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Tesi etd-09052014-111129


Thesis type
Tesi di laurea magistrale
Author
RUFFA, ANNALISA
email address
annalisa.ruffa@hotmail.it
URN
etd-09052014-111129
Title
In Silico Modeling of Aryl Hydrocarbon Receptor Binding Affinities of a Series of Mixed Halogenated Aromatic Compounds
Struttura
SCIENZE DELLA TERRA
Corso di studi
SCIENZE AMBIENTALI
Supervisors
relatore Tozzi, Maria Grazia
correlatore Saçan, Melek Türker
controrelatore Raffaelli, Andrea
Parole chiave
  • modelli in silico
  • idrocarburi aromatici alogenati
  • recettore arilico
Data inizio appello
26/09/2014;
Consultabilità
Completa
Riassunto analitico
The Halogenated Aromatic Compounds (HAC) are considered an emerging group of persistent chemical pollutants dangerous and potentially harmful to human health. Their biological activities as the binding affinity to the Aryl Hydrocarbon Receptor (AhR) is of fundamental importance to detect the toxicity of these compounds on living organisms. AhR binding affinity has been used throughout the text as log RBA.
In this study, the Quantitative Structure-Activity Relationship/Quantitative Structure-Toxicity Relationship (QSAR/QSTR) methods were used to create some models developed on log RBA values of a data set of 108 congeners from halogenated aromatic compounds (PCBs, PCDDs, PCDFs, PBDDs, PBDEs, some substituted PCB groups and congeners from bromo chloro substituted dibenzo dioxin groups) by employing the Multiple Linear Regression (MLR).

The used descriptors were from DRAGON 06 and SPARTAN 04 software, whereas the models are developed from QSARINS (evaluation version b1.1 2012) software. All the best models were validated for their performance using all the criteria suggested by Economic Co-operation and Development principles (OECD, 2007), which involving the internal and external validation of the models, the analysis of the applicability domain (AD) and, when possible, a mechanistic interpretation of the models. External validation was provided by splitting the data set into training and test sets either choosing manually the compounds initially ordered according to the increasing order of their toxicity values or by applying the hierarchical clustering technique.

Finally, the proposed QSTR models were used to predict the log RBA values of PCBs, PCDDs, PCDFs, PBDDs and PBDE congeners (618 compounds) with no experimental data.
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