Anal. Chem. 2008, 80, 873-877
Use of Chemometrics To Optimize the Operation of an Ion Source Natalie D. de Sousa,* Christopher J. Ellett,† Michelle Gilbert, and Andrew D. Wright
AstraZeneca R&D Charnwood, Bakewell Road, Loughborough, Leics, UK, LE11 5RH
A chemometric approach has been used to optimize the Agilent multimode ion source. Initial factorial experimental design studies indicated that there was a significant degree of curvature in the experimental region, so further central composite design experiments were performed. Optimum conditions were found using statistical optimization tools, and these results were then validated. As a result, recommendations have been made for the value of each operational parameter in order to optimize response. In this paper, the optimization of parameters for the Agilent multimode source for use in the analysis of novel compounds of pharmaceutical interest is described. This was performed using fractional factorial experimental design1-3 followed by central composite design techniques.1-4 This enabled optimization to be performed on all relevant variables by conducting the minimum number of experiments. An optimal set of conditions was derived, and these were tested against the “default” settings provided by the manufacturer5 on a number of samples of pharmaceutical interest. It was found that, by employing experimental design procedures, conditions were obtained that provided a larger absolute response for all compounds tested. It is concluded that this type of experimental design procedure offers a significant saving in time and effort for the optimization of operating conditions on this instrument and provides, ultimately, conditions that lead to improved performance of the instrument over that suggested by the manufacturer. Electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization are routine ionization techniques in the LCMS analysis of novel compounds of pharmaceutical interest. It is widely acknowledged that these techniques are complimentary such that compounds that do not ionize efficiently using one method are likely to ionize * To whom correspondence should be addressed. E-mail: natalie.desousa@ astrazeneca.com. † Current address: AstraZeneca R & D, Mereside, Alderley Park, Macclesfield, Cheshire, England, SK10 4TG. (1) Gabrielsson, J.; Lundberg, N-O.; Lundstedt, T. J. Chemom. 2002, 16, 141160. (2) Bebe, K. R.; Pell, R. J.; Seasholtz, M. B. Chemometrics: A Practical Guide; Wiley: New York, 1998. (3) Deming, S. N.; Morgan, S. L. Experimental Design: A Chemometric Approach; 2nd ed.; Elsevier: New York, 1993. (4) Myers, R. H.; Montgomery, D. M. Response Surface Methodology; Wiley: New York, 2002. (5) Agilent G1978A Multimode Ion Source: Users Guide, Agilent Technologies Inc,, 2005. 10.1021/ac701966r CCC: $40.75 Published on Web 01/05/2008
© 2008 American Chemical Society
using one of the others.6 In the past, this has led to compounds having to be run on two instruments to obtain molecular weight information. To alleviate this problem, some manufacturers have sought to provide ion sources that combine two ionization techniques in one ion source.7,8 One of these is the Agilent multimode source. This source enables simultaneous or separate collection of APCI and ESI spectra.8 The variables that could be responsible for the quality of the data obtained are many and differing depending on which mode the source is used in. It is therefore a considerable problem to design a manageable number of pertinient experiments to optimize the signal produced by the ion source in both single ionization mode and mixed mode. It was therefore decided to employ factorial experimental design (FED)1-3 to optimize multimode operation. As a result of the data obtained from the fractional FED, further experiments were conducted using central composite design (CCD).1-4 Factorial experimental design is a systematic and structured approach to experimentation. Factors that are selected for optimization are varied simultaneously over two levels (high and low), providing information on whether they are exhibiting a positive, negative, or neutral effect on the response of interest. In addition, FED provides important information on interactions, i.e., how the effect of one variable depends on another, which is not achieved by more traditional approaches. Building replicated center points into the design allows for the testing for curvature in the experimental region and for testing of the statistical validity of the model. Factorial designs can be reduced to fractional factorial designs by aliasing terms together. This essentially allows the information to be gathered over fewer experimental runs, while not overcompromising on the resolution needed to determine the importance of each of the factors investigated. In cases where more information is required about a specific factor, response surface methods can be employed.4 In the case of these experiments, a CCD was chosen, which allows the modeling of curvature within the experimental space with the aim of locating any optimum that may exist within the confines of the factor settings used for the FED experiments. CCD provides an estimation of all coefficients in a quadratic model, which allows the curvature within the experimental space to be calculated. CCDs are created from a two-level factorial design augmented (6) Lim, C-K.; Lord, G. Biol. Pharm. Bull. 2002, 25, 547-557. (7) Gallagher, R. T.; Balogh, M. P.; Davey, P.; Jackson, M. R.; Sinclair, I.; Southern, L. J. Anal. Chem. 2003, 75, 973-977. (8) Agilent Multimode Source: Simultaneous ESI and APCI for Maximum Throughput, Agilent Technologies Inc., 2005.
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Table 1. HPLC Methods for Evaluation of the Multimode Source 1 mL min-1
gradient/% time/min
0.65 mL min-1
0.3 mL min-1
MeOH
MeCN
MeOH
MeCN
MeOH
MeCN
5-95 9.5
30-95 3.5
5-95 10.5
30-95 4.5
5-95 12.5
30-95 8.5
with center points and axial points. This leads to a total of five levels for each factor. In this paper, a series of experiments is described to gain a greater understanding of the operation of the multimode ion source, allowing optimum conditions for a range of compounds to be set. EXPERIMENTAL SECTION Materials. Naproxen, caffeine, atenolol, propranolol, ibuprofen, methoxyverapamil, amitriptyline, and N-acetylprocainamide were purchased from Sigma Aldrich (Poole, UK). Solvents and buffers were purchased from Fisher Scientific (Loughborough, UK). The 2.0 × 50 mm Gemini C18 (3 µm) columns were purchased from Phenomenex (Macclesfield, UK). The 2.1 × 50 mm Symmetry C18 (3.5 µm) columns were purchased from Waters (Elstree, UK) Instrumentation. All experiments were conducted using an Agilent 1100 LCMS comprising an 1100 binary pump, an 1100 well plate autosampler, an 1100 thermostatted column compartment, an 1100 diode array detector, and an 1100 MSD equipped with a multimode source. Compounds. The test mix was composed of a mixture of naproxen, caffeine, atenolol, propranolol, ibuprofen, methoxyverapamil, amitriptyline, and N-acetylprocainamide each at 1 mg mL-1 in DMSO. The validation test set comprised 14 compounds taken from lead optimization projects at AstraZeneca Charnwood. These were dissolved in methanol at concentrations of ∼1 mg mL-1. Chromatography Conditions. Since the performance of an ion source is known to be dependent on solvent and flow rate, chromatographic methods were developed to encompass a range of these combinations. The details of these methods are outlined above (Table 1). All separations during the model building were performed on a 2.0 × 50 mm Gemini C18 (3 µm) column. One microliter injections of the test mix were made in each case. All experiments conducted during the validation phase were performed on a 2.1 × 50 mm Symmetry C18 (3.5 µm) column. Chromatography was conducted on a gradient from 95% trifluoroacetic acid (0.1% aq)/5% acetonitrile to 5% trifluoroacetic acid (0.1% aq)/5% acetonitrile over 2.5 min at a flow rate of 1 mL min-1. The column temperature was 40 °C. Experimental Design. Experimental design was performed using Design Expert 6 and Design Expert 7.01 software. RESULTS AND DISCUSSION In the case of multimode operation, there were six factors to be studied. The factors believed to affect the intensity of the signal in multimode operation are nebulizer pressure, vaporizer temperature, capillary voltage, corona current, flow rate, and solvent.8 With the exception of solvent (which is a categorical factor) each parameter was varied across a high and low setting. In a full 874 Analytical Chemistry, Vol. 80, No. 3, February 1, 2008
Figure 1. Half-normal plot of the model parameters. 2 indicates error from replicates. A is nebulizer pressure, B is vaporizer temperature, C is capillary voltage, D is corona current, E is flow rate, and F is solvent. A red 9 indicates a potentially signficant effect. A black 9 indicates a potentially insignificant effect.
factorial experimental design, all combinations of the extreme values are included in the experiment; i.e., if there are x variables, there will be 2x experiments. In addition, center points are included to test for curvature in the experimental region. Repeat center points also allow for testing of the statistical validity of the model. Fractional factorial experimental designs aim to cover the entire experimental space without conducting all of the experiments. The number of experiments was therefore decreased from 64 to 16 (with the addition of four center points), by performing a quarter factorial rather than a full factorial. By performing a fractional factorial experimental design, information about the main effects is confounded with interaction effects leading to a loss of information, but the time savings are substantial. In this case, each main effect is aliased to two three-factor interactions, and the twofactor interactions were each aliased to another two-factor interaction. Consequently, a total of 20 experiments were performed (see Table 2). Additionally, the set of 20 experiments was repeated three times to further eliminate any systematic error. The data could be analyzed in a number of ways. An average of all the peak areas in a batch could be taken providing three points (one from each batch) for each set of conditions to form the model. Alternatively, a mean could then also be taken of these three numbers to provide just one point for each set of conditions. Otherwise, an average for each compound over the three batches could be taken, (providing eight points for each set of experimental conditions), and in addition, the mean of these numbers across the batches could be taken, providing one point per set of experimental conditions. Statistical analyses were performed in each of these ways, but since, largely, the recommendations for conditions of use of the instrument that were defined were largely the same irrespective of the method of analysis, only the analysis of the data that was performed by averaging across compounds and across batches shall be considered in this paper. Analysis of data gained from experiments is performed in three stages, namely, evaluation of the raw data to check that it is
Table 2. Factorial Experimental Design for Multimode Operation
run
nebulizer pressure (psi)
vaporizer temp (°C)
capillary voltage (V)
corona current (µA)
flow rate (mL min-1)
solvent
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
60 20 20 20 40 60 20 60 20 40 20 60 20 40 60 40 20 60 60 60
250 100 250 100 175 250 100 100 250 175 250 100 100 175 250 175 250 250 100 100
3000 500 500 3000 1750 500 500 500 500 1750 3000 3000 3000 1750 3000 1750 3000 500 3000 500
5 5 1 5 3 5 1 1 5 3 5 1 1 3 1 3 1 1 5 5
1 0.3 1 1 0.65 0.3 0.3 1 1 0.65 0.3 0.3 1 0.65 1 0.65 0.3 0.3 0.3 1
acetonitrile acetonitrile acetonitrile methanol methanol methanol methanol methanol methanol acetonitrile acetonitrile acetonitrile acetonitrile methanol methanol acetonitrile methanol acetonitrile methanol acetonitrile
Table 3. Showing the Gross Effect of Various Parameters on the Operation of the Multimode Source nebulizer pressure
vaporizer temperature
capillary voltage
corona current
flow rate
-
+
+
+
-
normally distributed and to check that the raw error is no larger than the total variation in the design points. Second, models are built that link the design data to the recorded responses, and these are then interpreted and refined. Finally, the model is tested to see if its recomendations are valid. 1. Evaluation of Raw Data. Normal and half-normal plots are a useful tool for visualizing effects when building a statistical model. Each term (variable) in the model has a coefficient. If all the terms in the model have no effect on the magnitude of the response, then the coefficients will be normally distributed. This can be represented by a bell-shaped normal distribution plot. Alternatively, this can be described by a cumulative probablilty curve, which can then be straightened to describe the normal distribution as a line (a normal plot). If an effect diverges significantly from the line, it is probably describing something more significant than experimental noise. Half-normal plots do not take into account the sign of the effect, just the magnitude. For ease, half-normal plots will be used throughout this discussion. It may be seen (Figure 1) that for multimode operation capillary voltage (C) and the interaction between capillary voltage and vaporizer temperature (BC) are significant. Flow rate (E) and corona current (D) can also be seen to be important as is the interaction between corona current and vaporizer temperature (BD). Analysis of variance (ANOVA) calculations were performed on the model to determine the statistical significance of the effects highlighted in the half-normal plots. ANOVA calculations, or the total variation in response calculations, examines the overall significance of each term in the model compared to the residual error. Terms found to have a probability value of less than 0.1
Table 4. CCD Experiments in Multimode Operation
run
A: nebulizer pressure (psi)
B: vaporizer temp (°C)
C: capillary voltage (V)
D: corona current (µA)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
40 40 50 50 50 40 40 30 30 60 30 50 40 50 40 30 20 30 40 50 30 30 30 50 50 40
175 175 212.5 137.5 212.5 175 175 212.5 137.5 175 212.5 212.5 175 137.5 175 212.5 175 137.5 100 137.5 137.5 212.5 137.5 137.5 212.5 250
1750 500 2375 1125 2375 1750 1750 1125 1125 1750 1125 1125 1750 1125 3000 2375 1750 1125 1750 2375 2375 2375 2375 2375 1125 1750
3 3 2 4 4 5 3 4 2 3 2 4 1 2 3 2 3 4 3 4 4 4 2 2 2 3
are considered to be significant. The model terms selected using the half-normal plot (capillary voltage (C), the interaction between capillary voltage and vaporizer temperature (BC), flow rate (E), and corona current (D), and the interaction between corona current and vaporizer temperature (BD)) were tested for statistical significance using ANOVA calculations. All the terms in the model, with the exception of temperature (B) and corona current (D) (which were included to satisfy the hierarchy of the model), were found to be statistically significant. Curvature of the model was tested using the center points. Significant curvature (p < 0.0001) was observed; i.e., the magniAnalytical Chemistry, Vol. 80, No. 3, February 1, 2008
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Figure 2. Vaporizer temperature vs peak area plot for multimode operation. Other operating conditions are nebulizer pressure 40 psi, capillary voltage 1750 V, corona current 3 kV, flow rate 0.65 mL min -1, and solvent methanol. Table 5. Operating Conditions for the Multimode Source Suggested by Design Expert and by Agilent
nebulizer pressure (psi) vaporizer temperature (°C) capillary voltage (V) corona current (uA)
Agilent9
Design Expert
40 200 2000 1
20 213 1839 2.3
tude of the peak observed at the conditions defined by the center points is statistically unlikely to have the value predicted by the model. In addition, repeat center points allowed pure error in the measurements to be estimated and therefore determine the lack of fit of the selected model (i.e., is the error associated with repeated experiments statistically different from the remaining error having fitted a given model). The lack of fit was found to be statistically significant. However, due to the fact that the optimum conditions are likely to be close to the center points, from the initial interpretation, the variability associated with the repeat center points is not likely to be representative of true pure error within the experimental space. Therefore, the significant lack of fit will be ignored in this particular case. Graphical interpretation can be used to visualize the consequences of the main effects, e.g., vaporizer temperature, capillary voltage, etc., on the peak area (See Figure 2.). These should be viewed with caution, however, as all the factors in this model are involved in an interaction. Similarly, interactions can be plotted against peak area, (See Figure 3.), but again these give a rather simplistic view. An insight can be gained, however, into the basic operation of the multimode source from these plots (See Table 3). These findings broadly agree with the manufacturer’s recomendations for the operating conditions for the multimode source.9 For instance, the model suggests that for optimum (9) Information captured from the “Help” files in Agilent ChemStation. After completion of this work, Agilent published revised recommendations in a technical note: Publication No. 5989-6463EN, Agilent Technologies Inc., 2007.’
876 Analytical Chemistry, Vol. 80, No. 3, February 1, 2008
Figure 3. Effect of vaporizer temperature and capillary voltage on peak area. 9 indicates capillary voltage 500 V. 2 indicates capillary voltage 3000 V. Nebulizer pressure 40 psi, corona current 3 kV, flow rate 0.65 mL min-1, and solvent methanol.
Figure 4. 3-D surface plot for ibuprofen showing the effect of capillary voltage and vaporizer temperature on MS response. Nebulizer pressure 40 psi and corona current 3 kV.
operation the source should be operated with a high capillary voltage, which agrees with the recomendations from Agilent.9 As the ANOVA calculations suggest, if there is a significant degree of curvature in the model it was likely that the optimum points in experimental space were being missed by the original design. 2. Model Refinement. It was therefore considered pertinent to conduct further experiments to try to find the optimum point in the experimental space for each variable using a response surface method. The method chosen was CCD. Central composite designs incorporates either a full or fractional factorial experimental design and, in addition, replicate experiments at the center points and further experiments at points along each of the axes
Figure 5. Showing the peak area obtained using the mutimode source operated under manufacturer’s conditions and under the conditions suggested by the CCD experiments.
where all factors bar one are set to their center point values (axial points). The designs were all performed using Design Expert 6 software. The number of experiments performed was restricted by fixing the flow rate (at 1 mL min -1) and using only acetonitrile. This was done for two reasons: first, solvent was shown to have no contribution to compound response in the FED experiments, and second, these are the most commonly used conditions within the laboratory. In addition, as there had been insignificant batchto-batch error in the FED experiments, each experiment was only run once. The CCD experiments are oulined in Table 4. There are a number of methods that can be used to build statistical models from response surface method designs, namely, stepwise elimination, forward selection, and backward regression. Backward regression was chosen in this case as it was believed that it would build the most robust model. The model was then tested using ANOVA calculations as described previously. Data from response surface methods can be displayed using 3-D surface plots (e.g., Figure 4). Obviously these only allow the effect of two parameters at a time to be visualized, but nonetheless they are a useful tool. It may be seen from Figure 4 that, as predicted from the fractional factorial experimental design, there is significant curvature in the experimental region. However, it is obviously not possible to view the optimum conditons for a model that has been
built using four factors from a 3-D plot. The optimization tools in Design Expert were used for this purpose. The optimum conditions recomended by Design Expert are shown in Table 5. 3. Model Validation. The operating conditions suggested by the model were tested against the manufacturer’s suggested operating conditions9 using compounds from a broad range of current lead optimization projects at AstraZeneca Charnwood. The peak area obtained for each compound using the manufacturer’s operating conditions9 was compared to that obtained using the conditions proposed by the model. These results are shown in Figure 5. It can be seen that the conditions supplied from the CCD experiments provided improved sensitivity in all cases. These conditions were adopted in the laboratory but were found to be unsatisfactory for practical use. A nebulizer pressure of 20 psi was found to produce an inconsistent spray leading to a lack of reproducibility. A nebulizer pressure of 40 psi was therefore adopted, which produced a more consistent spray and therefore more consistent results. All other parameters were set at the optimum found using the CCD experiments. Received for review September 19, 2007. Accepted October 30, 2007. AC701966R
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