Computer vision and machine‐learning techniques for quantification and predictive modeling of intracellular anticancer drug delivery by nanocarriers

Abstract

The field of cancer nanomedicine has made significant progress, but its clinical translation is impeded by many challenges, such as the difficulty in analyzing intracellular anticancer drug release by the nanocarriers due to the lack of suitable tools. Here, we propose the development of an image-based strategy involving machine learning (ML) to evaluate anticancer drug such as doxorubicin hydrochloride (DOX) released by a nanocarrier inside the HCT116 colon cancer cells and its subsequent intracellular accumulation. This technique combines fluorescent cell imaging with ML-based image analysis to assess and quantify the delivery of DOX by nanoparticles within them. We show that DOX in HCT116 cells was higher for multifunctional CNT-DOX-Fe 3 O 4 nanocarrier than free DOX, indicating efficient and steady release of DOX as well as superior retentive property of the nanocarrier. Initially (1 and 4 hours), the luminance intensity of DOX in the cell cytoplasm delivered by CNT-DOX-Fe 3 O 4 nanocarrier was ~0.34 and ~0.42 times lesser than that of free DOX delivered normally. However, at 24 and 48 hours posttreatment, the luminance intensity of DOX for CNT-DOX-Fe 3 O 4 nanocarrier was ~1.98 and ~1.92 times higher than that of free DOX. Furthermore, the luminance intensity of DOX for CNT-DOX-Fe 3 O 4 in the whole cell was ~1.35 and ~1.62 times higher than that of free DOX at 24 and 48 hours, respectively. The high-throughput nature of our image analysis workflow allowed us to automate the process of DOX retention analysis and enabled us to devise ML-based modeling to predict the percentage of anticancer drug retention in cells. The development of models to automatically quantify and predict intracellular drug release in cancer cells could benefit personalized treatments by optimizing the design of nanocarriers.