An integrated quantitative structure and mechanism of action-activity relationship model of human serum albumin binding

Angela Serra,Dario Greco,Pietro Coretto,Serli Önlü

doi:10.1186/s13321-019-0359-2

Abstract

BackgroundTraditional quantitative structure-activity relationship models usually neglect the molecular alterations happening in the exposed systems (the mechanism of action, MOA), that mediate between structural properties of compounds and phenotypic effects of an exposure.ResultsHere, we propose a computational strategy that integrates molecular descriptors and MOA information to better explain the mechanisms underlying biological endpoints of interest. By applying our methodology, we obtained a statistically robust and validated model to predict the binding affinity to human serum albumin. Our model is also able to provide new venues for the interpretation of the chemical-biological interactions.ConclusionOur observations suggest that integrated quantitative models of structural and MOA-activity relationships are promising complementary tools in the arsenal of strategies aiming at developing new safe- and useful-by-design compounds.

Highlights

Quantitative structure-activity relationship (QSAR) models are increasingly applied in various fields, such as toxicity assessment and drug design [1]
Considering the organization for economic co-operation and development (OECD) criteria [2], we propose a computational approach that combines molecular descriptors (MDs) and MOA information to develop integrated quantitative structure and mechanism of action-activity relationship (QSMARt) models with the potential to better explain the role of specific structural properties in a bio-mechanistic way
Upon rigorous evaluation based on the OECD validation principles [2], we selected a final model of six structural/MOA features: three molecular descriptors and three gene expression patterns (Eq 3)

Summary

Introduction

Quantitative structure-activity relationship (QSAR) models are increasingly applied in various fields, such as toxicity assessment and drug design [1]. Traditional QSAR models usually neglect the primary biological fingerprint of the exposure, consisting of the ensemble of molecular alterations happening at various cellular compartments of the exposed biological system, hereafter denoted as the mechanism of action (MOA). Integrating MOA information, such as gene expression or external bioassay data, into QSAR modelling would expand our understanding of the chemical-biological interactions, paving the way to the development of the generations of safeand useful-by-design compounds [6, 7]. Traditional quantitative structure-activity relationship models usually neglect the molecular alterations happening in the exposed systems (the mechanism of action, MOA), that mediate between structural properties of compounds and phenotypic effects of an exposure

Methods

Results

Conclusion