Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

193
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
193
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

257
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
257
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

181
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
181
Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

2.8K
The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
2.8K
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

227
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
227
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.7K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
1.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

INHERITANCE OF INTRACELLULAR VIRAL RNA IN A MULTISCALE MODEL OF HEPATITIS C INFECTION.

SIAM journal on applied mathematics·2026
Same author

Handheld hyperspectral imaging dataset of annual sowthistle and little mallow under abiotic stress for machine learning.

Data in brief·2026
Same author

Dynamics, Noise, Delays and the Gibbs and Conditional Entropy.

Entropy (Basel, Switzerland)·2026
Same author

Genuine and spurious bistability in a simple epidemic model with waning immunity.

Mathematical biosciences·2026
Same author

Growth rate-driven modelling suggests that phenotypic adaptation drives drug resistance in BRAFV600E-mutant melanoma.

Communications biology·2026
Same author

Viral evolution during primary infection in immunocompromised hosts.

PLoS computational biology·2026

Related Experiment Video

Updated: Dec 13, 2025

Modeling Chemotherapy Resistant Leukemia In Vitro
08:41

Modeling Chemotherapy Resistant Leukemia In Vitro

Published on: February 9, 2016

9.4K

Characterizing Chemotherapy-Induced Neutropenia and Monocytopenia Through Mathematical Modelling.

Tyler Cassidy1, Antony R Humphries2,3, Morgan Craig4,5

  • 1Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.

Bulletin of Mathematical Biology
|August 2, 2020
PubMed
Summary

Chemotherapy causes low neutrophil and monocyte counts. Monocyte reduction predicts neutrophil reduction by 3 days, suggesting monocyte levels can guide G-CSF treatment to reduce chemotherapy toxicity.

Keywords:
Cyclic chemotherapyG-CSFMathematical modelingMonocytesNeutrophilsTherapy rationalization

More Related Videos

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia
08:31

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia

Published on: October 17, 2025

435
Isolation and Characterization of Neutrophils with Anti-Tumor Properties
10:15

Isolation and Characterization of Neutrophils with Anti-Tumor Properties

Published on: June 19, 2015

25.3K

Related Experiment Videos

Last Updated: Dec 13, 2025

Modeling Chemotherapy Resistant Leukemia In Vitro
08:41

Modeling Chemotherapy Resistant Leukemia In Vitro

Published on: February 9, 2016

9.4K
Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia
08:31

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia

Published on: October 17, 2025

435
Isolation and Characterization of Neutrophils with Anti-Tumor Properties
10:15

Isolation and Characterization of Neutrophils with Anti-Tumor Properties

Published on: June 19, 2015

25.3K

Area of Science:

  • Hematology
  • Computational Biology
  • Oncology

Background:

  • Cytotoxic chemotherapy is a cornerstone of cancer treatment but causes significant hematopoietic toxicity.
  • Chemotherapy-induced neutropenia (a lack of neutrophils) can limit treatment efficacy and increase infection risk.
  • Granulocyte colony-stimulating factor (G-CSF) is used to mitigate neutropenia, but optimal timing remains a challenge.

Purpose of the Study:

  • To develop and parameterize a mechanistic model of monocytopoiesis.
  • To analyze the relationship between neutrophil and monocyte counts during cyclic chemotherapy.
  • To identify potential clinical markers for G-CSF dosing to manage chemotherapy-induced cytopenias.

Main Methods:

  • Constructed a mathematical model of monocytopoiesis.
  • Utilized patient data from a cohort of childhood acute lymphoblastic leukemia patients.
  • Analyzed neutrophil and monocyte concentrations during chemotherapy cycles with and without G-CSF support.

Main Results:

  • Monocytopenia (low monocyte count) was observed to precede neutropenia (low neutrophil count) by approximately 3 days.
  • The model successfully predicted the dynamics of both neutrophil and monocyte responses to chemotherapy.
  • Established a rationale for using the onset of monocytopenia as a clinical marker for G-CSF administration.

Conclusions:

  • Monocyte dynamics provide a predictive marker for neutrophil nadirs during chemotherapy.
  • The onset of monocytopenia can serve as a clinical indicator for initiating G-CSF therapy.
  • This approach offers a refined strategy for managing chemotherapy-induced hematopoietic toxicity, improving patient outcomes.