Synergistic Computational Modeling Approaches as Team Players in the Game of Solubility Predictions
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Authors
Author (Corporation)
Publication date
17.11.2020
Typ of student thesis
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Collections
Type
01A - Journal article
Editors
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Supervisor
Parent work
Journal of Pharmaceutical Sciences
Special issue
DOI of the original publication
Series
Series number
Volume
110
Issue / Number
1
Pages / Duration
22-34
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Publisher / Publishing institution
Elsevier
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Edition
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Abstract
Several approaches to predict and model drug solubility have been used in the drug discovery and development processes during the last decades. Each of these approaches have their own benefits and place, and are typically used as standalone approaches rather than in concert. The synergistic effects of these are often overlooked, partly due to the need of computational experts to perform the modeling and simulations as well as analyzing the data obtained. Here we provide our views on how these different approaches can be used to retrieve more information on drug solubility, ranging from multivariate data analysis over thermodynamic cycle modeling to molecular dynamics simulations. We are discussing aqueous solubility as well as solubility in more complex mixed solvents and media with colloidal structures present. We conclude that the field of computational pharmaceutics is in its early days but with a bright future ahead. However, education of computational formulators with broad knowledge of modeling and simulation approaches is imperative if computational pharmaceutics is to reach its full potential.
Keywords
Colloid(s), Computational ADME, Dissolution, Drug-excipient interaction(s), Lipid-based formulation(s), Solubility, Solubilization
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ISBN
ISSN
0022-3549
1520-6017
1520-6017
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
License
Citation
Kuentz, M. (2020). Synergistic Computational Modeling Approaches as Team Players in the Game of Solubility Predictions. Journal of Pharmaceutical Sciences, 110(1), 22–34. https://doi.org/10.1016/j.xphs.2020.10.068