Current trends in the development of new antidepressants.

Early antidepressant medications e.g. tricyclic antidepressants (TCAs) and monoamine oxidase inhibitors (MAOIs) are effective because they enhance either noradrenergic or serotonergic mechanisms, or both. Unfortunately, these compounds block cholinergic, histaminergic and alpha-1-adrenergic receptor sites, interact with a number of other medications and bring about numerous undesirable side effects. Several chemically unrelated agents have been developed and introduced in the past decade to supplement the early antidepressants. These include selective inhibitors of the reuptake of serotonin (the selective serotonin reuptake inhibitors (SSRIs)) or noradrenaline (reboxetine) or both (SNRIs: milnacipran and venlafaxine), as well as drugs with distinct neurochemical profiles such as mirtazapine, nefazodone, moclobemide and tianeptine. All these newer compounds are the results of rational developmental strategies to find drugs that were as effective as the TCAs but of higher safety and tolerability profile. In spite of the remarkable structural diversity, most currently introduced antidepressants are monoamin based and modulating monoamine activity as a therapeutic strategy continues to dominate antidepressant research. It must be emphasised, however, that these newer antidepressants are far from the ideal ones, also resulting in undesirable side effects and requiring 2-6 weeks of treatment to produce therapeutic effect. Furthermore, approximately 30% of the population do not respond to current therapies. An important new development has been the emergence of potential novel mechanisms of action beyond the monoaminergic synapse. The results of recent novel developmental approaches have suggested that modulation of N-methyl-D-aspartate (NMDA), neuropeptide (substance P and corticotrophin-releasing factor) receptors and the intracellular messenger system may provide an entirely new set of potential therapeutic targets. This paper discusses the advances from monoamine-based treatment strategies and looks at the future developments in the treatment of depression.

The algorithmic complexity of multichannel EEGs is sensitive to changes in behavior.

Symbolic measures of complexity provide a quantitative characterization of the sequential structure of symbol sequences. Promising results from the application of these methods to the analysis of electroencephalographic (EEG) and event-related brain potential (ERP) activity have been reported. Symbolic measures used thus far have two limitations, however. First, because the value of complexity increases with the length of the message, it is difficult to compare signals of different epoch lengths. Second, these symbolic measures do not generalize easily to the multichannel case. We address these issues in studies in which both single and multichannel EEGs were analyzed using measures of signal complexity and algorithmic redundancy, the latter being defined as a sequence-sensitive generalization of Shannon's redundancy. Using a binary partition of EEG activity about the median, redundancy was shown to be insensitive to the size of the data set while being sensitive to changes in the subject's behavioral state (eyes open vs. eyes closed). The covariance complexity, calculated from the singular value spectrum of a multichannel signal, was also found to be sensitive to changes in behavioral state. Statistical separations between the eyes open and eyes closed conditions were found to decrease following removal of the 8- to 12-Hz content in the EEG, but still remained statistically significant. Use of symbolic measures in multivariate signal classification is described.