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

High-Performance Liquid Chromatography: Elution Process01:05

High-Performance Liquid Chromatography: Elution Process

546
In High-Performance Liquid Chromatography (HPLC), the elution process is critical to the separation of analytes and the quality of chromatographic results. Elution describes how compounds move through the column and separate based on their interactions with the mobile and stationary phases. This process determines the resolution, peak shape, and retention times in the chromatogram, which are essential for identifying and quantifying components in complex mixtures. Understanding the elution...
546
Optimizing Chromatographic Separations01:15

Optimizing Chromatographic Separations

442
Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...
442
High-Performance Liquid Chromatography: Introduction01:11

High-Performance Liquid Chromatography: Introduction

2.1K
High-performance liquid chromatography(HPLC), formerly referred to as High-pressure liquid chromatography, is a powerful technique used to separate, identify, and quantify components in complex mixtures. The term "high pressure" refers to using high pressure to push the liquid mobile phase through the tightly packed columns.
In HPLC, two phases play a critical role in the separation process:
2.1K
High-Performance Liquid Chromatography: Instrumentation00:57

High-Performance Liquid Chromatography: Instrumentation

1.9K
High-performance liquid chromatography, or HPLC, is an analytical technique that separates liquid samples under high pressures. An HPLC instrument consists of glass bottles for storing solvents called mobile phase reservoirs. HPLC-grade solvents are used to maintain high purity, and the dissolved gases are removed using a degasser, such as a vacuum pumping system or sparging with helium. The solvents are then pumped into the analytical column using a screw-driven syringe or reciprocating pumps.
1.9K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

101
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...
101
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

832
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
832

You might also read

Related Articles

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

Sort by
Same author

Predicting mortality risk in pediatric severe pneumonia using a CNN-BiLSTM model with dynamic clinical indicators.

Respiratory medicine·2026
Same author

Optimization-based framework with flux balance analysis (FBA) and metabolic pathway analysis (MPA) for identifying metabolic objective functions.

PLoS computational biology·2025
Same author

Proteomic profiling of human plasma for anxiety and depression: Discovery of potential biomarkers and mechanistic insights.

Journal of affective disorders·2025
Same author

A Novel, Site-Specific N-Linked Glycosylation Model Provides Mechanistic Insights Into the Process-Condition Dependent Distinct Fab and Fc Glycosylation of an IgG1 Monoclonal Antibody Produced by CHO VRC01 Cells.

Biotechnology and bioengineering·2024
Same author

Probabilistic pressure-flow operating space for chromatographic resins using mechanistic modeling.

Journal of chromatography. A·2024
Same author

Flux balance analysis and peptide mapping elucidate the impact of bioreactor pH on Chinese hamster ovary (CHO) cell metabolism and N-linked glycosylation in the fab and Fc regions of the produced IgG.

Metabolic engineering·2024

Related Experiment Video

Updated: Jul 28, 2025

Automated Hydrophobic Interaction Chromatography Column Selection for Use in Protein Purification
10:21

Automated Hydrophobic Interaction Chromatography Column Selection for Use in Protein Purification

Published on: September 21, 2011

43.8K

Hybrid model development for parameter estimation and process optimization of hydrophobic interaction chromatography.

Chaoying Ding1, Christopher Gerberich2, Marianthi Ierapetritou1

  • 1Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA.

Journal of Chromatography. A
|June 2, 2023
PubMed
Summary

A hybrid model combining a multi-component Langmuir isotherm and neural network accurately predicts Hydrophobic Interaction Chromatography (HIC) behavior for therapeutic protein purification, improving process development efficiency.

Keywords:
Hybrid modelHydrophobic interaction chromatographyMechanistic modelNeural networkOptimization

More Related Videos

Curtain Flow Column: Optimization of Efficiency and Sensitivity
06:44

Curtain Flow Column: Optimization of Efficiency and Sensitivity

Published on: June 12, 2016

6.6K
Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems
14:42

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Published on: September 23, 2021

4.9K

Related Experiment Videos

Last Updated: Jul 28, 2025

Automated Hydrophobic Interaction Chromatography Column Selection for Use in Protein Purification
10:21

Automated Hydrophobic Interaction Chromatography Column Selection for Use in Protein Purification

Published on: September 21, 2011

43.8K
Curtain Flow Column: Optimization of Efficiency and Sensitivity
06:44

Curtain Flow Column: Optimization of Efficiency and Sensitivity

Published on: June 12, 2016

6.6K
Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems
14:42

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Published on: September 23, 2021

4.9K

Area of Science:

  • Biopharmaceutical Process Development
  • Chromatographic Separation Science
  • Computational Modeling

Background:

  • Hydrophobic Interaction Chromatography (HIC) is crucial for therapeutic protein purification, but its complex, salt-dependent adsorption mechanism hinders accurate modeling.
  • Developing precise mechanistic models for HIC is challenging due to unclear protein-resin interactions.
  • Accelerated process development and cost reduction necessitate advanced model-based design and optimization strategies.

Purpose of the Study:

  • To develop an accurate and efficient hybrid model for Hydrophobic Interaction Chromatography (HIC) processes.
  • To overcome the limitations of traditional mechanistic models in capturing complex HIC behavior.
  • To enable faster process development and optimization for therapeutic protein purification.

Main Methods:

  • A hybrid model was constructed by integrating a multi-component Langmuir isotherm with a neural network (NN).
  • A modified isotherm and an equilibrium dispersive model were employed to represent the HIC process.
  • Regularization strategies were incorporated during parameter estimation to prevent overfitting and investigate NN structures.

Main Results:

  • The hybrid model, particularly with a simple NN structure, significantly outperformed the mechanistic model in accuracy (62% calibration, 31.4% validation).
  • Model structure was found to be critical for predictive accuracy.
  • The developed hybrid model demonstrated generalizability through in-silico testing and enabled process optimization under product quality constraints.

Conclusions:

  • A hybrid multi-component Langmuir isotherm and neural network model offers a powerful approach for accurate HIC process modeling.
  • This hybrid modeling strategy enhances efficiency and accuracy in therapeutic protein purification process development.
  • The developed model facilitates optimal operating condition determination, contributing to cost savings and faster development timelines.