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Related Concept Videos

Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

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Solid dosage forms such as tablets and capsules undergo rigorous manufacturing processes to ensure stability and effectiveness. Their dissolution and absorption properties are influenced significantly by the choice of excipients (inactive ingredients that serve various roles in the formulation), and the methodology applied during production. The manufacturing parameters, such as compression force and granulation techniques, significantly affect dissolution rates. Elevated compression forces...
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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.
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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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When a drug follows nonlinear pharmacokinetics, its bioavailability, the amount of the drug that reaches the systemic circulation, can change with different doses. This is due to the presence of a saturable pathway. The pathway becomes saturated as the drug concentration increases, decreasing the absorption rate. Consequently, the drug's bioavailability may be lower than expected at higher doses.
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Updated: Sep 13, 2025

Preparation and Characterization of Individual and Multi-drug Loaded Physically Entrapped Polymeric Micelles
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Machine Learning Predicts Drug Release Profiles and Kinetic Parameters Based on Tablets' Formulations.

Chrystalla Protopapa1, Angeliki Siamidi1, Amelia Adibe Eneli2

  • 1Section of Pharmaceutical Technology, Department of Pharmacy, National and Kapodistrian University of Athens, 157 84, Athens, Greece.

The AAPS Journal
|July 28, 2025
PubMed
Summary

Machine learning (ML) models can predict drug release profiles from direct compression (DC) formulations, accelerating pharmaceutical development. This approach offers insights into kinetic parameters, improving formulation optimization for solid dosage forms.

Keywords:
artificial intelligencedirect compression tabletsdrug developmentoral dosage formspredictive modellingrelease kinetic model

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Materials Science

Background:

  • Direct compression (DC) is a widely used method for manufacturing solid dosage forms.
  • Formulation optimization for DC is traditionally time-consuming and resource-intensive.

Purpose of the Study:

  • To evaluate the utility of machine learning (ML) in predicting drug release profiles under dynamic conditions.
  • To accelerate the development and optimization of direct compression formulations.

Main Methods:

  • 377 direct compression formulations were produced and their dynamic dissolution profiles measured.
  • Six machine learning techniques were employed to predict release profiles and kinetic parameters.
  • Random forest (RF) and extreme gradient boosting (XGB) models were assessed using R-squared values.

Main Results:

  • ML models, particularly RF and XGB, demonstrated capability in predicting entire drug release profiles.
  • Achieved a fivefold cross-validation R-squared of 0.635 ± 0.047 (RF) and 0.601 ± 0.091 (XGB).
  • A secondary strategy predicting kinetic parameters yielded comparable results, enhancing model interpretability.

Conclusions:

  • Machine learning can significantly accelerate the prediction of drug release during dynamic dissolution studies.
  • ML models provide valuable insights into kinetic parameters, aiding pharmaceutical researchers in formulation development.
  • Future research will focus on developing more 'kinetic-informed' ML models for enhanced predictive power.