Drug delivery: Experiments, mathematical modelling and machine learning

Daniela P. Boso, Daniele Di Mascolo, Raffaella Santagiuliana, Paolo Decuzzi, Bernhard A. Schrefler

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Article number103820
Pages (from-to)103820
JournalComputers in Biology and Medicine
StatePublished - Aug 2020


  • Artificial neural network
  • Cancer
  • Drug delivery
  • Mathematical model
  • Oncophysics
  • Physical parameter identification
  • Tumor spheroids

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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