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.

高内涵成像（HCI）提供了对人体体外细胞模型的化学作用的定量和信息丰富的测量。从HCI获得的细胞特征中识别可区分的表型终点是准确评估潜在化学危害所必需的。但是，基于这些特征使用次优指标量化化学物质的浓度-响应曲线（CRC）可能会掩盖歧视性特征，并导致非预测性终点和不良的化学分类或危害评估。在这里，我们提出了一个基于数据的研究，对不同的CRC指标在识别基于图像的表型特征方面的性能进行了研究，这些特征可以准确地对具有已知体内毒性的参考化学品进行分类。我们研究了四个以前的HCI体外肾毒性或肺毒性数据集，其中包含来自不同细胞和特征类型的表型特征测量值。在一个要素类型中，我们发现较高化学浓度下的功效指标倾向于提供更高的分类准确性，而效能指标在不同的响应水平上没有明显的趋势。在不同的细胞和特征类型中，功效指标通常比效能指标和曲线下面积（AUC）具有更高的分类准确性。我们的结果表明，疗效指标，尤其是在较高浓度时，更有可能帮助我们识别区别性的表型终点。因此，用于毒理学应用的HCI实验应包括在足够高的化学浓度下进行的测量，和功效指标应始终进行分析。所识别的特征可用作进一步的化学危害评估的特定毒性终点。