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Rationale: H(3)-receptor inverse agonists raise a great interest as innovative therapeutics in several central disorders. Whereas their procognitive properties are well established, their antipsychotic-like properties are still debated., Objectives: We further explored the effect of maximal doses (3-10 mg/kg) of ciproxifan, BF2.649, and ABT-239, three selective H(3)-receptor inverse agonists, on deficits of prepulse inhibition (PPI) induced by apomorphine, MK-801, and phencyclidine (PCP). Their effect was also investigated on stereotypies induced by apomorphine and methamphetamine., Results: Ciproxifan, BF2.649, and ABT-239 did not reverse the PPI impairment produced by apomorphine (0.5 mg/kg, subcutaneous) in rats. Ciproxifan and BF2.649 did not reverse the impairment induced in mice by MK-801 (0.3 mg/kg). Ciproxifan and BF2.649 also failed to reverse the disruption induced in mice by PCP (5-10 mg/kg). Low to moderate doses of haloperidol (0.1-0.4 mg/kg, intraperitoneal), alone or co-administered with BF2.649, did not reverse MK-801-induced PPI disruption. A high dose (1 mg/kg) of haloperidol partially reversed the MK-801-induced deficit and BF2.649 tended to increase this effect, although nonsignificantly. Whereas stereotypies induced in mice by apomorphine and methamphetamine were totally suppressed by haloperidol, the decrease induced by ciproxifan was partial against apomorphine and very low, if any, against methamphetamine., Conclusions: Their total absence of effect in several validated animal models of the disease does not support antipsychotic properties of H(3)-receptor inverse agonists. However, their positive effects previously reported in behavioral tasks addressing learning, attention, and memory maintain the interest of H(3)-receptor inverse agonists for the treatment of cognitive symptoms of schizophrenia as adjunctive medications.

Automated signal generation aims to focus the attention of pharmacovigilance experts on drug-ADR associations which are disproportionally present in a spontaneous reporting system. Since 1986, we could find several signals using classic pharmacovigilance techniques with case reports registered in our pharmacovigilance regional centre. From this dataset 3,324 cases were related to spontaneous reporting. Drug-ADR associations were generated by using a Data Mining Algorithm (DMA) proposed by Evans et al. Potential signals were evaluated by reviewing case reports related to the unlabelled associations. The DMA generated 523 associations of which 107 were not described in the SPC. Most potential signals were false positives. Although the DMA generated little additional knowledge compared to signals already detected using classic techniques, the whole process helped us to focus our case review on a very small subset of the whole dataset (9.6%).