Machine learning-assisted high-content imaging analysis of 3D MCF7 microtissues for estrogenic effect prediction.

Endocrine-disrupting chemicals (EDCs) pose a significant threat to human well-being and the ecosystem. However, in managing the many thousands of uncharacterized chemical entities, the high-throughput screening of EDCs using relevant biological endpoints remains challenging. Three-dimensional (3D) culture technology enables the development of more physiologically relevant systems in more realistic biochemical microenvironments. The high-content and quantitative imaging techniques enable quantifying endpoints associated with cell morphology, cell-cell interaction, and microtissue organization. In the present study, 3D microtissues formed by MCF-7 breast cancer cells were exposed to the model EDCs estradiol (E2) and propyl pyrazole triol (PPT). A 3D imaging and image analysis pipeline was established to extract quantitative image features from estrogen-exposed microtissues. Moreover, a machine-learning classification model was built using estrogenic-associated differential imaging features. Based on 140 common differential image features found between the E2 and PPT group, the classification model predicted E2 and PPT exposure with AUC-ROC at 0.9528 and 0.9513, respectively. Deep learning-assisted analysis software was developed to characterize microtissue gland lumen formation. The fully automated tool can accurately characterize the number of identified lumens and the total luminal volume of each microtissue. Overall, the current study established an integrated approach by combining non-supervised image feature profiling and supervised luminal volume characterization, which reflected the complexity of functional ER signaling and highlighted a promising conceptual framework for estrogenic EDC risk assessment.

Machine Learning-Assisted High-Content Imaging Analysis of 3D MCF7 Microtissues for Estrogenic Effect Prediction.

Endocrine-disrupting chemicals (EDCs) pose a significant threat to human well-being and the ecosystem. However, in managing the many thousands of uncharacterized chemical entities, the high-throughput screening of EDCs using relevant biological endpoints remains challenging. Three-dimensional (3D) culture technology enables the development of more physiologically relevant systems in more realistic biochemical microenvironments. The high-content and quantitative imaging techniques enable quantifying endpoints associated with cell morphology, cell-cell interaction, and microtissue organization. In the present study, 3D microtissues formed by MCF-7 breast cancer cells were exposed to the model EDCs estradiol (E2) and propyl pyrazole triol (PPT). A 3D imaging and image analysis pipeline was established to extract quantitative image features from estrogen-exposed microtissues. Moreover, a machine-learning classification model was built using estrogenic-associated differential imaging features. Based on 140 common differential image features found between the E2 and PPT group, the classification model predicted E2 and PPT exposure with AUC-ROC at 0.9528 and 0.9513, respectively. Deep learning-assisted analysis software was developed to characterize microtissue gland lumen formation. The fully automated tool can accurately characterize the number of identified lumens and the total luminal volume of each microtissue. Overall, the current study established an integrated approach by combining non-supervised image feature profiling and supervised luminal volume characterization, which reflected the complexity of functional ER signaling and highlighted a promising conceptual framework for estrogenic EDC risk assessment.

The frontline of alternatives to animal testing: novel in vitro skin model application in drug development and evaluation.

The FDA Modernization Act 2.0 has brought nonclinical drug evaluation into a new era. In vitro models are widely used and play an important role in modern drug development and evaluation, including early candidate drug screening and preclinical drug efficacy and toxicity assessment. Driven by regulatory steering and facilitated by well-defined physiology, novel in vitro skin models are emerging rapidly, becoming the most advanced area in alternative testing research. The revolutionary technologies bring us many in vitro skin models, either laboratory-developed or commercially available, which were all built to emulate the structure of the natural skin to recapitulate the skin's physiological function and particular skin pathology. During the model development, how to achieve balance among complexity, accessibility, capability, and cost-effectiveness remains the core challenge for researchers. This review attempts to introduce the existing in vitro skin models, align them on different dimensions, such as structural complexity, functional maturity, and screening throughput, and provide an update on their current application in various scenarios within the scope of chemical testing and drug development, including testing in genotoxicity, phototoxicity, skin sensitization, corrosion/irritation. Overall, the review will summarize a general strategy for in vitro skin model to enhance future model invention, application, and translation in drug development and evaluation.