March 10, 2022
by Sanji Bhal, Director, Marketing & Communications, ACD/Labs
Separation scientists at pharmaceutical and chemical companies have the challenging task of developing appropriate separation methods for various outcomes. These include developing stability indicating methods, impurity identification, method transfer, and method scale up. Thankfully trial-and-error method development is becoming a thing of the past with the majority of R&D labs using a combined manual and computer assisted approach. As method development groups continue to investigate the ways in which software can support their work, creative applications of method simulation have resulted in exciting developments that provide better separations with greater efficiency.
We were recently joined by Imad Haidar Ahmad, an associate principal scientist and scientific supervisor at Merck, to speak about the traditional and creative approaches on liquid chromatographic (LC) separations of pharmaceutically relevant compounds. Imad and his team have been conducting and publishing this type of work using ACD/Labs’ method optimization software—Method Selection Suite.
Imad shared his experience with us in a recent webinar. The topics discussed include:
- Application of software in traditional and creative ways to aid in the separation of pharmaceutically relevant small and large molecules
- Developing separation methods suitable for scale-up so components can be isolated with high purity
- Exploiting modeling and Multifactorial Peak Crossover (MPC) to effectively separate closely related components
- Application of modeling to 2D LC techniques to separate mixtures containing many closely related components
Here’s how Imad and his team incorporate optimization software into their workflow to develop methods more efficiently.
Method Optimization Supported by Chromatographic Simulation
Imad and his team use modeling to develop LC, ion exchange, and other types of chromatographic methods to efficiently separate many types of pharmaceutically relevant components (APIs, impurities, degradants, etc.). This can include small molecules or large complex biomolecules such as proteins, peptides, vaccines, and monoclonal antibodies (MABs).
When developing reverse-phase liquid chromatography (LC) and ion exchange methods for the separation of small molecules, Imad’s team first identify, through screening, the column/mobile phase combination that will give the best selectivity as a starting point. Then, in the optimization phase, a matrix of three temperatures and three gradients is acquired and fed into the simulation software to generate an interactive predictive model.
The automatically generated resolution map enables Imad and his group to see the ideal conditions and visualize simulated chromatograms which can then be verified experimentally. This also allows users to see areas of robustness and edges of failure for a method.
Accurate retention modeling of small molecules has been available for decades and is afforded through correlating data with a first degree linear fit. Due to their tertiary structure and complex interaction with the stationary phase, modeling of large molecules requires the use of second order polynomial fit to obtain accurate retention time modelling. Imad and his group have reported guidelines under which circumstances it would be required to use a linear or polynomial fit for the separation of large molecules. They reported excellent correspondence between experimental and predicted retention times for several classes of biomolecules.1
“When a protein goes through a column,” says Imad, “the retention mechanism is not a simple two-point interaction.”
As a final step in their process, Imad’s team store their final methods; “Building a database that we can use in case we ever need to separate and detect the same or similar component,” said Imad.
Preparative Scale Method Development
The group also uses chromatographic modeling to develop methods suitable for scale up.2 One of the most important aspects of method optimization for scale-up is being able to load large quantities of sample on the column while maintaining separation of the peak(s) of interest. Using simulation to help reduce method and retention time, Imad’s team reported a 200-fold increase in productivity. “A purification that could take up to month, we completed in less than a day,” recalled Imad.
Multifactorial Peak Crossover
For peaks that are difficult to separate due to co-elution, i.e., a small impurity that is eluting slightly before the target peak they are trying to isolate, Imad’s team use multipeak crossover and modeling to identify methods with different selectivity to isolate specific peaks for scale up.
Imad explained, “When we look at the resolution map and find a specific condition where we can move the smaller peak away from the front, so it’s loading after the peak we are trying to purify, we could easily increase loading by 20x.”
In the short movie below Imad moves around the resolution map for a complex separation to select both a robust method and ideal peak positions.
Two Dimensional Liquid Chromatography (2D LC)
“In some of the purifications when we have very challenging mixtures, it’s almost impossible to be able to purify an analyte in one step because you may have closely related compounds co-eluting together,” Imad stated. These separations require a two-step (2D) purification process. Imad and his team have successfully applied simulation software to model separations in both dimensions for 2D LC, thereby further increasing the efficiency gains achieved with this up-and-coming approach. In an example 2D separation, Imad’s team developed a system to simultaneously screen multiple columns and mobile phases in two dimensions for a complex mixture of chiral and achiral components.3
To hear more about Imad’s creative approaches to method optimization and how you might apply them in your own work, watch the full webinar here.
- Ahmad, et al. (2019). Multi-column ultra-high performance liquid chromatography screening with chaotropic agents and computer-assisted separation modeling enables process development of new drug substances. Analyst, 144, 2872–2880.
- Bennett, et al. (2019). Mapping the Separation Landscape of Pharmaceuticals: Rapid and Efficient Scale-Up of Preparative Purifications Enabled by Computer-Assisted Chromatographic Method Development. Organic Process Research and Development, 23(12), 2678–2684.
- Wang, et al. (2020). Introducing online multicolumn two-dimensional liquid chromatography screening for facile selection of stationary and mobile phase conditions in both dimensions. J Chromatogr A, 1622.