Computer-Aided Discovery and Redesign for Respiratory Sensitization: A Tiered Mechanistic Model to Deliver Robust Performance Across a Diverse Chemical Space

Asthma
is among the most common occupational diseases with considerable
public health and economic costs. Chemicals that induce hypersensitivity
in the airways can cause respiratory distress and comorbidities with
respiratory infections such as COVID. Robust predictive models for
this end point are still elusive due to the lack of an experimental
benchmark and the over-reliance of existing in silico tools on structural alerts and structural (vs chemical) similarities.
The Computer-Aided Discovery and REdesign (CADRE) platform is a proven
strategy for providing robust computational predictions for hazard
end points using a tiered hybrid system of expert rules, molecular
simulations, and quantum mechanics calculations. The recently developed
CADRE model for respiratory sensitization is based on a highly curated
data set of structurally diverse chemicals with high-fidelity biological
data. The model evaluates absorption kinetics in lung mucosa using
Monte Carlo simulations, assigns reactive centers in a molecule and
possible biotransformations via expert rules, and determines subsequent
reactivity with cell proteins via quantum-mechanics calculations using
a multi-tiered regression. The model affords an accuracy above 0.90,
with a series of external validations based on literature data in
the range of 0.88–0.95. The model is applicable to all low-molecular-weight
organics and can inform not only chemical substitution but also chemical
redesign to advance development of safer alternatives.