1. Optimum concentration–response curve metrics for supervised selection of discriminative cellular phenotypic endpoints for chemical hazard assessment
- Author
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James Alastair Miller and Lit-Hsin Loo
- Subjects
0301 basic medicine ,Computer science ,Health, Toxicology and Mutagenesis ,010501 environmental sciences ,Toxicology ,Risk Assessment ,01 natural sciences ,Hazardous Substances ,Chemical effects ,03 medical and health sciences ,Discriminative model ,Toxicity Tests ,Humans ,Potency ,Selection (genetic algorithm) ,High content imaging ,0105 earth and related environmental sciences ,business.industry ,Pattern recognition ,General Medicine ,Specific toxicity ,Chemical hazard ,Benchmarking ,030104 developmental biology ,Feature (computer vision) ,Area Under Curve ,Artificial intelligence ,business - Abstract
High-content imaging (HCI) provides quantitative and information-rich measurements of chemical effects on human in vitro cell models. Identification of discriminative phenotypic endpoints from cellular features obtained from HCI is required for accurate assessments of potential chemical hazards. However, the use of suboptimal metrics to quantify the concentration-response curves (CRC) of chemicals based on these features may obscure discriminative features, and lead to non-predictive endpoints and poor chemical classifications or hazard assessments. Here, we present a systematic and data-driven study on the performances of different CRC metrics in identifying image-based phenotypic features that can accurately classify the effects of reference chemicals with known in vivo toxicities. We studied four previous HCI in vitro nephro- or pulmono-toxicity datasets, which contain phenotypic feature measurements from different cell and feature types. Within a feature type, we found that efficacy metrics at higher chemical concentrations tend to give higher classification accuracy, whereas potency metrics do not have obvious trends across different response levels. Across different cell and feature types, efficacy metrics generally gave higher classification accuracy than potency metrics and area under the curve (AUC). Our results suggest that efficacy metrics, especially at higher concentrations, are more likely to help us to identify discriminative phenotypic endpoints. Therefore, HCI experiments for toxicological applications should include measurements at sufficiently high chemical concentrations, and efficacy metrics should always be analyzed. The identified features may be used as specific toxicity endpoints for further chemical hazard assessment.
- Published
- 2020