Metabolism of AM404 From Acetaminophen at Human Therapeutic Dosages in the Rat Brain.

BACKGROUND: Acetaminophen, an analgesic and antipyretic drug, has been used clinically for more than a century. Previous studies showed that acetaminophen undergoes metabolic transformations to form an analgesic compound, N-(4-hydroxyphenyl) arachidonamide (AM404), in the rodent brain. However, these studies were performed with higher concentrations of acetaminophen than are used in humans. OBJECTIVES: The aim of the present study was to examine the metabolism of AM404 from acetaminophen in the rat brain at a concentration of 20 mg/kg, which is used in therapeutic practice in humans, and to compare the pharmacokinetics between them. MATERIALS AND METHODS: We used rat brains to investigate the metabolism of AM404 from acetaminophen at concentrations (20 mg/kg) used in humans. In addition, we determined the mean pharmacokinetic parameters for acetaminophen and its metabolites, including AM404. RESULTS: The maximum plasma concentrations of acetaminophen and AM404 in the rat brain were 15.8 µg/g and 150 pg/g, respectively, with corresponding AUC0-2h values of 8.96 µg hour/g and 117 pg hour/g. The tmax for both acetaminophen and AM404 was 0.25 hour. CONCLUSIONS: These data suggest that AM404's concentration-time profile in the brain is similar to those of acetaminophen and its other metabolites. Measurement of blood acetaminophen concentration seems to reflect the concentration of the prospective bioactive substance, AM404.

Development of a virtual screening method for identification of "frequent hitters" in compound libraries.

A computer-based method was developed for rapid and automatic identification of potential "frequent hitters". These compounds show up as hits in many different biological assays covering a wide range of targets. A scoring scheme was elaborated from substructure analysis, multivariate linear and nonlinear statistical methods applied to several sets of one and two-dimensional molecular descriptors. The final model is based on a three-layered neural network, yielding a predictive Matthews correlation coefficient of 0.81. This system was able to correctly classify 90% of the test set molecules in a 10-times cross-validation study. The method was applied to database filtering, yielding between 8% (compilation of trade drugs) and 35% (Available Chemicals Directory) potential frequent hitters. This filter will be a valuable tool for the prioritization of compounds from large databases, for compound purchase and biological testing, and for building new virtual libraries.