Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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 relationship...
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the lowest drug...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Noncompartmental Analysis: Mean Transit, Absorption and Dissolution Time01:02

Noncompartmental Analysis: Mean Transit, Absorption and Dissolution Time

When drugs are administered extravascularly, a comprehensive evaluation through noncompartmental analysis becomes imperative. This analytical approach considers various parameters that play a crucial role in understanding the pharmacokinetics of these drugs.
One of the key parameters is the mean transit time (MTT), which refers to the total duration required for drug molecules to transit through the body. MTT is determined by calculating the ratio of the area under the moment curve to the area...
Pharmacokinetic–Pharmacodynamic Relationship: Problems01:24

Pharmacokinetic–Pharmacodynamic Relationship: Problems

The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Correction: Sphingosine-1-phosphate receptor 1/5 selective agonist alleviates ocular vascular pathologies.

Scientific reports·2026
Same author

Rapid bedside assessment of serum caffeine concentrations using an immunochromatographic screening kit: evaluation of analytical and diagnostic performance in acute caffeine poisoning.

Clinical toxicology (Philadelphia, Pa.)·2026
Same author

Discrimination of crystal polymorphism in active pharmaceutical ingredients using time-domain <sup>1</sup>H NMR relaxation combined with multivariate statistical process control.

International journal of pharmaceutics·2026
Same author

Development of an artificial intelligence-based computer-aided detection system for routine gastric biopsy diagnosis.

Journal of pathology informatics·2026
Same author

Association of single nucleotide polymorphisms in FILIP1-SENP6 and FTO with temporomandibular joint osteoarthritis: clinical and in silico study.

Scientific reports·2026
Same author

Evaluation of the stability and cross-reactivity of zopiclone and eszopiclone using immunoassay kits.

The Journal of toxicological sciences·2026

Related Experiment Video

Updated: May 13, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Self-organizing map analysis using multivariate data from theophylline tablets predicted by a thin-plate spline

Akihito Yasuda1, Yoshinori Onuki, Yasuko Obata

  • 1Department of Pharmaceutics, Hoshi University, Shinagawa-ku, Tokyo, Japan.

Chemical & Pharmaceutical Bulletin
|March 2, 2013
PubMed
Summary

This study used thin-plate spline interpolation and Kohonen

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Related Experiment Videos

Last Updated: May 13, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Pharmaceutical Sciences
  • Formulation Development
  • Data Visualization

Background:

  • Quality by Design (QbD) in pharmaceutical development necessitates a science-based approach and defined design space.
  • Understanding the complex relationships between formulation variables and drug product performance is crucial.

Purpose of the Study:

  • To integrate thin-plate spline (TPS) interpolation and Kohonen's self-organizing map (SOM) for visualizing causal factors and responses in pharmaceutical formulations.
  • To establish a science-based rationale and design space for theophylline tablet development.

Main Methods:

  • Preparation of theophylline tablets using a standard formulation.
  • Measurement of tensile strength, disintegration time, and stability as response variables.
  • Application of nonlinear TPS for quantitative prediction and SOM for data clustering and correlation analysis.

Main Results:

  • High accuracy in predicting experimental tablet responses using TPS.
  • Successful classification of tablet data into distinct clusters using SOM, revealing correlations between causal factors and tablet characteristics.
  • Identification of optimal microcrystalline cellulose (MCC) proportions for tensile strength, disintegration time, and stability.

Conclusions:

  • The integrated TPS and SOM approach effectively visualizes relationships between formulation factors and theophylline tablet properties.
  • Microcrystalline cellulose (MCC) proportion impacts tensile strength, disintegration time, and stability, with an optimal range identified.
  • Compression force and magnesium stearate content also influence tablet properties, highlighting the utility of this technique for formulation optimization.