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

Retrovirus Life Cycles01:10

Retrovirus Life Cycles

47.0K
Retroviruses have a single-stranded RNA genome that undergoes a special form of replication. Once the retrovirus has entered the host cell, an enzyme called reverse transcriptase synthesizes double-stranded DNA from the retroviral RNA genome. This DNA copy of the genome is then integrated into the host’s genome inside the nucleus via an enzyme called integrase. Consequently, the retroviral genome is transcribed into RNA whenever the host’s genome is transcribed, allowing the...
47.0K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

132
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...
132
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

234
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...
234
Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

2.1K
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...
2.1K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.2K
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...
1.2K
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

133
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...
133

You might also read

Related Articles

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

Sort by
Same author

Glycemic variability and muscle loss in elderly type 2 diabetes: insights from continuous glucose monitoring and chest CT in 303 patients.

Frontiers in endocrinology·2026
Same author

Arabidopsis MYC2-ARF16-ABI5 complex functions as a transcriptional switch to regulate jasmonate, auxin, and abscisic acid signaling synergy.

Plant physiology·2026
Same author

Nitroxyl relieves acute kidney injury by suppressing SLC31A1-mediated cuproptosis in renal tubular epithelial cells.

Life sciences·2026
Same author

A prognostic exosome-related mRNAs risk signature correlates with the immune microenvironment in breast cancer.

Discover oncology·2026
Same author

<i>TIMP1</i> and <i>DPP4</i> Promote Tumor Progression by Regulating Lactate Metabolism in Papillary Thyroid Carcinoma.

Cancers·2026
Same author

Unveiling GDF-15: a new frontier in combating diabetic osteoporosis.

Frontiers in medicine·2026

Related Experiment Video

Updated: Sep 20, 2025

Humanized NOD/SCID/IL2r&#947;null (hu-NSG) Mouse Model for HIV Replication and Latency Studies
07:10

Humanized NOD/SCID/IL2rγnull (hu-NSG) Mouse Model for HIV Replication and Latency Studies

Published on: January 7, 2019

15.8K

HIV dynamics under multi-drug combination therapy: mathematical modelling and data fitting.

Ning Bai1,2,3, Rui Xu4,5

  • 1Complex Systems Research Center, Shanxi University, Taiyuan, 030006, Shanxi, China.

Journal of Mathematical Biology
|May 29, 2025
PubMed
Summary

Combination HIV therapy, like tenofovir disoproxil fumarate (TDF), lamivudine (3TC), and efavirenz (EFV), is more effective than monotherapy. Early treatment initiation and adherence are crucial for viral load suppression and successful HIV management.

Keywords:
Data fittingHIV infectionMulti-drug combination therapyPharmacodynamicsPharmacokinetics

More Related Videos

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.5K
Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors
05:46

Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors

Published on: April 9, 2014

18.0K

Related Experiment Videos

Last Updated: Sep 20, 2025

Humanized NOD/SCID/IL2r&#947;null (hu-NSG) Mouse Model for HIV Replication and Latency Studies
07:10

Humanized NOD/SCID/IL2rγnull (hu-NSG) Mouse Model for HIV Replication and Latency Studies

Published on: January 7, 2019

15.8K
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.5K
Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors
05:46

Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors

Published on: April 9, 2014

18.0K

Area of Science:

  • Mathematical modeling of infectious diseases
  • Pharmacokinetics and pharmacodynamics
  • Computational biology and bioinformatics

Background:

  • The National Free AIDS Antiviral Drug Treatment manual (2023) recommends TDF, 3TC, and EFV as first-line HIV treatment.
  • Understanding the necessity of combination therapy and the impact of different regimens on viral load dynamics is critical.

Purpose of the Study:

  • To investigate the reasons behind multi-drug combination therapy for HIV viral load suppression.
  • To analyze the effects of various medication regimens on viral load dynamics.
  • To evaluate the impact of medication adherence on treatment outcomes.

Main Methods:

  • Developed a within-host HIV infection model integrating viral dynamics and pharmacokinetics.
  • Utilized a two-compartment model to describe drug concentration over time.
  • Applied Markov-chain Monte-Carlo (MCMC) with the Metropolis-Hastings (M-H) algorithm for parameter estimation.

Main Results:

  • Monotherapy can suppress viral load with strict adherence, but adherence impact is more pronounced.
  • Multi-drug combination therapy shows a diminished impact of adherence compared to monotherapy.
  • Early initiation of first-line treatment is vital for successful HIV treatment outcomes.

Conclusions:

  • Combination therapy is essential for effective HIV viral load suppression.
  • Medication adherence plays a differential role in monotherapy versus combination therapy.
  • Timely initiation of antiretroviral therapy improves treatment success rates and guides clinical practice.