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The Norton–Simon hypothesis: designing more effective and less toxic chemotherapeutic regimens

Nature Clinical Practice Oncology volume 3, pages 406–407 (2006)Cite this article

Successful treatment of bacterial infections is largely a result of our ability to exploit the biochemical differences between bacteria and human cells so as to achieve toxic drug concentrations in the former while sparing the latter. Unfortunately, such high selectivity is at present elusive in the chemotherapy of human cancers. Hence, great effort is required to determine dose schedules that maximize the benefit/toxicity ratio, particularly for multiple agent regimens.

Extensive clinical experience has taught us that trial-and-error, in the absence of guiding principles, is inefficient in this regard. Seeking guiding principles, we used clinical and laboratory observations to derive a phenomenological law relating the effect of cytotoxic chemotherapy on tumor size to the growth dynamics of the tumor. This relationship was referred to by others as the Norton–Simon hypothesis, i.e. that chemotherapy results in a rate of regression in tumor volume that is proportional to the rate of growth for an unperturbed tumor of that size.1,2,3 This relationship differed from the existing “log kill” model, which stated that a given dose of chemotherapy killed the same fraction of tumor cells, regardless of the size of the tumor at the time of administration.4 The log kill model worked for experimental leukemia because the growth dynamics of the cancer were constant during the course of observation, but it failed when applied to human and experimental solid tumors in which tumor size approached a plateau level, dynamics that were often adequately captured by Gompertzian growth curves.5

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References

  1. Norton L and Simon R (1976) Tumor size, sensitivity to therapy and the design of treatment protocols. Cancer Treat Rep 61: 1307–1317

    Google Scholar 

  2. Norton L and Simon R (1977) The growth curve of an experimental solid tumor following radiotherapy. J Natl Cancer Inst 58: 1735–1741

    Article  CAS  Google Scholar 

  3. Norton L and Simon R (1986) The Norton-Simon hypothesis revisited. Cancer Treat Rep 70: 163–169

    CAS  PubMed  Google Scholar 

  4. Skipper HE (1986) Laboratory models: some historical perspectives. Cancer Treat Rep 70: 3–7

    CAS  PubMed  Google Scholar 

  5. Norton L et al. (1976) Predicting the course of Gompertzian growth. Nature 264: 542–545

    Article  CAS  Google Scholar 

  6. Bonadonna G et al. (1995) Sequential or alternating doxorubicin and CMF regimens in breast cancer with more than three positive nodes. Ten-year results. JAMA 273: 542–547

    Article  CAS  Google Scholar 

  7. Bonadonna G et al. (2004) Clinical relevance of different sequencing of doxorubicin and cyclophosphamide, methotrexate and fluorouracil in operable breast cancer. J Clin Oncol 22: 1614–1620

    Article  CAS  Google Scholar 

  8. Goldie JH and Coldman AJ (1979) A mathematical model for relating the drug sensitivity of tumors to their spontaneous mutation rate. Cancer Treat Rep 63: 1727–1733

    CAS  PubMed  Google Scholar 

  9. Citron ML et al. (2003) 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

    Article  CAS  Google Scholar 

  10. Venturini M et al. (2005) Dose-dense adjuvant chemotherapy in early breast cancer patients: Results from a randomized trial. J Natl Cancer Inst 97: 1724–1733

    Article  CAS  Google Scholar 

  11. Norton L (2005) Conceptual and practical implications of breast tissue geometry: toward a more effective, less toxic therapy. Oncologist 10: 370–381

    Article  CAS  Google Scholar 

  12. Norton L et al. (2005) Optimizing chemotherapeutic dose-schedule by Norton-Simon modeling: capecitabine. In 96th Annual Meeting of the American Association of Cancer Research: 2005 April 16–20; Anaheim, abstract #5007

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Authors and Affiliations

  1. Chief of the Biometric Research Branch at the National Cancer Institute, Bethesda, MD, USA

    Richard Simon

  2. Memorial Hospital,

    Larry Norton

  3. Evelyn H. Lauder Breast Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA

    Larry Norton

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  1. Richard Simon
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  2. Larry Norton
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Correspondence to Richard Simon.

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The authors declare no competing financial interests.

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Simon, R., Norton, L. The Norton–Simon hypothesis: designing more effective and less toxic chemotherapeutic regimens. Nat Rev Clin Oncol 3, 406–407 (2006). https://doi.org/10.1038/ncponc0560

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