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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

417
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
417
Information Processing Approach01:30

Information Processing Approach

549
The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
549
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

330
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...
330
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

516
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...
516
Biopharmaceutics and Pharmacokinetics: Overview01:28

Biopharmaceutics and Pharmacokinetics: Overview

3.9K
Understanding drugs, drug products, and their performance in pharmaceutical science is pivotal. Drugs, whether simple molecules or complex compounds, are designed to interact with the body's biological systems to diagnose, treat, or prevent diseases. Drug products include various delivery systems such as tablets, capsules, injections, and inhalers. The performance of these drug products is gauged by their ability to deliver the active ingredient to the desired site of action at the...
3.9K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

543
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...
543

You might also read

Related Articles

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

Sort by
Same author

Advanced glucose control strategies leveraging Raman spectroscopy for optimized mammalian cell culture manufacturing.

Biotechnology progress·2026
Same author

Firm, Yellow, and Now Fluid-Filled!

Clinical and experimental dermatology·2026
Same author

Yeast at Forty.

Yeast (Chichester, England)·2026
Same author

Turning struggles into strengths: A qualitative exploration of academic difficulty in medical school.

Medical teacher·2026
Same author

Hotspot Interactions between Two Fab Molecules in Molecular Dynamics Simulations Improve Predictive Models of Aggregation Kinetics.

Molecular pharmaceutics·2026
Same author

Mecp2 deficiency impairs microscale cortical network topology and dynamics in a Rett syndrome mouse model.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Jan 24, 2026

Author Spotlight: A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management
10:23

Author Spotlight: A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management

Published on: June 23, 2023

3.5K

Metaheuristic approaches in biopharmaceutical process development data analysis.

Nishanthi Gangadharan1, Richard Turner2, Ray Field2

  • 1Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.

Bioprocess and Biosystems Engineering
|May 24, 2019
PubMed
Summary
This summary is machine-generated.

This review explores metaheuristic methods for big data analysis in biopharmaceutical production. It offers guidelines for data handling and modeling to improve process development and predict future culture performance.

Keywords:
Biopharmaceutical process development dataData modellingInteractive data visualisationMeta-feature selectionMissing data handling

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

486
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.8K

Related Experiment Videos

Last Updated: Jan 24, 2026

Author Spotlight: A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management
10:23

Author Spotlight: A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management

Published on: June 23, 2023

3.5K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

486
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.8K

Area of Science:

  • Biopharmaceutical Process Development
  • Data Science
  • Process Engineering

Background:

  • Biopharmaceutical industries generate large datasets due to advanced process control and multivariate monitoring.
  • Real-time data on critical quality and performance attributes are crucial for understanding and modeling bioprocesses.
  • Data mining offers potential for extracting insights and predicting bioprocess performance.

Purpose of the Study:

  • To review and evaluate metaheuristic methods for big data analysis in biopharmaceutical process development.
  • To assess methods for data pre-processing (handling missing data, visualization, dimension reduction).
  • To assess methods for data processing (modeling and optimization).

Main Methods:

  • Literature review of metaheuristic methods for data mining.
  • Evaluation of techniques for data pre-processing and processing.
  • Discussion of advantages and challenges of different methodologies.

Main Results:

  • Identified various metaheuristic methods suitable for biopharmaceutical big data.
  • Discussed the applicability of these methods in data pre-processing and processing stages.
  • Highlighted the benefits and challenges associated with each method.

Conclusions:

  • Metaheuristic methods are valuable tools for handling and analyzing big data in biopharmaceutical process development.
  • A guideline is proposed for effective data handling and analysis.
  • Further research can optimize the application of these methods for improved bioprocess outcomes.