MK-0249

Current antipsychotic treatments have little impact on the cognitive deficits associated with schizophrenia. It has been proposed that agents which promote histamine release may enhance cognition. We evaluated whether the H3 inverse agonist MK-0249 might improve cognitive deficits in patients with schizophrenia.

Outpatients (N=55) with schizophrenia between ages 21 and 55 who were clinically stable, experienced no more than mild to moderate overall symptoms (PANSS score total 36-75), and were taking a stable dose of antipsychotic medication were randomized to MK-0249 10mg and placebo in a multi-center, randomized, double-blind, 2-period (4-weeks per period), cross-over study. The primary efficacy endpoint was the mean change from baseline at 4-weeks of treatment in the total cognitive score on the Brief Assessment of Cognition in Schizophrenia (BACS) Battery. Other assessments of cognition were also performed.

A total of 46 patients completed the study. MK-0249 10mg did not demonstrate a statistically significant difference compared to placebo in the mean change from baseline in the total cognitive score on the BACS battery after 4-weeks of treatment (-0.1, 95% CI: -2.3, 2.1) or with regard to secondary measures of attention/processing speed, episodic memory, or working memory after 4-weeks of treatment. The incidence of adverse events was greater during the MK-0249 treatment period (25/52 patients, 48.1%) compared to the placebo treatment period (15/51 patients, 29.4%).

MK-0249 10mg once daily was not superior to placebo in the treatment of cognitive impairment in patients with schizophrenia after 4-weeks. (Clinicaltrials.gov: NCT00506077).

Virtual fragment screening (VFS) is a promising new method that uses computer models to identify small, fragment-like biologically active molecules as useful starting points for fragment-based drug discovery (FBDD). Training sets of true active and inactive fragment-like molecules to construct and validate target customized VFS methods are however lacking. We have for the first time explored the possibilities and challenges of VFS using molecular fingerprints derived from a unique set of fragment affinity data for the histamine H(3) receptor (H(3)R), a pharmaceutically relevant G protein-coupled receptor (GPCR). Optimized FLAP (Fingerprints of Ligands and Proteins) models containing essential molecular interaction fields that discriminate known H(3)R binders from inactive molecules were successfully used for the identification of new H(3)R ligands. Prospective virtual screening of 156,090 molecules yielded a high hit rate of 62% (18 of the 29 tested) experimentally confirmed novel fragment-like H(3)R ligands that offer new potential starting points for the design of H(3)R targeting drugs. The first construction and application of customized FLAP models for the discovery of fragment-like biologically active molecules demonstrates that VFS is an efficient way to explore protein-fragment interaction space in silico.