Table 1.

New approach methodologies for exposure science.

Exposure NAM class	Description	Traditional Approach	Makes use of
			Measurement	Toxicokinetics	Models	Descriptors	Evaluation	Machine learning
Measurements	New techniques including screening analyses capable of detecting hundreds of chemicals present in a sample	Targeted (chemical-specific) analyses	–	•	•	•		•
Toxicokinetics	High-throughput methods using in vitro data to generate chemical-specific models	Analyses based on in vivo animal studies	•	–		•		•
HTE models	Models capable of making predictions for thousands of chemicals	Models requiring detailed, chemical- and scenario- specific information	•	•	–	•
Chemical descriptors	Informatic approaches for organizing chemical information in a machine- readable format	Tools targeted at single- chemical analyses by humans				–		•
Evaluation	Statistical approaches that use the data from many chemicals to estimate the uncertainty in a prediction for a new chemical	Comparison of model predictions to data on a per- chemical basis	•	•	•	•	–	•
Machine learning	Computer algorithms to identify patterns	Manual inspection of the data	•	•		•		–
Prioritization	Integration of exposure and other NAMs to identify chemicals for follow-up study	Expert decision-making	•	•	•	•	•	•

HTE, high-throughput exposure; NAMs, new approach methodologies.

We describe six broad categories of NAMs that are being used or applied to inform exposure along with other chemical risk prioritization NAMs (Table 1).

• indicates that the NAM also makes use of the NAM in the indicated column, while - indicates that these are the same NAM.