Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure–Activity Relationships

The aim of human toxicity risk assessment is to determine
a safe
dose or exposure to a chemical for humans. This requires an understanding
of the exposure of a person to a chemical and how much of the chemical
is required to cause an adverse effect. To do this computationally,
we need to understand how much of a chemical is required to perturb
normal biological function in an adverse outcome pathway (AOP). The
molecular initiating event (MIE) is the first step in an adverse outcome
pathway and can be considered as a chemical interaction between a
chemical toxicant and a biological molecule. Key chemical characteristics
can be identified and used to model the chemistry of these MIEs. In
this study, we do just this by using chemical substructures to categorize
chemicals and 3D quantitative structure–activity relationships
(QSARs) based on comparative molecular field analysis (CoMFA) to calculate
molecular activity. Models have been constructed across a variety
of human biological targets, the glucocorticoid receptor, mu opioid
receptor, cyclooxygenase-2 enzyme, human ether-à-go-go related
gene channel, and dopamine transporter. These models tend to provide
molecular activity estimation well within one log unit and electronic
and steric fields that can be visualized to better understand the
MIE and biological target of interest. The outputs of these fields
can be used to identify key aspects of a chemical’s chemistry
which can be changed to reduce its ability to activate a given MIE.
With this methodology, the quantitative chemical activity can be predicted
for a wide variety of MIEs, which can feed into AOP-based chemical
risk assessments, and understanding of the chemistry behind the MIE
can be gained.