Peter Feenstra

Prediction of drug-packaging interactions via molecular dynamics (MD) simulations

The interaction between packaging materials and drug products is an important issue for the pharmaceutical industry, since during manufacturing, processing and storage a drug product is continuously exposed to various packaging materials. The experimental investigation of a great variety of different packaging material-drug product combinations in terms of efficacy and safety can be a costly and time-consuming task. In our work we used molecular dynamics (MD) simulations in order to evaluate the applicability of such methods to pre-screening of the packaging material-solute compatibility. The solvation free energy and the free energy of adsorption of diverse solute/solvent/solid systems were estimated. The results of our simulations agree with experimental values previously published in the literature, which indicates that the methods in question can be used to semi-quantitatively reproduce the solid-liquid interactions of the investigated systems.

Particle-loaded monolithic materials for separations via planar electrochromatography

The synthesis and characterization of novel stationary phases for analytical applications as well as for planar electrochromatography are presented. The stationary phases are C8-functionalized silica-based monoliths, which are supported by silica particles in order to avoid shrinkage and to increase mechanical stability. After optimizing the separation conditions in capillaries for capillary electrochromatography (CEC), the novel materials were implemented in a planar test cell and tested for the electrochromatochraphic separation of polyaromatic hydrocarbons (PAHs). Although the change of geometries implied a scale-up of the filling procedure, high separation performances with theoretical plate numbers of up to 30,000 m−1 and throughputs of 50 mL h−1 could be achieved.

Retention-time prediction for polycyclic aromatic compounds in reversed-phase capillary electro-chromatography

Log Po/w based models are often used for the retention time prediction of reversed phase liquid chromatography. Here, we present the investigation of the applicability of log Po/w based retention time predictions for the separation in capillary electro-chromatography (CEC). A test set of five polycyclic aromatic hydrocarbons was separated using two different stationary phases with three different mobile phases each. The resulting retention times were correlated with the experimental log Po/w values as well as with calculated log Po/w values. The used methods include quantitative structure property relationship (QSPR) models as well as molecular dynamic methods such as the linear interaction energy (LIE) or the Bennett acceptance ratio (BAR). The results indicate that rigorous simulation models are capable of accurately reproducing experimental results and that the electrophoretic mobility of analytes in CEC separations leads to significant deviations in the retention time prediction.

Investigation of Migrant–Polymer Interaction in Pharmaceutical Packaging Material Using the Linear Interaction Energy Algorithm

The interaction between drug products and polymeric packaging materials is an important topic in the pharmaceutical industry and often associated with high costs because of the required elaborative interaction studies. Therefore, a theoretical prediction of such interactions would be beneficial. Often, material parameters such as the octanol water partition coefficient are used to predict the partitioning of migrant molecules between a solvent and a polymeric packaging material. Here, we present the investigation of the partitioning of various migrant molecules between polymers and solvents using molecular dynamics simulations for the calculation of interaction energies. Our results show that the use of a model for the interaction between the migrant and the polymer at atomistic detail can yield significantly better results when predicting the polymer solvent partitioning than a model based on the octanol water partition coefficient.