Literature DB >> 25880026

Major Source of Error in QSPR Prediction of Intrinsic Thermodynamic Solubility of Drugs: Solid vs Nonsolid State Contributions?

Yuriy A Abramov1.   

Abstract

The main purpose of this study is to define the major limiting factor in the accuracy of the quantitative structure-property relationship (QSPR) models of the thermodynamic intrinsic aqueous solubility of the drug-like compounds. For doing this, the thermodynamic intrinsic aqueous solubility property was suggested to be indirectly "measured" from the contributions of solid state, ΔGfus, and nonsolid state, ΔGmix, properties, which are estimated by the corresponding QSPR models. The QSPR models of ΔGfus and ΔGmix properties were built based on a set of drug-like compounds with available accurate measurements of fusion and thermodynamic solubility properties. For consistency ΔGfus and ΔGmix models were developed using similar algorithms and descriptor sets, and validated against the similar test compounds. Analysis of the relative performances of these two QSPR models clearly demonstrates that it is the solid state contribution which is the limiting factor in the accuracy and predictive power of the QSPR models of the thermodynamic intrinsic solubility. The performed analysis outlines a necessity of development of new descriptor sets for an accurate description of the long-range order (periodicity) phenomenon in the crystalline state. The proposed approach to the analysis of limitations and suggestions for improvement of QSPR-type models may be generalized to other applications in the pharmaceutical industry.

Keywords:  QSPR/QSAR; crystal packing contribution; error propagation; free energy of fusion; free energy of mixing; thermodynamic intrinsic aqueous solubility

Mesh:

Year:  2015        PMID: 25880026     DOI: 10.1021/acs.molpharmaceut.5b00119

Source DB:  PubMed          Journal:  Mol Pharm        ISSN: 1543-8384            Impact factor:   4.939


  4 in total

1.  Quantifying the chameleonic properties of macrocycles and other high-molecular-weight drugs.

Authors:  Adrian Whitty; Mengqi Zhong; Lauren Viarengo; Dmitri Beglov; David R Hall; Sandor Vajda
Journal:  Drug Discov Today       Date:  2016-02-15       Impact factor: 7.851

2.  Intermolecular interaction as a direct measure of water solubility advantage of meloxicam cocrystalized with carboxylic acids.

Authors:  Piotr Cysewski
Journal:  J Mol Model       Date:  2018-04-21       Impact factor: 1.810

3.  The influence of solid state information and descriptor selection on statistical models of temperature dependent aqueous solubility.

Authors:  Richard L Marchese Robinson; Kevin J Roberts; Elaine B Martin
Journal:  J Cheminform       Date:  2018-08-29       Impact factor: 5.514

4.  Solubility prediction in the bRo5 chemical space: where are we right now?

Authors:  Giuseppe Ermondi; Vasanthanathan Poongavanam; Maura Vallaro; Jan Kihlberg; Giulia Caron
Journal:  ADMET DMPK       Date:  2020-07-08
  4 in total

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