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Related Concept Videos

Drug Dissolution: Requirements and Profile Comparison01:14

Drug Dissolution: Requirements and Profile Comparison

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The acceptance criteria for dissolution profile data are anchored in Q values, representing the percentage of drug dissolved within a specified period. This assessment unfolds in three stages:First Stage: The test passes if all six drug dosage units are equal to or greater than Q plus 5%; otherwise, the sample proceeds to the second stage.Second Stage: The average of twelve units must be equal to or greater than Q, with no unit falling below Q - 15% to pass; if not, it progresses to the final...
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Molecular and Ionic Solids02:54

Molecular and Ionic Solids

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Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
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In Vitro Drug Dissolution: Compendial Testing Models I01:13

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Compendial dissolution methods are standardized procedures defined by pharmacopeias to evaluate the rate at which a drug dissolves in a specific medium. These methods ensure batch-to-batch consistency, enable quality control, and support the prediction of drug bioavailability. They are critical for both immediate and modified-release drug products.The apparatuses used for dissolution testing differ in their design and mechanical function, but all aim to simulate the physiological environment of...
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In Vitro Drug Dissolution: Compendial Testing Models II01:09

In Vitro Drug Dissolution: Compendial Testing Models II

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Various dissolution methods are utilized to assess a drug’s dissolution rate, including the flow-through cell, paddle-over-disk, cylinder, and reciprocating disk methods.The flow-through cell apparatus (USP (United States Pharmacopeia) method 4) comprises a reservoir for the dissolution medium and a pump that propels the medium through the cell containing the test sample. This method is crucial for assessing modified-release dosage forms with minimally soluble active ingredients,...
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In Vitro Drug Dissolution: Alternative Methods01:17

In Vitro Drug Dissolution: Alternative Methods

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Alternative drug dissolution methods include the rotating bottle, intrinsic dissolution test, peristalsis, and the Franz diffusion cell method. The rotating bottle method involves meticulously rotating tightly capped controlled-release beads in a temperature-controlled bath. Periodic decanting of samples allows for residue assay, followed by refilling with fresh medium and testing at various pH levels to emulate the gastrointestinal tract conditions.In contrast, the intrinsic dissolution test...
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The Small x Assumption02:20

The Small x Assumption

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If a reaction has a small equilibrium constant, the equilibrium position favors the reactants. In such reactions, a negligible change in concentration may occur if the initial concentrations of reactants are high and the Kc value is small. In such circumstances, the equilibrium concentration is approximately equal to its initial concentration.  This estimation can be used to simplify the equilibrium calculations by assuming that some equilibrium concentrations are equal to the initial...
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PharmSD: A novel AI-based computational platform for solid dispersion formulation design.

Jie Dong1, Hanlu Gao1, Defang Ouyang1

  • 1State Key Laboratory of Quality Research in Chinese Medicine, ICMS, University of Macau, China.

International Journal of Pharmaceutics
|May 15, 2021
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Summary

Machine learning models predict solid dispersion properties, improving drug formulation design. The PharmSD platform offers free, web-based tools for efficient drug-polymer screening and formulation development.

Keywords:
Computational platformDrug designFormulationMachine learningSolid dispersionWeb server

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Materials Science

Background:

  • Solid dispersion enhances oral bioavailability of poorly soluble drugs.
  • Traditional methods for evaluating solid dispersion formulations are time-consuming and labor-intensive.
  • Machine learning offers a powerful alternative for predicting formulation properties.

Purpose of the Study:

  • To develop a systematic strategy and a web-based platform (PharmSD) for assisting solid dispersion formulation design using machine learning.
  • To provide efficient prediction of physical stability, dissolution type, and dissolution rate.
  • To enable virtual screening of drug-polymer combinations for optimal formulation development.

Main Methods:

  • Development of robust machine learning models based on advanced algorithms.
  • Integration of prediction models into a user-friendly web platform (PharmSD).
  • Implementation of a virtual screening pipeline and tools for model domain evaluation and dissolution curve similarity calculation.

Main Results:

  • Established robust machine learning models for predicting solid dispersion properties.
  • Developed PharmSD, a freely available web platform for formulation design.
  • PharmSD provides independent predictions for physical stability, dissolution type, and rate, enabling virtual screening.

Conclusions:

  • PharmSD is the first freely available, machine learning-driven web platform for solid dispersion formulation design.
  • The platform streamlines the identification of optimal drug-polymer combinations.
  • PharmSD is expected to significantly assist researchers in the field of solid dispersion formulation.