Quantitative non-targeted analysis: Bridging the gap between contaminant discovery and risk characterization

Abstract

Chemical risk assessments follow a long-standing paradigm that integrates hazard, dose-response, and exposure information to facilitate quantitative risk characterization. Targeted analytical measurement data directly support risk assessment activities, as well as downstream risk management and compliance monitoring efforts. Yet, targeted methods have struggled to keep pace with the demands for data regarding the vast, and growing, number of known chemicals. Many contemporary monitoring studies therefore utilize non-targeted analysis (NTA) methods to screen for known chemicals with limited risk information. Qualitative NTA data has enabled identification of previously unknown compounds and characterization of data-poor compounds in support of hazard identification and exposure assessment efforts. In spite of this, NTA data have seen limited use in risk-based decision making due to uncertainties surrounding their quantitative interpretation. Significant efforts have been made in recent years to bridge this quantitative gap. Based on these advancements, quantitative NTA data, when coupled with other high-throughput data streams and predictive models, are poised to directly support 21st-century risk-based decisions. This article highlights components of the chemical risk assessment process that are influenced by NTA data, surveys the existing literature for approaches to derive quantitative estimates of chemicals from NTA measurements, and presents a conceptual framework for incorporating NTA data into contemporary risk assessment frameworks.

Keywords: Exposure modeling; Non-targeted analysis; Quantitation; Risk characterization.

Fig. 1.

Relationship between sample processing for MS analysis (top) and level of chemical information derived (bottom). NTA techniques primarily focus on the identification chemical species in MS analysis (bottom-right), but quantitative modeling approaches allow estimation of the likelihood of given concentrations in prepared sample extracts and parent samples (bottom-left).

Fig. 2.

Integration of targeted and non-targeted analysis data (ovals) into the traditional risk assessment paradigm (rectangles). Current non-targeted analysis provides primarily qualitative data for hazard identification (via compound discovery) and occurrence information (presence/absence) for exposure assessment; this influences the development of targeted analysis methods for follow-up quantitative examinations. Further connection of NTA to existing dose–response, exposure assessment, and risk-characterization tools, designed for compatibility with targeted analysis approaches, requires reformulation of NTA data to adhere to workflows designed for quantitative inputs.

Fig. 3.

Prioritizing chemical features by statistically significant fold change using volcano plots of individual chemical features compared between two study groups (data reprocessed from Rager et al.). The fold-change (FC) for each feature intensity (where FC = MeanIntensity_Group1 / MeanIntensity_Group2) is plotted versus p-values for the unadjusted (left) and Benjamini–Hochberg corrected (right) group differences. Dashed lines show designated p-value (horizontal lines; p = 0.05) and FC (vertical lines; FC = 3) thresholds. Red chemical features are elevated (above designated thresholds) in study group 1 and blue chemical features are elevated in study group 2.

Fig. 4.

Estimation of the concentration of an unknown compound from a measured intensity (horizontal green line) and multiple surrogate calibration curves (blue and orange lines) with 95% prediction bands (grey envelopes). In this theoretical example, the uncertainty incurred from surrogate selection (i.e., calibration curve 1 vs. 2) far outweighs the calibration estimate uncertainty associated with either individual curve.

Fig. 5.

Toxicity/bioactivity assay measures (e.g. ToxCast AC₅₀, top left) can be scaffolded to lower bound acceptable concentrations via in vitro to in vivo extrapolation (IVIVE) and exposure modeling. Chemical instrument response from NTA experiments (bottom left) can likewise be converted to protective upper-bound estimates via quantitative NTA modeling and estimation of sample recovery. Risk assessment decision making can then be based on the comparison between upper-bound estimated concentration and a lower-bound acceptable concentration. Even sizable estimation errors may be acceptable for risk-based prioritization if the degree of separation is large.