Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

Pharmacokinetic Models: Overview

Pharmacokinetic Models: Overview

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...

Nonlinear Pharmacokinetics: Causes of Nonlinearity

Nonlinear Pharmacokinetics: Causes of Nonlinearity

Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...

Nonlinear Pharmacokinetics: Overview

Nonlinear Pharmacokinetics: Overview

Nonlinear or dose-dependent pharmacokinetics is a phenomenon that occurs when the pharmacokinetic parameters of certain drugs deviate from linear pharmacokinetics at higher doses. These drugs do not follow the expected first-order kinetics, where the rate of drug elimination is directly proportional to the drug concentration. Instead, they exhibit a nonlinear relationship, which can be attributed to several factors.
Nonlinearity can arise due to the saturation of plasma protein-binding or...

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

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...

You might also read

Related Articles

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

Sort by

Same author

Imaging findings of hepatic storage diseases.

Radiologia·2026

Same author

Search for Signatures of Dark Matter Annihilation in the Galactic Center with HAWC.

Physical review letters·2026

Same author

QuiCAT: a scalable and flexible framework for mapping synthetic sequences.

Bioinformatics (Oxford, England)·2025

Same author

Neuroimaging and immunofluorescence of the Pseudopus apodus brain: unraveling its structural complexity.

Brain structure & function·2025

Same author

Changes in biogeographic patterns of coastal fishes: Indicators of tropicalization in the Canary Islands over the last 40 years.

Marine environmental research·2025

Same author

Novel nanostructured lipid carriers loading Apigenin for anterior segment ocular pathologies.

International journal of pharmaceutics·2024

Search research articles

Related Experiment Video

Updated: Nov 15, 2025

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Published on: January 19, 2019

Combination anti-coronavirus therapies based on nonlinear mathematical models.

J A González¹, Z Akhtar², D Andrews³

¹Department of Physics, Florida International University, Miami, Florida 33199, USA.

Chaos (Woodbury, N.Y.)

|March 3, 2021

Summary

New combination therapies for COVID-19 were designed using mathematical models and experimental data. These novel approaches leverage nonlinear mathematical models and extensive data for improved treatment strategies.

More Related Videos

Engineering Antiviral Agents via Surface Plasmon Resonance

Engineering Antiviral Agents via Surface Plasmon Resonance

Published on: June 14, 2022

Diagonal Method to Measure Synergy Among Any Number of Drugs

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Related Experiment Videos

Last Updated: Nov 15, 2025

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Published on: January 19, 2019

Engineering Antiviral Agents via Surface Plasmon Resonance

Engineering Antiviral Agents via Surface Plasmon Resonance

Published on: June 14, 2022

Diagonal Method to Measure Synergy Among Any Number of Drugs

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Area of Science:

Mathematical Biology
Infectious Disease Research
Pharmacology

Background:

COVID-19 remains a significant global health challenge.
Existing therapies have limitations in efficacy and resistance.
The need for innovative treatment strategies is critical.

Purpose of the Study:

To design novel combination therapies for COVID-19.
To utilize nonlinear mathematical models for therapeutic development.
To integrate experimental data for robust treatment design.

Main Methods:

Development of nonlinear mathematical models.
Analysis of laboratory and clinical study data.
In silico design and validation of combination therapies.

Main Results:

Identification of promising synergistic drug combinations.
Mathematical models accurately predicted therapeutic outcomes.
Experimental validation confirmed the efficacy of designed therapies.

Conclusions:

Nonlinear mathematical modeling is a powerful tool for designing COVID-19 combination therapies.
The designed therapies show potential for improved clinical outcomes.
Further research and clinical trials are warranted to evaluate these novel treatments.