Keywords: HPLC method development; Computer simulation; DOE; surface response design; Box-Behnken design; statistical analysis
The same QbD principles have been applied to the HPLC method development [4-6]. Using Design of Experiment (DOE) technique, the Method Operable Design Region (MODR) can be established. Operating the method within this MODR will guarantee that the Critical Quality Attributes (CQAs) of the method, such as separation resolution and peak tailing factors, can be controlled within a reasonable range, resulting in a robust method that consistently delivers the intended performance. In addition, by DOE based strategies, detailed information about how the operational parameters affect the CQAs within a narrow region near the optimum operational conditions can be easily obtained using statistical analysis methods [7], which not only provides information for the quantitative evaluation of the method robustness, but also provides critical information for the fundamental study of the separation mechanisms.
Although numerous papers have been published on reversed phase HPLC method development and optimization by DOE strategies [5,8-10], method development strategies that fully rely on DOE need to search the entire high dimensional design space, which usually requires large amount of experimental trials, especially for complex samples.
Computer simulation has been widely used in HPLC method development and has been proved to be highly efficient to find the optimum operational conditions [11-25]. By allowing DOE to only study the narrow region of the design space near the optimum, computer simulation can greatly speed up the process for defining the MODR by DOE [20]. It should be noted that HPLC method development is a complicated process where many operational parameters, including column selectivity parameters, column kinetic properties, mobile phase compositions, column temperature, ion pair types and concentration, gradient profile, and their interactions need to be considered. Although computerassisted method development methods can greatly speed up the method development process, understanding of the fundamental separation mechanisms and the operators’ method development experience are still critical to method development.
In this paper, a framework is reported that combines the computer simulation and surface response design for HPLC method development. The goals of this manuscript are the following: First, to demonstrate that by combining computer simulation and surface response design, HPLC methods can be effectively developed for challenging separation problems by using reasonable amount of experimental trials; Second, to demonstrate that UHPLC/UPLC can be used as an efficient tool for conventional HPLC method development. Although UHPLC/UPLC has been widely used in pharmaceutical industry, conventional HPLC is still used in many cases due to instrument compatibility and method transfer issues. This work showed that the solute retention parameters obtained from an acquity BEH C18 UPLC column operated on a UPLC system can be directly applied to X Bridge C18 column for conventional HPLC method development by computer simulation. Due to the faster separation speed [26,27]. And lower mobile phase consumption of UHPLC/UPLC compared to conventional HPLC, this can greatly reduce the time and cost required for conventional HPLC method development; Third, to demonstrate that surface response design together with the following statistical analysis not only provides a critical tool to quantitatively evaluate the robustness of HPLC methods and define the MODR, but also provides further insights into the fundamental mechanisms of the separation. Finally, to demonstrate an interactive simulation program that integrated various column parameters for gradient method development. The Graphic User Interfaces (GUI) of this tool makes it easy to use for users without in-depth knowledge and understanding of gradient elution theory and simulation. Results of this work showed this simulation program can accurately predict the retention times and propose the optimum operational conditions based on the constraints set up by users.
It should be noted that this manuscript only discusses the framework for finding the operational conditions that satisfy the resolutions and peak shape quality attributes, and method validation is beyond the scope of this work.
Solutes used in the isocratic flow study were obtained from Sigma-Aldrich. The sample mixture was prepared by appropriately diluting individual stock solutions of the solutes in 40:60 acetonitrile-water, resulting in a sample mixture solution containing acetophenone (0.2mg/ mL), propiophenone (0.4 mg/ mL), butyrophenone (1.0 mg/ mL) and valerophenone (1.0 mg/ mL). Uracil was purchased from Sigma and dissolved in pure water at a concentration that gave an adequate signal to determine the column dead volume.
A mixture sample consisting of Active Pharmaceutical Ingredient (API) and process impurities developed by Eisai Inc. was used for the method development. The molecular weights of API and major impurities were measured by LC-MS.
The HPLC experiments were conducted using an Agilent 1100 HPLC system equipped with a column heating compartment and a VWD UV detector. Detection wavelength was set at 260 nm for the pharmaceutical mixture sample and 240 nm for alkylphenones.
The instrument gradient void volumes were measured to be 114 μL and 1.33 mL for UPLC and HPLC systems, respectively.
Eighteen columns commonly used by the Eisai Inc. Analytical Chemistry team for method development were characterized by flow study. The names, dimensions, pore diameters, and particle sizes of these columns are listed in table 1.
In addition to the plate counts, column pressure for each column at each flow rate was recorded. These column pressures, after corrected for instrumental pressure at the corresponding flow rates, were used to calculate the column flow resistance.
Column Name |
Column I.D. (mm) |
Column Length (cm) |
Pore diameter (Å) |
Particle Diameter (µm) |
Zorbax Bonus RP RRHD |
2.1 |
5 |
80 |
1.8 |
Zorbax Extend C18 RRHD |
2.1 |
5 |
80 |
1.8 |
ZorbaxStableBond C18 RRHD |
2.1 |
5 |
80 |
1.8 |
Acquity BEH C18 |
2.1 |
5 |
130 |
1.7 |
Acquity CSH C18 |
2.1 |
5 |
130 |
1.7 |
Acquity BEH Shield RP18 |
2.1 |
5 |
130 |
1.7 |
Acquity HSS T3 |
2.1 |
5 |
90 |
1.8 |
XBridge C18 |
4.6 |
15 |
130 |
3.5 |
XBridgeBEH Shield RP18 |
4.6 |
15 |
130 |
3.5 |
Atlantis T3 |
4.6 |
10 |
101 |
3 |
Sunfire C18 |
4.6 |
15 |
100 |
3.5 |
Symmetry Shield RP18 |
4.6 |
15 |
100 |
5 |
Luna C18(2) |
4.6 |
10 |
100 |
3 |
Zorbax Eclipse XDB-C18 |
4.6 |
15 |
80 |
3.5 |
Zorbax Eclipse XDB-Phenyl |
4.6 |
15 |
80 |
3.5 |
Zorbax Bonus RP |
4.6 |
15 |
80 |
3.5 |
Zorbax Stable Bond 80A C18 |
4.6 |
15 |
80 |
3.5 |
YMC-Pack Pro C18 RS |
4.6 |
15 |
80 |
3 |
Due to the severe peak tailing observed when 0.1% FA aqueous solution was used in the mobile phase, only mobile phases consisting of methanol-0.1% TFA aqueous, methanol-0.1% NH4OH aqueous (pH = 10.5), ACN-0.1% TFA aqueous and ACN-0.1% NH4OH aqueous (pH = 10.5)were fully screened. The methanol-0.1% NH4OH aqueous and ACN-0.1% NH4OH aqueous mobile phases were only used on Zorbax Extend C18 RRHD and Acquity BEH C18 columns since only these two columns are compatible with basic mobile phase conditions. The ACN-0.1% TFA aqueous and methanol-0.1% TFA aqueous mobile phases were screened for all the five columns.
For each column under a given mobile phase composition condition, three gradient scouting runs with different gradient times were completed. The gradient started at 75:25% aqueous: organic and ramped to 10:90% aqueous: organic for all three runs. The flow rates were fixed at 0.5 mL/ min. The gradient times of these runs were 6, 18 and 30 minutes, respectively. The API and seven other impurity peaks that distributed within the entire gradient time window were chosen to represent the API mixture sample for the gradient condition optimization (see Figure 1). For a given column-mobile phase combination, if reasonable resolutions of the critical pairs were observed during the scouting runs, retention times of API and the seven impurities obtained from the three scouting runs were submitted to the Excel simulation software for optimization. Based on the retention times of API and the seven impurities under the three gradient scouting run conditions, the retention parameters (k’w)and S values) of each peak were calculated and used by the simulation software to predict the retention times and peak widths, and optimize the gradient conditions based on the linear solvent strength theory [24,25,28,29].
Gradient conditions: B% increased from 25% to 90% linearly in 6 minutes. Solvent A: 0.1%TFA in water. Solvent B: acetonitrile. Flow rate = 0.5 mL/min. Detection wavelength was set at 260 nm. Injection volume was 0.5 μL. The column temperature was fixed at 35 ºC. The separation was carried out on a 2.1 I.D. × 100 mm BEH-C18 column packed with 1.7 μm particles.
The author believes that integrating these column parameters in the simulation program will be helpful, especially for optimizing the separation conditions for complex samples where high peak capacities obtained by coupled column systems are needed [34,39,42]. In addition, integrating these column parameters in the simulation programs allows users to easily optimize both the gradient operational conditions and column types for HPLC and UPLC method development by using the user-friendly graphic user interface of the simulation program.
Although several papers have been published on optimizing HPLC/UPLC methods for complex samples based on carefully characterized columns using the flow study method reported in this manuscript [34,42,43], these papers only characterized a few specific types of columns. As far as the author knows, this
Column Name |
A |
B |
C |
S.E.a |
Zorbax Bonus RP RRHD |
1.10 ± 0.07 |
10.78 ± 0.34 |
0.057 ± 0.003 |
0.02 |
Zorbax Extend C18 RRHD |
1.11 ± 0.02 |
9.97 ± 0.08 |
0.045 ± 0.001 |
0.007 |
ZorbaxStableBond C18 RRHD |
1.04 ± 0.02 |
11.41 ± 0.11 |
0.038 ± 0.001 |
0.009 |
Acquity BEH C18 |
0.38 ± 0.06 |
11.88 ± 0.20 |
0.069 ± 0.003 |
0.04 |
Acquity CSH C18 |
0.37 ± 0.05 |
12.51 ± 0.17 |
0.075 ± 0.003 |
0.04 |
Acquity BEH Shield RP18 |
0.37 ± 0.05 |
10.59 ± 0.17 |
0.081 ± 0.003 |
0.04 |
Acquity HSS T3 |
0.70 ± 0.10 |
11.23 ± 0.32 |
0.071 ± 0.006 |
0.07 |
XBridge C18 |
0.75 ± 0.03 |
11.32 ± 0.09 |
0.048 ± 0.002 |
0.02 |
XBridgeBEH Shield RP18 |
0.93 ± 0.07 |
8.61 ± 0.19 |
0.033 ± 0.003 |
0.05 |
Atlantis T3 |
0.91 ± 0.14 |
16.48 ± 0.34 |
0.049 ± 0.008 |
0.12 |
Sunfire C18 |
0.64 ± 0.07 |
14.05 ± 0.19 |
0.052 ± 0.004 |
0.05 |
Symmetry Shield RP18 |
1.32 ± 0.03 |
7.36 ± 0.10 |
0.042 ± 0.001 |
0.02 |
Luna C18(2) |
1.06 ± 0.09 |
10.96 ± 0.22 |
0.030 ± 0.005 |
0.08 |
Zorbax Eclipse XDB-C18 |
1.37 ± 0.05 |
9.95 ± 0.14 |
0.028 ± 0.003 |
0.05 |
Zorbax Eclipse XDB-Phenyl |
0.72 ± 0.04 |
7.74 ±0.10 |
0.052 ± 0.002 |
0.04 |
Zorbax Bonus RP |
|
6.58 ± 0.08 |
0.054 ± 0.002 |
0.03 |
ZorbaxStableBond 80A C18 |
0.70 ± 0.04 |
9.48 ± 0.09 |
0.051 ±0.002 |
0.03 |
YMC-Pack Pro C18 RS |
1.26 ± 0.17 |
9.72 ± 0.67 |
0.108 ± 0.009 |
0.05 |
Column Name |
Pore diameter (Å) |
Surface Area (m2/g) |
Interstitial porositya (εe) |
Total porosity (εtol) |
Flow resistance |
Zorbax Bonus RP RRHD |
80 |
180 |
0.386b |
0.523 |
443 |
Zorbax Extend C18 RRHD |
80 |
180 |
0.386b |
0.480 |
523 |
Zorbax Stable Bond C18 RRHD |
80 |
180 |
0.386b |
0.506 |
508 |
Acquity BEH C18 |
130 |
185 |
0.353b |
0.546 |
443 |
Acquity CSH C18 |
130 |
185 |
0.353b |
0.616 |
393 |
Acquity BEH Shield RP18 |
130 |
185 |
0.353b |
0.554 |
462 |
Acquity HSS T3 |
90 |
150 |
0.38e |
0.55 |
428 |
XBridge C18 |
130 |
185 |
0.34c |
0.555 |
539 |
XBridge BEH Shield RP18 |
130 |
185 |
0.34c |
0.582 |
441 |
Atlantis T3 |
101 |
315 |
0.38d |
0.620 |
381 |
Sunfire C18 |
100 |
340 |
0.38e |
0.535 |
572 |
Symmetry Shield RP18 |
100 |
335 |
0.38e |
0.518 |
513 |
Luna C18(2) |
100 |
400 |
0.383d |
0.560 |
528 |
Zorbax Eclipse XDB-C18 |
80 |
180 |
0.426d |
0.496 |
457 |
Zorbax Eclipse XDB-Phenyl |
80 |
180 |
0.426d |
0.547 |
417 |
Zorbax Bonus RP |
80 |
180 |
0.426d |
0.548 |
400 |
Zorbax Stable Bond 80A C18 |
80 |
180 |
0.426d |
0.502 |
472 |
YMC-Pack Pro C18 RS |
80 |
330 |
0.38e |
0.510 |
406 |
b. Results reported by Desmet and co-workers [39]
c. Results reported by Desmet and co-workers [40]
d. Results reported by Gritti and Guiochon [41]
A default value of 0.38 was used
HPLC conditions |
Peak # |
tr,pre (min) |
tr,exp(min) |
tr,error(%)e |
wpre (min) |
wexp (min) |
Werror(%)d |
Condition Aa |
1 |
7.52 |
7.60 |
1.06 |
0.05 |
0.06 |
17 |
|
2 |
8.56 |
8.57 |
0.07 |
0.06 |
0.06 |
0 |
|
3 |
9.10 |
9.09 |
-0.09 |
0.06 |
0.08 |
25 |
|
4 |
9.61 |
9.68 |
0.76 |
0.06 |
0.09 |
33 |
|
5 |
9.80 |
9.91 |
1.12 |
0.06 |
0.07 |
14 |
|
6 |
11.16 |
11.27 |
0.95 |
0.06 |
0.07 |
14 |
|
7 |
12.08 |
12.14 |
0.45 |
0.06 |
0.07 |
14 |
|
8 |
14.51 |
14.50 |
- 0.03 |
0.06 |
0.08 |
25 |
Condition Bb |
1 |
9.35 |
10.06 |
7.04 |
0.05 |
0.08 |
38 |
|
2 |
10.76 |
11.32 |
4.94 |
0.06 |
0.08 |
25 |
|
3 |
11.40 |
11.96 |
4.69 |
0.06 |
0.08 |
25 |
|
4 |
12.02 |
12.70 |
5.36 |
0.06 |
0.11 |
45 |
|
5 |
12.26 |
12.99 |
5.61 |
0.06 |
0.09 |
33 |
|
6 |
13.86 |
14.62 |
5.18 |
0.06 |
0.11 |
45 |
|
7 |
14.95 |
14.89 |
-0.38 |
0.06 |
0.10 |
40 |
|
8 |
17.95 |
18.09 |
2.66 |
0.07 |
0.10 |
30 |
Condition Cc |
1 |
5.26 |
5.57 |
5.57 |
0.06 |
0.08 |
25 |
|
2 |
7.70 |
7.70 |
0 |
0.07 |
0.08 |
12 |
|
3 |
8.61 |
8.51 |
-1.23 |
0.07 |
0.09 |
22 |
|
4 |
9.63 |
9.63 |
0.04 |
0.07 |
0.14 |
50 |
|
5 |
10.04 |
10.07 |
0.29 |
0.08 |
0.10 |
20 |
|
6 |
12.72 |
12.67 |
-0.43 |
0.08 |
0.09 |
11 |
|
7 |
14.72 |
14.54 |
-1.26 |
0.09 |
0.10 |
10 |
|
8 |
20.47 |
19.98 |
-2.46 |
0.10 |
0.11 |
9 |
b. Gradient conditions: B% increased from 20 to 62% linearly in 20 min; column length: 25 cm; F = 1.1 mL/min.
c. Gradient conditions:B% increased from 33 to 53% linearly in 20 min; column length: 40 cm; F = 1.0 mL/min.
d. Werror(%) = |wpre- wexp|/wexp × 100%
e. tr,error(%) = (tr,pre- tr,exp)/tr,exp × 100%
All the gradient runs were conducted using XBridgeC18 columns packed with 3.5 μm particles with the pore size of 130 Å. 0.1% TFA in water was as solvent A and acetonitrile was used as solvent B. The column temperature was fixed at 35°C. Detection wavelength was set at 260 nm.
HPLC conditions |
Peak # |
tr,pre (min) |
tr,exp(min) |
tr,error(%)e |
wpre (min) |
wexp (min) |
Werror(%)d |
Condition Aa |
1 |
1.295 |
1.284 |
0.86 |
0.008 |
0.017 |
53 |
2 |
1.567 |
1.510 |
3.77 |
0.010 |
0.016 |
38 |
|
3 |
1.688 |
1.634 |
3.30 |
0.010 |
0.018 |
44 |
|
4 |
1.808 |
1.777 |
1.74 |
0.010 |
0.025 |
60 |
|
5 |
1.852 |
1.895 |
-2.27 |
0.010 |
0.024 |
58 |
|
6 |
2.160 |
2.165 |
-0.23 |
0.011 |
0.018 |
39 |
|
7 |
2.371 |
2.302 |
3.00 |
0.010 |
0.020 |
50 |
|
8 |
3.836 |
3.787 |
1.29 |
0.012 |
0.020 |
40 |
|
Condition Bb |
1 |
1.739 |
1.794 |
-3.05 |
0.015 |
0.021 |
29 |
2 |
2.087 |
2.256 |
-7.49 |
0.018 |
0.018 |
2 |
|
3 |
2.404 |
2.541 |
-5.40 |
0.019 |
0.026 |
27 |
|
4 |
2.758 |
2.803 |
-1.62 |
0.020 |
0.052 |
62 |
|
5 |
2.864 |
2.896 |
-1.11 |
0.021 |
0.029 |
28 |
|
6 |
3.541 |
3.709 |
-4.53 |
0.023 |
0.033 |
30 |
|
7 |
4.156 |
4.283 |
-2.97 |
0.022 |
0.033 |
33 |
|
8 |
7.969 |
7.951 |
0.23 |
0.031 |
0.038 |
18 |
|
Condition Cc |
1 |
1.962 |
2.064 |
-4.94 |
0.020 |
0.018 |
11 |
2 |
2.456 |
2.615 |
-6.31 |
0.024 |
0.031 |
23 |
|
3 |
2.803 |
3.015 |
-7.03 |
0.026 |
0.033 |
21 |
|
4 |
3.309 |
3.380 |
-2.10 |
0.028 |
0.070 |
60 |
|
5 |
3.453 |
3.501 |
-1.37 |
0.029 |
0.040 |
28 |
|
6 |
4.406 |
4.736 |
-6.97 |
0.033 |
0.040 |
18 |
|
7 |
5.428 |
5.637 |
-3.70 |
0.034 |
0.052 |
35 |
|
8 |
11.249 |
11.269 |
-0.18 |
0.048 |
0.055 |
13 |
b. Gradient conditions: B% increased from 25 to 90% linearly in 18 min; column length: 10 cm; F = 0.5 mL/min.
c. Gradient conditions: B% increased from 25 to 90% linearly in 30 min; column length: 10 cm; F = 0.5 mL/min.
d. Werror(%) = |wpre- wexp|/wexp × 100%
e. tr,error(%) = (tr,pre- tr,exp)/tr,exp × 100%
All the gradient runs were conducted using a 2.1mm I.D. ×100 mm BEHC18 UPLC columns packed with 1.7 μm particles with the pore size of 130 Å. 0.1% TFA in water was used as solvent A and acetonitrile was used as solvent B. The column temperature was fixed at 35 ºC. Detection wavelength was set at 260 nm.
Experiment No. |
TFA% |
Temperature (ºC) |
Flow rate (mL/min) |
1 |
0.1 |
35 |
0.8 |
2 |
0.05 |
38 |
0.8 |
3 |
0.05 |
35 |
0.9 |
4 |
0.1 |
38 |
0.9 |
5 |
0.15 |
35 |
0.7 |
6 |
0.1 |
38 |
0.7 |
7 |
0.1 |
32 |
0.7 |
8 |
0.15 |
32 |
0.8 |
9 |
0.1 |
32 |
0.9 |
10 |
0.15 |
38 |
0.8 |
11 |
0.05 |
32 |
0.8 |
12 |
0.1 |
35 |
0.8 |
13 |
0.1 |
35 |
0.8 |
14 |
0.15 |
35 |
0.9 |
15 |
0.05 |
35 |
0.7 |
Quality Attribute |
Term |
Estimate |
Standard Error |
t Ratio |
Prob> |t| |
API peak tailing |
intercept |
1.50 |
0.005 |
281.20 |
< 10-8 |
TFA% (0.05, 0.15) |
-0.19 |
0.005 |
-37.61 |
< 10-8 |
|
TFA% × TFA% |
0.09 |
0.007 |
12.19 |
< 10-8 |
|
tr, API |
intercept |
16.50 |
0.054 |
305.07 |
< 10-8 |
TFA% (0.05, 0.15) |
0.56 |
0.04 |
14.00 |
2 × 10-7 |
|
temperature (32, 38) |
-0.19 |
0.040 |
-4.85 |
9.1 × 10-7 |
|
flow rate (0.7, 0.9) |
-1.40 |
0.040 |
-35.27 |
< 10-8 |
|
TFA% × TFA% |
-0.54 |
0.058 |
-9.21 |
7.1 × 10-6 |
|
flow rate × flow rate |
0.20 |
0.058 |
3.34 |
0.0086 |
|
tr, imp4 |
intercept |
15.83 |
0.051 |
312.50 |
< 10-8 |
TFA% (0.05, 0.15) |
0.61 |
0.037 |
16.64 |
1 × 10-7 |
|
temperature (32, 38) |
-0.16 |
0.037 |
-4.30 |
0.002 |
|
flow rate (0.7, 0.9) |
-1.37 |
0.037 |
-36.78 |
< 10-8 |
|
TFA% × TFA% |
-0.51 |
0.055 |
-9.41 |
5.9 × 10-6 |
|
flow rate × flow rate |
0.19 |
0.055 |
3.39 |
0.0079 |
|
Resolution 1a |
intercept |
2.20 |
0.020 |
108.71 |
< 10-8 |
TFA% (0.05, 0.15) |
0.14 |
0.015 |
9.39 |
6 × 10-6 |
|
temperature (32, 38) |
-0.03 |
0.015 |
-1.84 |
0.098 |
|
TFA% × temperature |
-0.08 |
0.021 |
-3.67 |
0.0051 |
|
TFA% × TFA% |
-0.21 |
0.022 |
-9.38 |
6 × 10-6 |
|
temperature × temperature |
-0.10 |
0.022 |
-4.36 |
0.0018 |
|
Resolution 2b |
intercept |
3.75 |
0.081 |
46.25 |
< 10-8 |
TFA% (0.05, 0.15) |
-3.00 |
0.111 |
-27.01 |
< 10-8 |
|
flow rate (0.7, 0.9) |
0.29 |
0.111 |
2.61 |
0.023 |
b. Defined as the resolution between API and the unknown impurity (see Figure. 5C)
Five UPLC/UHPLC columns were selected for column and mobile phase composition screening. These five columns included three C18 columns, namely Zorbax SB C18, Zorbax Extend C18, and Acquity BEH C18. Zorbax SB C18 column was selected due to its extremely good stability under acidic mobile phase conditions. Water BEH C18 was selected due to its unique stability in both acidic and basic mobile phases. Extend C18 column was selected mainly to test the peak shape improvement of API under basic mobile phase conditions. In addition, two Polar Embedded Group (PEG) columns, Zobax RP bonus and Acquity BEH Shield RP18 columns were selected due to their unique selectivity that was very different from the selectivity of C18 columns [48].
Another challenge for this method development was the separation of API and impurity 4 (see Figure 1 and Figure 5). LC-MS results showed that these two molecules had very similar structures. The only structural difference between these two molecules was an extra double bond in API molecule. Column screening results showed that under all the tested gradient scouting conditions, only the Acquity BEH C18 column partially resolved this critical pair using ACN-0.1% TFA aqueous as the mobile phase. Using the ACN-0.1% TFA aqueous mobile phase, the Zorbax Extend C18 only produced marginally partial separation. For all the other columns and mobile phase conditions, no separation of this critical pair was observed. Therefore, retention times of the API and the seven chosen impurities obtained from the three gradient scouting runs on the Acquity BEH C18 column using ACN- 0.1% TFA aqueous mobile phase were used in the next step for further optimization.
It should be noted that the simulation program used the retention times of API and representative impurities measured on an Acquity BEH C18 UPLC column using an UPLC instrument system, and predicted retention times and peak widths on an X Bridge C18 column operated on an HPLC system with good accuracy. Applying UPLC systems to the method development of conventional HPLC can greatly reduce the time and solvent consumption required for the method development.
An interesting question is if it is possible to predict the
The gradient conditions in figure 5. Were proposed by the simulation program as optimum conditions under different constraints (25 cm maximum column length and 15 min gradient
Figure 5B gradient conditions: B% increased from 20 to 62% in 20 min; column length: 25 cm; F = 1.1 mL/min. The resolution of API and impurity 4 in this figure was 1.6.
Figure 5C gradient conditions: B% increased from 33 to 53% in 20 min; column length: 40 cm (obtained by coupling a 25 cm and another 15 cm columns); F = 1.0 mL/min. The resolution of API and impurity 4 in this figure was 2.1.
It should be noted that although changing the column temperature has been reported as one of the most effective ways to optimize the resolution and peak capacity of gradient elution[23,24,35,50], and optimizing the column temperature using simulation programs has been successfully demonstrated [13,14,23,24,50], the column temperature was not optimized by the simulation program in this work. The column temperature was fixed at 35 °C for all column screening experiments and the following simulations and optimization experiments in order to prevent degradation of process impurities and the API that might occur at higher column temperatures. Since the optimum operational condition obtained from the simulation program had already achieved the resolution criteria, exploring higher column temperatures was not considered in this step. In the next step, surface response design was used to explore a narrow column temperature range of 30-40 °C to define the operable region.
Although Kzakevich model only considers the ion pairing equilibration in the system, the dynamic ion exchange process can be described by the same format as expressed by Equation 3 [44]. As a result, with the increase of TFA concentration in the mobile phase, the retention factor of the analyte governed by the dynamic ion exchange process will finally asymptotically approaches the limiting retention factor corresponding to the maximum adsorption of TFA anions on the column. Since retention factors governed by ion pairing and dynamic ion exchange processes both asymptotically approach their maximum values with the increase of TFA concentration, it is not surprising to see the nonlinear trend of retention time versus the TFA concentration in figure 6A.
It should be noted that the regression model listed in table 7 was used to approximate API retention versus operational parameters in a narrow region near the optimized operational conditions. It does not mean that the relationship between the retention and TFA concentration is fundamentally governed by a second order polynomial model. In fact, according to the fundamental separation mechanisms, the retention factor of basic compounds is a complicated function of the ion pair reagent concentration [44,53]. In addition, from figure 6A, increasing flow rate greatly reduces the retention time, while temperature only slightly affects the retention time.
Figure 7A shows that resolution 1changes with TFA% in a nonlinear way, with the optimum TFA% at ~ 0.1%. As shown in the previous sections, both the retention times and peak widths of API and impurity 4 change with TFA% nonlinearly. Therefore, it is not surprising to observe a complicated, nonlinear effect of TFA% on the resolution between them.
From table 7, It is interesting to see that resolution 1is affected by temperature ×TFA% and temperature ×temperature, but not by temperature itself (prob>|t| > 0.05). In addition, the interaction profiles in figure 7A Show strong interaction effects between TFA% and temperature, indicating that temperature affects resolution 1 by affecting the interactions between TFA and the basic analytes (API and impurity 4).
The linear regression model of the resolution between API and this impurity (defined as resolution 2) versus operational parameters is shown in table 7. Plots generated by JMP software based on this model are shown in figure 7B. Figure 7B Showed that the resolution between API and this impurity decreases with the increase of TFA%. This can be explained by the different interactions between API and the impurity with TFA. As a consequence of interacting with TFA by ion pairing and ion exchange, the retention time of API increases with the increase of TFA%. However, the retention time of the impurity does not change with the increase of TFA% because it does not interact with TFA. Since the impurity is eluted after API, with the increase of TFA%, the API peak will move closer to the impurity peak, leading to a continuous decrease of the resolution between them. In addition, the interaction profiles of resolution 2 (right hand side of figure 7B show parallel lines corresponding to resolution 2 versus flow rate when TFA% are 0.15% and 0.05%, respectively. The same parallel lines are observed for resolution 2 versus TFA% when flow rates are 0.7 and 0.9 mL/min, respectively. These parallel lines indicate that no interactive effects exist between flow rate and TFA%. This is consistent to the fact that fundamentally flow rate should not affect ion pairing and ion exchange processes.
Resolution 1 is the resolution between API and impurity 4 (see Figure. 5)
Resolution 2 is the resolution between API and unknown impurity (see Figure. 5C)
The settings for the contour plots are: resolution 1 >2.0; resolution 2 > 2.0.
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