Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity

Area of Science:

• Environmental toxicology
• Nanomaterial safety
• Computational toxicology

Background:

• Co-exposure to nanomaterials and chemicals can lead to complex mixture toxicity.
• Predictive models, like concentration addition (CA) and independent action (IA), are established for chemical mixtures.
• Limited research existed on predictive models for nano-mixtures before June 2020.

Purpose of the Study:

• To review existing data and predictive models for nano-mixture toxicity.
• To identify data and model availability gaps for in silico nano-mixture toxicity assessment.
• To outline research challenges for developing robust predictive models.

Main Methods:

• Systematic literature search and data curation.
• Analysis of 183 studies on nano-mixture toxicity.
• Identification of trends in data and model development.

Main Results:

• Significant gaps exist in available datasets and predictive models for nano-mixtures.
• Existing models for chemical mixtures may not be directly applicable to nano-mixtures.
• The review highlights the need for standardized data collection and model validation.

Conclusions:

• Addressing data and model gaps is essential for advancing in silico nano-mixture toxicity assessment.
• Further experimental studies are needed to fill data gaps and develop reliable predictive models.
• Facilitating the development of predictive models will reduce reliance on extensive laboratory testing.