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Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics

[Image: see text] We demonstrate that physics-based calculations of intrinsic aqueous solubility can rival cheminformatics-based machine learning predictions. A proof-of-concept was developed for a physics-based approach via a sublimation thermodynamic cycle, building upon previous work that relied...

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Autores principales: Fowles, Daniel J., Palmer, David S., Guo, Rui, Price, Sarah L., Mitchell, John B. O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190954/
https://www.ncbi.nlm.nih.gov/pubmed/33988381
http://dx.doi.org/10.1021/acs.jctc.1c00130
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author Fowles, Daniel J.
Palmer, David S.
Guo, Rui
Price, Sarah L.
Mitchell, John B. O.
author_facet Fowles, Daniel J.
Palmer, David S.
Guo, Rui
Price, Sarah L.
Mitchell, John B. O.
author_sort Fowles, Daniel J.
collection PubMed
description [Image: see text] We demonstrate that physics-based calculations of intrinsic aqueous solubility can rival cheminformatics-based machine learning predictions. A proof-of-concept was developed for a physics-based approach via a sublimation thermodynamic cycle, building upon previous work that relied upon several thermodynamic approximations, notably the 2RT approximation, and limited conformational sampling. Here, we apply improvements to our sublimation free-energy model with the use of crystal phonon mode calculations to capture the contributions of the vibrational modes of the crystal. Including these improvements with lattice energies computed using the model-potential-based Ψ(mol) method leads to accurate estimates of sublimation free energy. Combining these with hydration free energies obtained from either molecular dynamics free-energy perturbation simulations or density functional theory calculations, solubilities comparable to both experiment and informatics predictions are obtained. The application to coronene, succinic acid, and the pharmaceutical desloratadine shows how the methods must be adapted for the adoption of different conformations in different phases. The approach has the flexibility to extend to applications that cannot be covered by informatics methods.
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spelling pubmed-81909542021-06-11 Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics Fowles, Daniel J. Palmer, David S. Guo, Rui Price, Sarah L. Mitchell, John B. O. J Chem Theory Comput [Image: see text] We demonstrate that physics-based calculations of intrinsic aqueous solubility can rival cheminformatics-based machine learning predictions. A proof-of-concept was developed for a physics-based approach via a sublimation thermodynamic cycle, building upon previous work that relied upon several thermodynamic approximations, notably the 2RT approximation, and limited conformational sampling. Here, we apply improvements to our sublimation free-energy model with the use of crystal phonon mode calculations to capture the contributions of the vibrational modes of the crystal. Including these improvements with lattice energies computed using the model-potential-based Ψ(mol) method leads to accurate estimates of sublimation free energy. Combining these with hydration free energies obtained from either molecular dynamics free-energy perturbation simulations or density functional theory calculations, solubilities comparable to both experiment and informatics predictions are obtained. The application to coronene, succinic acid, and the pharmaceutical desloratadine shows how the methods must be adapted for the adoption of different conformations in different phases. The approach has the flexibility to extend to applications that cannot be covered by informatics methods. American Chemical Society 2021-05-14 2021-06-08 /pmc/articles/PMC8190954/ /pubmed/33988381 http://dx.doi.org/10.1021/acs.jctc.1c00130 Text en © 2021 The Authors. Published by American Chemical Society Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Fowles, Daniel J.
Palmer, David S.
Guo, Rui
Price, Sarah L.
Mitchell, John B. O.
Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics
title Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics
title_full Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics
title_fullStr Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics
title_full_unstemmed Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics
title_short Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics
title_sort toward physics-based solubility computation for pharmaceuticals to rival informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190954/
https://www.ncbi.nlm.nih.gov/pubmed/33988381
http://dx.doi.org/10.1021/acs.jctc.1c00130
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