In Molecules (Basel, Switzerland)
BACKGROUND : Wide use of oxaliplatin as an antitumor drug is limited by severe neuropathy with pharmacoresistant cold hypersensitivity as the main symptom. Novel analgesics to attenuate cold hyperalgesia and new methods to detect drug candidates are needed.
METHODS : We developed a method to study thermal preference of oxaliplatin-treated mice and assessed analgesic activity of intraperitoneal duloxetine and pregabalin used at 30 mg/kg. A prototype analgesiameter and a broad range of temperatures (0-45 °C) were used. Advanced methods of image analysis (deep learning and machine learning) enabled us to determine the effectiveness of analgesics. The loss or reversal of thermal preference of oxaliplatin-treated mice was a measure of analgesia.
RESULTS : Duloxetine selectively attenuated cold-induced pain at temperatures between 0 and 10 °C. Pregabalin-treated mice showed preference towards a colder plate of the two used at temperatures between 0 and 45 °C.
CONCLUSION : Unlike duloxetine, pregabalin was not selective for temperatures below thermal preferendum. It influenced pain sensation at a much wider range of temperatures applied. Therefore, for the attenuation of cold hypersensitivity duloxetine seems to be a better than pregabalin therapeutic option. We propose wide-range measurements of thermal preference as a novel method for the assessment of analgesic activity in mice.
Sałat Kinga, Furgała-Wojas Anna, Awtoniuk Michał, Sałat Robert
control, deep learning, duloxetine, image analysis, mice, oxaliplatin, pregabalin, thermal preference