ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-03212022-095053


Tipo di tesi
Tesi di laurea magistrale LM5
Autore
BERNARDONI, BIANCA LAURA
URN
etd-03212022-095053
Titolo
Development of synthetic ADAMTS7 selective inhibitors potentially useful for the treatment of cardiovascular diseases
Dipartimento
FARMACIA
Corso di studi
CHIMICA E TECNOLOGIA FARMACEUTICHE
Relatori
relatore Prof.ssa Nuti, Elisa
relatore Dott.ssa Cuffaro, Doretta
Parole chiave
  • ADAMTS
  • hydroxamic acids
  • metalloproteinases
  • atherosclerosis
  • CAD
  • ADAMTS7
Data inizio appello
06/04/2022
Consultabilità
Non consultabile
Data di rilascio
06/04/2025
Riassunto
ADAMTS (A Disintegrin And Metalloproteinase with Thrombospondin Motifs) family belongs to the superfamily of Metzincins and comprises 19 secreted zinc metalloproteinases. An altered homeostasis of ADAMTS enzymes is associated to pathological conditions, rendering these proteases an attractive pharmacological target. In particular, in vivo studies confirmed that ADAMTS7 is involved in both Coronary Artery Disease (CAD) and atherosclerosis. Overall, recent findings revealed the role of ADAMTS7 as promising target for intervention and its inhibition as a potential pharmacological approach. So far, no selective ADAMTS7 inhibitors have been reported.
My Thesis project focused on the synthesis of potent and selective hydroxamate-based ADAMTS7 inhibitors, starting from the already published ADAMTS7 inhibitor EDV33. The optimization process led to the synthesis of new compounds, which were tested for enzymatic activity and selectivity by a fluorometric assay by Dr. Santamaria (Imperial College London, UK).
The optimized compounds displayed a good activity on the target enzyme and a significant improvement in selectivity.
Even though further modifications have to be done in order to develop a SAR study and achieve a high affinity and selectivity for ADAMTS7, final compounds reported in this Thesis showed promising results, thus representing a good starting point for future optimizations.
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