Effects of peripheral FAAH blockade on NTG-induced hyperalgesia--evaluation of URB937 in an animal model of migraine.

BACKGROUND: Systemic nitroglycerin (NTG) activates brain nuclei involved in nociceptive transmission as well as in neuroendocrine and autonomic functions in rats. These changes are considered relevant for migraine because NTG consistently provokes spontaneous-like migraine attacks in migraineurs. Several studies have suggested a relationship between the endocannabinoid levels and pain mediation in migraine. URB937, a peripheral inhibitor of fatty acid amide hydrolase (FAAH)-the enzyme that degrades anandamide, produces analgesia in animal models of pain, but there is no information on its effects in migraine. AIM: We evaluated whether URB937 alters nociceptive responses in the animal model of migraine based on NTG administration in male rats, using the tail flick test and the plantar and orofacial formalin tests, under baseline conditions and after NTG administration. Furthermore, we investigated whether URB937 affects NTG-induced c-Fos expression in the brain. RESULTS: During the tail flick test, URB937 showed an antinociceptive effect in baseline conditions and it blocked NTG-induced hyperalgesia. URB937 also proved effective in counteracting NTG-induced hyperalgesia during both the plantar and orofacial formalin tests. Mapping of brain nuclei activated by NTG indicates that URB937 significantly reduces c-Fos expression in the nucleus trigeminalis caudalis and the locus coeruleus. CONCLUSIONS: The data suggest that URB937 is capable of changing, probably via indirect mechanisms, the functional status of central structures that are important for pain transmission in an animal model of migraine.

A genetic algorithms' approach to the exploration of parameter space in mesoscopic multicellular tumour spheroid models.

The design of accurate in silico cancer models capable of quantitatively predicting tumor growth is an important goal in cancer research today. Mesoscopic models have shown great promise in this scenario; however, their use is often inhibited by the difficulty in correctly assigning parameter values. In this paper, enabled by an extremely computationally efficient mesoscopic model, we propose a generic algorithms' (GAs) approach to the exploration of parameter space. Analysis of the results suggest that this novel application of GAs to tumor growth models both facilitates the attribution of parameter values to the fitting of experimental data and, more importantly, lends insight to the role played by the different parameters in regulating the tumor model growth.