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Title: Scheduling Chemotherapy: Catch 22 between Cell Kill and Resistance Evolution

Abstract

Dose response curves show that prolonged drug exposure at a low concentration may kill more cells than short exposures at higher drug concentrations, particularly for cell cycle phase specific drugs. Applying drugs at low concentrations for prolonged periods, however, allows cells with partial resistance to evolve higher levels of resistance through stepwise processes such as gene amplification. Models are developed for cell cycle specific (CS) and cell cycle nonspecific (CNS) drugs to identify the schedule of drug application that balances this tradeoff. The models predict that a CS drug may be applied most effectively by splitting the cumulative dose into many (>40) fractions applied by long-term chemotherapy, while CNS drugs may be better applied in fewer than 10 fractions applied over a shorter term. The model suggests that administering each fraction by continuous infusion may be more effective than giving the drug as a bolus, whether the drug is CS or CNS. In addition, tumors with a low growth fraction or slow rate of cell division are predicted to be controlled more easily with CNS drugs, while those with a high proliferative fraction or fast cell division rate may respond better to CS drugs.

Authors:
 [1]
  1. NERC Centre for Population Biology, Imperial College at Silwood Park, Ascot, Berkshire SL5 7PY, UK, Biology and Biotechnology Research Program, Lawrence Livermore National Laboratory, P.O. Box 808, L-452, Livermore, CA 94551-0452, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1198191
Grant/Contract Number:  
W7405-ENG-48
Resource Type:
Published Article
Journal Name:
Journal of Theoretical Medicine
Additional Journal Information:
Journal Name: Journal of Theoretical Medicine Journal Volume: 2 Journal Issue: 3; Journal ID: ISSN 1027-3662
Publisher:
Hindawi Publishing Corporation
Country of Publication:
Country unknown/Code not available
Language:
English

Citation Formats

Gardner, Shea N. Scheduling Chemotherapy: Catch 22 between Cell Kill and Resistance Evolution. Country unknown/Code not available: N. p., 2000. Web. doi:10.1080/10273660008833047.
Gardner, Shea N. Scheduling Chemotherapy: Catch 22 between Cell Kill and Resistance Evolution. Country unknown/Code not available. doi:10.1080/10273660008833047.
Gardner, Shea N. Sat . "Scheduling Chemotherapy: Catch 22 between Cell Kill and Resistance Evolution". Country unknown/Code not available. doi:10.1080/10273660008833047.
@article{osti_1198191,
title = {Scheduling Chemotherapy: Catch 22 between Cell Kill and Resistance Evolution},
author = {Gardner, Shea N.},
abstractNote = {Dose response curves show that prolonged drug exposure at a low concentration may kill more cells than short exposures at higher drug concentrations, particularly for cell cycle phase specific drugs. Applying drugs at low concentrations for prolonged periods, however, allows cells with partial resistance to evolve higher levels of resistance through stepwise processes such as gene amplification. Models are developed for cell cycle specific (CS) and cell cycle nonspecific (CNS) drugs to identify the schedule of drug application that balances this tradeoff. The models predict that a CS drug may be applied most effectively by splitting the cumulative dose into many (>40) fractions applied by long-term chemotherapy, while CNS drugs may be better applied in fewer than 10 fractions applied over a shorter term. The model suggests that administering each fraction by continuous infusion may be more effective than giving the drug as a bolus, whether the drug is CS or CNS. In addition, tumors with a low growth fraction or slow rate of cell division are predicted to be controlled more easily with CNS drugs, while those with a high proliferative fraction or fast cell division rate may respond better to CS drugs.},
doi = {10.1080/10273660008833047},
journal = {Journal of Theoretical Medicine},
number = 3,
volume = 2,
place = {Country unknown/Code not available},
year = {2000},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1080/10273660008833047

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