Development of a predictive group-contribution platform for the phase behaviour and the effect of pH on the solubility of pharmaceuticals and excipients
Creators: name: Wehbe, Malak
Publisher: name: Imperial College London
Year: 2022

description: Predictive thermodynamic modeling tools play an important role in the design of pharmaceutical processes by focusing experimental efforts on promising areas and ultimately enabling a more efficient approach to drug product development and manufacturing. In this thesis, we explore the applicability of the SAFT-γ Mie GC approach, a heteronuclear GC version of the statistical associating fluid theory (SAFT) equation of state (EoS), for predicting the phase behaviour and the pH-dependent solubility of pharmaceutical mixtures. An area of particular interest to the industry is enhancing the solubility of ionisable active pharmaceutical ingredients (APIs). Salt formation is commonly used for this purpose and is monitored via a pH-solubility profile of the API and its salt. Due to the extremely low solubilities of many APIs in water, experiments are difficult to perform, and reliable predictive tools can be especially useful in this context. The SAFT-γ Mie GC EoS is used to predict the phase diagrams and the pH-dependent solubility of two acidic monoprotic APIs: ibuprofen and ketoprofen. We consider ibuprofen and ketoprofen in inorganic and organic basic and acidic buffering agents. A new group, NH+3 , is characterised for the case of amine buffers and salts. Equilibrium constants for the dissociation of the APIs and the formation of salts (pKa and Ksp values) are also incorporated in the model using experimental values from literature. Predictions of the complete phase diagrams of the APIs in water are presented, including the vapour–liquid, liquid–liquid (oiling out), and solid–liquid (solubility) equilibria. The SAFT-γ Mie approach is shown to provide accurate predictions of the phase behaviour of the APIs, as well as the pH-solubility profiles of the APIs at 298.15 K and 310.15 K for the range of buffers and salts considered. We extend the applicability of the SAFT-γ Mie GC approach by modeling the pH-solubility profile of the weakly basic diprotic API procaine, in aqueous solutions buffered by NaOH and HCl. Three new groups: aCNH2, aCCOO and aCNH+3 , in addition to their unlike interactions with other groups, are characterised to study the neutral and ionic forms of procaine within the SAFT-γ Mie framework. The newly developed groups and group interactions are shown to deliver accurate predictions of the intrinsic solubility and the pH-solubility profiles of the API; demonstrating the transferability of the SAFT-γ Mie group interactions, as no data related to procaine were used to obtain the group parameters. Subsequently, we demonstrate the relevance of the GC approach for the prediction of the phase behaviour of more-complex pharmaceutical mixtures, by studying mixtures containing formaldehyde, a common degradation agent produced by some pharmaceutical excipients during the storage of drug products. Formaldehyde is usually processed in the form of aqueous solutions, with methanol added for stability. In these solutions, formaldehyde reacts with the solvents, to produce a variety of oligomers; and these chemical reactions have a significant influence on the fluid-phase equilibrium of formaldehyde mixtures. The SAFT-γ Mie GC approach is used to obtain the phase behaviour of binary and ternary mixtures of formaldehyde with water and methanol. The oligomerization reactions taking place in formaldehyde solutions are modeled implicitly using a physical approach, which is possible within the SAFT-γ Mie framework by adding association (reactive) sites to mediate the formation of the reaction products. A new group, CH2O, characterising formaldehyde within the SAFT-γ Mie GC approach, is developed. Experimental data for the vapour-liquid equilibria (VLE) in binary mixtures of formaldehyde + water and formaldehyde + methanol are used to obtain the optimal unlike interaction parameters between the corresponding SAFT-γ Mie groups. The newly developed parameters are then used to predict the VLE of ternary formaldehyde + water + methanol mixtures, providing an excellent agreement with experimental data. Additionally, the nature of chemical speciation in formaldehyde + water, formaldehyde + methanol and formaldehyde + water + methanol mixtures is studied using this approach, obtaining accurate predictions of the distribution of reaction products (oligomers) in the binary and ternary mixtures. The findings in this thesis demonstrate the applicability of the SAFT-γ Mie GC approach for thermodynamic property prediction of pharmaceutical mixtures. The SAFT-γ Mie EoS is proven to be a powerful predictive tool that can contribute to increasing the efficiency and lowering the cost of drug product development and manufacturing in the pharmaceutical industry.