TY - JOUR
T1 - Drug delivery
T2 - Experiments, mathematical modelling and machine learning
AU - Boso, Daniela P.
AU - Di Mascolo, Daniele
AU - Santagiuliana, Raffaella
AU - Decuzzi, Paolo
AU - Schrefler, Bernhard A.
PY - 2020/8/1
Y1 - 2020/8/1
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Cancer
KW - Drug delivery
KW - Mathematical model
KW - Oncophysics
KW - Physical parameter identification
KW - Tumor spheroids
UR - http://www.scopus.com/inward/record.url?scp=85087275421&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85087275421&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2020.103820
DO - 10.1016/j.compbiomed.2020.103820
M3 - Article
C2 - 32658778
AN - SCOPUS:85087275421
VL - 123
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
SN - 0010-4825
M1 - 103820
ER -