Pharmacological evaluation of anticancer drugs using 3D in vitro models provides invaluable information for predicting in vivo activity. Artificial matrices are currently available that scale up and increase the power of such 3D models. The aim of the present study was to propose an efficient and robust imaging and analysis pipeline to assess with quantitative parameters the efficacy of a particular cytotoxic drug. HCT116 colorectal adenocarcinoma tumor cell multispheres were grown in a 3D physiological hyaluronic acid matrix. 3D microscopy was performed with structured illumination, whereas image processing and feature extraction were performed with custom analysis tools. This procedure makes it possible to automatically detect spheres in a large volume of matrix in 96-well plates. It was used to evaluate drug efficacy in HCT116 spheres treated with different concentrations of topotecan, a DNA topoisomerase inhibitor. Following automatic detection and quantification, changes in cluster size distribution with a topotecan concentration-dependent increase of small clusters according to drug cytotoxicity were observed. Quantitative image analysis is thus an effective means to evaluate and quantify the cytotoxic and cytostatic activities of anticancer drugs on 3D multicellular models grown in a physiological matrix.

Source: Evaluation by quantitative image analysis of anticancer drug activity on multicellular spheroids grown in 3D matrices



Grégory MAUBON is Chief Data Officer and digital coordinator at HCS Pharma, a biotech startup focused in high content screening and complex diseases. He manages IT missions and leads digital usages linked to company needs. He is also a Augmented Reality Evangelist (presenter and lecturer) since 2008, where he created and founded in 2010 RA'pro (the augmented reality promotion association). He helped many companies (in several domains) to define precisely their augmented reality needs and supported them in the implementation.


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