The atypical anxiolytic drug, tofisopam, selectively blocks phosphodiesterase isoenzymes and is active in the mouse model of negative symptoms of psychosis

Tofisopam is a member of the 2,3-benzodiazepine compound family which is marketed for the treatment of anxiety in some European countries. In contrast to classical 1,4-benzodiazepines, the compound does not bind to the benzodiazepine binding site of the γ-aminobutyric acid receptor and its psychopharmacological profile differs from such compounds. In addition to anxiolytic properties, antipsychotic effects are reported. We now show that tofisopam, 50 mg/kg intraperitoneally (i.p.), administered in parallel to repeated doses of dizocilpine 0.2 mg/kg i.p. can ameliorate dizocilpine-induced prolongation of immobility, which is considered to be a model of negative symptoms of psychosis. We further show that tofisopam acts as an isoenzyme-selective inhibitor of phosphodiesterases (PDEs) with highest affinity to PDE-4A1 (0.42 μM) followed by PDE-10A1 (0.92 μM), PDE-3 (1.98 μM) and PDE-2A3 (2.11 μM). The data indicate that tofisopam is an interesting candidate for the adjuvant treatment of psychosis with focus on negative symptoms. Combined partial inhibition of PDE-4 and PDE-10 as well as PDE-2 may be the underlying mechanism to this activity. Due to the good safety profile of tofisopam as evident from long-term use of this agent in patients, it may be concluded that dual or triple inhibition of PDE isoenzymes with additive or synergistic effects may be an interesting approach to pharmacological activity, resulting in active compounds with beneficial safety profile. Dose-limiting side effects such as emesis induced by selective inhibition of PDE-4 may be prevented by such strategies

State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency

[1] An ensemble Kalman filter for state estimation and a bias estimation algorithm were applied to estimate individual soil moisture profiles in a small corn field with the CLM2.0 model through the assimilation of measurements from capacitance probes. Both without and with inclusion of bias correction, the effect of the assimilation frequency, the assimilation depth, and the number of observations assimilated per profile were studied. Assimilation of complete profiles had the highest impact on deeper soil layers, and the optimal assimilation frequency was about 1-2 weeks, if bias correction was applied. The optimal assimilation depth depended on the calibration results. Assimilation in the surface layer had typically less impact than assimilation in other layers. Through bias correction the soil moisture estimate greatly improved. In general, the correct propagation of the innovations for both the bias-blind state and bias filtering from any layer to other layers was insufficient. The approximate estimation of the a priori (bias) error covariance and the choice of a zero-initialized persistent bias model made it impossible to estimate the bias in layers for which no observations were available. Copyright 2007 by the American Geophysical Union.status: publishe