Data belonging to the thesis: Corpora Non Agunt Nisi Fixata: Ligand Receptor Binding Kinetics in G Protein-Coupled Receptors.

Datacite citation style:
Xia, Lizi (2018): Data belonging to the thesis: Corpora Non Agunt Nisi Fixata: Ligand Receptor Binding Kinetics in G Protein-Coupled Receptors. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:dce5b3f9-5bd8-49a3-8108-936ded3051f9
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The present thesis focuses on the pharmacological concept of drug-target interaction, which dates back to the beginning of modern pharmacology. A traditional equilibrium metrics-based rationale (i.e. optimization of drug affinity leads to better efficacy and safety) is unable to prevent current high attrition rates in the early phase of drug discovery. In the past decade drug-target binding kinetics (i.e. association and dissociation rate constants, residence time) has been gaining more and more attention, which constitutes a paradigm shift to better predict parameters of drug efficacy and safety. We decided to investigate binding kinetics of G protein-coupled receptors (GPCR), since GPCR are involved in various critical physiological and pharmacological functions, being the target of about 30% of all drugs on the market. Both the human cannabinoid receptor 1 and the human adenosine A1 and A3 receptors were chosen as prototypical GPCR as well as potential drug targets. The binding kinetics investigations described in this thesis provide a better and multi-faceted understanding of drug-target interactions and offer suggestions for the design of better ligands with an appropriate kinetic profile, new technologies for rapid kinetic assessment, and ultimately suitable evaluation schemes for a better translation towards effective and safe drugs.
history
  • 2018-09-27 first online, published, posted
publisher
Leiden University
format
media types: application/octet-stream, application/pdf, application/vnd.ms-excel, application/vnd.openxmlformats-officedocument.presentationml.presentation, application/vnd.openxmlformats-officedocument.wordprocessingml.document, application/zip, image/gif, image/jpg, image/png, text/csv, text/html, text/plain
organizations
Leiden Academic Centre for Drug Research (LACDR)

DATA

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