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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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The one-compartment model is a pharmacokinetic tool that models the body as a single, uniform compartment, facilitating the understanding of drug distribution and elimination. This model is particularly beneficial for intravenous (IV) bolus administration, where the drug rapidly circulates throughout the body.
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Factors Influencing Drug Absorption: Physicochemical Parameters01:22

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The physicochemical characteristics of drugs play a crucial role in formulating stable and bioavailable drug products. The solubility of a drug, governed by the varying pH along the GI tract and its dissociation constant (pKa), is pivotal in determining its ionization state and absorption rate. Notably, weak acids and bases remain unionized and are absorbed more rapidly.
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On the complexity of predicting tablet capping.

J Meynard1, F Amado-Becker2, P Tchoreloff1

  • 1Univ. Bordeaux, CNRS, Arts et Metiers Institute of Technology, Bordeaux INP, INRAE, I2M Bordeaux, F-33400 Talence, France.

International Journal of Pharmaceutics
|June 25, 2022
PubMed
Summary
This summary is machine-generated.

Predicting tablet capping, a common pharmaceutical manufacturing defect, is challenging. A new decision tree model using in-die tablet properties accurately predicts capping for most formulations, aiding risk assessment.

Keywords:
CappingCompactionDecision treeTablet

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Area of Science:

  • Pharmaceutical Manufacturing
  • Materials Science
  • Process Engineering

Background:

  • Tablet capping is a significant defect in pharmaceutical manufacturing, impacting product quality and production efficiency.
  • Existing methods for predicting capping lack a general consensus and universal applicability.
  • Characterizing defect-free tablets to predict defects in biconvex tablets is an underexplored area.

Purpose of the Study:

  • To evaluate existing parameters and predictive indices for their ability to forecast tablet capping.
  • To develop a novel predictive model for tablet capping using in-die tablet properties.
  • To assess the model's performance across a diverse range of pharmaceutical formulations.

Main Methods:

  • Characterization of defect-free flat-faced tablets (tensile strength, solid fraction, elastic recovery).
  • Evaluation of literature-based single parameters and predictive indices on 18 formulations (8 with capping).
  • Development of a predictive model using decision tree analysis based on in-die properties (plastic energy per volume, in-die elastic recovery, residual die-wall pressure).
  • Validation of the model on an additional set of 13 formulations.

Main Results:

  • Literature-based parameters and indices were insufficient for predicting capping across different formulations.
  • The developed decision tree model, relying on three key in-die properties, demonstrated high predictive accuracy.
  • The model successfully predicted the capping behavior for 29 out of 31 total formulations studied.
  • Only two formulations were misclassified, showing the model's robustness.

Conclusions:

  • A novel predictive model based on in-die tablet properties offers a reliable approach for assessing capping risk in pharmaceutical manufacturing.
  • The model's reliance on specific in-die characteristics provides a more accurate prediction than previously studied parameters.
  • This approach can significantly enhance risk analysis and quality control during tablet production.