(640g) Prediction of the Solubility of Active Pharmaceutical Ingredients Using the SAFT-? Mie Group Contribution Approach

Wehbe, M. - Presenter, Imperial College London
Febra, S., Imperial College London
Kournopoulos, S., Imperial College London
Jackson, G., Imperial College London
Adjiman, C. S., Imperial College London
Galindo, A., Imperial College London
The pharmaceutical industry is facing a constantly rising demand for drugs of growing complexity and more efficient manufacturing processes to quickly address market needs1. The solubility of pharmaceuticals is a key property during the drug formulation and the subsequent design of the processes involved in the manufacturing of the pharmaceutical drug. Common challenges in the manufacturing process are linked to solvent selection procedures for unit operations such as reaction, solvent extraction and crystallisation. An increasingly large number of experiments is required when more solvents and solvent blends are considered, which is usually time-consuming, expensive and can leave out good candidates. Computer-aided approaches provide an attractive alternative to performing numerous and costly experiments. Specifically, molecular modelling approaches can deliver physical properties predictively, including solubility.

The SAFT-𝛾 Mie group contribution (GC) equation of state (EoS)2,3 is such a predictive thermodynamic modelling technique. In the SAFT-𝛾 Mie framework, molecules are modelled as heteronuclear chains formed from fused spherical segments, which represent the distinct chemical moieties (or functional groups) comprising the molecule. In this framework, it is assumed that the properties of a molecule or a mixture can be determined from the weighted contributions of the functional groups present in the system of interest, with the assumption that the parameters characterising the functional groups are fully transferable across molecules.

We first demonstrate the validity of the SAFT- 𝛾 Mie EoS in the prediction of thermodynamic equilibrium properties of neutral active pharmaceutical ingredients (APIs) including solubility Moreover, as it is well known that the bioavailability of a drug is improved by salt formulation4, we test our approach for the solubility prediction of ionisable APIs, and their salts, under changing pH. The SAFT- 𝛾 Mie EoS accounts for the complex speciation phenomena that take place under pH changes including fully ionised (strong electrolytes)5 and partially ionised (weak electrolytes) systems. We investigate in particular, the solubility of the acidic API, ibuprofen, its speciation and salt formation, to develop the pH-solubility profile of this drug.

  1. Mckenzie, P., Kiang, S., Tom, J., Rubin, A. E. & Futran, M. Can Pharmaceutical Process Development Become High Tech? Am. Inst. Chem. Eng. 52, 3990–3994 (2006).
  2. Papaioannou, V. et al. Group contribution methodology based on the statistical associating fluid theory for heteronuclear molecules formed from Mie segments. Chem. Phys. 140, (2014).
  3. Dufal, S. et al. Prediction of thermodynamic properties and phase behavior of fluids and mixtures with the SAFT-γ mie group-contribution equation of state. J. Chem. Eng. Data 59, 3272–3288 (2014).
  4. Newton, D. W. Drug incompatibility chemistry. Am J Heal. Pharm 66, 348–357 (2009).
  5. Eriksen, D. K. et al. Development of intermolecular potential models for electrolyte solutions using an electrolyte SAFT-VR Mie equation of state. Mol. Phys. 114, 2724–2749 (2016).


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