Prediction of drug response variability in human carboxylesterase 1 by computational structure- and ligand-based methods

Response to clinical drugs varies widely between individuals due to a combination of several factors, which can be inherited, environmental or physiological. Their interplay affects mostly pharmacokinetic parameters, but also pharmacodynamic properties, resulting in drug failure or worse, severe adverse drug reactions. In recent years, progress in structural biology, along with the development of computational molecular modeling methods has led to an explosion of available three-dimensional structures of proteins and the opportunity to investigate atomic-level details of interactions between proteins and small molecules. Consequently, this has massively contributed to elucidate the mechanisms underlying variability in therapeutic outcomes at a protein structural perspective.

The work presented in this thesis shows three different contexts in which molecular modeling approaches were used to examine the impact of the aforementioned factors on the metabolism of drugs mediated by human carboxylesterase 1 (hCES1), in order to gain a better understanding of how variability in the response to these drugs occur.

Firstly, we investigated genetic variations, namely nonsynonymous single nucleotide polymorphisms (nsSNPs) in hCES1. We described how the use of computational prediction tools for prioritizing functional nsSNPs, in combination with docking studies and molecular dynamics simulations can provide insight into the alteration of hCES1 structure, affecting the protein’s ability to interact with drugs and other small molecules and leading to significantly reduced hCES1 activity and, ultimately, poor response to drugs metabolized by this enzyme.

Secondly, we focused on the role of hCES1 in xenobiotic metabolism in order to assess how clinical drugs and food compounds can modulate the activity of hCES1, hence potentially producing adverse effects when administered with hCES1-metabolized drugs. For the purpose, we combined docking-based virtual screening and fingerprint-based similarity search and identified a number of drugs and food compounds with a potential to inhibit hCES1.

Finally, with the aim of understanding the role of hCES1 in mediating physiological processes and the possible implications on drug metabolism, we evaluated interactions between this enzyme and endogenous metabolites, namely cholesterol derivatives and fatty acids using pharmacophore-based and docking-based screenings. This allowed identifying endogenous compounds that interact with hCES1 via different binding sites.

Collectively, findings from the studies presented in this thesis provide further insight into the structural basis of interactions between hCES1 and small molecules and its role in mediating various pharmacological and physiological functions. Ultimately, these findings could contribute to decipher unexplained variability in response to drugs whose metabolism is mediated by hCES1, thus providing a basis for personalized therapy of these drugs.