(574c) Solvent Screening and Pure Component Thermophysical Property Prediction for Pharmaceutical Process Design

The selection of optimal
solvent or a mixture of solvents is essentially useful to achieve
pharmaceutical production with desirable product and production qualities as
well as a minimum cost of production and a maximum space-time yield. Despite the
availability of high-throughput technologies, experimental solvent screening is
most often expensive and laborious and sometimes simply not possible because of
lack of sufficient amount of API or relevant impurities. This is especially the
case when many combinations of design needs to be tested for identifying an optimal
mixture of binary or ternary solvents made up among several common solvents in
pharmaceutical industry. To this end, application of predictive thermodynamic
models for estimation of phase behaviors in pure and mixed solvent systems have
been shown to be a supporting and complementary tool to design pharmaceutical
processes. In this work, different predictive thermodynamic models such as
UNIFAC, COSMO-SAC, COSMO-RS and NRTL-SAC were applied for solubility prediction
of a steroid-like structure antibiotic. The predicted solubility data were
compared with experimental solubility data in n-butanol, isobutanol, methyl
tert-butyl ether, methyl isobutyl ketone, ethyl acetate, hexane, methanol,
acetone and toluene by calculating the root mean square error (RMSE) in order
to evaluate the models capability. Therefore, a model which gives more accurate
solubility prediction can be selected. For this specific case study, NRTL-SAC is
capable of predicting solubility in a good agreement with experimental solubility
with RMSE of 4.5%. Moreover, the application of such predictive thermodynamic
models for the design of separation/purification unit operations is sometimes
limited by the lack of pure component thermophysical properties like melting
point and enthalpy of fusion. This mostly happens in the design of
separation/purification unit operations to separate analytically identified but
not commercially available impurities. Therefore, in addition to the application
of those predictive thermodynamic models for solubility prediction, a group-contribution⁺
(GC⁺) based model is applied for pure component thermosphysical
property prediction. However, the GC⁺ based model has a limited
capability for a large and complex organic molecules that pure component
thermosphysical property prediction is prone to uncertainty. In such
circumstance, a reference solvent approachcan be applied to predict
solubility[1].
The reference solvent approach requires no pure component thermosphysical
properties but an experimental solubility data in a single solvent. The
experimental solubility data of a noncommercially available impurity can be
measured by concentrating a sample containing the impurity. This work concludes
by critical examination of the performance and the applicability of the above
mentioned four property models under different pure component data constraints
especially for solubility calculations in life science product (API) separation
applications.

Acknowledgment:
We would like to thank the Danish Council for
Independent Research (DFF) for financing the project with grant ID:
DFF-6111600077B.