Evaluation of Empirical Thermodynamic Solubility Modeling in Formulation Vehicles
Posted on April 30, 2015
Thermodynamic solubility of three compounds with differing physico-chemical properties [Indomethacin (MW: 357.78, XlogP3: 4.3), Posaconazole (MW: 700.78, XlogP3: 4.6) and Levothyroxine (MW: 798.85, XlogP3: 2.4)] were determined in several formulation vehicles and common solvents. Experimental solubility data along with thermal data were used to regress the solubility behavior of the compounds via the empirical non-random two-liquid segment activity coefficient (NRTL-SAC) model and predict their solubility in formulation vehicles.
Development of formulations based on softgel technology is
usually pursued for poorly soluble compounds to improve their bioavailability. In most cases, optimization of the bioavailability can be achieved by utilizing suitable vehicles which maximizes the solubility of the API. The process of selecting vehicles generally involves screening a set of vehicles exhibiting a diverse range of hydrophilicity, lipophilicity and solubilizing properties. It may also involve the evaluation of solubility in both neat vehicles as well as in mixtures.
In this presented study, we utilized NRTL-SAC (non-linear two liquid segment activity coefficient) model to predict the solubility of APIs in formulation vehicles and evaluate its feasibility as a tool for selecting potential vehicles. The use of NRTL-SAC and other activity models in predicting solubility of APIs in common solvents have been widely investigated. The model calculates activity coefficient based on (1) entropy of mixing (compositional, combinatorial, nonspecific) and (2) segment-segment (specific) pair-wise interactions. Each molecule in the model is defined by four
conceptual descriptors (±polarity, hydrophilic, hydrophobic) As an empirical model, NRTL-SAC has several advantages over other solubility models: (1) the approach does not rely on pre-defined functional group activity coefficients; (2)
correlative and predictive; and (3) minimally, requires only four experimental solubility data as an input.
A set of 21 vehicles consisting of common solvents as well as
potential softgel vehicles were used in this study. Table 1 presents the description of the vehicles as well as the experimental equilibrium solubility data obtained. Of the three model compounds, indomethacin exhibited generally better solubility across all vehicles compared to the two model compounds. A training set involving four common solvents only as well as training sets combining four solvents and three softgel vehicles were initially attempted to model the solubility behavior of indomethacin. Figure 1 shows the correlation of the results between experimental and predicted values in the two models for indomethacin. Overall, modeling involving solvents and softgel vehicles resulted in a better correlation.
The same training sets of seven solvents and softgel vehicles were utilized to model the solubility behavior of all three model compounds. Results of the solubility prediction are shown in Figure 2. In general, the predicted solubility values for the softgel vehicles were lower compared to actual experimental values. However, the rank-order of the vehicles in terms of solubility is good overall. Evaluation of the overall correlation of experimental vs predicted data sets suggest indomethacin > posaconazole > levothyroxine. Interestingly, this rank order qualitatively correlates with the physical stability of the compound during the solubility determination.
Thermodynamic solubility modeling can provide adequate correlation between experimental and predicted solubility data and can be useful in selecting vehicles for initial formulation design.
Related Topics and Keywords
API, bioavailability, Catalent, common solvents, Empiracal Solubility Modeling, empirical thermodynamic solubility, experimental solubility data, formulation vehicles, G. K. Rashid, hydrophilicity, I. McGuffy Catalent, Indomethacin, Levothyroxine, lipophilicity, MW: 357.78, MW: 700.78, MW: 798.85, non-random two-liquid segment activity coefficient, NRTL-SAC, P. Batheja, P. Chen, physico-chemical properties, Posaconazole, S. Carino, S. Sukumar, softgel technology, solubilizing properties, thermal data, Thermodynamic solubility, XlogP3: 2.4, XlogP3: 4.3, XlogP3: 4.6
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