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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

54
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...
54
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

545
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...
545
Drug Delivery: Overview01:16

Drug Delivery: Overview

271
The selection of a drug's delivery route depends upon its physicochemical properties, including lipid or water solubility and ionization, as well as the therapeutic requirement, such as immediate or sustained effect. These routes can be divided into three primary categories: enteral, parenteral, and topical.
Enteral delivery involves administering drugs directly through swallowing, sublingual placement, or buccal application. Orally administered drugs predominantly navigate the...
271
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

38
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.
38
One-Compartment Open Model for Extravascular Administration: Zero-Order Absorption Model01:12

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

50
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...
50
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

72
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
72

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Related Experiment Video

Updated: May 27, 2025

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

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Utilizing machine learning for predicting drug release from polymeric drug delivery systems.

Sareh Aghajanpour1, Hamid Amiriara2, Mehdi Esfandyari-Manesh3

  • 1Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran; Department of Pharmaceutics, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran.

Computers in Biology and Medicine
|February 20, 2025
PubMed
Summary

Machine learning (ML) enhances polymeric drug delivery systems (PDDS) by accurately predicting drug release profiles. Artificial Neural Networks show particular promise for complex systems like 3D-printed dosage forms.

Keywords:
Artificial neural networksDeep learningDrug release prediction modelsIntelligent pharmaceuticsMachine learningPolymeric drug delivery system

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Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Computational Chemistry

Background:

  • Polymeric drug delivery systems (PDDS) are vital for controlled release but face formulation and prediction challenges.
  • Traditional methods struggle with the complexity of PDDS and numerous influencing variables.
  • Machine learning (ML) offers a promising approach to overcome these limitations in drug delivery.

Purpose of the Study:

  • To review ML strategies for predicting drug release from PDDS.
  • To highlight key ML applications across seven sustained-release systems.
  • To discuss challenges, solutions, and future directions in ML-based drug release prediction.

Main Methods:

  • Overview of fundamental ML principles applied to PDDS.
  • Analysis of current research on ML for matrix tablets, microspheres, implants, hydrogels, films, and 3D-printed forms.
  • Evaluation of Artificial Neural Networks (ANNs) and ensemble models for release prediction.

Main Results:

  • Artificial Neural Networks demonstrate superior performance in PDDS release prediction compared to other ML methods.
  • Ensemble models are effective for complex release profiles with multiple parameters.
  • ML shows significant potential for 3D-printed dosage forms, enabling personalized medicine.

Conclusions:

  • ML, particularly ANNs, offers powerful tools for predicting drug release from diverse PDDS.
  • ML facilitates advancements in complex drug delivery systems, including 3D-printed forms.
  • ML-based prediction paves the way for personalized medicine and precise drug delivery.