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Related Concept Videos

Factors Influencing Drug Absorption: Presystemic Elimination01:24

Factors Influencing Drug Absorption: Presystemic Elimination

The pharmacokinetic journey of oral drugs begins with a crucial first pass through the hepatic portal system, called the first-pass effect. This first pass significantly impacts bioavailability — the proportion of a drug that enters systemic circulation and is available for therapeutic action. The primary route sees the drug absorbed by intestinal membranes and then shunted to the liver via the hepatic portal vein. Here, pre-systemic elimination occurs as drugs face metabolism or biliary...
Factors Influencing Drug Absorption: Anatomical Parameters01:23

Factors Influencing Drug Absorption: Anatomical Parameters

Drug absorption involves the movement of drugs from the point of administration into the systemic circulation. Initially, Gastrointestinal (GI) motility propels the drug through the digestive tract and into the stomach. However, the stomach's high acidity and limited surface area restrict its role in drug absorption for most drugs. The drug then moves from the stomach to the small intestine via gastric emptying, which can be slowed by various factors, including interactions with other...
Methods for Studying Drug Absorption: In vitro01:16

Methods for Studying Drug Absorption: In vitro

In vitro experiments are crucial for understanding the transport and absorption of drugs through biological materials. These studies employ varied methods such as the diffusion cell method, the everted sac technique, and the everted ring technique.
The diffusion cell method uses a two-compartment cell, including a donor compartment with the drug solution, which simulates the environment where the drug is applied, and a receptor compartment with a buffer solution, which simulates the environment...
Factors Influencing Drug Absorption: Physicochemical Parameters01:22

Factors Influencing Drug Absorption: Physicochemical Parameters

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...
Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

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...
Factors Influencing Drug Absorption: Disease States and Pharmacology01:25

Factors Influencing Drug Absorption: Disease States and Pharmacology

Multiple disease states can significantly influence the oral drug absorption process by affecting blood flow and the functionality of the gastrointestinal (GI) system. Various GI diseases, including conditions that alter GI motility, such as diarrhea, decreased acid secretions (achlorhydria), and infections, have been associated with reduced drug absorption.
Substances such as alcohol and specific drugs, including antineoplastics, can also negatively impact drug absorption. For instance,...

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Related Experiment Video

Updated: May 7, 2026

Using Caco-2 Cells to Study Lipid Transport by the Intestine
07:00

Using Caco-2 Cells to Study Lipid Transport by the Intestine

Published on: August 20, 2015

Pre-processing feature selection for improved C&RT models for oral absorption.

Danielle Newby1, Alex A Freitas, Taravat Ghafourian

  • 1Medway School of Pharmacy, Universities of Kent and Greenwich , Chatham, Kent ME4 4TB, U.K.

Journal of Chemical Information and Modeling
|September 21, 2013
PubMed
Summary
This summary is machine-generated.

Feature selection methods improve Quantitative Structure-Activity Relationships (QSAR) models for predicting oral absorption. A two-stage approach using pre-processing filters and misclassification costs enhanced model accuracy and interpretability.

Related Experiment Videos

Last Updated: May 7, 2026

Using Caco-2 Cells to Study Lipid Transport by the Intestine
07:00

Using Caco-2 Cells to Study Lipid Transport by the Intestine

Published on: August 20, 2015

Area of Science:

  • * Cheminformatics and computational toxicology.
  • * Development of predictive models for drug absorption.
  • * Application of machine learning in drug discovery.

Background:

  • * Thousands of molecular descriptors exist, but using all in Quantitative Structure-Activity Relationships (QSAR) modeling leads to overfitting and reduced performance.
  • * Traditional decision tree algorithms like Classification and Regression Trees (C&RT) have limitations in feature selection due to diminishing sample sizes at deeper tree levels.
  • * Oral absorption datasets are often biased, with more highly absorbed compounds than poorly absorbed ones, posing challenges for model building.

Purpose of the Study:

  • * To compare two feature selection approaches for improving QSAR model prediction accuracy for oral absorption.
  • * To investigate the effectiveness of a two-stage feature selection process versus a one-stage C&RT approach.
  • * To incorporate misclassification costs to address biased datasets and enhance model reliability.

Main Methods:

  • * Implemented a two-stage feature selection: pre-processing filter methods followed by C&RT.
  • * Applied a one-stage approach using C&RT as the sole feature selection technique.
  • * Utilized misclassification costs during model building to handle imbalanced oral absorption data.

Main Results:

  • * The two-stage feature selection approach generally yielded higher model accuracy compared to the one-stage method.
  • * Using the top 20 molecular descriptors identified by random forest predictor importance resulted in the most accurate C&RT classification model.
  • * Filter pre-processing methods and misclassification costs significantly improved interpretability and predictability for oral absorption QSAR models.

Conclusions:

  • * Pre-processing feature selection methods, particularly filter methods, are effective in enhancing QSAR model performance for oral absorption prediction.
  • * Combining filter feature selection with misclassification costs leads to more interpretable and predictable QSAR models.
  • * The two-stage feature selection strategy offers a superior approach for building robust QSAR models compared to using C&RT alone.