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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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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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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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Related Experiment Video

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Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture
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A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine

Ilgaz Akseli1, Jingjin Xie1, Leon Schultz1

  • 1R&D, Pharmaceutical Development, Boehringer-Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877.

Journal of Pharmaceutical Sciences
|March 26, 2017
PubMed
Summary

This study introduces a new method using ultrasonics and machine learning to predict tablet breaking force and disintegration time. This non-destructive approach accelerates drug product development and optimizes material usage.

Keywords:
disintegration timeneuroevolutionnondestructive testingpredictive modelingtablet breaking forcetablet formulation

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Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Data Science

Background:

  • Quality by Design (QbD) necessitates correlating material properties and process variables with product performance.
  • Conventional quality-by-test methods for tablet attributes are destructive, time-consuming, and labor-intensive.
  • Advances in material characterization, statistics, and machine learning offer potential for non-destructive drug product analysis.

Purpose of the Study:

  • To develop a methodology for predicting tablet breaking force and disintegration time using non-destructive ultrasonics and machine learning.
  • To establish a quantitative correlation between material properties, process variables, and tablet performance attributes.

Main Methods:

  • Utilized non-destructive ultrasonics to gather data on tablet formulations.
  • Developed a machine learning model incorporating intrinsic formulation properties and extrinsic process variables.
  • Applied the model to predict breaking force and disintegration time.

Main Results:

  • Successfully predicted tablet breaking force and disintegration time using the developed methodology.
  • Demonstrated the model's applicability with small quantities of active pharmaceutical ingredient and prototype formulations.
  • Validated the potential for non-destructive, rapid, and accurate assessment of tablet properties.

Conclusions:

  • The novel approach advances rational drug product design by providing insight into material performance.
  • This methodology can expedite drug product development timelines and reduce active pharmaceutical ingredient usage.
  • The non-destructive ultrasonic and machine learning approach enhances overall process efficiency and quality control.