Evaluating the efficacy of neurokinin-1 (substance P) receptor antagonist CP-99994 on facial grimacing and allodynia in mice postsurgery: a comparative study with known analgesics

Abstract

Neurokinin-1 (substance P) receptor (NK1R) antagonists failed to effectively treat pain in humans despite having antinociceptive properties in animals. Here, we sought to evaluate the efficacy of NK1R antagonist CP-99994 at reducing facial grimacing in white-coated CD-1 mice after laparotomy surgery when compared to the analgesics carprofen and buprenorphine. To enable this investigation, we developed a machine learning algorithm to automatically score facial grimacing in white-coated mice using the PainFace software platform. This algorithm detects 5 facial action units of the mouse grimace scale (MGS; orbitals, nose, ears, whiskers, cheeks) and assigns a facial grimace score (0-10) for each video frame analyzed. Carprofen and buprenorphine significantly reduced mean MGS scores and percentage of high grimace (MGS scores ≥5) frames for up to 4 hours postsurgery across multiple doses. In contrast, CP-99994 showed limited efficacy, with only the highest 30 mg/kg dose reducing grimacing at 2 hours. Likewise, principal component analysis of grimace data over time indicated that carprofen and buprenorphine were effective at reducing facial grimacing, whereas CP-99994 was not. However, both buprenorphine and CP-99994 reduced mechanical allodynia at the incision site. These findings reveal a dissociation between the effects of CP-99994 on a spontaneous pain measure (grimacing) and an evoked nociceptive response, whereas a known analgesic reduced both measures. Our study suggests that using facial grimacing to assess spontaneous pain alongside traditional nociceptive assays may better predict analgesic potential and possibly reduce risk of translational failures when selecting drug candidates for clinical advancement.

Keywords: Grimace; Machine learning; Nociception; Pain.