ARTICLE pubs.acs.org/ac
A Variable Column Length Strategy To Expedite Method Development Deirdre Cabooter,*,† David Clicq,‡ Filip De Boever,‡ Franc-ois Lestremau,§ Roman Szucs,§ and Gert Desmet† †
Department of Chemical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium Analytical Development & Industrialization, UCB Pharma, Chemin du Foriest, B-1420 Braine-L'Alleud, Belgium § Pfizer Global Research and Development, Ramsgate Road, Sandwich, Kent, CT13 9NJ, U.K. ‡
bS Supporting Information ABSTRACT: A variable length method development (or VL-MD)strategy, exploiting the potential of an automatic column coupling system, is proposed and has been applied to a number of different pharmaceutical and environmental samples with a varying degree of complexity. The proposed strategy consistently produced separation methods that had at least an equally good critical pair resolution and an equally short run time to those of methods produced using commercially available MD assistance software. In some cases, the VL-MD strategy allowed the MD time to be drastically shortened from >30 h to an overnight run of only 12 h. The developed strategy has the potential to become fully automated provided that reliable chromatogram read-out software becomes available. The advantage of combining different stationary phase types to improve the available selectivity and the integration into the general VL-MD strategy was also demonstrated.
M
ethod development (MD) for high-performance liquid chromatography (HPLC) has become an essential task in the analytical lab of today and is widely employed in many industries such as the chemical, pharmaceutical, and food industry, as well as in clinical and environmental analysis.1-7 MD is traditionally carried out on a single column with a given length, using a variation of the stationary phase, the mobile phase composition, and/or the temperature as a means to improve the quality of the separation.8 New MD strategies have been proposed that make use of new column technologies, such as the coupling of different stationary phases9 or the exploitation of the effect of temperature on coupled columns with a different selectivity.10 Because MD is such an important and timedemanding task, a number of hardware and software tools have become available to facilitate the development of a method. Computer simulation software that predicts the outcome of either isocratic or gradient experiments as a function of changes in experimental conditions can save a lot of method development time and provide better resolution and/or shorter run times for the final method.11 A number of simulation software packages from different companies are commercially available, such as DryLab (Molnar Institut),11-14 Chromsword (Iris Technologies),2,15 and ACD/Labs Method Development Suite (Advanced Chemistry Development).16,17 Most computer simulation programs work in a similar way: usually two initial gradients, wherein only the gradient time is varied, are conducted on a column with a fixed length. From the retention times obtained in these two runs and the experimental conditions used for each run, the program allows the predictions of separation upon changing one or more of the experimental conditions. Additional experiments wherein other conditions r 2010 American Chemical Society
affecting the separation, such as temperature, are changed can be added to the initial two experiments to make predictions about their effect on the separation. Some computer-simulation packages can develop new methods or improve existing methods fully automatically. ChromSword-Auto, for example, can optimize the composition of the mobile phase (isocratic, linear, and multistep gradients) and column temperature in an unattended way by interacting with the software of the instrument. Two types of automated method development options are offered by ChromSword-Auto: rapid method development and fully optimized method development.15 The fully optimized method development option performs a number of isocratic runs with decreasing percentage of organic modifier to study the elution behavior of the sample components and assess their total number. The system then either determines the best percentage organic modifier for an isocratic separation or it performs a number of gradient runs before recommending a finalized gradient method. The rapid method development option is much faster and offers a more crude method that may still be acceptable: only three gradient runs are performed aimed at separating all the components in the sample. Before the automated method optimization is initiated, the starting conditions for optimization need to be specified. This involves an automated screening of different columns, organic modifiers, and buffers, followed by a selection of the best conditions by the analyst. Besides MD software, a number of hardware tools have also become available to expedite the MD process, hereby mainly Received: October 15, 2010 Accepted: November 30, 2010 Published: December 30, 2010 966
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Analytical Chemistry aiming at a fast and unattended screening of different solvents and columns. An example of such a hardware tool is a quaternary pump, allowing the evaluation of four different buffer/modifier combinations in an unattended way. When more solvents need to be screened (up to 12 different solvents), solvent selection valves can be used.18,19 Column selection valves for the unattended screening of different columns are available as well. These valves allow the sequential screening of different columns (containing particles with different selectivities), without the need to disconnect them from the system.14,20,21 In a recent publication,22 our group has proposed a new hardware system that can serially connect a number of identical or different columns. This so-called automatic column coupler (ACC) consists of two rotor-stator valves with a custom-made connection groove pattern.22 The availability of a flexible system that can automatically change the length of a column in several steps has led us to investigate whether it would be possible to use the ACC in a “brute force” MD strategy wherein first a large number of scouting runs is run on a very short column, followed by a series of peak overlap verification runs and kinetic and gradient optimization runs on the longer column lengths. To explore this possibility, several alternative strategies have been investigated and tested on a variety of pharmaceutical and environmental samples with a varying degree of complexity (containing between 5 and 15 components). The possibility to use different stationary phase types to improve the available selectivity has been studied as well. To maximize the challenge, the tests were designed such that no a priori knowledge about the sample was needed, nor did the individual components have to be injected one by one. In addition, the number of scouting runs was intentionally constrained by imposing that the total MD time (from initial scouting run to kinetically optimized method) be completed overnight (hence within 10 to 14 h). Obviously this time constraint can be varied, depending on the complexity of the sample and on the importance of obtaining a robust method that meets the criteria of, for example, official instances. Another imposed criterion was that the finally developed strategy should be amenable to full automatization, provided that reliable peak detection software becomes available that can automatically read out chromatograms and calculate the resolution between each pair of peaks. Lacking such software, the present study was performed by mimicking the operation of an automatic MD program, but always without making use of any knowledge of the sample composition. The number of separated peaks and the obtained resolution were assessed manually (using the instrument software), but all MD decisions were made according to a fixed predetermined scheme. When necessary, peak tracking was realized by checking the peak areas. Obviously, more powerful peak tracking methods exist, such as multiwavelength UV absorption (DAD) and mass spectrometry. Integration of this type of spectral information is planned for future work. To test the performance of the finally developed MD strategy, further referred to as variable length (VL) MD strategy, against an independent reference, the considered separation problems were also solved using two commercial MD software packages (DryLab and Chromsword). The final methods obtained with both approaches were compared in terms of critical pair resolution, analysis time, and total method development time (including both the actual run times of each performed analysis and the time needed to equilibrate or flush the columns when
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Table 1. Properties of the Buffers Used in the Experiments buffer
concentration
pH
trifluoroacetic acid
0.1%
formic acid
0.1%
1.8 2.6
ammonium formate
10 mM
6.5
ammonium acetate
10 mM
ammonium hydroxide
0.1%
6.8 10.6
changing conditions). This, however, does not mean that we see the VL-MD strategy as an alternative to these packages. On the contrary, it is believed that the strength of the VL-MD strategy can be considerably improved by incorporating features of the existing MD software, while on the other hand the existing MD software could also benefit from incorporating some of the features of the VL-MD strategy.
’ EXPERIMENTAL SECTION Chemicals . Solvents. Acetonitrile (ACN) and methanol (MeOH), both LC-MS grade, were purchased from SigmaAldrich (Steinheim, Germany). HPLC grade water (H2O) was prepared in the laboratory using a Milli-Q gradient (Millipore, Bedford, MA) water purification system. Buffers. Five different aqueous buffers were used to study the effect of pH on the quality of the separation. Formic acid, ammonium formate, and ammonium hydroxide were purchased from Sigma-Aldrich. Trifluoroacetic acid (TFA) and ammonium acetate were purchased from Merck Chemicals (Darmstadt, Germany). Table 1 lists the properties of the buffers used in the experiments. Samples. The pharmaceutical mixture (used in Example 1: Separation of a Pharmaceutical Mixture) was provided by UCB Pharma (Braine-L'Alleud, Belgium) and consisted of eleven compounds: one active pharmaceutical ingredient (API) in a final concentration of 1 mg/mL and ten impurities/degradation products in a final concentration of 0.05 mg/mL. All compounds were dissolved in 5/95 vol%/vol% H2O/ACN. The structures of the compounds are confidential and therefore cannot be displayed. The simple wastewater pollutants (WWP) mixture (used in Example 2: Separation of a Simple Waste Water Pollutant Mixture) consisted of 5 compounds: acenaphthene, carbazole, fluorene, and indene, purchased from Sigma-Aldrich, and benzo[b]thiophene, purchased from VWR (Leuven, Belgium). The complex WWP mixture (used in Illustration 3: Separation of a Complex Waste Water Pollutant Mixture, Using Column Selectivity as an Additional Parameter) consisted of 15 compounds: 1-indanone, 2-naphthoic acid, 2-hydroxyquinoline, acenaphthene, benzofuran, carbazole, dibenzothiophene sulfoxide, fluorene, 9-hydroxyfluorene, indane, indene, 2-naphthalenol, quinoline, and dibenzofuran, purchased from Sigma-Aldrich and benzo[b]thiophene, purchased from VWR. All WWP were dissolved in a final concentration of 0.1 mg/mL in 5/95 vol%/vol% H2O/ACN. The compounds were selected based on a study performed by Mundt and Hollender.24 Columns. Acquity BEH C18, Phenyl, and Shield RP-18 columns (100 2.1 mm and 50 2.1 mm, 1.7 μm) were purchased from Waters (Milford, MA). Apparatus. The method development of the pharmaceutical sample (Example 1: Separation of a Pharmaceutical Mixture) was performed on an Acquity UHPLC system equipped with a binary solvent manager, sample manager, column manager, and photodiode 967
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Figure 1. Schematic representation of the variable length method development strategy.
array (DAD) detector (Waters). The detector was equipped with a 0.5 μL flow cell (10 mm path length). The Acquity system was operated with the Empower software. The maximum pressure on this system is 1032 bar. The injected sample mixture volume was 1 μL. Absorbance values were measured at 210 nm with a sample rate of 40 Hz. The UHPLC system was equipped with two high-pressure switching valves with a pressure limit of 1000 bar (TitanHT, HT715-000) from Rheodyne (Rhonert Park, CA). The rotors consisted of six peripheral ports with one central port and had a port-to-port volume of 300 nL. The stator was custom-made in order to allow the valves to be used in six different positions.22 The valves were operated with the Empower software. The method development of the WWP samples (Example 2: Separation of a Simple Waste Water Pollutant Mixture, and Illustration 3: Separation of a Complex Waste Water Pollutant Mixture, Using Column Selectivity as an Additional Parameter) was performed on an Agilent 1290 UHPLC system (Agilent Technologies, Waldbronn, Germany) with a quaternary pump, a diode array detector with a 1.0 μL flow cell (10 mm path length), an autosampler, and two heated column compartments (max. temperature = 100 °C), each with a high-pressure 9-port/ 10-position switching valve. The stator was again custom-made in order to allow the valves to be used in 8 different positions. The system was operated with Agilent Chemstation software. The maximum pressure on this system is 1200 bar. The injected sample mixture volume was 1 μL. Absorbance values were measured at 210 nm with a sample rate of 100 Hz. The connection tubing between injector and column coupler, and column coupler and detector (PEEKsil tubing, diameter 75 μm, Achrom, Machelen, Belgium), was kept as short as possible to reduce extracolumn band broadening. PEEKsil tubing (diameter 75 μm) was used to connect the columns to the switching valves. PEEKsil tubing is resistant to pressures up to 1034 bar, in the pH range of 0-10 and has a maximum tolerance of 75 ( 3 μm on its inner diameter. Each piece of tubing between valve and column had a maximum length of 10 cm. The return capillary had a length of 200 mm.22
contained columns of the same stationary phase and consisted of four Acquity C18 (2.1 mm i.d.) columns with a dp = 1.7 μm and column lengths of 5 and 10 cm, making automatic column length changes of 5, 10, 15, 20, 25, and 30 cm possible. The use of this system is illustrated in the following sections: Illustration 3: Separation of a Complex Waste Water Pollutant Mixture, Using Column Selectivity as an Additional Parameter, and Example 2: Separation of a Simple Waste Water Pollutant Mixture. The second ACC system consisted of six Acquity columns (2.1 mm i.d.) with a dp = 1.7 μm and with three different selectivities (C18, RP-18, and Phenyl). Each selectivity was available in a column length of 5 and 10 cm, making automatic column length changes of 5, 10, and 15 cm possible per column selectivity. The use of this second variant is shown in Illustration 3: Separation of a Complex Waste Water Pollutant Mixture, Using Column Selectivity as an Additional Parameter. Various strategies were evaluated on both systems, all with a maximal MD time constraint of 10-14 h. Solutions involving multistep gradients were not pursued for the variable length MD, as they are generally less robust than linear gradients. The finally selected strategy consists of five different steps and is described in Variable Length Method Development Strategy and Figure 1. Variable Length Method Development Strategy. The most important conclusion drawn from the exploratory work leading to the finally established MD strategy was that the chance to completely separate the sample can be greatly increased by incorporating a step wherein the gradient conditions are adapted in such a way that the first peak in the chromatogram elutes with an effective retention factor (k) of around 2 and the last peak in the chromatogram elutes with k = 10-15 for those scouted conditions where only a narrow elution window is obtained. The effective retention factor is calculated as k = (tR - t0)/t0, where tR is the retention time of the compound under consideration and t0 the column void time. This is step 2 in the scheme represented in Figure 1. The maximum number of visible peaks (including the partially separated ones) observed during the initial scouting runs (steps 1 and 2) was in many cases already very close to the total number of compounds that could finally be detected in the sample; i.e., the subsequent steps conducted on the longer column lengths only occasionally revealed the presence of one or two additional compounds. This can be understood from the following first
’ RESULTS AND DISCUSSION VL-MD strategies have been developed for two different automatic column coupling (ACC) systems. The first system 968
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such as the linear solvent strength model can be used:11,27
principles argumentation based on the work of Davis and Giddings.25 Assuming that the compounds are completely randomly distributed over the chromatogram, the probability (Ps) that a peak in a given chromatogram is a singlet peak, i.e., does not overlap with another compound, depends on the peak capacity np of the column as well as on the total number of compounds nc in the sample and is given by: Ps ¼ expð - 2nc =np Þ
tR ¼
tG 2:303SΔφt0 k0 log þ t0 þ tdwell SΔφ tG log kw ¼ log k0 þ Sφ0
ð2Þ ð3Þ
In this model, tG is the gradient time, S the solvent strength parameter (constant for a given solute and organic solvent), Δφ the change in organic modifier composition (hence the difference in begin- and end-concentration), tdwell the system dwell time, k0 the value of k at the beginning of the gradient, and kw the value of k in pure water (kw= k(φ=0)). Because two gradient times are evaluated for each combination of pH and organic modifier in step 1 and hence two retention times are available for the first and the last compound under these conditions (one obtained for tG,1 and one obtained for tG,2), the parameters S and k0 can be obtained by numerically solving eq 2 for every compound. This can, for example, be implemented relatively easily using the solver function of a spreadsheet calculator, such as MS Excel. Equation 3 can then be used to calculate the values of kw. Once S, kw, and k0 have been determined, the conditions of tG, initial gradient composition (φ0), and final gradient composition (φe), wherein the first compound elutes around 1 < k < 3 and the last one around 10 < k < 15, can be determined for each evaluated combination of pH and organic modifier, again using eqs 2 and 3 and the solver function of the spreadsheet calculator. An example of how a spreadsheet calculator can be used for the above calculations is given in the Supporting Information (section S-1). If the initial guess of the improved tG, φ0, and φe does not lead to the desired widening of the elution window, one or more additional gradients can be run with new tG, φ0, and φe values, adapted according to the information obtained in the previous run(s). At this stage in the development of the VL-MD strategy, only the effect of changes in gradient conditions (gradient steepness, begin- and end-concentration of organic modifier) on the retention behavior of the compounds is evaluated. For the current applications, the relation between the logarithm of the retention and tG was always so close to linear that the linear solvent strength model was sufficiently adequate. Other retention relationships, such as quadratic, cubic, or other polynomial models, that lead to a higher accuracy of the retention modeling can, however, also be used28-31 and will be necessary when evaluating the effect of parameters that have a nonlinear relation with retention, such as pH. These models, however, require a higher number of initial gradient experiments. Within the overnight constraint, the time that can typically be spent on step 2 is about 2 h. Step 3: Selection of Best Run and Repeat on a Longer Column Length (detection of peak overlap). Step 3 starts by comparing the runs obtained in steps 1 and 2 and selecting the best one. The best run is defined as the separation that (1) leads to the largest number of separated compounds and (2) the highest critical pair resolution. Additional criteria that can be considered are the total run time (where a shorter run time is preferred over a longer run time) and the tailing factor of the main compound. This “best run” is then repeated on the longest available column length and, if necessary, an intermediate column length, while keeping the ratio of tG/t0 and tdwell/t0 constant to ascertain constant effective retention factors for each compound,
ð1Þ
When a sample containing 10 compounds and a column producing a peak capacity of np = 50 (which would correspond to the performance of a 5 cm column packed with sub-2 μm particles and a ratio of tG/t0 of 10-30) is considered, the number of missed components nmissed (with nmissed = 1- ncPs) would be equal to nmissed = 3.3 (the calculation does not return an integer number because a statistical average is considered). In a column that would be 4 times longer, i.e., L = 20 cm, the peak capacity would only be doubled (np = 100), because np generally scales with the square root of L (for the same ratio of tG/t0). Subsequently recalculating nmissed with the new value of np returns a value of nmissed = 1.8. This implies that the extra peak capacity generated by going from a 5 to a 20 cm column is generally only sufficient to detect one additional peak (1.5 to be statistically exact). Note that the calculation based on random peak distribution only gives a first indication of the required peak capacity for real-world samples. On the one hand, the use of selectivity optimization can significantly reduce the required peak capacity compared to random chromatograms.13,26 However, when the compounds are related in structure, avoiding missed peaks may be much more challenging than the random selection method. The results discussed above reflect the well-known limited power of the separation efficiency as a means to increase the separation resolution.10 This is exploited in the present approach by conducting the scouting runs in a column that is too short to obtain a baseline separation for all components but nevertheless provides enough peak capacity to ensure that not more than one, or maximally a few compounds, are missed via peak overlap (provided a sufficient number of different scouting runs is carried out). The subsections below provide an overview of the different steps in the VL-MD strategy. Step 1: Scouting Runs. First, a high number of scouting runs are performed on a very short (e.g., 5 cm) column to assess the best possible mobile phase conditions for the sample under consideration. During these scouting runs, the effect of changes in organic modifier and gradient steepness are evaluated at discrete values of mobile phase pH. Adopting common MD practice,10,11 a minimum of two different gradient slopes (tG) is evaluated for every combination of mobile phase pH and organic modifier, with tG,1 g 3tG,2. By performing these initial runs on a very short column, a large number of different conditions can be scouted in a relatively short time, thus saving a lot of initial method development time. Within the constraint of a total MD time of 10-14 h, the time that would typically be available for this step is approximately 4 h. Step 2: Adapting the Elution Window. For most combinations of organic modifier and mobile phase pH tested in step 1, it is necessary to adapt the gradient conditions (tG, φ0, and φe) to obtain a sufficiently wide elution window, preferably one wherein the first compound elutes with an effective retention factor around 1 < k < 3 and the last compound around 10 < k < 15. To direct the adaptation of the elution window, retention models 969
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independent of the column length.22 Normally when comparing the chromatograms obtained on the different column lengths, the width of the peaks that appear to be separated with Rs g 1.0 should increase in proportion to the square root of the column length. Any deviation from this behavior can indicate the presence of multiple compounds under one observed peak. Admittedly, this method works especially well when the two coeluting components have a similar contribution to the detected signal. Its efficiency will, however, decrease when the contribution of the two compounds to the detected signal is significantly different. As an estimate, the time that can be allotted to these additional column length runs within an overnight program is on the order of 2-3 h. Step 4: Fine Tuning of the Separation on the Optimum Column Length. In the fourth step, the separation is further optimized by continuing the method development on either the longest possible column length, or on the shortest possible column that still gives a baseline separation for every compound in the sample. This second option can of course only be pursued when the length control runs in step 3 have indicated that none of the separated peaks consist of a coeluting pair or multiplet. To aid in the fine-tuning, the approach of step 2 can again be used by conducting runs at a different gradient steepness and using eqs 2 and 3 to predict the gradient conditions (tG, φ0, φe) that will lead to an improvement of the critical pair resolution and/or a shortening of the run time. Within the frame of an overnight MD program, 3-4 h can be spent on the fine-tuning of the separation. Since there is always a possibility that the fine-tuning step will not lead to a baseline separation of all individual peaks, the algorithm should allow returning to step 3 and allow step 4 to be redone with one of the other highly ranked conditions identified in steps 1 and 2. This re-evaluation is represented by the dashed loop in Figure 1. This loop can be continued until either a set of conditions is found that leads to a baseline separation in step 4 or until the total available method development time is exceeded. In the latter case, it can either be concluded that more MD time is needed, that a human interference is needed, or that the sample is too complex to be solved with the available selectivity and efficiency. In this case, another VL-MD run can be started with another type of stationary phase(s). Step 5: Kinetic Optimization of the Separation. When a satisfactory separation is obtained for all compounds in the sample, the separation can be expedited by switching to a higher flow rate and/or a different column length,23 provided that the resolution of the critical pair is not significantly reduced by this kinetic optimization step. This kinetic optimization step can take 1 or 2 h and follows the selectivity fine-tuning without the need to manually change the column length. Examples of the Variable Length Method Development Strategy. Example 1: Separation of a Pharmaceutical Mixture. A first example that was considered with the VL-MD strategy was the separation of the pharmaceutical mixture (Samples). The goal was to develop a UHPLC method that would lead to a baseline separation of all compounds in a maximum analysis time of 15 min. Step 1: Scouting Runs. Four scouting runs, wherein the gradient time and mobile phase pH were varied, were carried out at an arbitrary temperature of 40 °C on the shortest possible column length (5 cm). The resulting separations are shown in Figure S-3 in the Supporting Information. The largest obtained number of separated peaks was eleven. A flow rate of 0.2 mL/min
was chosen to ensure that all column lengths could be used at the same flow rate without encountering pressure problems. Step 2: Adapting the Elution Window. From the retention data obtained in step 1 and using eqs 2 and 3, gradient conditions leading to k ≈ 2 for the first compound and k ≈ 12 for the last compound were predicted for the different pHs. The accuracy of the predictions was subsequently verified by running the conditions experimentally. The resulting chromatograms are shown in Figure S-4 in the Supporting Information and demonstrate the good quality of the predictions obtained with the linear solvent strength model. Step 3: Selection of the Best Run and Repetition on a Longer Column Length. Comparing the chromatograms obtained in steps 1 and 2, a maximum of eleven separated peaks was observed for the separation conditions shown in Figures S-3b, S-3d, and S-4b. The highest resolution for the critical pair (compounds 7 and 8) was obtained for the separation shown in Figure S-4b (Rs = 1.1 compared to Rs = 1.0 in Figures S-3b and S-3d). Therefore, this run was selected as the best run and repeated on the longest possible column (25 cm) and an intermediate column length of 15 cm. A comparison of the peak widths obtained on the 15 and 25 cm columns was performed to assess whether all compounds were identified. The ratio of the peak widths is shown in Figure S-5 in the Supporting Information and shows a good correlation for all separated peaks, indicating that each peak corresponds to a single compound. Step 4: Fine Tuning Runs. Because a baseline separation was obtained for all compounds on the 15 cm column, and because step 3 indicated that all compounds were detected on this column length, the fine-tuning of the separation was performed on the 15 cm column instead of the longest available column. A retention model and a spreadsheet calculator were used to predict the gradient conditions that would lead to a shortening of the analysis time while maintaining the critical pair resolution. This fine-tuning is elaborated in the Supporting Information (section S-2). Step 5: Kinetic Optimization of the Separation. The obtained separation was subsequently kinetically optimized by switching to a shorter column length and/or a higher flow rate. Shortening the column length to 10 cm while maintaining the flow rate at F = 0.2 mL/min resulted in a baseline separation in an analysis time of 12 min. Increasing the speed of separation by increasing the flow rate, however, led to an incomplete separation of the critical pair. Maintaining the length of the column at 15 cm while increasing the flow rate until the maximum pressure of the system was obtained resulted in a baseline separation for all compounds (Rs,critical = 1.8) in an analysis time of 8 min, thus easily satisfying the requirement of a sub-15 min separation (see Figure 2a). The total time spent on the development of the method was 9 h: the scouting runs in steps 1 and 2 took 160 min, repeating the runs on the longer column lengths took 115 min, the fine-tuning of the separation took 240 min, and the kinetic optimization was done in 45 min. Comparison of the VL-MD Strategy with a ComputerAssisted Method Development Routine and a Fixed Column Length System. Subsequently, a method was developed for the same sample using a column with a fixed length and computerassisted method development software (DryLab). This conventional approach consisted of performing a number of scouting runs on a 10 cm Acquity column wherein the effect of pH and 970
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Figure 2. (a) Chromatogram obtained for the pharmaceutical mixture with the VL-MD strategy: column = 15 cm Acquity C18; φ0 = 0.1 ACN; φe = 0.36 ACN; tG = 15 min; mobile phase pH = 2 (0.1% TFA), F = 0.5 mL/min (Pmax = 900 bar), and T = 40 °C. (b) Chromatogram obtained for the same mixture using Drylab: column = 10 cm Acquity C18; φ0 = 0.1 ACN; φe = 0.37 ACN; tG = 9 min; mobile phase pH = 2 (0.1% TFA), F = 0.4 mL/min, and T = 30 °C. The critical pair resolutions are shown.
organic modifier on the separation was evaluated. ACN and MeOH were chosen as organic modifiers, and four different buffers were used to evaluate pHs of 1.8 (TFA), 2.6 (formic acid), 6.5 (ammonium formate), and 10.6 (ammonium hydroxide). The obtained chromatograms were subsequently analyzed in terms of number of separated compounds and resolution of the critical pair. A combination of ACN and TFA resulted in the best separation. These conditions were therefore used for the optimization of the separation, which involved selecting the flow rate, temperature, and gradient conditions leading to a baseline separation in 15 min. For this purpose, four experimental gradients with a sufficiently large difference in gradient time and temperature were imported into DryLab to model their effect on the separation. Because four compounds coeluted during some of the gradient runs, they were injected separately in addition to the complete sample mixture, to confirm their retention times and areas, following the standard approach of the pharmaceutical company (UCB Pharma). Using the Gradient Editor Section of DryLab, three methods were proposed for a baseline separation of the sample within the predetermined analysis time. Upon experimental verification, only two of the three proposed methods led to a complete baseline separation. The best separation is shown in Figure 2b where a baseline separation was obtained in 8.5 min (Rs,critical= 2.4). The experimental conditions are specified in the caption of the figure. The total time needed to develop this method was 11 h: the solvent and pH screening was performed in 320 min, the input runs were performed in 275 min, and verification of the runs took 55 min. Both approaches hence led to very similar separations in comparable analysis times. The critical pair resolution obtained with the DryLab approach was slightly better than with the column coupler approach, whereas the time spent on the
development of the method was slightly longer with the DryLab approach. This longer MD time essentially resulted from the need to separately inject some of the individual compounds to gather the necessary information for the computer modeling, which was in this case possible, as all standards were available. As an alternative, additional gradients with an intermediate steepness can be run and peak tracking can be performed by checking the areas of the compounds to determine which peaks overlap. Example 2: Separation of a Simple Waste Water Pollutant Mixture. As a second example, the separation of a simple WWP mixture was considered. This sample was a simplified version of the complex WWP mixture considered in Illustration 3: Separation of a Complex Waste Water Pollutant Mixture, Using Column Selectivity as an Additional Parameter. Steps 1 and 2: Scouting Runs and Adaptation of the Elution Window. Four scouting runs were again carried out on the shortest possible column length (5 cm), wherein pH and gradient time were varied. With the information obtained from these runs, predictions were made for gradient conditions that would result in a sufficiently broad elution window, as explained in the Supporting Information (section S-1). Step 3: Select Best Run from Step 1 and Step 2 and Repeat on a Longer Column Length. The best run obtained from steps 1 and 2 was repeated on the longest available column length (30 cm) and a 15 cm column length. Figure S-7 (Supporting Information) shows the results for the different column lengths. The ratio of peak widths obtained on the 15 and 30 cm columns revealed a discrepancy for the second compound (Figure S-8a, see Supporting Information). A closer inspection of the chromatogram from the 15 cm column (Figure S-7b), however, indicated that the resolution between compounds 2 and 3 was less than 1.0, making the assessment of the peak width at half height rather troublesome for compound 2. The flexibility of the 971
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Figure 3. (a) Chromatogram obtained for the simple WWP mixture with the VL-MD strategy: column = 30 cm Acquity C18; φ0= 0.76 ACN; φe= 1.00 ACN, tG = 4.6 min; mobile phase pH = 7 (10 mM ammonium acetate), F = 0.2 mL/min, and T = 23 °C. (b) Chromatogram obtained for the same sample using the fully optimized MD function of Chromsword: column = 10 cm Acquity C18; gradient conditions: 47%B; 44%B; 42%B; 31%B; 31%B in 0.0; 0.7; 3.5; 5.4; 14.3 min. Organic modifier (B): ACN, aqueous buffer (A): 10 mM ammonium acetate (pH = 7), F = 0.2 mL/min, and T = 23 °C (room). The critical pair resolutions are shown.
on the fine-tuning on the 30 cm column length, and 90 min were spent on the attempted kinetic optimization. Comparison of the VL-MD Strategy with a ComputerAssisted Method Development Routine and a Fixed Column Length System. An alternative method was developed for this sample using a column with a fixed length of 10 cm and method development software. For this particular sample, the fully optimized method development function of Chromsword-Auto was used, together with the starting conditions that led to the best separation on the automated column coupler. Hence, ACN and ammonium acetate buffer (pH = 7) were used as organic modifier and aqueous buffer, respectively. This resulted in eight gradient runs (all multistep gradients). The best run in gradient mode is shown in Figure 3b and resulted in the separation of only four compounds, with a maximum critical pair resolution of 0.99. The employed fixed length of 10 cm is in this particular case hence too short for a complete baseline resolution of all compounds. Even with a multistep gradient, not all compounds can be separated. The total time spent on the development of this method was 31 h. Using the same software on a system with a longer column length would undoubtedly also have led to a full resolution of the sample. Illustration 3: Separation of a Complex Waste Water Pollutant Mixture, Using Column Selectivity as an Additional Parameter. For the MD of the most complex sample (15 wastewater
column coupler allowed switching to a second intermediate column length (25 cm), where the resolution between compounds 2 and 3 was now sufficient for an accurate determination of the peak width (Figure S7-d). The ratio of peak widths obtained on the 25 and 30 cm columns is shown in the Supporting Information (Figure S-8b). The perfect agreement between the ratios suggests that indeed only five compounds were present in the sample and that they were all separated. Step 4: Fine Tuning Runs on a 30 cm Column. Because no baseline separation was observed on any of the intermediate lengths, the fine-tuning of the separation was continued on the longest column length. For this purpose, a second gradient was run on the 30 cm column. With the retention data obtained from the two runs on the 30 cm column, the conditions leading to a baseline separation of the critical pair were predicted (as explained in section S-1 and section S-2 in the Supporting Information). Figure 3a shows the fastest baseline separation that was obtained for the simple pesticide mixture. The critical pair resolution was 1.5 and the total analysis time 9 min. Step 5: Kinetic Optimization. A decrease in column length or an increase in flow rate led to a decrease in resolution of the critical pair. Therefore, the separation could not be further optimized in terms of separation speed. The time spent on the development of this method was slightly over 13 h: 190 min were spent on the scouting runs in steps 1 and 2, 200 min were spent on the runs on the longer columns in step 3, 325 min were spent 972
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Figure 4. Schematic representation of the automated column coupler with two 8-position/9-port switching valves allowing the coupling of columns with three different selectivities. The configuration of the ports needed to change the length of the first column selectivity between (a) 5 cm, (b) 10 cm, and (c) 15 cm is shown. A similar operation allows the length of the other two selectivities to be changed.
pollutants), the comparison and combination of different stationary phases were considered. For this purpose, the second ACC setup was used (see Figure 4). Steps 1 and 2: Scouting Runs and Adaptation of the Elution Window. Scouting runs, wherein mobile phase pH and gradient time were varied, were performed on the 5 cm format of each stationary phase type. With the information obtained from these runs, gradient conditions leading to a sufficiently broad elution window were predicted, as explained in the Supporting Information (section S-1). Step 3: Selection of the Best Run and Repeat on a Longer Column Length. The best run was obtained on a 5 cm Phenyl column and led to the separation of 15 compounds. This run was therefore repeated on a 10 cm and a 15 cm Phenyl column, and the ratio of the peak widths obtained for all
compounds was determined. Figure S-9 shows the chromatograms on the three different column lengths. The gradient conditions are specified in the caption. Figure S-10 shows the ratio of the peak widths obtained for the 10 cm and the 15 cm columns. From this comparison it was clear that all compounds present in the sample were detected on the 15 cm column. Step 4: Fine Tuning on the 15 cm Column. The separation was subsequently optimized on the 15 cm column by running a second gradient to have retention data for all compounds at two different levels of gradient steepness. With these retention data, the gradient conditions leading to a critical pair resolution of Rs g 1.5 were predicted. This fine-tuning led to a complete baseline separation in an analysis time of 25 min and with a critical pair resolution of 1.9. 973
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Figure 5. (a) Chromatogram obtained for the complex WWP mixture with the VL-MD strategy: column = 10 cm Acquity Phenyl; φ0 = 0.24 ACN; φe = 0.59 ACN; tG= 5.6 min; mobile phase pH = 7 (10 mM ammonium acetate), F = 0.6 mL/min (Pmax= 900 bar), and T = 23 °C (room temperature). (b) Chromatogram obtained for the same mixture using the fully optimized method development function of Chromsword. Gradient conditions: 23%B; 24%B; 47%B; 47%B in 0.0; 0.9; 2.47; 13.87 min. Organic modifier (B): ACN, aqueous buffer (A): 10 mM ammonium acetate (pH = 7), F = 0.6 mL/min, and T = 23 °C (room). The critical pair resolutions are shown.
Step 5: Kinetic Optimization of the Separation. Because a sufficiently high resolution was obtained for the critical pair on the 15 cm column at a flow rate of 0.2 mL/min, the separation was kinetically optimized by switching to a shorter column length and a higher flow rate. This resulted in the separation shown in Figure 5a, where a baseline separation was obtained for all compounds on a 10 cm column operated at the maximum pressure of the instrument. The critical pair resolution was 1.8 and the total analysis time 6 min. The total time spent on the development of this method was 12 h: the scouting runs on the 5 cm columns in step 1 and step 2 took 365 min, step 3 took 130 min, the fine-tuning on the 15 cm column took 150 min and the kinetic optimization 70 min. Comparison of the VL-MD Strategy with a ComputerAssisted Method Development Routine and a Fixed Column Length System. Finally, an alternative method was again developed for this particular sample using a column with a fixed length of 10 cm and method development software (ChromswordAuto). Because the optimal column selectivity, mobile phase pH, and organic modifier were already assessed with the automated column coupler in Step 3: Selection of the Best Run and Repeat on a Longer Column Length, Chromsword-Auto was again used in fully optimized method development mode using these starting conditions. Eight gradient runs (all multistep gradients) were obtained, which all resulted in the separation of 15 compounds. The best separation led to a critical pair resolution of 2.3. Because the fully
optimized method development mode of Chromsword-Auto does not optimize the flow rate, the separation speed was increased manually until the maximum flow rate was obtained (and making sure the gradient volume remained the same). This resulted in the separation shown in Figure 5b. The total time spent on the development of this method was 35 h. Both methods led to a baseline separation of the sample in similar analysis times. The time spent on the development of the method with Chromsword-Auto was significantly larger than with the automated column coupler. Note that the initial flow rate was set to 0.2 mL/min for both approaches. This flow rate was chosen initially for the column coupler approach to ensure that the same flow rate could be used on all column lengths without encountering pressure problems. A rather low flow rate, however, significantly increases the analysis, equilibration, and therefore method development times. If the optimization of the method with Chromsword-Auto had been performed at a higher flow rate (e.g., F = 0.4 mL/min), the development would have taken 18 h. A similar decrease in method development time can, however, also be realized by performing some steps of the VL-MD strategy at higher flow rates.
’ CONCLUSIONS A generic method development strategy (variable length method development, VL-MD) has been developed for the enhanced method development of samples with an unknown 974
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Analytical Chemistry composition. The strategy makes maximal use of the possibilities of a recently proposed automated column coupling device that allows the length and/or composition of a chromatographic system to be changed in an automated, unattended way. Whereas MD is usually accomplished on a fixed column length, the VL-MD offers the distinct advantage of different and optimized column lengths for each step in the MD process. Starting from an extensive set of scouting runs and following a predetermined set of decision rules, the VL-MD strategy is generic and has the potential to become fully automated and turn into a “push button” MD strategy using the existing link between the chromatographic instrument and the controlling software. Despite the natural link to the automatic column coupler concept and the inherent automation possibilities, the VL-MD strategy can also be applied using manually coupled columns or using single-piece columns of different lengths. In brief, the VL-MD strategy consists of the following steps: (1) Running a multitude of scouting runs on very short columns to rapidly screen a high number of different mobile and stationary phase conditions. (2) Adapting the gradient conditions to pursue a sufficiently wide elution window (1-3 < k < 10-15) for each of the scouted conditions considered in the first step, (3) Selection of the best conditions from the runs in steps 1 and 2 and comparison of the width of the separated peaks on different column lengths under the same experimental conditions to verify the potential coelution of compounds. (4) Fine-tuning the separation on a sufficiently long column length to optimize the selectivity of the method. (5) Kinetic optimization of the separation to minimize the run time of the final method. The VL-MD strategy was applied to develop separation methods for a number of different pharmaceutical and environmental samples with varying degrees of complexity. The resulting methods had at least an equally good critical pair resolution and an equally short run time as the methods produced using commercially available MD assistance software such as Drylab or Chromsword-Auto. In some cases, the VL-MD strategy drastically shortened the MD (from >30 h to an overnight run of only 12 h). An additional advantage of the VL-MD process is that it does not require the injection of individual compounds and also automatically indicates whether peaks in the sample can be suspected of coelution (cf. step 3). Currently, the length of the chromatographic support can be changed in an automated way, but the obtained chromatograms still need to be manually analyzed. Therefore, the VL-MD does not yet work in a fully automatic way. Future work will focus on the development of appropriate software that can automatically determine the number of peaks, the resolution between peaks, and the areas of the peaks in a chromatogram. A potential drawback of the VL-MD strategy is that it will yield methods that require the use of coupled columns. It is perfectly feasible, however, to add an additional constraint to the decision protocol that the final method should be run in a single column format.
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’ AUTHOR INFORMATION Corresponding Author
*Tel.: (þ)32.(0)2.629.33.30. Fax: (þ)32.(0)2.629.32.48. E-mail:
[email protected].
’ ACKNOWLEDGMENT Deirdre Cabooter is a Postdoctoral Fellow of the Research Foundation Flanders (FWO Vlaanderen). Iristech is kindly thanked for the evaluation version of Chromsword 4.0. Agilent Technologies is kindly thanked for supplying the custom-made 9-port/8-position valves. ’ REFERENCES (1) Dutta, A. K.; Avery, B. A.; Wyandt, C. M. J. Chromatogr., A 2006, 1110, 35. (2) Xiao, K. P.; Xiong, Y.; Liu, F. Z.; Rustum, A. M. J. Chromatogr., A 2007, 1163, 145. (3) Seifrtova, M.; Aufartova, J.; Vytlacilova, J.; Pena, A.; Solich, P.; Novakova, L. J. Sep. Sci. 2010, 33, 2094. (4) McLaughhn, G. M.; Weston, A.; Hauffe, K. D. J. Chromatogr., A 1996, 744, 123. (5) Schmidt, A. H.; Molnar, I. J. Chromatogr., A 2002, 948, 51. (6) Storton, M.; Exarchakis, J.; Waters, T.; Hao, Z.; Parker, B.; Knapp, M. J. Sep. Sci. 2010, 33, 982. (7) Berente, B.; De la Calle Garca, D.; Reichenbacher, M.; Danzer, K. J. Chromatogr., A 2000, 871, 95. (8) Neue, U. D. HPLC columns: Theory Technology and Practice; Wiley-VCH: New York, 1997. (9) De Beer, M.; Lynen, F.; Chen, K.; Ferguson, P.; Hanna-Brown, M.; Sandra, P. Anal. Chem. 2010, 82, 1733. (10) Mao, Y.; Carr, P. W. Anal. Chem. 2001, 73, 1821. (11) Snyder, L. R.; Dolan, J. W. High-Performance Gradient Elution: The Practical Application of the Linear-Solvent-Strength Model; WileyInterscience: Hoboken, NJ, 2007. (12) Sippola, E.; David, F.; Sandra, P. J. High Res. Chromatogr. 1993, 16, 95. (13) Dolan, J. W.; Snyder, L. R.; Djordjevic, N. M.; Hill, D. W.; Saunders, D. L.; Van Heukelem, L.; Waeghe, T. J. J. Chromatogr., A 1998, 803, 1. (14) Krisko, R. M.; McLaughlin, K.; Koenigbauer, M. J.; Lunte, C. E. J. Chromatogr., A 2006, 1122, 186. (15) Hewitt, E. F.; Lukulay, P.; Galushko, S. J. Chromatogr., A 2006, 1107, 79. apka, V.; Carter, S. J.; Bennett, P. K. (16) Meng, M.; Rohde, L.; C J. Pharm. Biomed. Anal. 2010 in press. (17) T€ ornblom, J. K.; Bureyko, T. F. W.; MacKinnon, C. D. J. Chromatogr., A 2005, 1095, 68. (18) Zhao, Y.; Woo, G.; Thomas, S.; Semin, D.; Sandra, P. J. Chromatogr., A 2003, 1003, 157. (19) Ceccato, A.; Reus, E.; Brione, W.; Vanderweyen, A.; Flament, A.; Caliaro, G.; Tilstam, U. Org. Process Res. Dev. 2007, 11, 223. (20) Mazzeo, J. R.; Grumbach, E. S.; Collier, S. LC-GC 2002, 20, 538. (21) Pfeffer, M.; Windt, H.; Fresenius, J. Anal. Chem. 2001, 369, 36. (22) Cabooter, D.; Decrop, W.; Eeltink, S.; Swart, R.; Ursem, M.; Lestremau, F.; Desmet, G. Anal. Chem. 2010, 82, 1054. (23) Desmet, G.; Cabooter, D. LC-GC Eur. 2009, 22, 70. (24) Mundt, M.; Hollender, J. J. Chromatogr., A 2005, 1065, 211. (25) Davis, J. M.; Giddings, J. C. Anal. Chem. 1983, 55, 418. (26) Neue, U. D. J. Chromatogr., A 2008, 1184, 107. (27) Snyder, L. R.; Dolan, J. W. J. Chromatogr., A 1996, 721, 3. (28) Neue, U. D. Chromatographia 2006, 63, S45. (29) Schoenmakers, P. J.; Billiet, H. A. H.; de Galan, L. J. Chromatogr. 1979, 185, 179. (30) Schoenmakers, P. J.; Billiet, H. A. H.; de Galan, L. J. Chromatogr. 1983, 282, 107. (31) Jandera, P.; Churacek, J. In Journal of Chromatography Library 31; Elsevier: Amsterdam, 1985.
’ ASSOCIATED CONTENT
bS
Supporting Information. Additional information as noted in the text. This material is available free of charge via the Internet at http://pubs.acs.org. 975
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