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Related Experiment Video

Updated: Jul 15, 2026

Analysis of Complex Molecules and Their Reactions on Surfaces by Means of Cluster-Induced Desorption/Ionization Mass Spectrometry

Analysis of Complex Molecules and Their Reactions on Surfaces by Means of Cluster-Induced Desorption/Ionization Mass Spectrometry

Published on: March 1, 2020

Statistical cluster analysis of pharmaceutical solvents.

Dong Xu¹, Nancy Redman-Furey

¹Computational Science Research Center, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182-1245, USA.

International Journal of Pharmaceutics

|April 17, 2007

Summary

Selecting optimal pharmaceutical solvents for polymorph screening is crucial for efficiency. A 20-cluster solvent solution, derived from 57 International Council for Harmonisation (ICH) solvents, offers a representative and resource-efficient approach.

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Last Updated: Jul 15, 2026

Analysis of Complex Molecules and Their Reactions on Surfaces by Means of Cluster-Induced Desorption/Ionization Mass Spectrometry

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Published on: March 1, 2020

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

Area of Science:

Pharmaceutical Science
Solid-State Chemistry
Computational Chemistry

Background:

Efficient polymorph screening and crystallization optimization are vital in pharmaceutical development.
Evaluating all pharmaceutical solvents (Classes 2 and 3) is resource-intensive.
International Council for Harmonisation (ICH) guidelines recognize specific pharmaceutical solvents.

Purpose of the Study:

To develop a more efficient solvent selection strategy for polymorph screening.
To reduce the time and resources required for solid-state studies.
To identify a representative subset of pharmaceutical solvents.

Main Methods:

Utilized a 17-descriptor dataset for 57 pharmaceutical solvents (ICH Classes 2 and 3).
Applied principal component analysis (PCA) for dimensionality and collinearity reduction.
Performed two-stage cluster analysis (hierarchical and K-means) to group solvents.

Main Results:

A robust 20-cluster solvent solution was identified with strong statistical support.
Hierarchical and K-means clustering demonstrated excellent agreement in cluster assignments.
The 20-cluster solution provides a chemically interpretable representation of solvent properties.

Conclusions:

The proposed 20-cluster solvent set serves as an efficient option for solid-state screening design.
Including one solvent from each cluster adequately represents the full spectrum of 57 pharmaceutical solvents.
This approach enhances efficiency in polymorph screening and crystallization optimization studies.