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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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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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Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

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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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The pharmacokinetic journey of drugs from solid oral dosage forms into systemic circulation is multifaceted. It begins with disintegration, a prerequisite ensuring a solid dosage form's subdivision into minute particles. Dissolution occurs next as these granulated entities solubilize in gastrointestinal fluids. This solubilization is crucial for the succeeding stage, permeation, which describes the traversal of the drug across the intestinal membrane and its subsequent entry into the blood...
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Drug Dissolution: Requirements and Profile Comparison01:14

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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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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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Data-Driven Modeling of the Bicalutamide Dissolution from Powder Systems.

Aleksander Mendyk1, Adam Pacławski2, Joanna Szafraniec-Szczęsny2

  • 1Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Medyczna 9 St, 30-688, Kraków, Poland. aleksander.mendyk@uj.edu.pl.

AAPS Pharmscitech
|April 3, 2020
PubMed
Summary

Developing predictive models for drug dissolution is crucial for pharmaceutical development. This study used artificial intelligence and machine learning to create accurate models for predicting bicalutamide (BCL) dissolution from solid dispersions, aiding in dosage form design.

Keywords:
artificial intelligencedissolution modelingmulti-scale modelingmultivariate modelingsolubility enhancement

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Area of Science:

  • Pharmaceutical Technology
  • Computational Chemistry
  • Drug Delivery

Background:

  • Low solubility of active pharmaceutical ingredients (APIs) presents a significant hurdle in developing effective dosage forms.
  • Bicalutamide (BCL), a poorly water-soluble API, serves as a model compound for investigating dissolution challenges.

Purpose of the Study:

  • To develop and analyze empirical models for predicting the dissolution of bicalutamide (BCL) from solid dispersions.
  • To compare models derived from literature data versus in-house experimental data.
  • To identify key variables influencing BCL dissolution using artificial intelligence and machine learning (AI/ML).

Main Methods:

  • Collected and curated two distinct datasets: one from existing literature and another from in-house experiments.
  • Employed a range of AI/ML techniques, including artificial neural networks, decision trees, rule-based systems, and evolutionary computations.
  • Conducted ab initio modeling for in silico simulations to explore potential bicalutamide-excipient interactions.

Main Results:

  • Models derived from in-house data demonstrated superior predictive accuracy compared to those from literature data due to data homogeneity and formulation characterization.
  • Evolutionary computations yielded classical mathematical equations with the lowest prediction error.
  • The most effective predictive model incorporated transmittance from the IR spectrum at 1260 cm⁻¹ as a key covariate.

Conclusions:

  • AI/ML tools automatically identified crucial variables, leading to simple yet predictive models applicable to Quality by Design (QbD) strategies.
  • Data-driven modeling using AI/ML offers valuable predictive and investigational tools for pharmaceutical technology.
  • This approach can uncover new knowledge and optimize the development of drug formulations with poor solubility.