Di Dato, Antonio
(2017)
Application of Computational Methods for the Design of New Potential Therapeutic Agents.
[Tesi di dottorato]
| Tipologia del documento: |
Tesi di dottorato
|
| Lingua: |
English |
| Titolo: |
Application of Computational Methods for the Design of New Potential Therapeutic Agents |
| Autori: |
| Autore | Email |
|---|
| Di Dato, Antonio | antonio.didato@unina.it |
|
| Data: |
2017 |
| Numero di pagine: |
155 |
| Istituzione: |
Università degli Studi di Napoli Federico II |
| Dipartimento: |
dep05 |
| Dottorato: |
phd071 |
| Ciclo di dottorato: |
30 |
| Coordinatore del Corso di dottorato: |
| nome | email |
|---|
| D'Auria, Maria Valeria | madauria@unina.it |
|
| Tutor: |
| nome | email |
|---|
| Fattorusso, Caterina | [non definito] |
|
| Data: |
2017 |
| Numero di pagine: |
155 |
| Parole chiave: |
Computational, Modelling, Pharmaceutical |
| Settori scientifico-disciplinari del MIUR: |
Area 03 - Scienze chimiche > CHIM/08 - Chimica farmaceutica |
[error in script]
[error in script]
| Depositato il: |
19 Dic 2017 13:05 |
| Ultima modifica: |
20 Mar 2019 12:03 |
| URI: |
http://www.fedoa.unina.it/id/eprint/12181 |
Abstract
Computer-aided drug discovery (CADD) represents a very useful tool to search for potential drug candidates and plays a strategic role in the discovery of new potential therapeutic agents for both pharmaceutical companies and academic research groups. Nevertheless, the modelling of biological systems still represents a challenge for computational chemists, and, at present, a single computational method able to face such challenge is not available. Computational tools are therefore evolving in the direction of combining molecular-mechanic (MM), molecular dynamics (MD), and quantum-mechanical (QM) approaches in order to achieve an overall better simulation of the actual molecular behaviour. In addition, many sampling methods have been developed and applied for the characterisation and comparison of the collective motions of protein structures related to the dynamics of proteins, protein folding and ligand-protein docking simulations. This prompted us, as computational medicinal chemists, to develop various CADD approaches, depending on the specific case under study, integrating theoretical and experimental data. In particular, the research activity carried out during the three years of my PhD led to: i) the development of three-dimensional (3-D) pharmacophore models for the analysis of 3-D structure-activity relationships (SARs) of bioactive compounds, ii) the identification of new molecular targets, iii) the simulation of large-scale protein conformational changes, iv) the simulation of protein/protein and ligand/protein interactions, and v) the design of new bioactive compounds. Computational studies were always performed in the frame of multi-disciplinary projects guided by a unique research strategy, which involved several international and national research groups, and were carried out by integrating and validating our computational studies with the experimental data coming from the other researchers involved in the various projects.
The results obtained enabled to: i) identify a new class of anticancer agents against paclitaxel resistant cancer cells, ii) provide important information on the mechanism of action of cationic porphyrins, a novel class of proteasome conformational regulators with great potentiality as “lead” pharmacophores, and iii) optimise the cellular pharmacokinetic and pharmacodynamic properties of a new series of antimalarial agents.
In addition, I spent a training period abroad of eight-months at the Institute of Research in Biomedicine (IRB) in Barcelona, under the supervision of prof. Modesto Orozco, during which I have had the opportunity to extend my computational background by learning and, then, performing metadynamic and MD simulations, investigating the open/close conformational transition of 20S human proteasome by molecular dynamics simulations.
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