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

Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

158
Solid dosage forms such as tablets and capsules undergo rigorous manufacturing processes to ensure stability and effectiveness. Their dissolution and absorption properties are influenced significantly by the choice of excipients (inactive ingredients that serve various roles in the formulation), and the methodology applied during production. The manufacturing parameters, such as compression force and granulation techniques, significantly affect dissolution rates. Elevated compression forces...
158
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.5K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.5K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.6K
VSEPR Theory for Determination of Electron Pair Geometries
34.6K
Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

13.6K
Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
13.6K
Factors Influencing Drug Absorption: Physicochemical Parameters01:22

Factors Influencing Drug Absorption: Physicochemical Parameters

331
The physicochemical characteristics of drugs play a crucial role in formulating stable and bioavailable drug products. The solubility of a drug, governed by the varying pH along the GI tract and its dissociation constant (pKa), is pivotal in determining its ionization state and absorption rate. Notably, weak acids and bases remain unionized and are absorbed more rapidly.
Enhanced drug absorption can be achieved by reducing particle sizes and increasing surface areas, thereby facilitating...
331
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

11.9K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
11.9K

You might also read

Related Articles

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

Sort by
Same author

Physio Avatar RM: Effects of Self-Avatar Integration with Foot-Elongated Avatar via Virtual Self-Touch on Human Gait.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

A methodological study of virtual self-touch: effects on perception and motor control strategies.

Frontiers in bioengineering and biotechnology·2026
Same author

A pilot study on the effects of a physio-avatar EB experience on motor performance.

Frontiers in bioengineering and biotechnology·2026
Same author

Machine Learning-Based Prediction of Polymer Chemical Resistance to Organic Solvents.

ACS omega·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

Loss of Fbxw7 disrupts lipid homeostasis and autophagy in hepatocellular carcinoma cells.

Medical molecular morphology·2026

Related Experiment Video

Updated: Jul 28, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.7K

A Data-Driven Approach to Predicting Tablet Properties after Accelerated Test Using Raw Material Property Database

Yoshihiro Hayashi1,2, Yuri Nakano2, Yuki Marumo2

  • 1Pharmaceutical Technology Division, Nichi-Iko Pharmaceutical Co., Ltd.

Chemical & Pharmaceutical Bulletin
|May 31, 2023
PubMed
Summary

Molecular descriptors significantly impact tablet properties like tensile strength and disintegration time. Machine learning models, particularly boosted neural networks, accurately predict these properties after accelerated testing.

Keywords:
data-drivenmachine learningmaterial librarymolecular descriptorquantitative structure–property relationshiptablet

More Related Videos

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.7K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Related Experiment Videos

Last Updated: Jul 28, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.7K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.7K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Computational Chemistry

Background:

  • Tablet properties are crucial for drug efficacy and stability.
  • Predicting post-stress tablet behavior is challenging due to complex material interactions.

Purpose of the Study:

  • Develop a predictive model for tablet properties after accelerated testing.
  • Investigate the influence of molecular descriptors and compression pressure on tablet properties.

Main Methods:

  • Prepared tablets using 81 active pharmaceutical ingredients under varying compression pressures.
  • Measured tensile strength, disintegration time, and swelling properties before and after accelerated testing.
  • Utilized random forest and eight machine learning types for feature selection and model development.

Main Results:

  • Molecular descriptors were among the top features influencing tablet properties.
  • A boosted neural network model achieved high accuracy in predicting tablet properties.
  • Over half of the top 25 features identified were molecular descriptors.

Conclusions:

  • Molecular descriptors are key determinants of tablet properties.
  • Data-driven, machine learning approaches effectively predict tablet performance under stress.
  • This study highlights the utility of computational methods in pharmaceutical formulation development.