A guide to selecting the correct HPLC stationary phase.
An excerpt from LCGC's e-learning tutorial on HPLC stationary phases at CHROMacademy.com
There is a bewildering array of stationary-phase choices available for reversed-phase high performance liquid chromatography (HPLC), and even within each phase designation (such as "C18") the selectivity of each phase can vary widely.
Let's be honest, a lot of our method development is carried out using trial and error, typically based on phases that have worked for us in the past or are the "new best thing" from our favourite manufacturer. Even those of us with advanced "screening" platforms containing arrays of carefully considered orthogonal chemistries and computer-optimized eluent design systems, sometimes have to resort to "chromatographer's instinct".
Figure 1: Retention mechanisms of some common reversed-phase ligands and residual silica surface species.
Retention in reversed-phase HPLC is typically based on an equilibrium between the analyte, the mobile phase, and the bonded stationary phase (C18, for example), and the nature and accessibility of the silica surface onto which the ligand is bonded. The chemistry of the bonded phase, the nature of the silica surface treatment, and the surface accessibility all need to be considered and classified to properly understand the retention mechanisms that are influencing a separation, which will then influence the initial column choice or method optimization.
Figure 2: Radar plots of similar (right) and orthogonal (left) stationary phases based on data from the PQRI database, which in turn is based on the hydrophobic subtraction model.
Dispersive interactions are predominant in most reversed-phase separations, especially those using unmodified alkyl ligands (C18, C8, C4), and retention will be proportional to the hydrophobicity of the analyte. Charge transfer (or π–π) interactions are in play when aromatic or unsaturated phases or analytes are analyzed. Dipole-hydrogen bonding interactions are important for the retention of polar compounds, and stationary phases such as "cyano" enhance this type of retention. Electrostatic interactions occur between ionized sites on the analyte molecule and the silica surface, and in most cases are caused by ionized residual silanol groups.
Many column-classification systems exist, based on the use of various chemical test probes that are known to describe unique characteristics of a stationary phase, and one very useful example is the Product Quality Research Institute (PQRI) database hosted within the USP website (http://www.usp.org/app/USPNF/columnsDB.html). This database uses the hydrophobic subtraction model of retention (1,2) to describe phases based on hydrophobicity (H), the ability to distinguish between analytes of similar hydrophobicity but different shape or hydrodynamic volume, shape selectivity (S), hydrogen bonding (the ability to act as a Lewis acid [A] or a Lewis base [B]), and electrostatic interaction (C) at pH 7.0 (total silanol activity) and pH 2.8 (acidic silanol activity likely to cause tailing with polar or ionizable analytes). More unique or orthogonal phases tend to have larger values for S, B, and C (7.0). These large databases are useful to compare phase characteristics and "radar plots" are a very useful way to do this.
Table 1 shows a summary of the major classifications of phases currently available and their applicability.
(1) L.R. Snyder, J.W. Dolan, and P.W. Carr, J. Chromatogr. A 1060, 77–116 (2004).
(2) L.R. Snyder, J.W. Dolan, and P.W. Carr, Anal. Chem. 79, 3255–3261 (2007).
AI and GenAI Applications to Help Optimize Purification and Yield of Antibodies From Plasma
October 31st 2024Deriving antibodies from plasma products involves several steps, typically starting from the collection of plasma and ending with the purification of the desired antibodies. These are: plasma collection; plasma pooling; fractionation; antibody purification; concentration and formulation; quality control; and packaging and storage. This process results in a purified antibody product that can be used for therapeutic purposes, diagnostic tests, or research. Each step is critical to ensure the safety, efficacy, and quality of the final product. Applications of AI/GenAI in many of these steps can significantly help in the optimization of purification and yield of the desired antibodies. Some specific use-cases are: selecting and optimizing plasma units for optimized plasma pooling; GenAI solution for enterprise search on internal knowledge portal; analysing and optimizing production batch profitability, inventory, yields; monitoring production batch key performance indicators for outlier identification; monitoring production equipment to predict maintenance events; and reducing quality control laboratory testing turnaround time.