Drug delivery: Experiments, mathematical modelling and machine learning. (August 2020)

• Record Type: Journal Article
• Authors: Boso, Daniela P.; Di Mascolo, Daniele; Santagiuliana, Raffaella; Decuzzi, Paolo; Schrefler, Bernhard A.
• Is Part Of: Computers in biology and medicine. Volume 123(2020)
• Publication Date: 2020-08
• Subjects: Drug delivery -- Tumor spheroids -- Artificial neural network -- Cancer -- Mathematical model -- Physical parameter identification -- Oncophysics; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103820
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: We address the problem of determining from laboratory experiments the data necessary for a proper modeling of drug delivery and efficacy in anticancer therapy. There is an inherent difficulty in extracting the necessary parameters, because the experiments often yield an insufficient quantity of information. To overcome this difficulty, we propose to combine real experiments, numerical simulation, and Machine Learning (ML) based on Artificial Neural Networks (ANN), aiming at a reliable identification of the physical model factors, e.g. the killing action of the drug. To this purpose, we exploit the employed mathematical-numerical model for tumor growth and drug delivery, together with the ANN - ML procedure, to integrate the results of the experimental tests and feed back the model itself, thus obtaining a reliable predictive tool. The procedure represents a hybrid data-driven, physics-informed approach to machine learning. The physical and mathematical model employed for the numerical simulations is without extracellular matrix (ECM) and healthy cells because of the experimental conditions we reproduce. Graphical abstract: Image 1 Highlights: Hybrid data-driven, physics-informed approach to machine learning. Combination of real tests, physics-based simulation and Artificial Neural Networks. Extracting parameters for drug efficiency modeling from insufficient laboratory data. Integration of experimental tests data with data from simulation.