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Title: Prediction of partition coefficients and solubilities of active pharmaceutical ingredients with the SAFT-γ Mie group-contribution approach
Author: Hutacharoen, Panatpong
ISNI:       0000 0004 6496 4298
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2017
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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.
Supervisor: Jackson, George ; Galindo, Amparo ; Adjiman, Claire S. Sponsor: Pfizer Ltd
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral