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Lipophilicity Studies on Thiosemicarbazide Derivatives
Agata Paneth1, Anna Hawrył2, Tomasz Plech3
1Department of Organic Chemistry, Medical University, Chodźki 4a, 20-093 Lublin, Poland. agata.paneth@umlub.pl.
Molecules (Basel, Switzerland)
|June 9, 2017
Summary
This study evaluated the lipophilicity of thiosemicarbazide derivatives using RP-HPLC. Different computational methods accurately predicted lipophilicity, with specific algorithms showing strong correlations for distinct compound series.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Analytical Chemistry
Background:
- Lipophilicity is a critical physicochemical property influencing drug absorption, distribution, metabolism, and excretion (ADME).
- Accurate prediction of lipophilicity (logP) is essential for efficient drug design and development.
- Thiosemicarbazide derivatives represent a class of compounds with diverse biological activities.
Purpose of the Study:
- To experimentally determine the lipophilicity of two series of thiosemicarbazide derivatives.
- To compare experimental lipophilicity values with predictions from various computational methods (AClogP, XlogP2, XlogP3, quantum chemical calculations).
- To evaluate the reliability and correlation of different predictive algorithms for thiosemicarbazide lipophilicity.
Main Methods:
- Reversed-phase high-performance liquid chromatography (RP-HPLC) using an RP-18 column and a methanol-water mobile phase.
- Determination of experimental distribution coefficients (logP).
- Comparison with in silico methods including AClogP, XlogP2, XlogP3, and quantum chemical calculations.
- Statistical evaluation using correlation matrices and Principal Component Analysis (PCA).
Main Results:
- High correlation between experimental and predicted logP values was observed for 4-benzoylthiosemicarbazides using the XlogP3 algorithm.
- Strong correlation for 4-aryl/(cyclohexyl)thiosemicarbazides was found with the XlogP2 parameter.
- PCA and correlation analysis confirmed the reliability of specific computational methods for different thiosemicarbazide structures.
Conclusions:
- The study successfully validated computational methods for predicting lipophilicity in thiosemicarbazide derivatives.
- The choice of predictive algorithm (XlogP3 or XlogP2) is crucial and depends on the specific structural class of thiosemicarbazides.
- These findings support the use of validated in silico tools for optimizing the design of thiosemicarbazide-based compounds.
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