological behavior of mice, digging. Stable digging behavior was observed over time in naive mice of both sexes. By contrast, deficits in digging were seen after acute knee inflammation. The analgesia conferred by meloxicam and gabapentin was compared in the monosodium iodoacetate knee osteoarthritis model, with meloxicam more effectively ameliorating digging deficits, in line with human patients finding meloxicam more effective. Finally, in a visceral pain model, the decrease in digging behavior correlated with the extent of disease. Ultimately, we make a case for adopting ethological assays, such as digging, in studies of pain in laboratory animals, which we believe to be more representative of the human experience of pain and thus valuable in assessing clinical potential of novel analgesics in animals.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要