Review Article | Published:

Computational oncology — mathematical modelling of drug regimens for precision medicine

Nature Reviews Clinical Oncology volume 13, pages 242254 (2016) | Download Citation

Abstract

Computational oncology is a generic term that encompasses any form of computer-based modelling relating to tumour biology and cancer therapy. Mathematical modelling can be used to probe the pharmacokinetics and pharmacodynamics relationships of the available anticancer agents in order to improve treatment. As a result of the ever-growing numbers of druggable molecular targets and possible drug combinations, obtaining an optimal toxicity–efficacy balance is an increasingly complex task. Consequently, standard empirical approaches to optimizing drug dosing and scheduling in patients are now of limited utility; mathematical modelling can substantially advance this practice through improved rationalization of therapeutic strategies. The implementation of mathematical modelling tools is an emerging trend, but remains largely insufficient to meet clinical needs; at the bedside, anticancer drugs continue to be prescribed and administered according to standard schedules. To shift the therapeutic paradigm towards personalized care, precision medicine in oncology requires powerful new resources for both researchers and clinicians. Mathematical modelling is an attractive approach that could help to refine treatment modalities at all phases of research and development, and in routine patient care. Reviewing preclinical and clinical examples, we highlight the current achievements and limitations with regard to computational modelling of drug regimens, and discuss the potential future implementation of this strategy to achieve precision medicine in oncology.

Key points

  • With the ever-increasing processing power of new computer hardware and software, the mathematical sciences are constantly advancing and the use of mathematical models in medicine is expanding

  • To date, computational oncology has been implemented mostly in systems-biology studies as a means to better understand cancer biology

  • Computation oncology can be applied to refine drug dosing and scheduling, which could benefit from sophisticated modelling of pharmacokinetics (PK)/pharmacodynamics (PD) relationships and the formulation of innovative clinical trial designs

  • Mathematical strategies can be used to predict tumour responses, efficacy, and toxicity during treatment planning or adaptation

  • Provision of personalized medicine could be improved through systems-biology approaches that incorporate both multiscale modelling and PK/PD modelling to ensure a better efficacy–toxicity balance of treatment

  • The use of dedicated models to rationally design drug regimens in oncology should result in improved efficacy of treatment and decreased toxicity

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Affiliations

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

    • Dominique Barbolosi
    • , Joseph Ciccolini
    • , Bruno Lacarelle
    •  & Fabrice Barlési
  2. Aix Marseille Université, INSERM, CRO2 UMR_S 911, Marseille 13005, France.

    • Nicolas André
  3. Multidisciplinary Oncology Unit, Nord University Hospital of Marseille, Assistance Publique - Hôpitaux de Marseille (AP-HM), Marseille 13000, France.

    • Fabrice Barlési
  4. Department of Paediatric Haematology and Oncology, Hospital of La Timone, AP-HM, Marseille 13005, France.

    • Nicolas André
  5. Centre d'Essais Précoce Cancérologie Marseille (CEPCM), Hospital of La Timone, AP-HM, Marseille 13005, France.

    • Fabrice Barlési
    •  & Nicolas André

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Contributions

D.B. and J.C. contributed equally to this work. D.B., J.C., F.B., and N.A. researched data for article; D.B., J.C., F.B., B.L., and N.A. contributed substantially to discussion of content; D.B., J.C., F.B., and N.A. wrote the article; and J.C., B.L., and N.A. reviewed/edited the manuscript before submission.

Competing interests

J.C. has received research grant support from Roche. F.B. has received research grant support from Pierre Fabre. The other authors declare no competing interests.

Corresponding author

Correspondence to Nicolas André.

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https://doi.org/10.1038/nrclinonc.2015.204

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