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Quantitative structure-(chromatographic) retention relationship models for dissociating compounds.

Łukasz Kubik1, Paweł Wiczling1

  • 1Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen. J. Hallera 107, 80-416 Gdańsk, Poland.

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Summary

This study develops quantitative structure-retention relationship (QSRR) models to predict drug retention times in chromatography. These models accurately link molecular structure to hydrophobicity and dissociation constants, aiding in retention time prediction.

Keywords:
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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Medicinal Chemistry

Background:

  • Accurate prediction of analyte retention times in reversed-phase liquid chromatography (RPLC) is crucial for drug discovery and development.
  • Quantitative Structure-Retention Relationship (QSRR) models offer a powerful approach to correlate molecular structure with chromatographic behavior.

Purpose of the Study:

  • To develop and validate QSRR models for predicting analyte retention times in RPLC.
  • To establish mathematical relationships between analyte hydrophobicity, dissociation constant (pKa), and molecular structure.
  • To compare the predictive performance of models based on quantum chemistry descriptors versus commercially available software descriptors.

Main Methods:

  • Development of three QSRR models using Lasso, Stepwise, and Partial Least Squares (PLS) regression.
  • Calculation of structural descriptors using molecular modeling for both dissociated and nondissociated analyte forms.
  • Utilization of retention-related parameters: logkw (hydrophobicity), S (Snyder-Soczewinski slope factor), and pKa.
  • Comparison of quantum chemistry-based QSRR equations with those derived from ACD/Labs software parameters.
  • Validation of model predictive performance using 10-fold cross-validation and external validation sets.

Main Results:

  • Successfully developed simple QSRR equations with satisfactory predictive performance for chromatographic retention times.
  • Demonstrated that both quantum chemistry-based and ACD/Labs software-based descriptors yield comparable accuracy in predicting retention times.
  • Established robust mathematical models linking molecular structure to key chromatographic parameters like hydrophobicity and pKa.

Conclusions:

  • The developed QSRR models provide a reliable and efficient method for predicting analyte retention times in RPLC.
  • The study highlights the utility of both computational and commercial software approaches for descriptor generation in QSRR modeling.
  • These findings can significantly streamline chromatographic method development and compound characterization in pharmaceutical research.