# Framework for In Silico Toxicity Screening of Novel Odorants

## TL;DR

This paper introduces a new in silico method to estimate safe maximum concentrations of novel odorants based on toxicity predictions.

## Contribution

The novel contribution is a framework combining open-source models to predict inhalation toxicity and derive safe concentration thresholds.

## Key findings

- The approach achieved health-protective predictions for 98.6% of a test dataset of 143 chemicals.
- Thresholds of toxicologic concern were assigned based on predicted hazard classifications using decision trees.
- Vapor pressure predictions from MPBPWIN™ were used to calculate safe solution concentrations.

## Abstract

Toxicological risk assessment of chemicals without experimental toxicity data often relies on in silico predictions. However, models designed to predict inhalation toxicity associated with exposure to volatile chemicals in solution are unavailable. The aim of this research was to develop an approach to estimate toxicology-based maximum solution concentrations for novel odorants using in silico structure-based predictions. The decision trees were adapted from established open-source models for assessing mutagenicity (rule-based, ISS in vitro mutagenicity decision tree) and systemic toxicity (revised Cramer decision tree). These were implemented using Toxtree (v3.1.0), a freely available program. Thresholds of toxicologic concern (TTC) were then assigned based on the predicted hazard classification. We then used predicted vapor pressure derived from MPBPWIN™ using US EPA EPI Suite to calculate a solution concentration where inhalation exposure to a defined headspace volume would not exceed the TTC. The approach was evaluated using a published dataset of 143 chemicals with repeat exposure inhalation toxicity data, yielding health-protective predictions for 98.6% of the test set. This demonstrates that the proposed in silico approach enables the estimation of safe toxicology-based maximum solution concentrations for chemicals using open-source models and software.