Predicting the solvation of organic compounds in aqueous environments: from alkanes and alcohols to pharmaceuticals

The development of accurate models to predict the solvation, solubility, and partitioning of nonpolar and amphiphilic compounds in aqueous environments remains an important challenge. We develop state-of-the-art group-interaction models that deliver an accurate description of the thermodynamic properties of alkanes and alcohols in aqueous solution. The group-contribution formulation of the statistical associating fluid theory based on potentials with a variable Mie form (SAFT-γ Mie) is shown to provide accurate predictions of the phase equilibria, including liquid–liquid equilibria, solubility, free energies of solvation, and other infinite-dilution properties. The transferability of the model is further exemplified with predictions of octanol–water partitioning and solubility for a range of organic and pharmaceutically relevant compounds. Our SAFT-γ Mie platform is reliable for the prediction of challenging properties such as mutual solubilities of water and organic compounds which can span over 10 orders of magnitude, while remaining generic in its applicability to a wide range of compounds and thermodynamic conditions. Our work sheds light on contradictory findings related to alkane–water solubility data and the suitability of models that do not account explicitly for polarity

Prediction of partition coefficients and solubilities of active pharmaceutical ingredients with the SAFT-γ Mie group-contribution approach

Partition coefficient and solubility are very useful properties in a variety of product and process design problems. Especially in the octanol-water system, partition coefficients (Ki_OW) are used as indicators for a drug's lipophilicity which is a key physicochemical property in drug design. Solid phase solubility is a fundamental parameter in the design of crystallisation processes commonly used in the pharmaceutical and agrochemical industries. The ability to predict these properties from the molecular structure of compounds is therefore highly desirable. In this thesis, the recently developed SAFT-γ Mie group-contribution (GC) equation of state is used as a predictive framework to study the thermodynamic properties of multifunctional compounds. The SAFT-γ Mie approach allows one to determine the thermo-physical properties of molecules in terms of the constituent functional groups that represent their unique molecular identity. The parameters for each functional group are developed from fluid-phase equilibrium data for simple compounds and, once estimated, they are applied to the study of more complex molecules in a predictive manner. Novel SAFT-γ models are developed for fundamental systems such as alkane + water and alcohol + water mixtures, which are typically involved in various chemical and biological applications. These GC models are able to describe accurately mutual solubilities of water and hydrocarbons which span more than ten orders of magnitude, and are also transferable to the modelling of multifunctional compounds. As a result, a quantitative prediction of Ki_OW and solubility is achieved for several active pharmaceutical ingredients (API) including ibuprofen, ketoprofen, lovastatin, and simvastatin. We find that an important factor that needs to be taken into account in modelling these complex APIs is the formation of intramolecular hydrogen bonds (IMHB). IMHB have a pronounced effect on molecular structure and thermodynamic properties, but are often overlooked by other predictive approaches. Modelling complex organic molecules with the consideration of IMHB is challenging, especially for GC approaches which do not take into account details of molecular conformation. In this thesis, an effective treatment for IMHB is developed within the SAFT-γ Mie framework and proven to improve the property prediction of molecules with IMHB, especially in highly associated solvents. The findings in this thesis validate the applicability of the SAFT-γ Mie approach in modelling complex multifunctional molecules and demonstrate its broad relevance for the pharmaceutical industry.Open Acces