Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Computational oncology — mathematical modelling of drug regimens for precision medicine

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

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Possible implementation of mathematical modelling in computational oncology.
Figure 2: Multiscale modelling in oncology.
Figure 3: PK/PD modelling of haematological toxicity and dosing optimization.
Figure 4: Example of PK/PD simulation to optimize a vinorelbine treatment regimen.

References

  1. Shrager, J. & Tenenbaum, J. M. Rapid learning for precision oncology. Nat. Rev. Clin. Oncol. 11, 109–118 (2014).

    PubMed  Google Scholar 

  2. Powathil, G. G., Swat, M. & Chaplain, M. A. Systems oncology: towards patient-specific treatment regimen informed by multiscale mathematical modelling. Semin. Cancer Biol. 30, 13–20 (2015).

    PubMed  Google Scholar 

  3. Agur, Z., Elishmereni, M. & Kheifetz, Y. Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration. Wiley Interdiscip. Rev. Syst. Biol. Med. 6, 239–253 (2014).

    PubMed  Google Scholar 

  4. Heist, R. S. et al. Improved tumor vascularization after anti-VEGF therapy with carboplatin and nab-paclitaxel associates with survival in lung cancer. Proc. Natl Acad. Sci. USA 112, 1547–1552 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Karajannis, M. A. et al. Phase II study of sorafenib in children with recurrent or progressive low-grade astrocytomas. Neuro Oncol. 16, 1408–1416 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Ebos, J. M. et al. Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell 15, 232–239 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Mollard, S. et al. A new mathematical model for describing metastatic spreading: validation in tumor-bearing mice, confrontation with clinical data and in silico simulations to optimize treatment modalities [abstract]. Cancer Res. 73 (8 Suppl.), a402 (2013).

    Google Scholar 

  8. Paci, A. et al. Review of therapeutic drug monitoring of anticancer drugs part 1 — cytotoxics. Eur. J. Cancer 50, 2010–2019 (2014).

    CAS  PubMed  Google Scholar 

  9. Joerger, M. Covariate pharmacokinetic model building in oncology and its potential clinical relevance. AAPS J. 14, 119–132 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Widmer, N. et al. Review of therapeutic drug monitoring of anticancer drugs part two — targeted therapies. Eur. J. Cancer 50, 2020–2036 (2014).

    CAS  PubMed  Google Scholar 

  11. Gao, B. et al. Evidence for therapeutic drug monitoring of targeted anticancer therapies. J. Clin. Oncol. 30, 4017–4025 (2012).

    CAS  PubMed  Google Scholar 

  12. Atkinson, B. J. et al. Clinical outcomes for patients with metastatic renal cell carcinoma treated with alternative sunitinib schedules. J. Urol. 191, 611–618 (2014).

    CAS  PubMed  Google Scholar 

  13. Bjarnason, G. A. et al. Outcomes in patients with metastatic renal cell cancer treated with individualized sunitinib therapy: correlation with dynamic microbubble ultrasound data and review of the literature. Urol. Oncol. 32, 480–487 (2014).

    CAS  PubMed  Google Scholar 

  14. Prasad, V., Massey, P. R. & Fojo, T. Oral anticancer drugs: how limited dosing options and dose reductions may affect outcomes in comparative trials and efficacy in patients. J. Clin. Oncol. 32, 1620–1629 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Klumpen, H. J., Samer, C. F., Mathijssen, R. H., Schellens, J. H. & Gurney, H. Moving towards dose individualization of tyrosine kinase inhibitors. Cancer Treat. Rev. 37, 251–260 (2011).

    PubMed  Google Scholar 

  16. Rousseau, A. & Marquet, P. Application of pharmacokinetic modelling to the routine therapeutic drug monitoring of anticancer drugs. Fundam. Clin. Pharmacol. 16, 253–262 (2002).

    CAS  PubMed  Google Scholar 

  17. Beumer, J. H. Without therapeutic drug monitoring, there is no personalized cancer care. Clin. Pharmacol. Ther. 93, 228–230 (2013).

    CAS  PubMed  Google Scholar 

  18. Picard, N. & Marquet, P. The influence of pharmacogenetics and cofactors on clinical outcomes in kidney transplantation. Expert Opin. Drug Metab. Toxicol. 7, 731–743 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. André, F. et al. Personalized medicine in oncology: where have we come from and where are we going? Pharmacogenomics 14, 931–939 (2013).

    PubMed  Google Scholar 

  20. Bardin, C. et al. Therapeutic drug monitoring in cancer — are we missing a trick? Eur. J. Cancer 50, 2005–2009 (2014).

    CAS  PubMed  Google Scholar 

  21. ICON. Welcome to ICON [online], (2015).

  22. Lixoft. Modelling & Simulation for Drug Development [online], (2015).

  23. Certara. Phoenix NLME — Nonlinear Mixed Effects Modeling [online], (2015).

  24. Diestelhorst, C. et al. Predictive performance of a physiologically based pharmacokinetic model of busulfan in children. Pediatr. Hematol. Oncol. 31, 731–742 (2014).

    CAS  PubMed  Google Scholar 

  25. Patoux, A. et al. Comparison of nonlinear mixed-effect and non-parametric expectation maximisation modelling for Bayesian estimation of carboplatin clearance in children. Eur. J. Clin. Pharmacol. 57, 297–303 (2001).

    CAS  PubMed  Google Scholar 

  26. Rubie, H. et al. Individual dosing of carboplatin based on drug monitoring in children receiving high-dose chemotherapy. Eur. J. Cancer 39, 1433–1438 (2003).

    CAS  PubMed  Google Scholar 

  27. Dupuis, C. et al. High-dose methotrexate in adults with osteosarcoma: a population pharmacokinetics study and validation of a new limited sampling strategy. Anticancer Drugs 19, 267–273 (2008).

    CAS  PubMed  Google Scholar 

  28. Monjanel-Mouterde, S. et al. Bayesian population model of methotrexate to guide dosage adjustments for folate rescue in patients with breast cancer. J. Clin. Pharm. Ther. 27, 189–195 (2002).

    CAS  PubMed  Google Scholar 

  29. Monjanel-Mouterde, S. et al. Population pharmacokinetics of cisplatin after 120-h infusion: application to routine adaptive control with feedback. J. Clin. Pharm. Ther. 28, 109–116 (2003).

    CAS  PubMed  Google Scholar 

  30. Salas, S. et al. Therapeutic drug monitoring for dose individualization of cisplatin in testicular cancer patients based upon total platinum measurement in plasma. Ther. Drug Monit. 28, 532–539 (2006).

    PubMed  Google Scholar 

  31. Mercier, C. et al. Dose individualization of carboplatin after a 120-hour infusion schedule: higher dose intensity but fewer toxicities. Ther. Drug Monit. 28, 212–218 (2006).

    PubMed  Google Scholar 

  32. Woo, M. H. et al. Phase I targeted systemic exposure study of paclitaxel in children with refractory acute leukemias. Clin. Cancer Res. 5, 543–549 (1999).

    CAS  PubMed  Google Scholar 

  33. Tuntland, T. et al. Implementation of pharmacokinetic and pharmacodynamic strategies in early research phases of drug discovery and development at Novartis Institute of Biomedical Research. Front. Pharmacol. 5, 174 (2014).

    PubMed  PubMed Central  Google Scholar 

  34. Zhou, Q. & Gallo, J. M. The pharmacokinetic/pharmacodynamic pipeline: translating anticancer drug pharmacology to the clinic. AAPS J. 13, 111–120 (2011).

    PubMed  PubMed Central  Google Scholar 

  35. Balducci, L. New paradigms for treating elderly patients with cancer: the comprehensive geriatric assessment and guidelines for supportive care. J. Support. Oncol. 1 (Suppl. 2), 30–37 (2003).

    PubMed  Google Scholar 

  36. Beumer, J. H., Chu, E. & Salamone, S. J. Body-surface area-based chemotherapy dosing: appropriate in the 21st century? J. Clin. Oncol. 30, 3896–3897 (2012).

    PubMed  Google Scholar 

  37. Chatelut, E. et al. Dose banding as an alternative to body surface area-based dosing of chemotherapeutic agents. Br. J. Cancer 107, 1100–1106 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Felici, A., Verweij, J. & Sparreboom, A. Dosing strategies for anticancer drugs: the good, the bad and body-surface area. Eur. J. Cancer 38, 1677–1684 (2002).

    CAS  PubMed  Google Scholar 

  39. Loos, W. J. et al. Evaluation of an alternate dosing strategy for cisplatin in patients with extreme body surface area values. J. Clin. Oncol. 24, 1499–1506 (2006).

    CAS  PubMed  Google Scholar 

  40. Gao, B., Klumpen, H. J. & Gurney, H. Dose calculation of anticancer drugs. Expert Opin. Drug Metab. Toxicol. 4, 1307–1319 (2008).

    CAS  PubMed  Google Scholar 

  41. Ciccolini, J., Gross, E., Dahan, L., Lacarelle, B. & Mercier, C. Routine dihydropyrimidine dehydrogenase testing for anticipating 5-fluorouracil-related severe toxicities: hype or hope? Clin. Colorectal Cancer 9, 224–228 (2010).

    CAS  PubMed  Google Scholar 

  42. Yang, C. G. et al. DPD-based adaptive dosing of 5-FU in patients with head and neck cancer: impact on treatment efficacy and toxicity. Cancer Chemother. Pharmacol. 67, 49–56 (2011).

    CAS  PubMed  Google Scholar 

  43. Bai, S. et al. A guide to rational dosing of monoclonal antibodies. Clin. Pharmacokinet. 51, 119–135 (2012).

    CAS  PubMed  Google Scholar 

  44. Long-Boyle, J. R. et al. Population pharmacokinetics of busulfan in pediatric and young adult patients undergoing hematopoietic cell transplant: a model-based dosing algorithm for personalized therapy and implementation into routine clinical use. Ther. Drug Monit. 37, 236–245 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Canal, P., Chatelut, E. & Guichard, S. Practical treatment guide for dose individualisation in cancer chemotherapy. Drugs 56, 1019–1038 (1998).

    CAS  PubMed  Google Scholar 

  46. Chatelut, E. et al. Prediction of carboplatin clearance from standard morphological and biological patient characteristics. J. Natl Cancer Inst. 87, 573–580 (1995).

    CAS  PubMed  Google Scholar 

  47. Schmitt, A. et al. A universal formula based on cystatin C to perform individual dosing of carboplatin in normal weight, underweight, and obese patients. Clin. Cancer Res. 15, 3633–3639 (2009).

    CAS  PubMed  Google Scholar 

  48. Levêque, D., Santucci, R., Gourieux, B. & Herbrecht, R. Pharmacokinetic drug–drug interactions with methotrexate in oncology. Expert Rev. Clin. Pharmacol. 4, 743–750 (2011).

    PubMed  Google Scholar 

  49. Li, J., Zhao, M., He, P., Hidalgo, M. & Baker, S. D. Differential metabolism of gefitinib and erlotinib by human cytochrome P450 enzymes. Clin. Cancer Res. 13, 3731–3737 (2007).

    CAS  PubMed  Google Scholar 

  50. Plowchalk, D. R. & Rowland Yeo, K. Prediction of drug clearance in a smoking population: modeling the impact of variable cigarette consumption on the induction of CYP1A2. Eur. J. Clin. Pharmacol. 68, 951–60 (2012).

    CAS  PubMed  Google Scholar 

  51. Calvert, A. H. Dose optimisation of carboplatin in adults. Anticancer Res. 14, 2273–2278 (1994).

    CAS  PubMed  Google Scholar 

  52. Schmitt, A. et al. Factors for hematopoietic toxicity of carboplatin: refining the targeting of carboplatin systemic exposure. J. Clin. Oncol. 28, 4568–4574 (2010).

    CAS  PubMed  Google Scholar 

  53. Ciccolini, J., Mercier, C., Dahan, L. & André, N. Integrating pharmacogenetics into gemcitabine dosing — time for a change? Nat. Rev. Clin. Oncol. 8, 439–444 (2011).

    CAS  PubMed  Google Scholar 

  54. Narjoz, C. et al. Role of the lean body mass and of pharmacogenetic variants on the pharmacokinetics and pharmacodynamics of sunitinib in cancer patients. Invest. New Drugs 33, 257–268 (2015).

    CAS  PubMed  Google Scholar 

  55. Claret, L. et al. Evaluation of tumor-size response metrics to predict overall survival in Western and Chinese patients with first-line metastatic colorectal cancer. J. Clin. Oncol. 31, 2110–2114 (2013).

    CAS  PubMed  Google Scholar 

  56. Bruno, R., Mercier, F. & Claret, L. Model-based drug development in oncology: what's next? Clin. Pharmacol. Ther. 93, 303–305 (2013).

    CAS  PubMed  Google Scholar 

  57. Sharma, M. R., Maitland, M. L. & Ratain, M. J. Models of excellence: improving oncology drug development. Clin. Pharmacol. Ther. 92, 548–550 (2012).

    CAS  PubMed  Google Scholar 

  58. Friberg, L. E., Henningsson, A., Maas, H., Nguyen, L. & Karlsson, M. O. Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J. Clin. Oncol. 20, 4713–4721 (2002).

    PubMed  Google Scholar 

  59. Iliadis, A. & Barbolosi, D. Optimizing drug regimens in cancer chemotherapy by an efficacy–toxicity mathematical model. Comput. Biomed. Res. 33, 211–226 (2000).

    CAS  PubMed  Google Scholar 

  60. Vainas, O. et al. Personalising docetaxel and G-CSF schedules in cancer patients by a clinically validated computational model. Br. J. Cancer 107, 814–822 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Barbolosi, D. & Iliadis, A. Optimizing drug regimens in cancer chemotherapy: a simulation study using a PK–PD model. Comput. Biol. Med. 31, 157–172 (2001).

    CAS  PubMed  Google Scholar 

  62. Meille, C., Iliadis, A., Barbolosi, D., Frances, N. & Freyer, G. An interface model for dosage adjustment connects hematotoxicity to pharmacokinetics. J. Pharmacokinet. Pharmacodyn. 35, 619–633 (2008).

    CAS  PubMed  Google Scholar 

  63. Meille, C. et al. New adaptive method for phase I trials in oncology. Clin. Pharmacol. Ther. 83, 873–881 (2008).

    CAS  PubMed  Google Scholar 

  64. Joerger, M. et al. Evaluation of a pharmacology-driven dosing algorithm of 3-weekly paclitaxel using therapeutic drug monitoring: a pharmacokinetic-pharmacodynamic simulation study. Clin. Pharmacokinet. 51, 607–617 (2012).

    CAS  PubMed  Google Scholar 

  65. Paule, I. et al. Dose adaptation of capecitabine based on individual prediction of limiting toxicity grade: evaluation by clinical trial simulation. Cancer Chemother. Pharmacol. 66, 447–455 (2012).

    Google Scholar 

  66. Keizer, R. J. et al. Model based treatment optimization of a novel VEGFR inhibitor. Br. J. Clin. Pharmacol. 74, 315–326 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. O'Quigley, J., Pepe, M. & Fisher, L. Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics 46, 33–48 (1990).

    CAS  PubMed  Google Scholar 

  68. Wallin, J. E., Friberg, L. E. & Karlsson, M. O. Model-based neutrophil-guided dose adaptation in chemotherapy: evaluation of predicted outcome with different types and amounts of information. Basic Clin. Pharmacol. Toxicol. 106, 234–242 (2010).

    CAS  PubMed  Google Scholar 

  69. Østby, I., Kvalheim, G., Rusten, L. S. & Grøttum, P. Mathematical modeling of granulocyte reconstitution after high-dose chemotherapy with stem cell support: effect of post-transplant G-CSF treatment. J. Theor. Biol. 231, 69–83 (2004).

    PubMed  Google Scholar 

  70. Pastor, M. L. et al. Model-based approach to describe G-CSF effects in carboplatin-treated cancer patients. Pharm. Res. 30, 2795–2807 (2013).

    CAS  PubMed  Google Scholar 

  71. Gompertz, B. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Phil. Trans. R. Soc. 115, 513–585 (1825).

    Google Scholar 

  72. Mendelsohn, M. L. in Cell Proliferation: (eds Lamerton, L. F. & Fry, R. J.) 190–210 (Blackwell Scientific Publication, 1963).

    Google Scholar 

  73. Gerlee, P. The model muddle: in search of tumor growth laws. Cancer Res. 73, 2407–2411 (2013).

    CAS  PubMed  Google Scholar 

  74. Benzekry, S. et al. Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput. Biol. 10, e1003800 (2014).

    PubMed  PubMed Central  Google Scholar 

  75. Steel, G. G. & Lamerton, L. F. The growth rate of human tumours. Br. J. Cancer 20, 74–86 (1966).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Laird, A. K. Dynamics of tumor growth. Br. J. Cancer 13, 490–502 (1964).

    CAS  PubMed  Google Scholar 

  77. Norton, L. A Gompertzian model of human breast cancer growth. Cancer Res. 48, 7067–7071 (1988).

    CAS  PubMed  Google Scholar 

  78. Ribba, B. et al. A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis. CPT Pharmacometrics Syst. Pharmacol. 3, e113 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Matis, J. H., Kiffe, T. R. & Parthasarathy, P. R. On the cumulants of population size for the stochastic power law logistic model. Theor. Popul. Biol. 53, 16–29 (1998).

    CAS  PubMed  Google Scholar 

  80. Hahnfeldt, P., Panigrahy, D., Folkman, J. & Hlatky, L. Tumor development under angiogenic signaling: a dynamical theory of tumor growth, treatment response, and postvascular dormancy. Cancer Res. 59, 4770–4775 (1999).

    CAS  PubMed  Google Scholar 

  81. Agur, Z. & Vuk-Pavlovic´, S. Mathematical modeling in immunotherapy of cancer: personalizing clinical trials. Mol. Ther. 20, 1–2 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Yu, V. Y. et al. Incorporating cancer stem cells in radiation therapy treatment response modeling and the implication in glioblastoma multiforme treatment resistance. Int. J. Radiat. Oncol. Biol. Phys. 91, 866–875 (2015).

    PubMed  Google Scholar 

  83. Greene, J. M. et al. Modeling intrinsic heterogeneity and growth of cancer cells. J. Theor. Biol. 367, 262–277 (2015).

    PubMed  Google Scholar 

  84. Ledzewicz, U., Bratton, K. & Schättler, H. A 3-compartment model for chemotherapy of heterogeneous tumor populations. Acta Appl. Math. 135, 191–207 (2014).

    Google Scholar 

  85. Hartung, N. et al. Mathematical modeling of tumor growth and metastatic spreading: validation in tumor-bearing mice. Cancer Res. 74, 6397–6407 (2014).

    CAS  PubMed  Google Scholar 

  86. Barbolosi, D. et al. Modélisation du risque d'évolution métastatique chez les patients supposés avoir une maladie localisée [French]. Oncologie 13, 528–533 (2011).

    Google Scholar 

  87. Deisboeck, T. S., Wang, Z., Macklin, P. & Cristini, V. Multiscale cancer modeling. Ann. Rev. Biomed. Eng. 13 127–155 (2011).

    CAS  Google Scholar 

  88. Wang, Z., Butner, J. D., Kerketta, R., Cristini, V. & Deisboeck, T. S. Simulating cancer growth with multiscale agent-based modeling. Semin. Cancer Biol. 30, 70–78 (2015).

    PubMed  Google Scholar 

  89. Stamatakos, G. et al. The technologically integrated oncosimulator: combining multiscale cancer modeling with information technology in the in silico oncology context. IEEE J. Biomed. Health Inform. 18, 840–854 (2014).

    PubMed  Google Scholar 

  90. Stamatakos, G. S., Antipas, V. P. & Uzunoglu, N. K. A spatiotemporal, patient individualized simulation model of solid tumor response to chemotherapy in vivo: the paradigm of glioblastoma multiforme treated by temozolomide. IEEE Trans. Biomed. Eng. 53, 1467–1477 (2006).

    PubMed  Google Scholar 

  91. van der Graaf, P. H. & Benson, N. Systems pharmacology: bridging systems biology and pharmacokinetics-pharmacodynamics (PKPD) in drug discovery and development. Pharm. Res. 28, 1460–1464 (2011).

    CAS  PubMed  Google Scholar 

  92. Gallo, J. M. & Birtwistle, M. R. Network pharmacodynamic models for customized cancer therapy. Wiley Interdiscip. Rev. Syst. Biol. Med. 7, 243–251 (2015).

    PubMed  PubMed Central  Google Scholar 

  93. Fu, F., Nowak, M. A. & Bonhoeffer, S. Spatial heterogeneity in drug concentrations can facilitate the emergence of resistance to cancer therapy. PLoS Comput. Biol. 11, e1004142 (2015).

    PubMed  PubMed Central  Google Scholar 

  94. Vasalou, C., Helmlinger, G. & Gomes, B. A mechanistic tumor penetration model to guide antibody drug conjugate design. PLoS ONE 10, e0118977 (2015).

    PubMed  PubMed Central  Google Scholar 

  95. Wada, R. et al. Mechanistic pharmacokinetic/pharmacodynamic modeling of in vivo tumor uptake, catabolism, and tumor response of trastuzumab maytansinoid conjugates. Cancer Chemother. Pharmacol. 74, 969–980 (2014).

    CAS  PubMed  Google Scholar 

  96. Tate, S. C. et al. Semi-mechanistic pharmacokinetic/pharmacodynamic modeling of the antitumor activity of LY2835219, a new cyclin-dependent kinase 4/6 inhibitor, in mice bearing human tumor xenografts. Clin. Cancer Res. 20, 3763–3774 (2014).

    CAS  PubMed  Google Scholar 

  97. Wong, H. et al. Learning and confirming with preclinical studies: modeling and simulation in the discovery of GDC-0917, an inhibitor of apoptosis proteins antagonist. Drug Metab. Dispos. 41, 2104–2113 (2013).

    CAS  PubMed  Google Scholar 

  98. Wang, J., Iyer, S., Fielder, P. J., Davis, J. D. & Deng, R. Projecting human pharmacokinetics of monoclonal antibodies from nonclinical data: comparative evaluation of prediction approaches in early drug development. Biopharm. Drug Dispos. http://dx.doi.org/10.1002/bdd.1952 (2015).

  99. Ledzewicz, U., Olumoye, O. & Schattler, H. On optimal chemotherapy with a strongly targeted agent for a model of tumor-immune system interactions with generalized logistic growth. Math. Biosci. Eng. 10, 787–802 (2013).

    PubMed  Google Scholar 

  100. Ledzewicz, U., Schattler, H., Gahrooi, M. R. & Dehkordi, S. M. On the MTD paradigm and optimal control for multi-drug cancer chemotherapy. Math. Biosci. Eng. 10, 803–819 (2013).

    PubMed  Google Scholar 

  101. You, B. et al. A mechanistic model predicting hematopoiesis and tumour growth to optimize docetaxel + epirubicin (ET) administration in metastatic breast cancer (MBC): phase I trial [abstract]. J. Clin. Oncol. 18 (Suppl.), a13013 (2007).

    Google Scholar 

  102. Morales, S. et al. Docetaxel plus epirubicin is a highly active, well-tolerated, first-line chemotherapy for metastatic breast cancer: results of a large, multicentre phase II study. Cancer Chemother. Pharmacol. 53, 75–81 (2004).

    CAS  PubMed  Google Scholar 

  103. Nishimura, R. et al. Neoadjuvant epirubicin/docetaxel (ET) concomitant chemotherapy for primary breast cancer with tumor diameter ≥3.1 cm: results of the Kyushu ET therapy phase II trial. Anticancer Res. 32, 3259–3265 (2012).

    CAS  PubMed  Google Scholar 

  104. Citron, M. L. et al. Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: first report of Intergroup Trial C9741/Cancer and Leukemia Group B Trial 9741. J. Clin. Oncol. 21, 1431–1439 (2003).

    CAS  PubMed  Google Scholar 

  105. Cottu, P. H. et al. High-dose sequential epirubicin and cyclophosphamide with peripheral blood stem cell support for advanced breast cancer: results of a phase II study. Br. J. Cancer 85, 1240–1246 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Salminen, E. et al. Epirubicin/docetaxel regimen in progressive breast cancer-a phase II study. Anticancer Drugs 13, 925–929 (2002).

    CAS  PubMed  Google Scholar 

  107. Piccart-Gebhart, M. J. Mathematics and oncology: A match for life? J. Clin. Oncol. 21, 1425–1428 (2003).

    CAS  PubMed  Google Scholar 

  108. Friedrichs, K., Holzel, F. & Janicke, F. Combination of taxanes and anthracyclines in first-line chemotherapy of metastatic breast cancer: an interim report. Eur. J. Cancer 38, 1730–1738 (2002).

    CAS  PubMed  Google Scholar 

  109. André, N., Carre, M. & Pasquier, E. Metronomics: towards personalized chemotherapy? Nat. Rev. Clin. Oncol. 11, 413–431 (2014).

    PubMed  Google Scholar 

  110. Hahnfeldt, P., Folkman, J. & Hlatky, L. Minimizing long-term tumor burden: the logic for metronomic chemotherapeutic dosing and its antiangiogenic basis. J. Theor. Biol. 220, 545–554 (2003).

    PubMed  Google Scholar 

  111. Faivre, C., Barbolosi, D., Pasquier, E. & André, N. A mathematical model for the administration of temozolomide: comparative analysis of conventional and metronomic chemotherapy regimens. Cancer Chemother. Pharmacol. 71, 1013–1019 (2013).

    CAS  PubMed  Google Scholar 

  112. Barbolosi, D. et al. Metronomics chemotherapy: time for computational decision support. Cancer Chemother. Pharmacol. 74, 647–652 (2014).

    CAS  PubMed  Google Scholar 

  113. Mollard, S. et al. Model-based optimization of combined antiangiogenic + cytotoxics modalities: application to the bevacizumab-paclitaxel association in breast cancer models [abstract a3677] in Proceedings of the 105th Annual Meeting of the American Association for Cancer Research (AACR, 2014).

  114. Higgins, B. et al. Preclinical optimization of MDM2 antagonist scheduling for cancer treatment by using a model-based approach. Clin. Cancer Res. 20, 3742–3752 (2014).

    CAS  PubMed  Google Scholar 

  115. European Medicines Agency. Clinical efficacy and safety: clinical pharmacology and pharmacokinetics [online], (2015).

  116. Yang, J. J. et al. Inherited NUDT15 variant is a genetic determinant of mercaptopurine intolerance in children with acute lymphoblastic leukemia. J. Clin. Oncol. 33, 1235–1242 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Swen, J. J. et al. Pharmacogenetics: from bench to byte — an update of guidelines. Clin. Pharmacol. Ther. 89, 662–673 (2011).

    CAS  PubMed  Google Scholar 

  118. Panetta, J. C., Paugh, S. W. & Evans, W. E. Mathematical modeling of folate metabolism. Wiley Interdiscip. Rev. Syst. Biol. Med. 5, 603–613 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Sanga, S. et al. Mathematical modeling of cancer progression and response to chemotherapy. Expert Rev. Anticancer Ther. 6, 1361–1376 (2006).

    CAS  PubMed  Google Scholar 

  120. El-Madani, M. et al. Multiparameter phase I trials: a tool for model-based development of targeted agent combinations — example of EVESOR trial. Future Oncol. 11, 1511–1518 (2015).

    CAS  PubMed  Google Scholar 

  121. Hahnfeldt, P., Hlatky, L. & Klement, G. L. Center of cancer systems biology second annual workshop — tumor metronomics: timing and dose level dynamics. Cancer Res. 73, 2949–2954 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

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.

Corresponding author

Correspondence to Nicolas André.

Ethics declarations

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.

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barbolosi, D., Ciccolini, J., Lacarelle, B. et al. Computational oncology — mathematical modelling of drug regimens for precision medicine. Nat Rev Clin Oncol 13, 242–254 (2016). https://doi.org/10.1038/nrclinonc.2015.204

Download citation

  • Published:

  • Issue Date:

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

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer