Assessment of anti-cancer drugs in 3D in vitro models using OCT-derived quantitative metrics (Conference Presentation)

Ninety percent of drugs fail during clinical trials. Of these failures, approximately 50% fail due to lack of clinical efficacy. Recent developments in in vitro models such as 3D spheroids and organoids have led to some improvements in failure rates, but the relative lack of standardized evaluation methods for 3D cultures limits their utility in high-throughput screening. In this study we investigate the potential of optical coherence tomography as a tool for assessing drug efficacy in 3D in vitro models. First, we consider a 3D convolutional neural network-based classification model for assessing drug efficacy based on raw OCT volumes. Additionally, we investigate several quantitative OCT metrics for assessing the effectiveness of the drugs including spheroid volume, OCT-derived optical attenuation coefficient, and analysis of speckle statistics. Our results underscore the possibility that OCT could be used for high-throughput screening of drugs using 3D in vitro models.