Evaluation of cebranopadol, a dually acting nociceptin/orphanin FQ and opioid receptor agonist in mouse models of acute, tonic, and chemotherapy-induced neuropathic pain

Background Cebranopadol (a.k.a. GRT-6005) is a dually acting nociceptin/orphanin FQ and opioid receptor agonist that has been recently developed in Phase 2 clinical trials for painful diabetic neuropathy or cancer pain. It also showed analgesic properties in various rat models of pain and had a better safety profile as compared to equi-analgesic doses of morphine. Since antinociceptive properties of cebranopadol have been studied mainly in rat models, in the present study, we assessed analgesic activity of subcutaneous cebranopadol (10 mg/kg) in various mouse pain models. Methods We used models of acute, tonic, and chronic pain induced by thermal and chemical stimuli, with a particular emphasis on pharmacoresistant chronic neuropathic pain evoked by oxaliplatin in which cebranopadol was used alone or in combination with simvastatin. Key results As shown in the hot plate test, the analgesic activity of cebranopadol developed more slowly as compared to morphine (90-120 min vs. 60 min). Cebranopadol displayed a significant antinociceptive activity in acute pain models, i.e., the hot plate, writhing, and capsaicin tests. It attenuated nocifensive responses in both phases of the formalin test and reduced cold allodynia in oxaliplatininduced neuropathic pain model. Its efficacy was similar to that of morphine. Used in combination and administered simultaneously, 4 or 6 h after simvastatin, cebranopadol did not potentiate antiallodynic activity of this cholesterollowering drug. Cebranopadol did not induce any motor deficits in the rotarod test. Conclusion Cebranopadol may have significant potential for the treatment of various pain types, including inflammatory and chemotherapy-induced neuropathic pain

Impact of feature selection on system identification by means of NARX-SVM

Support Vector Machines (SVM) are widely used in many fields of science, including system identification. The selection of feature vector plays a crucial role in SVM-based model building process. In this paper, we investigate the influence of the selection of feature vector on model’s quality. We have built an SVM model with a non-linear ARX (NARX) structure. The modelled system had a SISO structure, i.e. one input signal and one output signal. The output signal was temperature, which was controlled by a Peltier module. The supply voltage of the Peltier module was the input signal. The system had a non-linear characteristic. We have evaluated the model’s quality by the fit index. The classical feature selection of SVM with NARX structure comes down to a choice of the length of the regressor vector. For SISO models, this vector is determined by two parameters: nu and ny. These parameters determine the number of past samples of input and output signals of the system used to form the vector of regressors. In the present research we have tested two methods of building the vector of regressors, one classic and one using custom regressors. The results show that the vector of regressors obtained by the classical method can be shortened while maintaining the acceptable quality of the model. By using custom regressors, the feature vector of SVM can be reduced, which means also the reduction in calculation time

Wide-range measurement of thermal preference : a novel method for detecting analgesics reducing thermally-evoked pain in mice

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. Pregabalintreated 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

Passiflora incarnata attenuation of neuropathic allodynia and vulvodynia apropos GABA-ergic and opioidergic antinociceptive and behavioural mechanisms

Background: Passiflora incarnata is widely used as an anxiolytic and sedative due to its putative GABAergic properties. Passiflora incarnata L. methanolic extract (PI-ME) was evaluated in an animal model of streptozotocininduced diabetic neuropathic allodynia and vulvodynia in rats along with antinociceptive, anxiolytic and sedative activities in mice in order to examine possible underlying mechanisms. Methods: PI-ME was tested preliminary for qualitative phytochemical analysis and then quantitatively by proximate and GC-MS analysis. The antinociceptive property was evaluated using the abdominal constriction assay and hot plate test. The anxiolytic activity was performed in a stair case model and sedative activity in an open field test. The antagonistic activities were evaluated using naloxone and/or pentylenetetrazole (PTZ). PI-ME was evaluated for prospective anti-allodynic and anti-vulvodynic properties in a rat model of streptozotocin induced neuropathic pain using the static and dynamic testing paradigms of mechanical allodynia and vulvodynia. Results: GC-MS analysis revealed that PI-ME contained predominant quantities of oleamide (9-octadecenamide), palmitic acid (hexadecanoic acid) and 3-hydroxy-dodecanoic acid, among other active constituents. In the abdominal constriction assay and hot plate test, PI-ME produced dose dependant, naloxone and pentylenetetrazole reversible antinociception suggesting an involvement of opioidergic and GABAergic mechanisms. In the stair case test, PI-ME at 200 mg/kg increased the number of steps climbed while at 600 mg/kg a significant decrease was observed. The rearing incidence was diminished by PI-ME at all tested doses and in the open field test, PI-ME decreased locomotor activity to an extent that was analagous to diazepam. The effects of PI-ME were antagonized by PTZ in both the staircase and open field tests implicating GABAergic mechanisms in its anxiolytic and sedative activities. In the streptozotocin-induced neuropathic nociceptive model, PI-ME (200 and 300 mg/kg) exhibited static and dynamic anti-allodynic effects exemplified by an increase in paw withdrawal threshold and paw withdrawal latency. PI-ME relieved only the dynamic component of vulvodynia by increasing flinching response latency. Conclusions: These findings suggest that Passiflora incarnata might be useful for treating neuropathic pain. The antinociceptive and behavioural findings inferring that its activity may stem from underlying opioidergic and GABAergic mechanisms though a potential oleamide-sourced cannabimimetic involvement is also discussed

Impact of feature selection on system identification by means of NARX-SVM

Support Vector Machines (SVM) are widely used in many fields of science, including system identification. The selection of feature vector plays a crucial role in SVM-based model building process. In this paper, we investigate the influence of the selection of feature vector on model’s quality. We have built an SVM model with a non-linear ARX (NARX) structure. The modelled system had a SISO structure, i.e. one input signal and one output signal. The output signal was temperature, which was controlled by a Peltier module. The supply voltage of the Peltier module was the input signal. The system had a non-linear characteristic. We have evaluated the model’s quality by the fit index. The classical feature selection of SVM with NARX structure comes down to a choice of the length of the regressor vector. For SISO models, this vector is determined by two parameters: nu and ny. These parameters determine the number of past samples of input and output signals of the system used to form the vector of regressors. In the present research we have tested two methods of building the vector of regressors, one classic and one using custom regressors. The results show that the vector of regressors obtained by the classical method can be shortened while maintaining the acceptable quality of the model. By using custom regressors, the feature vector of SVM can be reduced, which means also the reduction in calculation time