SVILUPPO DI UNA PIATTAFORMA CHEMOINFORMATICA PER LO STUDIO DELLA SELETTIVITA DEGLI INIBITORI DELLE ANIDRASI CARBONICHE

• Main Author: GALATI, SALVATORE
• Other Authors: Tuccinardi, Tiziano, Martinelli, Adriano
• Format: Text
• Language: Italian
• Published: Pisa University 2019
• Subjects: FARMACIA; Carbonic acid
• Online Access: http://etd.adm.unipi.it/theses/available/etd-09102019-093344/

Carbonic anhydrases (CAs) constitute a family of 16 metalloenzyme isoforms implied in a plethora of cellular processes, since they catalyze the rapid interconversion of carbon dioxide into carbonic acid and protons. For instance, CAs are involved in intracellular pH regulation, electrolytes secretion, gluconeogenesis, lipogenesis and bone resorption, depending also on their tissue and organ location. The various CA isoforms found in mammals are divided into four broad subgroups (cytosolic, mitochondrial, secreted and membrane-associated isoforms). Clinically, CAs are involved in various diseases such as glaucoma, hypoxic tumors and osteoporosis. Therefore, small molecule ligands with inhibitory activity against certain CA isoforms can be used for the treatment of such diseases. In this context, the selectivity for a specific isoform is an important property a CA ligand should be endowed with, in order to represent a valuable potential therapeutic tool. This thesis project deals with the development of a chemoinformatic platform focused on the study of small molecules characterized by selective inhibitory activity towards different CA isoforms. The developed protocol is based on the data extracted from scientific literature and stored in publicly available databases. The methodology is focused exclusively on the analysis of ligand structures translated into fingerprints, without any direct reference to the target receptor, and is able to provide useful guidelines for the design of selective CA ligands. The protocol has been implemented in KNIME Analytics Platform software by creating a specific workflow. The development of a KNIME model allows a fast and efficient data processing, enabling the access to the platform also to non-expert users.