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Related Concept Videos

Factors Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism01:21

Factors Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism

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Polymorphism refers to the existence of a drug substance in multiple crystalline forms, known as polymorphs. Recently, this term has been expanded to include solvates (forms containing a solvent), amorphous forms (non-crystalline forms), and desolvated solvates (forms from which the solvent has been removed).
Some polymorphic crystals possess lower aqueous solubility than their amorphous counterparts, leading to incomplete absorption. For instance, the oral suspension of Chloramphenicol, which...
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Changes in polymorphic forms can significantly influence the bioavailability of poorly soluble drugs. Although the FDA defines pharmaceutical equivalence based on having the same active ingredient, dosage form, and route of administration, it does not automatically disqualify products with different polymorphic forms. This means two products with different polymorphs can still be deemed pharmaceutically equivalent. However, polymorphic differences can affect properties like wettability,...
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Polymer Classification: Crystallinity01:21

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Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
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Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the...
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Synthesis of Monodisperse Cylindrical Nanoparticles via Crystallization-driven Self-assembly of Biodegradable Block Copolymers
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Micro-scale prediction method for API-solubility in polymeric matrices and process model for forming amorphous solid

Esther S Bochmann1, Dirk Neumann2, Andreas Gryczke3

  • 1Department of Pharmaceutical Technology and Biopharmaceutics, University of Bonn, Bonn, Germany.

European Journal of Pharmaceutics and Biopharmaceutics : Official Journal of Arbeitsgemeinschaft Fur Pharmazeutische Verfahrenstechnik E.V
|June 29, 2016
PubMed
Summary

A new model predicts amorphous solid dispersion (ASD) solubility and processing temperatures using differential scanning calorimetry (DSC) and mathematical modeling. This method aids in optimizing hot-melt extrusion (HME) for drug formulation.

Keywords:
Amorphous solid dispersionDSCDipyridamole (PubChem CID: 3108)Hot-melt extrusionIndomethacin (PubChem CID: 3715)Itraconazole (PubChem CID: 55283)Melt rheologyNifedipine (PubChem CID: 4485)Solubility

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Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Chemical Engineering

Background:

  • Amorphous solid dispersions (ASDs) enhance drug solubility and bioavailability.
  • Hot-melt extrusion (HME) is a key manufacturing process for ASDs.
  • Predictive modeling is crucial for optimizing ASD formulation and processing.

Purpose of the Study:

  • To develop a predictive micro-scale model for ASD solubility and HME processing.
  • To utilize differential scanning calorimetry (DSC) and mathematical modeling for predictions.
  • To determine the minimal processing temperature for ASD formation during HME.

Main Methods:

  • Differential scanning calorimetry (DSC) with an annealing step and glass transition temperature (Tg) analysis.
  • Application of the BCKV-equation to model Tg dependency on API/polymer ratio.
  • Validation using X-ray powder diffraction (XRPD) and melt rheological trials.

Main Results:

  • Accurate prediction of active pharmaceutical ingredient (API) solubility at ambient conditions (25°C).
  • Estimation of the minimal processing temperature for ASD formation via HME.
  • Confirmation of DSC method suitability through rheological and XRPD data.

Conclusions:

  • The developed DSC-based model provides a reliable method for predicting ASD solubility and HME processing parameters.
  • This approach facilitates the optimization of ASD formulations for improved drug delivery.
  • The study demonstrates the utility of micro-scale characterization for macro-scale process development.