title: Employing dynamical computational models for personalizing cancer immunotherapy.
abstract: BACKGROUND
  Recently, cancer immunotherapy has shown considerable success, but due to the complexity of the immune-cancer interactions, clinical outcomes vary largely between patients. A possible approach to overcome this difficulty may be to develop new methodologies for personal predictions of therapy outcomes, by the integration of patient data with dynamical mathematical models of the drug-affected pathophysiological processes.
  
  
  METHODS
  This review unfolds the story of mathematical modeling in cancer immunotherapy, and examines the feasibility of using these models for immunotherapy personalization. The reviewed studies suggest that response to immunotherapy can be improved by patient-specific regimens, which can be worked out by personalized mathematical models. The studies further indicate that personalized models can be constructed and validated relatively early in treatment.
  
  
  CONCLUSIONS
  The suggested methodology has the potential to raise the overall efficacy of the developed immunotherapy. If implemented already during drug development it may increase the prospects of the technology being approved for clinical use. However, schedule personalization, per se, does not comply with the current, 'one size fits all,' paradigm of clinical trials. It is worthwhile considering adjustment of the current paradigm to involve personally tailored immunotherapy regimens.
authors: a Institute for Medical BioMathematics (IMBM) , Bene Ataroth , Israel.
authors: Zvia
authors: Z
authors: Agur
authors: a Institute for Medical BioMathematics (IMBM) , Bene Ataroth , Israel.
authors: Karin
authors: K
authors: Halevi-Tobias
authors: a Institute for Medical BioMathematics (IMBM) , Bene Ataroth , Israel.
authors: Yuri
authors: Y
authors: Kogan
authors: a Institute for Medical BioMathematics (IMBM) , Bene Ataroth , Israel.
authors: Ofer
authors: O
authors: Shlagman
keywords: Mathematical model; adoptive cell transfer; cancer vaccination; immune checkpoint inhibitor; immunotherapy personalization; model validation