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Related Concept Videos

Bioavailability Enhancement: Drug Solubility Enhancement01:16

Bioavailability Enhancement: Drug Solubility Enhancement

Bioavailability is a critical factor in determining a drug's effectiveness. It refers to the proportion of a drug that enters the circulation when introduced into the body and is, as a result, able to have an active effect. Enhancing bioavailability is essential for drugs with poor solubility, as it can significantly impact their therapeutic efficacy. Various methods are employed to increase the solubility of drugs, thereby enhancing their bioavailability.Micronization and nanonization are...
Factors Affecting Solubility04:01

Factors Affecting Solubility

Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Chȃtelier’s principle. Consider the dissolution of silver iodide:
Drug Dissolution: Requirements and Profile Comparison01:14

Drug Dissolution: Requirements and Profile Comparison

The acceptance criteria for dissolution profile data are anchored in Q values, representing the percentage of drug dissolved within a specified period. This assessment unfolds in three stages:First Stage: The test passes if all six drug dosage units are equal to or greater than Q plus 5%; otherwise, the sample proceeds to the second stage.Second Stage: The average of twelve units must be equal to or greater than Q, with no unit falling below Q - 15% to pass; if not, it progresses to the final...
Supercritical Fluid Chromatography01:18

Supercritical Fluid Chromatography

Supercritical fluid chromatography (SFC) provides a beneficial substitute for gas chromatography (GC) and liquid chromatography (LC) for certain samples because it merges the top attributes of both techniques. SFC allows the separation and analysis of compounds that GC or LC does not easily manage. These compounds are traditionally nonvolatile or thermally unstable, making GC unsuitable and lacking functional groups required for HPLC analysis.
SFC utilizes a supercritical fluid mobile phase,...
In Vitro Drug Dissolution: Alternative Methods01:17

In Vitro Drug Dissolution: Alternative Methods

Alternative drug dissolution methods include the rotating bottle, intrinsic dissolution test, peristalsis, and the Franz diffusion cell method. The rotating bottle method involves meticulously rotating tightly capped controlled-release beads in a temperature-controlled bath. Periodic decanting of samples allows for residue assay, followed by refilling with fresh medium and testing at various pH levels to emulate the gastrointestinal tract conditions.In contrast, the intrinsic dissolution test...
Solubility03:00

Solubility

Solution, Solubility, and Solubility Equilibrium
A solution is a homogeneous mixture composed of a solvent, the major component, and a solute, the minor component. The physical state of a solution—solid, liquid, or gas—is typically the same as that of the solvent. Solute concentrations are often described with qualitative terms such as dilute (of relatively low concentration) and concentrated (of relatively high concentration).
In a solution, the solute particles (molecules, atoms, and/or ions)...

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Related Experiment Video

Updated: Jun 26, 2026

Solubility of Hydrophobic Compounds in Aqueous Solution Using Combinations of Self-assembling Peptide and Amino Acid
05:08

Solubility of Hydrophobic Compounds in Aqueous Solution Using Combinations of Self-assembling Peptide and Amino Acid

Published on: September 20, 2017

Automated machine-learning framework for predicting drug solubility in supercritical CO2 for sustainable process

Saad M Alshahrani1, Mahboubeh Pishnamazi2,3

  • 1Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj, 11942, Saudi Arabia.

Scientific Reports
|June 24, 2026
PubMed
Summary

This study presents an automated computational framework for predicting drug solubility in supercritical carbon dioxide (SC-CO2). The novel approach combines advanced regression algorithms with bio-inspired optimization for efficient and eco-friendly pharmaceutical process design.

Keywords:
Bio-inspired optimizersData-driven solubility estimationGreen processing technologiesHybrid regression systemsPredictive modelingSupercritical fluids

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Supercritical Nitrogen Processing for the Purification of Reactive Porous Materials
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Supercritical Nitrogen Processing for the Purification of Reactive Porous Materials

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Last Updated: Jun 26, 2026

Solubility of Hydrophobic Compounds in Aqueous Solution Using Combinations of Self-assembling Peptide and Amino Acid
05:08

Solubility of Hydrophobic Compounds in Aqueous Solution Using Combinations of Self-assembling Peptide and Amino Acid

Published on: September 20, 2017

Supercritical Nitrogen Processing for the Purification of Reactive Porous Materials
09:05

Supercritical Nitrogen Processing for the Purification of Reactive Porous Materials

Published on: May 15, 2015

Area of Science:

  • Chemical Engineering
  • Computational Chemistry
  • Green Chemistry

Background:

  • Experimental drug solubility measurements in supercritical carbon dioxide (SC-CO2) are slow and resource-intensive, hindering green pharmaceutical process development.
  • Existing methods limit the advancement of eco-friendly technologies like particle formation and solvent-free formulations.

Purpose of the Study:

  • To develop an automated computational framework for accurate and scalable prediction of drug solubility in SC-CO2.
  • To provide a practical alternative to extensive laboratory experiments for pharmaceutical process design.

Main Methods:

  • Coupling Adaptive Boosting Regression and Light Gradient Boosting Regression with bio-inspired optimization (Osprey Optimization Algorithm, Artificial Protozoa Optimizer).
  • Utilizing hybrid ensemble schemes and metaheuristic algorithms for model tuning.
  • Assessing model performance through cross-validation, accuracy metrics (RMSE, R2, MDAPE), statistical comparison, and sensitivity analysis.

Main Results:

  • The Artificial Protozoa Optimizer-driven ensemble (ALAP) demonstrated superior performance.
  • Achieved high accuracy with RMSE = 0.191, R2 = 0.982, and MDAPE = 15.6% on the test set.
  • Identified ALAP as the most reliable configuration through multi-criteria ranking (TOPSIS).

Conclusions:

  • The developed computational framework offers a reliable and efficient tool for predicting drug solubility in SC-CO2.
  • This data-driven approach supports the design of environmentally conscious pharmaceutical processes.
  • The framework serves as a practical, transferable alternative to traditional experimental methods.