DATA MINING TO FIND NEW POTENTIAL INHIBITORS OF ENDOCANNABINOID METABOLISM

EXEQUIEL O. J. PORTA; BRUNO HERNÁNDEZ CRAVERO; GABRIEL DI GRESIA; RENZO CARLUCCI; GUILLERMO R. LABADIE

Buzios

Simposio; BrazMedChem 2016; 2016

Sociedade Brasileira de Química

The endocannabinoid signaling system regulates diverse physiologic processes and has attracted considerable attention as a potential pharmaceutical target for treating diseases, such as pain, anxiety/depression, and metabolic disorders.The principal ligands of the endocannabinoid system are the lipid transmitters N-arachidonoylethanolamine (anandamide) and 2-arachidonoylglycerol (2-AG), which activate the two major cannabinoid receptors, CB1 and CB2. Anandamide and 2-AG signaling pathways in the nervous system are terminated by enzymatic hydrolysis mediated primarily by the serine hydrolases fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL), respectively.Our stratery for the rational design of new chemical entities (NCE) that interfere the endocanabinoids metabolism we use different modern chemoinformatics and bioinformatics tools (Figure 2). Our efforts have been directed to design new inhibitors that can be easy to synthesize looking to prepare mid-size collection in short time for biologic assay. Initially a curated database of the known inhibitors was prepared for all the target enzyme (size >5000 for FAAH and >1500 for MAGL). After that, data mining combined with chemoinformatics tools were used to filter the DB. Finally, the inhibitors were structurally clustered by Tanimoto, Dice and Tversky similarity coefficient.Then, examination of the structure-activity of each cluster, new potential inhibitors were design that should hit the endocanabinoids metabolism. A retrosynthetic analysis allowed us to scheme a synthetic route to prepare the new compounds.