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Molecular simulation as a computational pharmaceutics tool to predict drug solubility, solubilization processes and partitioning
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Swedish Drug Delivery Forum.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Swedish Drug Delivery Forum.ORCID iD: 0000-0002-8917-2612
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2019 (English)In: European journal of pharmaceutics and biopharmaceutics, ISSN 0939-6411, E-ISSN 1873-3441, Vol. 137, p. 46-55Article, review/survey (Refereed) Published
Abstract [en]

In this review we will discuss how computational methods, and in particular classical molecular dynamics simulations, can be used to calculate solubility of pharmaceutically relevant molecules and systems. To the extent possible, we focus on the non-technical details of these calculations, and try to show also the added value of a more thorough and detailed understanding of the solubilization process obtained by using computational simulations. Although the main focus is on classical molecular dynamics simulations, we also provide the reader with some insights into other computational techniques, such as the COSMO-method, and also discuss Flory-Huggins theory and solubility parameters. We hope that this review will serve as a valuable starting point for any pharmaceutical researcher, who has not yet fully explored the possibilities offered by computational approaches to solubility calculations.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2019. Vol. 137, p. 46-55
National Category
Theoretical Chemistry Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-382770DOI: 10.1016/j.ejpb.2019.02.007ISI: 000463304000005PubMedID: 30771454OAI: oai:DiVA.org:uu-382770DiVA, id: diva2:1313338
Funder
EU, FP7, Seventh Framework Programme, 115369Vinnova, Dnr 2017-02690EU, European Research Council, 638965Available from: 2019-05-03 Created: 2019-05-03 Last updated: 2019-05-03Bibliographically approved

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