Mixed-model QSAR at the glucocorticoid and liver X receptors.
PhD Thesis, University of Basel,
Faculty of Science.
Official URL: http://edoc.unibas.ch/diss/DissB_8730
The presence of hormonally active compounds in the biosphere has become a worldwide environmental concern, and measures such as policy acts and regulations try to address the problem, both in Europe and in the United States. Such compounds, referred to as endocrine disruptors, may alter the functions of the endocrine system and consequently cause adverse health effects in organism, or its progeny, or populations.1 A safe in silico identification of the toxic potential of drugs and chemicals is therefore highly desirable by both regulatory bodies, and the pharmaceutical industry. Nuclear receptors regulate biological functions such as cell growth and differentiation, metabolic processes, reproduction and development, intracellular signaling and can be involved in carcinogenesis through control of gene expression.2 Chemicals that disrupt the endocrine system interfere with the function of nuclear receptors, alter their functions and consequently cause adverse health effects.1 In this thesis, the development and validation of in silico three-dimensional models for the glucocorticoid and the liver X receptors, both belonging to the nuclear receptor superfamily, are presented. These models aim at the screening of drug candidates for glucocorticoid and liver X activity and of environmental chemicals for potential endocrine-disrupting activity. Different in silico-based tools and protocols were used to model receptor-ligand interactions. Molecular dynamics simulations enabled to gain an insight into the dynamical character of the protein-ligand interactions. An appropriate consideration of receptor flexibility (induced fit) was a prerequisite for the identification of realistic binding modes, which was performed with flexible docking. Once a suitable alignment was obtained, QSAR models were built, using two different technologies, and tested by the application to external validation sets, scramble tests and consensus scoring. The models have been added to the VirtualToxLab™3, 4 – a technology for the in silico identification of the toxic (endocrine-disrupting) potential of drugs and environmental chemicals. Special consideration was given to the role of hydrophobic effect in ligand binding. An empirical scoring function (Heidi: Hydrophobic Effect in Drug Interactions) was developed to quantify the hydrophobic effect for scoring protein–ligand binding energies. The use of HEidi, together with electrostatic, van der Waals and hydrogen bond energies, in the ranking of docking poses provided encouraging results when applied to glucocorticoid and liver X receptor complexes, but for a generalized statement more extensive evaluations are needed.
|Committee Members:||Langer, T.|
|Faculties and Departments:||05 Faculty of Science > Departement Pharmazeutische Wissenschaften > Pharmazie|
|Bibsysno:||Link to catalogue|
|Number of Pages:||153|
|Last Modified:||30 Jun 2016 10:41|
|Deposited On:||17 Jul 2009 08:20|
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