dc.creator | Murphy, Hope | |
dc.creator | McCarthy, Gabriel | |
dc.creator | Dobrovolny, Hana M. | |
dc.date.accessioned | 2021-01-19T19:26:22Z | |
dc.date.available | 2021-01-19T19:26:22Z | |
dc.date.issued | 2020-05-14 | |
dc.identifier.uri | https://doi.org/10.1371/journal.pone.0233031 | |
dc.identifier.uri | https://repository.tcu.edu/handle/116099117/43089 | |
dc.identifier.uri | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233031 | |
dc.description.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: emax 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 emax 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 emax. Using traditional methods, we then calculate the IC50 and emax 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 emax increases with increasing measurement day for most mathematical models. Unfortunately, the measured values of IC50 and emax 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 emax is more accurate at early measurement times. | |
dc.language.iso | en_US | en_US |
dc.publisher | Public Library of Science | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | PLoS One | |
dc.subject | tumor growth | |
dc.subject | cancer treatment | |
dc.subject | model | |
dc.subject | chemotherapy | |
dc.subject | dynamics | |
dc.subject | rates | |
dc.subject | mechanism | |
dc.subject | oncology | |
dc.subject | therapy | |
dc.subject | metrics | |
dc.title | Understanding the effect of measurement time on drug characterization | |
dc.type | Article | |
dc.rights.holder | 2020 Murphy et al | |
dc.rights.license | CC BY 4.0 | |
local.college | College of Science and Engineering | |
local.department | Physics and Astronomy | |
local.persons | All (PHYS) | |