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Computational oncology--mathematical modelling of drug regimens for precision medicine

Dominique Barbolosi1, Joseph Ciccolini1, Bruno Lacarelle1

  • 1SMARTc Unit, Aix Marseille Université, INSERM, CRO2 UMR_S 911, Marseille 13005, France.

Nature Reviews. Clinical Oncology
|November 25, 2015
PubMed

Insights

Computational oncology uses mathematical modeling to optimize cancer drug therapy, balancing toxicity and efficacy. This approach is crucial for advancing precision medicine in cancer treatment.

Area of Science:

  • Oncology
  • Computational Biology
  • Pharmacology

Background:

  • Computational oncology integrates computer-based modeling for tumor biology and cancer therapy.
  • Optimizing anticancer drug regimens is complex due to numerous targets and drug combinations, challenging traditional empirical methods.
  • Current clinical practice largely relies on standard drug schedules, not fully leveraging advanced modeling tools.

Purpose of the Study:

  • To explore the role of mathematical modeling in refining anticancer drug regimens.
  • To highlight achievements and limitations of computational modeling in preclinical and clinical oncology.
  • To discuss the potential of mathematical modeling for achieving precision medicine in cancer care.

Main Methods:

  • Review of preclinical and clinical studies involving computational modeling of drug regimens.
  • Analysis of pharmacokinetic and pharmacodynamic relationships of anticancer agents.
  • Exploration of mathematical modeling applications in optimizing toxicity-efficacy balance.

Main Results:

  • Mathematical modeling offers a rational approach to advance drug dosing and scheduling beyond empirical methods.
  • Existing implementations of mathematical modeling tools are insufficient to meet widespread clinical needs.
  • Computational modeling shows potential for refining treatment modalities across research and clinical settings.

Conclusions:

  • Mathematical modeling is a key resource for researchers and clinicians to advance precision medicine in oncology.
  • Further implementation of computational tools is necessary to transition towards personalized cancer care.
  • Computational oncology can significantly improve the rationalization of therapeutic strategies and patient outcomes.

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