3D Fluid-Dynamic Ovarian Cancer Model Resembling Systemic Drug Administration for Efficacy Assay

Recently, 3D in vitro cancer models have become important alternatives to animal tests for establishing the efficacy of anticancer treatments. In this work, 3D SKOV-3 cell-laden alginate hydrogels were established as ovarian tumor models and cultured within a fluid-dynamic bioreactor (MIVO (R)) device able to mimic the capillary flow dynamics feeding the tumor. Cisplatin efficacy tests were performed within the device over time and compared with (i) the in vitro culture under static conditions and (ii) a xenograft mouse model with SKOV-3 cells, by monitoring and measuring cell proliferation or tumor regression, respectively, over time. After one week of treatment with 10 mu M cisplatin, viability of cells within the 3D hydrogels cultured under static conditions remained above 80%. In contrast, the viability of cells within the 3D hydrogels cultured within dynamic MIVO (R) decreased by up to 50%, and very few proliferating Ki67-positive cells were observed through immunostaining. Analysis of drug diffusion, confirmed by computational analysis, explained that these results are due to different cisplatin diffusion mechanisms in the two culture conditions. Interestingly, the outcome of the drug efficacy test in the xenograft model was about 44% of tumor regression after 5 weeks, as predicted in a shorter time in the fluid-dynamic in vitro tests carried out in the MIVO (R) device. These results indicate that the in vivo-like dynamic environment provided by the MIVO (R) device allows to better model the 3D tumor environment and predict in vivo drug efficacy than a static in vitro model.

Automated summary

The study found that cells cultured within the dynamic MIVO(R) device showed a significant decrease in viability compared to cells cultured under static conditions, with the former experiencing up to 50% decrease. The results suggest that the dynamic environment provided by the MIVO(R) device better models the 3D tumor environment and predicts in vivo drug efficacy more accurately than static in vitro models.