Antidepressant potentiation of 5-hydroxytryptophan by L-Deprenil in affective illness

In an open trial study L-Deprenil, an irreversible selective MAO-B inhibitor without 'cheese effect' was given to 14 patients with unipolar and bipolar depression receiving L-5-Hydroxytryptophan (L-5-HTP) and benzerazide. Ten out of 14 patients showed a good response to the combination of drugs and a correlation was found between the degree of platelet MAO inhibition and clinical response. In a double-blind controlled study, 18 affectively ill patients were randomly allocated to L-Deprenil plus L-5-HTP and benzerazide, 21 patients were treated with L-5-HTP and benzerazide and 19 patients with placebo only. Patients treated with the combination of L-Deprenil and L-5-HTP showed a significantly greater clinical improvement than placebo patients but this was not the case for patients treated with 5-HTP alone. A positive relationship was demonstrated between mood improvement and degree of platelet MAO inhibition in patients treated with L-Deprenil.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

L-Deprenil, a selective monoamine oxidase type B inhibitor, in the treatment of depression: A double blind evaluation

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Discovery of Multi-Target Agents for Neurological Diseases via Ligand Design

Multi-target drug discovery (MTDD) is an emerging area of increasing interest to the drug discovery community. Drugs that modulate several targets have the potential for an improved balance of efficacy and safety compared to single targets agents. Although there are a number of marketed drugs that are thought to derive their therapeutic benefit by virtue of interacting with multiple targets, the majority of these were discovered accidentally. Written by world renowned experts, this is the first book to gather together knowledge and experiences of the rational discovery of multi-target drugs. It describes the current state of the art, the achievements and the challenges of the field and importantly the lessons learned by researchers to date and their application to future MTDD