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HIGH-THROUGHPUT IMAGING-BASED METHODS FOR PREDICTING CELL-TYPE-SPECIFIC TOXICITY OF XENOBIOTICS WITH DIVERSE CHEMICAL STRUCTURES
HIGH-THROUGHPUT IMAGING-BASED METHODS FOR PREDICTING CELL-TYPE-SPECIFIC TOXICITY OF XENOBIOTICS WITH DIVERSE CHEMICAL STRUCTURES
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机译：基于高通量成像的化学结构多样的异种药物的细胞类型特异性毒性预测方法
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摘要
The present invention provides methods for the prediction of in vivo cell-specific toxicity of a compound that combines high-throughput imaging of cultured cells, quantitative phenotypic profiling, and machine learning methods. More particularly, the invention provides a method for the prediction of in vivo renal proximal tubular-, bronchial-epithelial-, and alveolar-cell-specific toxicities of a soluble or particulate compound that comprises contacting cultured human kidney and pulmonary cells with the compound at a range of concentrations, then labeling the cells with DNA, γH2AX and actin markers and obtaining textural features, spatial correlation features, ratios of the markers, intensity features, cell count and morphology, estimating dose response curves and performing automatic classification of the compound using a random-forest algorithm.
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