IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0233031.html
   My bibliography  Save this article

Understanding the effect of measurement time on drug characterization

Author

Listed:
  • Hope Murphy
  • Gabriel McCarthy
  • Hana M Dobrovolny

Abstract

In order to determine correct dosage of chemotherapy drugs, the effect of the drug must be properly quantified. There are two important values that characterize the effect of the drug: εmax is the maximum possible effect of a drug, and IC50 is the drug concentration where the effect diminishes by half. There is currently a problem with the way these values are measured because they are time-dependent measurements. We use mathematical models to determine how the εmax and IC50 values depend on measurement time and model choice. Seven ordinary differential equation models (ODE) are used for the mathematical analysis; the exponential, Mendelsohn, logistic, linear, surface, Bertalanffy, and Gompertz models. We use the models to simulate tumor growth in the presence and absence of treatment with a known IC50 and εmax. Using traditional methods, we then calculate the IC50 and εmax values over fifty days to show the time-dependence of these values for all seven mathematical models. The general trend found is that the measured IC50 value decreases and the measured εmax increases with increasing measurement day for most mathematical models. Unfortunately, the measured values of IC50 and εmax rarely matched the values used to generate the data. Our results show that there is no optimal measurement time since models predict that IC50 estimates become more accurate at later measurement times while εmax is more accurate at early measurement times.

Suggested Citation

  • Hope Murphy & Gabriel McCarthy & Hana M Dobrovolny, 2020. "Understanding the effect of measurement time on drug characterization," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-15, May.
  • Handle: RePEc:plo:pone00:0233031
    DOI: 10.1371/journal.pone.0233031
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233031
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0233031&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0233031?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yu, Jui-Ling & Jang, Sophia R.-J., 2019. "A mathematical model of tumor-immune interactions with an immune checkpoint inhibitor," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.
    2. Víctor M Pérez-García & Luis E Ayala-Hernández & Juan Belmonte-Beitia & Philippe Schucht & Michael Murek & Andreas Raabe & Juan Sepúlveda, 2019. "Computational design of improved standardized chemotherapy protocols for grade II oligodendrogliomas," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-17, July.
    3. Nicolas Houy & François Le Grand, 2018. "Optimal dynamic regimens with artificial intelligence: The case of temozolomide," Post-Print halshs-01949651, HAL.
    4. Nicolas Houy & François Le Grand, 2018. "Optimal dynamic regimens with artificial intelligence : The case of temozolomide," Post-Print hal-02312154, HAL.
    5. Maria Pia Saccomani & Karl Thomaseth, 2018. "The Union between Structural and Practical Identifiability Makes Strength in Reducing Oncological Model Complexity: A Case Study," Complexity, Hindawi, vol. 2018, pages 1-10, February.
    6. Margaret M Demment & Karen Peters & J Andrew Dykens & Ann Dozier & Haq Nawaz & Scott McIntosh & Jennifer S Smith & Angela Sy & Tracy Irwin & Thomas T Fogg & Mahmooda Khaliq & Rachel Blumenfeld & Mehra, 2015. "Developing the Evidence Base to Inform Best Practice: A Scoping Study of Breast and Cervical Cancer Reviews in Low- and Middle-Income Countries," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nicolas Houy & François Le Grand, 2019. "Optimizing treatment combination for lymphoma using an optimization heuristic," Post-Print halshs-02386445, HAL.
    2. Nicolas Houy & François Le Grand, 2019. "Personalized oncology with artificial intelligence: The case of temozolomide," Post-Print halshs-02386458, HAL.
    3. Nicolas Houy & Julien Flaig, 2021. "Hospital-wide surveillance-based antimicrobial treatments: A Monte-Carlo look-ahead method," Post-Print halshs-03506952, HAL.
    4. Zhao, Zhong & Pang, Liuyong & Li, Qiuying, 2021. "Analysis of a hybrid impulsive tumor-immune model with immunotherapy and chemotherapy," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    5. Lam, Nicholas N. & Docherty, Paul D. & Murray, Rua, 2022. "Practical identifiability of parametrised models: A review of benefits and limitations of various approaches," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 199(C), pages 202-216.
    6. Bach Xuan Tran & Tracy Vo & Anh Kim Dang & Quang Nhat Nguyen & Cuong Tat Nguyen & Chi Linh Hoang & Khanh Nam Do & Carl A. Latkin & Cyrus S. H. Ho & Roger C. M. Ho, 2019. "Knowledge towards Cervical and Breast Cancers among Industrial Workers: Results from a Multisite Study in Northern Vietnam," IJERPH, MDPI, vol. 16(21), pages 1-12, November.
    7. Alejandro F. Villaverde, 2019. "Observability and Structural Identifiability of Nonlinear Biological Systems," Complexity, Hindawi, vol. 2019, pages 1-12, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0233031. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.