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Related Concept Videos

Nonlinear Pharmacokinetics: Drug Elimination for IV Bolus Injection00:59

Nonlinear Pharmacokinetics: Drug Elimination for IV Bolus Injection

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In pharmacokinetics, the elimination rate of a drug following a capacity-limited model is primarily controlled by two parameters: Vmax and KM. These parameters are crucial in how the drug behaves inside the body after administration.
Following the administration of a single intravenous (IV) bolus injection, we can determine the concentration of the drug in the plasma at any given time. This calculation is achieved using a specific equation that integrates the values of Vmax and KM.
We can also...
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One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance00:56

One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance

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Clearance is a key pharmacokinetic parameter that quantifies the volume of body fluid from which a drug is entirely removed within a specific time frame. It is crucial in assessing how a drug is eliminated from the body and has critical clinical applications.
In the one-compartment open model for intravenous (IV) bolus administration, clearance is estimated by dividing the elimination rate by the plasma drug concentration. This equation leverages the elimination rate constant and the apparent...
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Drug Delivery: Parenteral Route01:29

Drug Delivery: Parenteral Route

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The parenteral route is a critical method of drug administration. It delivers compounds directly into the systemic circulation and bypasses the gastrointestinal tract. This approach is particularly advantageous for drugs that exhibit poor absorption or instability when administered orally.
There are three primary parenteral routes: intravenous (IV), intramuscular (IM), and subcutaneous (SC). The IV route introduces the drug directly into the bloodstream, ensuring immediate action. The IM route...
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Applying Machine Learning to the Visual Inspection of Filled Injectable Drug Products.

Romain Veillon1, John Shabushnig2, Lars Aabye-Hansen3

  • 1GSK, Wavres, Belgium.

PDA Journal of Pharmaceutical Science and Technology
|June 15, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) enhances automated visual inspection (AVI) for injectable drugs, improving defect detection and reducing false rejects. This technology integrates seamlessly with existing hardware and validation processes.

Keywords:
Automated inspectionDeep learningImage labelingInjectable drugInspection qualificationMachine learningNeural networkSupervised learningUnsupervised learningVisual inspection

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Area of Science:

  • Pharmaceutical manufacturing
  • Computer vision
  • Artificial intelligence

Background:

  • Manual visual inspection (MVI) faces limitations in throughput and consistency.
  • Machine learning (ML) offers potential to enhance automated visual inspection (AVI).

Purpose of the Study:

  • To capture current experiences and provide considerations for applying ML to AVI of injectable drug products.
  • To highlight the benefits of ML in pharmaceutical quality control.

Main Methods:

  • Integration of ML into existing machine vision hardware for AVI.
  • Utilizing AI tools for accelerated recipe development and model configuration.

Main Results:

  • Demonstrated superior defect detection rates compared to conventional inspection tools.
  • Shown reduction in false reject rates.
  • ML implementation requires no modifications to current AVI qualification strategies.

Conclusions:

  • ML technology is readily available for AVI applications in pharmaceutical manufacturing.
  • Successful ML implementation ensures reliable performance in production environments through established validation strategies.