High-throughput methods are now routinely used to rapidly screen chemicals for potential hazard. However, hazard-based decision-making excludes important exposure considerations resulting in an incomplete estimation of chemical safety. Models to estimate exposure exist, but are generally unsuited to keep up with high-throughput demands. The High-Throughput Exposure Assessment Tool (HEAT) is designed to efficiently predict near-field exposure to consumers and workers via inhalation, oral and dermal routes. HEAT is based on well-known modeling algorithms and provides default model parameters to support reasonably conservative exposure estimates. Underlying chemical-specific data are uploaded or entered by the end user. HEAT’s main strength is the flexible tiered screening functionality, which enables exposure estimates for single or multiple chemicals simultaneously. Hypothetical case examples highlighting the application of HEAT to more complex exposure estimates for alternative and aggregate assessments are provided.

现在通常使用高通量方法来快速筛选化学药品是否存在潜在危险。但是，基于危害的决策不包括重要的接触考虑因素，从而导致对化学安全性的估计不完整。存在估算暴露的模型，但通常不适合满足高通量需求。高通量暴露评估工具（HEAT）旨在通过吸入，口服和皮肤途径有效预测消费者和工人的近场暴露。HEAT基于著名的建模算法，并提供默认模型参数以支持合理保守的暴露估计。最终用户可以上传或输入基础化学特定数据。HEAT的主要优势是灵活的分层筛选功能，可以同时估算一种或多种化学品的暴露量。假设的案例示例重点介绍了将HEAT应用到更复杂的暴露估计中，以进行替代评估和总体评估。