Development and validation of an in-silico tool for the study of therapeutic agents in 3D cell cultures. (March 2021)

• Record Type: Journal Article
• Authors: Cortesi, M.; Liverani, C.; Mercatali, L.; Ibrahim, T.; Giordano, E.
• Is Part Of: Computers in biology and medicine. Volume 130(2021)
• Publication Date: 2021-03
• Subjects: Computational biology -- In-silico simulations -- Drug testing -- 3D cell culture; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104211
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Computational models constitute a fundamental asset for cancer research and drug R & D, as they provide controlled environments for testing of hypotheses and are characterized by the total knowledge of the system. These features are particularly useful for 3D cell culture models where a complex interaction among cells and their environments ensues. In this work, we present a programmable simulator capable of reproducing the behavior of cells cultured in 3D scaffolds and their response to pharmacological treatment. This system will be shown to be able to accurately describe the temporal evolution of the density of a population of MDA-MB-231 cells following their treatment with different concentrations of doxorubicin, together with a newly described drug-resistance mechanism and potential re-sensitization strategy. An extensive technical description of this model will be coupled to its experimental validation and to an analysis aimed at identifying which variables and behaviors account for differences in the response to treatment. Comprehensively, this work contributes to the growing field of integrated in-silico / in-vitro analysis of biological processes which has great potential for both the increase of our scientific knowledge and the development of novel, more effective treatments. Graphical abstract: Image 1 Highlights: We present a programmable computational model capable of simulating drug testing in scaffold-based 3D cell cultures. We show that its results are equivalent to those obtained in-vitro even when drug-resistance mechanisms are considered. We analyse our results to determine which variables more accurately predict the therapeutic outcome within each simulation.