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Learning how to use IAM chromatography for predicting permeability.

Giuseppe Ermondi¹, Maura Vallaro¹, Giulia Caron¹

¹Molecular Biotechnology and Health Sciences Dept., Università degli Studi di Torino, via Quarello 15, 10135 Torino, Italy.

European Journal of Pharmaceutical Sciences : Official Journal of the European Federation for Pharmaceutical Sciences

|January 7, 2018

Summary

This summary is machine-generated.

Immobilized Artificial Membranes (IAM) chromatography shows promise for predicting drug permeability. This study establishes a dataset and uses block relevance analysis to link IAM data to passive permeability, aiding drug development.

Keywords:

BR Analysis Chromatography IAM MDCK-LE PAMPA Permeability VolSurf+

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Area of Science:

Pharmacokinetics and Drug Metabolism
Chromatographic Science
Computational Chemistry

Background:

Drug permeability is a critical factor in pharmacokinetics.
Immobilized Artificial Membranes (IAM) chromatography is gaining interest for predicting drug permeability.
Existing methods for permeability prediction require optimization.

Purpose of the Study:

To establish a comprehensive dataset for IAM chromatography and drug permeability.
To investigate the relationship between IAM retention data and passive permeability.
To develop models for rational application of IAM chromatography in drug permeability prediction.

Main Methods:

Compiled a dataset of 253 molecules with measured or retrieved IAM.PC.DD2 log KwIAM data.
Applied Block Relevance (BR) analysis on Partial Least Squares (PLS) models.
Analyzed intermolecular forces governing IAM retention.
Correlated IAM descriptors with passive permeability from PAMPA and MDCK-LE systems.

Main Results:

Developed PLS models using BR analysis to identify key intermolecular forces.
Established relationships between IAM.PC.DD2 log KwIAM, Δlog KwIAM, and passive permeability.
Demonstrated the utility of IAM chromatography in predicting drug permeability.

Conclusions:

IAM chromatography, coupled with BR analysis, provides a valuable tool for predicting drug permeability.
The established models support the rational use of IAM chromatography in drug discovery and development.
This approach can streamline the assessment of passive permeability for drug candidates.