The potential for wider use of two-dimensional liquid chromatography (2D-LC) becomes more apparent as the complexity of sample handling continues to increase in application areas ranging from biopharmaceuticals to bio-based consumer products. Although sophistication and ease of use have improved in recent years for commercial 2D-LC instruments, many analysts are still intimidated by the method development process for 2D methods due to the greater number of variables involved. compared to conventional liquid chromatography. In this article, I share my perspective on the trends in this field, and the developments we are likely to see in the field in the near future.
In 2022, on this 40th anniversary of the LCGC, I often find myself thinking about how to approach method development for two-dimensional liquid chromatography (2D-LC). Although 2D-LC is a powerful chromatographic technique, and today those beginning to use it have very good options in terms of purchasing commercial instruments and software, method development is a process which is still strongly influenced by the experience of the analyst. Although I am fortunate to have a lot of experience to draw on when developing new methods, new users are not so lucky and often feel overwhelmed and intimidated by the method development process. , which is understandable. While there are a number of variables that can be adjusted in method development for conventional LC (e.g., column chemistry and dimensions, flow rate, and temperature, to name a few- ones), then there are at least twice as many variables to deal with. for 2D-LC methods. Perhaps more importantly, many variables of the two dimensions are interdependent (1). Personally, I see this as the biggest obstacle to more widespread use of 2D-LC in the years to come. Reflecting on the technical program of the 2022 High Performance Liquid Chromatography (HPLC) conference held recently in San Diego, California, it seems to me that many parallels can be drawn between how development strategies of methods developed for one-dimensional LC (1D-LC) over the past 40 years, and how these strategies are currently developing for 2D-LC. Maybe we can learn from 1D-LC developments to accelerate the progress of 2D-LC method development strategies – I think there are good opportunities to work smarter instead of working harder. Thinking about the talks and posters at HPLC 2022 and the ongoing work in my group, I see at least three themes emerging.
First, we desperately need software tools that can help us in the method development process. In San Diego, John Dolan gave a talk called “In the Fullness of Time—The DryLab Story.” This conversation, in 2022, took place 33 years after the first articles were published describing what would become known as DryLab (2.3). In one of these papers (2), Snyder and others stated: “This procedure, which may be called ‘computer simulation’, attempts to imitate the strategy followed by experienced chromatographers, but uses the computer to reduce the time and effort required.”
Snyder and others were absolutely right. Reducing the time and effort required for the method development process is necessary for 2D-LC. There has been some research in this direction in recent years by the groups of Pirok and Regalado (4-8). For our part, Thomas Lauer gave a presentation at the HPLC 2022 meeting outlining a recent effort by my group to develop a freely available simulation tool for 2D-LC, but more is needed.
Second, it looks like our community is embarking on a new era where advanced simulation tools, informed in part by larger datasets than in the past (i.e. machine learning, intelligence artificial and big data) will play an important role in the chromatographic simulations of the future. The HPLC 2022 program featured several sessions dedicated to machine learning and artificial intelligence, which I believe is the first time that these topics have appeared with such prominence at this annual meeting. It will be fascinating to see what value these advanced simulation tools add to existing optimization strategies, and whether these approaches can also be applied to 2D-LC.
Finally, there are still significant gaps in the fundamental knowledge we need to successfully implement method development software for 2D-LC, and not all of the method development software schemes we will suggest will be equally effective. they could be until we fill in those knowledge gaps. An example of this that we have focused on quite intensively in recent years is how the volume and composition of the effluent from a first dimension (1D) separation affects the behavior of the second dimension (2D) 2D-LC separation. In 1D-LC, it is generally advisable not to use injection volumes much greater than about 1% of the dead volume of the column, as injection of larger volumes can adversely affect peak shape and resolution. However, in 2D-LC, limiting the volume of the 1D effluent in the 2Column D in this way is not an option in most cases as the detection sensitivity would be too low and would render the 2D-LC method useless. As a concrete example, it is common in our work to inject 80 μL of 1D effluent in an inside diameter of 50 mm x 2.1 mm 2Column D which has a dead volume of approximately 100 μL. In other words, the injected sample literally fills the column and effectively acts as the mobile phase for a short time. We (9–11) and others (12) have studied this issue and now have a better understanding of the conditions that can lead to severe performance degradation of the 2D separation (eg, analyte breakthrough and other types of distortion and peak broadening). However, most of this work so far has focused on relatively simple systems (e.g. injection of acetonitrile or buffer into acetonitrile or buffer where pH does not play a role). major). Many important applications of 2D-LC involve conditions where the differences between mobile phases and column chemistries are maximized in the interest of maximizing the complementarity of the two separations. Under these conditions, differences in mobile phase pH and additive concentrations can have significant effects on the 2D (13), and may appear as “unpredictable” behavior to those new to 2D-LC method development. In the long run, we will need to be able to account for these behaviors so that method development is more about following a data-informed process than guessing what conditions might be worth trying. We need to develop the knowledge and tools that enable us to work smarter instead of working harder.
I’m excited to see how the themes we observed at HPLC 2022 will impact the 2D-LC field in the near future, and I’m optimistic that by the 45th anniversary of LCGCmethod development for 2D-LC will seem much more systematic than it does today and accessible to a wide range of users.
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