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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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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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Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Bioequivalence in generic drugs, such as tablets and capsules, refers to their pharmaceutical equivalence to the brand-name counterparts. However, for therapeutic equivalence, manufacturers must also consider physical attributes like size, shape, and weight (FDA Guidance for Industry, December 2003). Discrepancies in these aspects could impact patient compliance and cause medication errors. For instance, swallowing difficulties, often experienced with larger tablets or capsules, can lead to...
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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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An insight into predictive parameters of tablet capping by machine learning and multivariate tools.

Shubhajit Paul1, Yukteshwar Baranwal2, Yin-Chao Tseng1

  • 1Boehringer Ingelheim Pharmaceuticals Inc., Department of Material and Analytical Sciences, Ridgefield, CT 06877, USA.

International Journal of Pharmaceutics
|March 4, 2021
PubMed
Summary

Understanding tablet capping is crucial for drug development. Key material and compaction properties, including brittleness and elastic modulus, predict and help prevent this common mechanical defect.

Keywords:
Machine learningMechanical propertyMultivariate analysisPowder compressionTablet capping

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Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Chemical Engineering

Background:

  • Tablet capping is a prevalent mechanical defect in pharmaceutical manufacturing.
  • Sub-optimal formulation composition and process parameters contribute to capping.
  • Addressing capping is vital for efficient drug product development.

Purpose of the Study:

  • To identify key tablet properties influencing capping propensity.
  • To develop a predictive model for capping based on these properties.
  • To enhance the understanding of factors governing tablet mechanical integrity.

Main Methods:

  • Characterization of 26 diverse formulations under commercial tableting conditions.
  • Application of machine learning and multivariate statistical tools.
  • Analysis of compaction parameters (e.g., pressure, stress transmission, Poisson's ratio).
  • Evaluation of material properties (e.g., brittleness, yield strength, bonding strength, elastic recovery).

Main Results:

  • Compaction parameters and material properties significantly influence capping propensity.
  • Brittleness, yield strength, particle bonding strength, and elastic recovery are key material indicators.
  • Poisson's ratio and radial stress transmission characteristics are critical compaction metrics.
  • The ratio of orthogonal elastic modulus and its variation with porosity quantitatively predict capping.

Conclusions:

  • A predictive model for tablet capping was successfully established using threshold properties.
  • Understanding these quantitative metrics allows for proactive mitigation of capping defects.
  • This research facilitates more robust and efficient tablet formulation and process development.