Traditional drug screening methods use monolayer (2D) tumor cell cultures, which lack
basic features of tumor complexity. As an alternative, 3D hydrogels have begun to
emerge as simple, time‐ and cost‐saving systems. One of the most promising candidates,
synthetic alkoxysilane‐PEG (polyethylene glycol)‐based hydrogels, are formed by "sol‐gel"
polymerization in an aqueous medium, which allows control over the incorporated elements.
Our aims were to optimize siloxane‐PEG hydrogels for three different cell lines of
skin origin and utilize these 3D hydrogels as a feasible drug (e.g. daunorubicin)
screening assay. A drastic increase in survival and the formation of cellular aggregates
(spheroids) could be observed in A2058 melanoma cells, but not in keratinocyte and
endothelial cell lines. A deep‐learning neural network was trained to recognize and
distinguish between the cellular formations and allowed the fast processing of hundreds
of microscopic images. We developed an artificial intelligence (AI)‐assisted application
( https://github.com/enyecz/CancerDetector2 ), which indicated that, in terms of average
area of the spheroids treated with daunorubicin, A2058 melanoma cell 3D aggregates
have better survival in a hydrogel containing 15% bis‐mono‐ethoxysilane‐PEG.
