Dataset Information

Understanding the effect of measurement time on drug characterization.

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: ?max 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 ?max 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 ?max. Using traditional methods, we then calculate the IC50 and ?max 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 ?max increases with increasing measurement day for most mathematical models. Unfortunately, the measured values of IC50 and ?max 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 ?max is more accurate at early measurement times.

SUBMITTER: Murphy H

PROVIDER: S-EPMC7224495 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Understanding the effect of measurement time on drug characterization.

Murphy Hope H McCarthy Gabriel G Dobrovolny Hana M HM

PloS one 20200514 5

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 mea ...[more]

PMID: 32407356

Similar Datasets

Project description:PURPOSE:The aim of this study was to analyze the effect of epoch length on intensity classification during continuous and intermittent activities. METHODS:Ten active students exercised under controlled conditions on a treadmill for four 5-min bouts by combining two effort intensities (running and walking) and two physical activity (PA) patterns (continuous or intermittent). The testing session was designed to generate a known level of moderate to vigorous PA (MVPA) for each condition. These PA levels were used as criterion measures to compare with the accelerometer measures. Data obtained from the accelerometer were reintegrated into 1-sec, 10-sec, 30-sec and 60-sec epochs. Equivalence testing was used to examine measurement agreements between MVPA values obtained with the different epochs and the reference values. Mean absolute percent errors (MAPE) were also calculated to provide an indicator of overall measurement error. RESULTS:During the intermittent conditions, only the value obtained with the 1-sec epoch was significantly equivalent to the reference value. With longer epochs the difference increased for both intermittent conditions but in an opposite way: with longer epochs, MVPA decreased during walking but increased during running. Regarding the measurement accuracy, the pattern of variations according to the epoch length selected during the intermittent conditions was identical between walking and running: MAPE increased with the increase in epoch length. MAPE remained low only for the 1-sec epoch (7.6% and 2.7% for walking and running, respectively), increased at 31.3% and 34% for the 10-sec epoch and until near 100% with the 30- and 60-sec epoch lengths. CONCLUSION:This study highlighted the misclassification of exercise intensity based on accelerometer measurement and described for the first time the extent and the direction of this misclassification. Moreover, we can confirm that the shorter epochs are more accurate to measure the real exercise intensity during intermittent PA whatever the intensity.

Project description:Antibody-drug conjugates (ADCs) are designed to target antigen expressing (Ag+) cells in a tumor. Once processed by the Ag+ cells, ADCs can release cytotoxic drug molecules that can diffuse out of Ag+ cells into the neighboring antigen-negative (Ag-) cells to induce their cytotoxicity. This additional efficacy of ADCs on Ag- cells in the presence of Ag+ cells is known as the 'bystander effect'. Although the importance of this phenomena is widely acknowledged for effective killing of a heterogeneous tumor, the rate and extent of the bystander killing in a heterogeneous system is not quantitatively understood yet. Thus, the objectives of this manuscript were to: (1) synthesize and characterize a tool ADC Trastuzumab-vc-MMAE that is capable of exhibiting bystander effect, (2) quantify the time course of the bystander effect for the tool ADC using in vitro co-culture systems created using mixture of various HER2-expressing cell lines, and (3) develop a pharmacodynamic (PD) model that is capable of characterizing the bystander effect of ADCs. Co-culture studies conducted using GFP labelled MCF7 cells as Ag- cells and N87, BT474, and SKBR3 as Ag+ cells revealed that the bystander effect of ADC increases with increasing fraction of Ag+ cells in a co-culture system, and with increased expression level of target on Ag+ cells. A notable lag time after ADC incubation was also observed prior to significant bystander killing of Ag- cells. Based on our results we hypothesize that there may be other determinants apart from the antigen expression level that can also influence the ability of Ag+ cells to demonstrate the bystander effect in a co-culture system. The co-culture analysis also suggested that the bystander effect of the ADC can dissipate over the period of time as the population of Ag+ cells declines. A novel PD model was developed to mathematically characterize the bystander effect of ADCs by combining two different cell distribution models to represent the population of Ag+ and Ag- cells in a co-culture system. This PD model can be integrated with the systems PK model for ADCs in the future to generate a quantitative framework that is capable of supporting the discovery and development of novel ADCs with optimal bystander killing capabilities.

Dataset Information

Understanding the effect of measurement time on drug characterization.

Publications

Understanding the effect of measurement time on drug characterization.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets