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

Drug Accumulation During Multiple Dosing: Repetitive IV Injections01:21

Drug Accumulation During Multiple Dosing: Repetitive IV Injections

256
Calculating drug dosage and accumulation in multiple-dose regimens is crucial for achieving therapeutic efficacy while avoiding toxicity. This involves determining the plasma drug concentrations over time to optimize dosing schedules. The principle of superposition is fundamental in this process, allowing for the prediction of drug concentration in plasma following multiple doses based on single-dose data.The principle of superposition asserts that the plasma concentration-time curves from...
256
Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

234
Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though...
234
One-Compartment Open Model for Extravascular Administration: Zero-Order Absorption Model01:12

One-Compartment Open Model for Extravascular Administration: Zero-Order Absorption Model

368
Extravascular administration, such as oral or intramuscular routes, is a non-invasive drug delivery method, often preferred for ease and patient compliance. A key factor here is absorption, which dictates how quickly and effectively the drug enters the bloodstream from the administration site. Absorption follows either zero-order or first-order kinetics.
Zero-order absorption maintains a steady rate irrespective of the amount of drug left to be absorbed, making it a constant process. In the...
368
One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution01:09

One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution

857
The one-compartment open model is a simplified approach used in pharmacokinetics to understand the distribution and elimination of a drug administered through an intravenous bolus. This model assumes rapid drug dispersal throughout the body and elimination using a first-order process. Key pharmacokinetic parameters, such as the elimination rate constant (k), half-life (t1/2), and the apparent volume of distribution (Vd), can be estimated from this model. The elimination rate is calculated...
857
Two-Compartment Open Model: Extravascular Administration01:12

Two-Compartment Open Model: Extravascular Administration

663
The two-compartment model for extravascular administration represents a drug's absorption and distribution process. It features a central compartment, where the drug is first absorbed, and a peripheral compartment, which illustrates the drug's distribution throughout the body. The rate of change in drug concentration in the central compartment is calculated by three exponents: absorption, distribution, and elimination.
The absorption exponent (ka) indicates the speed at which the drug...
663
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

292
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
292

You might also read

Related Articles

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

Sort by
Same author

Predicting early and complete drug release from long-acting injectables using explainable machine learning.

International journal of pharmaceutics·2026
Same author

Advances in Electrospun Poly(ε-caprolactone)-Based Nanofibrous Scaffolds for Tissue Engineering.

Polymers·2024
Same author

Attention-based Imputation of Missing Values in Electronic Health Records Tabular Data.

Proceedings. IEEE International Conference on Healthcare Informatics·2024
Same author

Deep Clustering of Electronic Health Records Tabular Data for Clinical Interpretation.

... IEEE International Conference on Telecommunications and Photonics. IEEE International Conference on Telecommunications and Photonics·2024
Same author

Deep imputation of missing values in time series health data: A review with benchmarking.

Journal of biomedical informatics·2023
Same author

Perturbation of deep autoencoder weights for model compression and classification of tabular data.

Neural networks : the official journal of the International Neural Network Society·2022
Same journal

Poisoning the Genome: Targeted Backdoor Attacks on DNA Foundation Models.

ArXiv·2026
Same journal

Mechanistic mathematical model of the in vitro infection dynamics of Bunyamwera and Batai viruses including MOI-dependent shortening of the eclipse phase.

ArXiv·2026
Same journal

AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics.

ArXiv·2026
Same journal

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI.

ArXiv·2026
Same journal

Feynman Kac Reweighted Schrödinger Bridge Matching for Surface-Based Tau PET Harmonization.

ArXiv·2026
Same journal

Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3.

ArXiv·2026
See all related articles

Related Experiment Video

Updated: Jan 18, 2026

Preparation and Characterization of Individual and Multi-drug Loaded Physically Entrapped Polymeric Micelles
07:32

Preparation and Characterization of Individual and Multi-drug Loaded Physically Entrapped Polymeric Micelles

Published on: August 28, 2015

11.9K

Predicting Early and Complete Drug Release from Long-Acting Injectables Using Explainable Machine Learning.

Karla N Robles1, Manar D Samad2

  • 1TIGER Institute of Advanced Materials, Tennessee State University, Nashville, TN, USA.

Arxiv
|January 16, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models predict drug release from polymer-based long-acting injectables (LAIs) by analyzing material properties. This approach optimizes controlled drug delivery for chronic diseases, improving therapeutic outcomes.

Keywords:
delayed releasedrug releaselong-acting injectablesmachine learning

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Slow-release Drug Delivery through Elvax 40W to the Rat Retina: Implications for the Treatment of Chronic Conditions
07:49

Slow-release Drug Delivery through Elvax 40W to the Rat Retina: Implications for the Treatment of Chronic Conditions

Published on: September 17, 2014

11.9K

Related Experiment Videos

Last Updated: Jan 18, 2026

Preparation and Characterization of Individual and Multi-drug Loaded Physically Entrapped Polymeric Micelles
07:32

Preparation and Characterization of Individual and Multi-drug Loaded Physically Entrapped Polymeric Micelles

Published on: August 28, 2015

11.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Slow-release Drug Delivery through Elvax 40W to the Rat Retina: Implications for the Treatment of Chronic Conditions
07:49

Slow-release Drug Delivery through Elvax 40W to the Rat Retina: Implications for the Treatment of Chronic Conditions

Published on: September 17, 2014

11.9K

Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Computational Chemistry

Background:

  • Polymer-based long-acting injectables (LAIs) offer controlled drug delivery for chronic diseases, reducing dosing frequency.
  • Optimizing LAI physicochemical properties for controlled release is complex and time-consuming.
  • Existing machine learning (ML) studies lack tailored analysis for LAI data, limiting insights into drug release modulation.

Purpose of the Study:

  • To develop and apply a novel explainable ML approach for LAI formulation analysis.
  • To predict early and complete drug release profiles from LAI formulations.
  • To identify key material characteristics influencing drug release dynamics.

Main Methods:

  • Utilized a dataset of 321 LAI formulations.
  • Employed data transformation and explainable ML techniques.
  • Performed predictions of drug release at 24, 48, and 72 hours, classification of release profiles, and prediction of complete release curves.

Main Results:

  • Achieved a strong correlation (>0.65) between predicted and true drug release at 72 hours.
  • Obtained an F1-score of 0.87 for classifying release profile types.
  • Developed a time-independent ML framework outperforming time-dependent methods for predicting complex release profiles (biphasic, triphasic).
  • Shapley additive explanations identified critical material properties influencing early and complete drug release.

Conclusions:

  • The novel ML approach provides actionable insights into LAI material characteristics and drug release.
  • This quantitative strategy can guide scientists in optimizing LAI drug release dynamics.
  • The study addresses limitations in previous in-vitro and ML-based analyses of LAIs.