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Related Concept Videos

Oral Drug Delivery Systems: Continuous-Release Systems01:26

Oral Drug Delivery Systems: Continuous-Release Systems

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Continuous-release drug delivery systems offer a strategic approach to maintaining therapeutic drug levels over extended periods following oral administration. By modulating the release rate of active pharmaceutical ingredients, these systems minimize fluctuations in plasma concentrations, which enhances clinical efficacy and reduces the need for frequent dosing. Such characteristics make them particularly advantageous in managing chronic diseases where patient adherence and stable drug...
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Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

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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...
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Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

834
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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Modified-Release Drug Delivery Systems: Drug Release Characteristics01:22

Modified-Release Drug Delivery Systems: Drug Release Characteristics

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Drug release from modified-release dosage forms is designed to achieve specific therapeutic effects by controlling the rate and extent of drug release. The classification of these drug release systems is based on key pharmacokinetic assumptions: drug disposition follows first-order kinetics, drug release is the rate-limiting step in absorption, and the released drug is rapidly and completely absorbed.There are four major models of drug release patterns. The first model is the slow zero-order...
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

426
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...
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Modified-Release Drug Delivery Systems: Rate-Programmed II01:19

Modified-Release Drug Delivery Systems: Rate-Programmed II

137
Rate-programmed drug delivery systems release drugs in a controlled manner to maintain therapeutic levels. Three main designs include reservoir, matrix, and hybrid systems.Reservoir systems consist of a drug core enclosed within a membrane that controls drug release. In non-swelling reservoir systems, polymers like ethyl cellulose or polymethacrylates are used. These do not hydrate in aqueous media and control release through membrane thickness, porosity, or insolubility. This type includes...
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Related Experiment Video

Updated: May 3, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting the viability of pharmaceutical formulations for continuous direct compression using machine learning

Laura Pereira Diaz1, Stéphanie Marchal2, Paul Kroll2

  • 1CMAC, University of Strathclyde, Technology and Innovation Centre, 99 George Street, Glasgow G1 1RD, UK; Strathclyde Institute of Pharmacy & Biomedical Sciences, 161 Cathedral St, Glasgow G4 0RE, UK.

International Journal of Pharmaceutics
|May 1, 2026
PubMed
Summary

Artificial intelligence and Machine Learning (ML) models predict pharmaceutical formulation viability for continuous direct compression (cDC). This data-driven approach accelerates development and improves manufacturing decisions.

Keywords:
Continuous direct compression (cDC)Machine learningPharmaceutical formulationPowder flowabilityPredictive modelling

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Area of Science:

  • Pharmaceutical Sciences
  • Chemical Engineering
  • Computational Science

Background:

  • Pharmaceutical formulation involves combining active pharmaceutical ingredients (API) and excipients, where changes can affect bulk properties like powder flowability.
  • These properties significantly influence manufacturing processes, highlighting the need for predictive techniques.
  • Subtle API physical property variations (e.g., particle size, shape) also impact drug product manufacturability.

Purpose of the Study:

  • To present a novel approach using Artificial Intelligence (AI) and Machine Learning (ML) to predict pharmaceutical formulation viability.
  • To support the optimization of the transition from formulation development to manufacturing.
  • To enable early assessment of formulation suitability for continuous direct compression (cDC).

Main Methods:

  • Development and integration of three complementary ML models.
  • Application of data-driven models for predictive screening of formulation properties.
  • Utilizing digital design principles to assess manufacturability.

Main Results:

  • The combined ML modeling approach provides a practical framework for early viability assessment.
  • Enables predictive screening of pharmaceutical formulations for cDC.
  • Supports informed decision-making in formulation development.

Conclusions:

  • AI and ML offer powerful tools to accelerate pharmaceutical development.
  • The presented modeling approach enhances the prediction of formulation manufacturability.
  • Facilitates a more efficient and data-driven process for developing viable pharmaceutical formulations for cDC.