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Related Concept Videos

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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Pharmaceutical Alternatives: Polymorphic Form-Related and Particle Size-Related Therapeutic Nonequivalence01:27

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Changes in polymorphic forms can significantly influence the bioavailability of poorly soluble drugs. Although the FDA defines pharmaceutical equivalence based on having the same active ingredient, dosage form, and route of administration, it does not automatically disqualify products with different polymorphic forms. This means two products with different polymorphs can still be deemed pharmaceutically equivalent. However, polymorphic differences can affect properties like wettability,...
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In Vitro Drug Dissolution: Compendial Testing Models I01:13

In Vitro Drug Dissolution: Compendial Testing Models I

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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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Formulation and Manufacturing Process: Physical Attributes of Generic Tablets and Capsules01:18

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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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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 Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism01:21

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Polymorphism refers to the existence of a drug substance in multiple crystalline forms, known as polymorphs. Recently, this term has been expanded to include solvates (forms containing a solvent), amorphous forms (non-crystalline forms), and desolvated solvates (forms from which the solvent has been removed).
Some polymorphic crystals possess lower aqueous solubility than their amorphous counterparts, leading to incomplete absorption. For instance, the oral suspension of Chloramphenicol, which...
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Predicting the tabletability of binary mixtures from individual powder compaction behavior.

Michael Ghijs1, Alexander Ryckaert2, Daan Van Hauwermeiren3

  • 1Elegent, Hippolyte Metdepenningenstraat 33, B-9000, Ghent, Belgium; Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, B-9000, Ghent, Belgium.

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A new neural network model predicts tablet tensile strength from material properties, outperforming traditional methods for poorly compactible active pharmaceutical ingredients (APIs) and complex mixtures.

Keywords:
Direct compressionMachine learningMixture modelingNeural network modelingTabletabilityTensile strength

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Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Computational Chemistry

Background:

  • Direct-compression (DC) formulation development is often limited by the poor compactibility of active pharmaceutical ingredients (APIs).
  • Predicting tablet properties like tensile strength is crucial for successful DC formulation.
  • Existing methods struggle with APIs exhibiting poor compaction behavior.

Purpose of the Study:

  • To develop a predictive model for tablet tensile strength using a neural network.
  • To evaluate the model's performance against traditional mixing rules.
  • To address challenges in formulating poorly compactible APIs.

Main Methods:

  • A neural network was trained on a dataset of over 200 formulations from 33 powders, including 17 APIs.
  • The model predicted tablet tensile strength from material properties and binary mixture composition.
  • Performance was compared to a power-law mixing rule based on tabletability parameters.

Main Results:

  • The neural network model accurately predicted tablet tensile strength across a wide range of compaction pressures.
  • The neural network outperformed the power-law mixing rule, especially for poorly compactible APIs.
  • The model successfully predicted properties for mixtures where pure components could not be compacted.

Conclusions:

  • A neural network approach offers a superior method for predicting tablet tensile strength in direct-compression formulations.
  • This model aids in rational formulation design, particularly for challenging APIs.
  • The model requires minimal material (3-5g) for characterization, facilitating rapid prediction for new powders.