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A New Straightforward Method for Lipophilicity logP Measurement using 19F NMR Spectroscopy
Published on: January 30, 2019
Perspectives in solubility measurement and interpretation
Christel A S Bergström1, Alex Avdeef2
1Department of Pharmacy, Uppsala University, BMC P.O. Box 580, SE-751 23 Uppsala, Sweden.
This study explores drug solubility, highlighting how pH affects ionizable molecules and discussing factors like supersaturation and complexation for better measurement and understanding of drug behavior in suspensions.
Area of Science:
- Pharmaceutical Science
- Physical Chemistry
- Drug Discovery
Background:
- Accurate solubility measurement is crucial for drug development and formulation.
- Traditional models like the Henderson-Hasselbalch equation may not fully capture complex solubility behaviors of ionizable molecules.
- Understanding solution equilibria and solid-state properties is key to predicting and controlling drug solubility.
Purpose of the Study:
- To summarize key aspects of solubility measurement and interpretation, focusing on pH-dependent solubility.
- To illustrate deviations from predicted solubility-pH curves and explore their underlying causes.
- To enhance understanding of solution chemistry relevant to drug suspensions and improve assay design.
Main Methods:
- Review and case study analysis of published solubility determinations as a function of pH.
- Discussion of limitations in kinetic solubility measurements.
- Exploration of advanced concepts such as supersaturation, pre-nucleation clusters, and hydrotropic solubilization.
Main Results:
- Ionizable molecules can exhibit solubility-pH curves that deviate from predictions based on the Henderson-Hasselbalch equation.
- Factors like drug-buffer complexation, hydrotropy, and cocrystal formation influence solubility.
- Supersaturation phenomena are linked to pre-nucleation clusters and sub-micellar aggregates.
Conclusions:
- A deeper understanding of solution equilibria and factors influencing solubility is essential for robust drug development.
- Improved assay design and data quality assessment are needed for reliable solubility measurements.
- Recognizing deviations from simple models and considering complex solution phenomena can lead to better drug formulations.
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