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You may be one of the many analytical scientists who look with envy at those laboratories who are equipped with sophisticated automated HPLC method development systems. These systems are indeed very nice and can be very efficient in narrowing down choices, however, they aren’t a universal panacea and one can achieve a lot with a simple, paired down approach.
You may be one of the many analytical scientists who look with envy at those laboratories who are equipped with sophisticated automated HPLC method development systems. These systems are typically equipped with several columns, eluent reservoirs, and column switching valves connected to smart software which can test a wide number of column and eluent combinations in an automated fashion, to search for the best combination of conditions for the initial phase of method development.
These systems are indeed very nice and can be very efficient in narrowing down choices, however, they aren’t a universal panacea and one can achieve a lot with a simple, pared down approach. In my experience, a well-designed single screening experiment can tell us a lot about the sample at hand and point the way to future method choices, however I find that the interpretation of the results of the initial screen is often the step at which less experienced method developers get stuck.
Never fear-I’ll present some simple scenarios to guide us to the next steps, and over the years I’ve designed some robust decision trees that can help guide future experimentation as we progress through the method development paradigm. Further-these trees are organised to maximize return on effort, so the simple experiments can be tried first before having to undertake more complex experiments or time-consuming steps-such as changing columns.
Step 1 – Arrange your screening experiment to shoot for k* (let’s call that an Average Gradient Retention Factor) of around 5.
The formula we use here is:
tg is the gradient time in min, F is the eluent flow rate in mL/min, DΦ is the change in eluent composition (i.e. 0.4 for a 20 to 60% B gradient), Vm is the interstitial volume of the column, which is estimated by:
S is a shape selectivity factor which can be estimated by,
(for analytes < 1000Da a value of 5 is typically used for S).
Some of this is predicated on the column dimensions used which determines Vm, and some of the more common column dimensions give the following interstitial column volumes (all calculated using Equation 2):
Table I: Interstitial Column Volumes (Vm) for various popular HPLC column dimensions.
For screening experiments-we like a nice wide gradient range to take care of the various polarities of analyte that we might encounter, so let’s say a range of 0.8 which will mean setting the gradient from 10%B to 90%B (not forgetting that the we hold the gradient at 90% for a considerable time to assess if there are any really strongly retained analytes).
I’m going to assume an eluent flow rate of 1.0 mL/min for the 4.6 mm internal diameter columns and 0.5 mL/min for the 2.1 mm internal diameter columns.
Armed with all this information, we can re-arrange Equation 1 to give us a gradient time figure which will result in a k* value of 5 for each of the columns shown in Table I:
Calculating for each of the column volume and flow rate combinations gives gradient times shown in Table II:
So now we have gathered some useful information and we can consider undertaking our initial experiments. I’ve proposed below a set of conditions based on a good quality C18 column, however if you know something of the physical chemical properties of your analyte, you may want to choose a more appropriate column chemistry (see Reference 1 and 2 for more details) or eluent pH value:
Column: C18 (150 x 4.6 mm)
Eluent: [A] MeCN [B] Water [C] 2% TFA v/v 200mM Ammonium Acetate (aq) using the following gradient table
Gradient: 10% to 80% in 34 min (see Table II for Gradient Time details)
(use an initial hold time (5% of the gradient time) if the method is expected to be transferred to different equipment or laboratories for dwell volume matching)
Here we have opted for an acidic mobile phase at constant ionic strength (0.1% v/v TFA, 10 mM ammonium acetate) which will very much help in the interpretation of where to go next when we obtain the results from the initial screening experiment.
Flow rate: 1 mL/min
(for this column volume – see Table II for relevant details for other column dimensions)
Diluent: 10% MeCN (aq)
(or as close as possible to this concentration given solubility constraints, to avoid peak shape deformation)
Detection: Electrospray API -MS in pos/neg switching mode if possible or UV detection at an appropriate wavelength for the sample type (choose 254 nm if the analyte chemistry is unknown)
Ok, so let’s assume we have undertaken our initial screening experiments. So, what do we do with the results, as unless you are extremely lucky, you are unlikely to get a perfect separation from the screening experiment? If you do, you can write to me and let me know!
I’m going to outline a few typical scenarios here and then show you some very simple logic flows and decision trees to follow in order to make the most efficient use of your method development time. The principle here is that we are adopting a “kill quickly” policy and performing a limited number of experiments post the initial screen before stopping optimization and changing to a new column. This avoids the funnel of diminishing returns, where one is tempted to perform an (often long) series of tweaks in order to achieve a satisfactory separation. This latter approach is both time consuming a rarely leads to robust HPLC methods, and the kill quickly philosophy is much more time efficient in the long run.
In this simulated experiment, we are separating a mixture of five pharmaceutical test compounds and three impurity peaks. We can expect the test compounds to be at a higher concentration than any of the impurities.
Scenario 1: Some of my peaks are missing
Scenario 2: All peaks elute very quickly
Where tr is the analyte retention time and t0 is the system hold-up time (dead volume)
Note that the use of retention factor (k) values within gradient analysis is not strictly indicative of retention behavior but these figures can be used as a rough guide to the applicability of the method
Scenario 3: All peaks elute very slowly
Same sample separated using 50–85% B in 20 min pH 5.5
Scenario 4: Peaks show wide range of retention behaviors
Chromatogram resulting from gradient slope of 5% per min at pH 5.5. While further development is required to improve resolution (and robustness), all peaks are separated within a reasonable retention range.
Scenario 5: Co-elution in the middle of the chromatogram / compressed retention range
Same separation carried using a shallow gradient 30–65 %B in 30 min.
Scenario 6: Co-elution at the start and end (or throughout) the chromatogram
Same separation carried out using Methanol as the modifier at pH 6.5.
While in almost all cases, further optimization work will be required at the end of each process, the order in which changes are undertaken and the types of change will lead to a significant reduction in effort for those developing separations without the aid of optimization software!
Tony Taylor is the technical director of Crawford Scientific and ChromAcademy. He comes from a pharmaceutical background and has many years research and development experience in small molecule analysis and bioanalysis using LC, GC, and hyphenated MS techniques. Taylor is actively involved in method development within the analytical services laboratory at Crawford Scientific and continues to research in LC-MS and GC-MS methods for structural characterization. As the technical director of the CHROMacademy, Taylor has spent the past 12 years as a trainer and developing online education materials in analytical chemistry techniques.