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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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Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

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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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In Vitro Drug Dissolution: Compendial Testing Models II01:09

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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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Aggregates contain pores of varying sizes; while some are completely enclosed within the particles, others open onto the surface, allowing water to penetrate. The porosity of aggregates is a major factor contributing to the overall porosity of concrete, given that aggregates constitute about three-quarters of concrete's volume.
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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Computational intelligence models to predict porosity of tablets using minimum features.

Mohammad Hassan Khalid1, Pezhman Kazemi1, Lucia Perez-Gandarillas2

  • 1Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland.

Drug Design, Development and Therapy
|February 1, 2017
PubMed
Summary
This summary is machine-generated.

Computational intelligence models predict tablet porosity by analyzing formulation and manufacturing variables. Artificial neural networks and symbolic regression offer accurate, in silico predictions for pharmaceutical quality-by-design.

Keywords:
artificial neural networkcomputational intelligencedie compactionfeature selectionporositysymbolic regression

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Intelligence
  • Materials Science

Background:

  • Understanding the impact of formulation and manufacturing on solid dosage form physical properties is crucial for the pharmaceutical industry.
  • Quality-by-design (QbD) principles necessitate in-depth knowledge of material properties and process parameters.
  • Computational intelligence (CI) provides tools for empirical modeling to predict system behavior and outcomes.

Purpose of the Study:

  • To develop and evaluate CI models for predicting tablet porosity.
  • To explore the influence of formulation (microcrystalline cellulose proportion) and processing conditions (granule size, compaction force) on tablet porosity.
  • To compare the predictive performance of different CI methods, including tree-based methods, artificial neural networks (ANNs), and symbolic regression.

Main Methods:

  • Experimental data on tablet porosity were generated by systematically varying microcrystalline cellulose proportion, granule size fraction, and die compaction force.
  • CI models, including ANNs and symbolic regression, were trained and screened using root-mean-square error (RMSE).
  • Model generalization ability was assessed using an external validation dataset.

Main Results:

  • ANNs achieved a normalized root-mean-square error (NRMSE) of 1% and symbolic regression achieved 4% on the training data.
  • Both ANNs and symbolic regression demonstrated reliable predictive behavior on external validation data, with the best symbolic regression model achieving an NRMSE of 3%.
  • Symbolic regression offered a more transparent modeling approach compared to black-box methods.

Conclusions:

  • CI models, particularly ANNs and symbolic regression, can accurately predict tablet porosity based on formulation and process parameters.
  • These models facilitate a deeper understanding of the system and support the implementation of QbD practices in pharmaceutical manufacturing.
  • The study highlights the importance of specific variables and demonstrates the transition towards interpretable predictive models in pharmaceutical research.