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Related Concept Videos

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

117
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.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
117
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

194
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.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
194
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

528
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
528
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

218
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
218
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

136
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
136
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

121
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
121

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Related Experiment Video

Updated: Sep 5, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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Comparison Between Pure Component Modeling Approaches for Monitoring Pharmaceutical Powder Blends with Near-Infrared

Adam J Rish1,2, Samuel R Henson1,2, Md Anik Alam3

  • 1Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA, 15282, USA.

The AAPS Journal
|July 13, 2022
PubMed
Summary
This summary is machine-generated.

Iterative optimization technology (IOT) algorithms show superior performance for near-infrared (NIR) spectroscopy in continuous manufacturing, offering a more flexible and efficient alternative to classical least squares (CLS) models for pharmaceutical process control.

Keywords:
classical least squares (CLS)continuous manufacturingiterative optimization technology (IOT)near-infrared spectroscopy (NIR)process analytical technology (PAT)

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Area of Science:

  • Analytical Chemistry
  • Process Analytical Technology (PAT)
  • Pharmaceutical Manufacturing

Background:

  • Near-infrared (NIR) spectroscopy is crucial for process monitoring in continuous manufacturing (CM).
  • Traditional partial least squares (PLS) models for NIR require significant time and material investment for calibration.
  • Pure component-based approaches offer greater flexibility and ease of development for NIR model calibration.

Purpose of the Study:

  • To evaluate and compare classical least squares (CLS) and iterative optimization technology (IOT) algorithms for pharmaceutical powder blend analysis using NIR.
  • To assess the performance and practical implementation of these pure component-based approaches in a continuous feed frame.
  • To identify a more efficient and effective modeling strategy for NIR-based process control in CM.

Main Methods:

  • Application of classical least squares (CLS) models to pharmaceutical powder blends.
  • Implementation of iterative optimization technology (IOT) algorithms for the same blends.
  • Comparative analysis of model performance and practical considerations for both CLS and IOT.

Main Results:

  • Iterative optimization technology (IOT) algorithms demonstrated superior performance in predicting drug content compared to classical least squares (CLS) models.
  • Both CLS and IOT were evaluated for their practical implementation feasibility in a continuous manufacturing setting.
  • IOT presents a more advantageous approach for NIR-based monitoring and control in pharmaceutical CM.

Conclusions:

  • IOT algorithms provide a more effective and efficient method for quantitative analysis of pharmaceutical powder blends using NIR spectroscopy.
  • The findings support the adoption of IOT over traditional CLS for developing robust NIR calibrations in continuous manufacturing.
  • This study highlights the potential of IOT to enhance process analytical technology (PAT) implementation in pharmaceutical CM.