Prediction of retention times of peptide nucleic acids during reversed-phase high-performance liquid chromatography

Modeling the drugs' passive transfer in the body based on their chromatographic behavior

One of the most challenging aims in modern analytical chemistry and pharmaceutical analysis is to create models for drugs' behavior based on simulation experiments. Since drugs' effects are closely related to their molecular properties, numerous characteristics of drugs are used in order to acquire a model of passive absorption and transfer in the human body. Importantly, such direction in innovative bioanalytical methodologies is also of stressful need in the area of personalized medicine to implement nanotechnological and genomics advancements. Simulation experiments were carried out by examining and interpreting the chromatographic behavior of 113 analytes/drugs (400 observations) in RP-HPLC. The dataset employed for this purpose included 73 descriptors which are referring to the physicochemical properties of the mobile phase mixture in different proportions, the physicochemical properties of the analytes and the structural characteristics of their molecules. A series of different software packages was used to calculate all the descriptors apart from those referring to the structure of analytes. The correlation of the descriptors with the retention time of the analytes eluted from a C4 column with an aqueous mobile phase was employed as dataset to introduce the behavior models in the human body. Their evaluation with a Partial Least Squares (PLS) software proved that the chromatographic behavior of a drug on a lipophilic stationary and a polar mobile phase is directly related to its drug-ability. At the same time, the behavior of an unknown drug in the human body can be predicted with reliability via the Artificial Neural Networks (ANNs) software.

A statistical learning approach to the modeling of chromatographic retention of oligonucleotides incorporating sequence and secondary structure data

We propose a new model for predicting the retention time of oligonucleotides. The model is based on nu support vector regression using features derived from base sequence and predicted secondary structure of oligonucleotides. Because of the secondary structure information, the model is applicable even at relatively low temperatures where the secondary structure is not suppressed by thermal denaturing. This makes the prediction of oligonucleotide retention time for arbitrary temperatures possible, provided that the target temperature lies within the temperature range of the training data. We describe different possibilities of feature calculation from base sequence and secondary structure, present the results and compare our model to existing models.

Native purification of biomolecules with temperature-mediated hydrophobic modulation liquid chromatography

The high-resolution purification of native enzymes is impeded by the limitations in the mobile-phase choices required for conventional hydrophobic separations such as in reverse-phase chromatography. To avoid problems associated with varying the composition of the mobile phase, we developed a stationary phase with a hydrophobicity that can be modulated by slight variations in temperature to bind and elute biomolecules. This chromatographic matrix was tested on nucleotide analogs, amino acids, and protein samples. Visualization of the temperature-dependent hydrophobic interaction with the chromatographic matrix was performed with fluorescence microscopy of CY3-ATP. Amino acids adsorbed to the column according to their known hydrophobicities, confirming the hydrophobic nature of their interaction with the matrix. Biomolecules were separated by modulating the hydrophobicity of the column matrix with slight adjustments to the running temperature between 22 and 37 degrees C without changing the mobile phase. Freedom in the choice of a mobile phase for both the loading and the elution of samples provides great practical advantages by eliminating the need for buffer-exchange steps and allowing more native conditions for purifying delicate enzymes, such as myosin.