Development of an Automated Headspace Gas Chromatography

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Development of an Automated Headspace Gas Chromatography Instrument for the Determination of Residual Solvents in Pharmaceutical Compounds and Reaction Mixtures Simon E. Hamilton,* Marc D. Rossington,* and Alexia Bertrand Merck Sharp and Dohme Ltd., Hoddesdon, Hertfordshire EN11 9BU, United Kingdom ABSTRACT: The use of gas chromatography with headspace sampling is commonplace in analytical laboratories for the analysis of residual solvents. In this article, we discuss the use of an internal standard-based calibration, utilizing relative response factors, to enable the generation of accurate weight % solvent data on 25 common solvents in a single chromatographic run. The total cycle time for analysis is less than 30 min. To facilitate this technology into the process chemistry environment, bespoke open access software has been developed to simplify the sample submission process such that virtually no training is required to analyze the sample, process the data, and generate a report. Furthermore, an automated calibration check workflow has been implemented to validate the quality of the data on a daily basis and alert the system administrator in the event of a problem with the system.

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INTRODUCTION The analysis of residual solvents used in the manufacture of pharmaceutical compounds is well-established1,2 since it is an integral part of the regulatory requirements associated with good manufacturing practice (GMP).3,4 Historically, analysis is performed by gas chromatography (GC), either via direct injection or headspace sampling techniques, using an external standard calibration. However, in some Merck Sharp and Dohme process chemistry laboratories, the familiar technique of nuclear magnetic resonance (NMR) analysis is often preferred for quantifying residual solvent levels in active pharmaceutical ingredients (API). While NMR is well-suited for the analysis of common solvents, the cost and availability of NMR instrumentation preclude widespread utilization in many laboratories. GC techniques are generally preferred by analytical chemistry groups because of the accuracy, sensitivity, and relatively low cost of instrumentation.5 Current barriers to the more widespread use of GC by process chemists include the burden associated with learning new software and instrumentation and the requirement of preparing analytical standards. In this article, we present a user-friendly automated approach to residual solvent analysis that is well-suited for routine use by process chemists with minimal training in GC. The system employs headspace sampling combined with software-driven internal standard calibration via the use of relative response factors. The resulting system allows the quantification of 25 residual solvents in a sample from a single preparation and injection. The total cycle time is less than 30 min, and the system is well-suited for use by nonexpert users. Automated User Interface. Headspace solvent analysis by GC is currently available with a high level of automation; however, operation of the instrumentation assumes that the user is competent in the use of the control and processing software. Fully automating solvent analysis using this technique, such that a chemist unfamiliar with the instrumentation and © XXXX American Chemical Society

software can utilize it, requires an additional level of automation currently not available off the shelf. Fully automating the use of GC for nonexpert users relies on the following software functionality. Simple sample log in and drop off, management of the sample queue, automated quantitation, and result reporting, ideally via e-mail. To achieve this, bespoke automation software has been developed through collaboration with JSB UK, Berkshire, United Kingdom. The software interfaces with Agilent Chemstation chromatography data systems for data processing. Daily at 5:00 a.m., a blank is selected from the assigned autosampler tray and analyzed. The chromatogram is automatically integrated, and checks for interferences and internal standard response are made against set criteria in the software. If the desired acceptance criteria are not met, then the system will stop and an e-mail message will be sent to the system administrator. If the criteria are met, then a quality control (QC) check standard will be analyzed. The system checks whether the solvent response factors are within ±10% of expected values. If this is the case, then the system is available for use and allows the submission of samples to the queue; if not, then the system stops and once again an e-mail message is sent to the system administrator (Figure 1). Sample preparation is simple. The sample is weighed directly to a headspace vial, and 1.0 mL of preprepared internal standard in dimethylacetamide (DMAc) solution is added via pipet. The chemist can then log the sample into the system (Figure 2). The user is then prompted to add information about the sample either via radio buttons, drop-down menus, or free text (Figure 3). The vials are added to the designated positions, and the system commences the analysis of the sample. After Received: November 5, 2015

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DOI: 10.1021/acs.oprd.5b00367 Org. Process Res. Dev. XXXX, XXX, XXX−XXX

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Figure 1. Schematic of walk-up headspace GC process flow.

Figure 2. Walk-up software login screen.

Figure 3. Sample registration and properties screen.

was performed using the gas chromatograph autosampler, with a 10 μL syringe (P/N 5181-1267). A 20 m × 0.18 mm × 1 μm DB-624 GC capillary column (Agilent Technologies) was used for the GC analysis. The operating conditions were as follows: helium carrier gas at 1.3 mL/min; split/splitless inlet at 240 °C; split ratio of 40:1; and GC oven temperature program of 45 °C for 1.5 min, 25 °C/ min to 90 °C, and then 35 °C/min to 200 °C, holding for 0.5 min. The headspace sampler operating conditions were as follows: incubation temperature at 85 °C, incubation time of 600 s, and syringe temperature 90 °C with injection volume of 500 μL. A 0.1% v/v QC check standard was used to monitor the system performance and injected daily. This was prepared by

completion, a PDF report is e-mailed to the user containing assay information for all solvents detected (Figure 4).

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EXPERIMENTAL SECTION The analysis was carried out using a gas chromatograph fitted with split/splitless inlet, liner (P/N 18740-80200), and flame ionization detector (FID) (Agilent Technologies Inc., Santa Clara, CA). A headspace autosampler (CTC Analytics, Zwingen, Switzerland) and 2.5 mL gastight syringe (P/N 203084/04) were attached. The instrument is controlled by a custom software interface for walk-up use in conjunction with the Agilent Chemstation (ver. B.04.01) chromatography data system. For direct injection analyses, liquid sample injection B

DOI: 10.1021/acs.oprd.5b00367 Org. Process Res. Dev. XXXX, XXX, XXX−XXX

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Figure 4. Example result report e-mailed to user.

diluting 1.0 mL of methanol, ethanol, i-propanol, t-butanol, ethyl acetate, i-propyl acetate, n-butanol, toluene, and p-xylene to 1000 mL with DMAc containing 1% v/v butan-2-ol (internal standard). For sample analysis, solids were weighed accurately, approximately 50 mg, into a GC headspace vial and diluted with 1.0 mL of DMAc containing 1% v/v butan-2-ol. For liquids, 50−100 μL of sample was diluted in 1.0 mL of DMAc containing 1% v/v butan-2-ol in a headspace vial. For the validation data documented herein, two API were selected from current laboratory supplies to assess recovery of solvents and matrix effects (Tables 4 and 5). Chromatographic conditions for the separation of 25 common process solvents already existed within Merck Laboratories, so this set of conditions was retained. For relative response factor (RRF) determination purposes (calibration), the 25 solvents were divided into three groups to ensure that partial co-elution of peaks did not impact the peak area determination (Table 1).

Table 1. Twenty Five Solvents That Can Be Quantified Using the Walk-Up GC System solvent group 1

solvent group 2

solvent group 3

methanol ethanol i-propanol t-butanol ethyl acetate i-propyl acetate n-butanol toluene p-xylene

diethyl ether acetonitrile methyl tert-butyl-ether (MTBE) n-propanol tetrahydrofuran (THF) methyl cyclohexane methyl isobutyl ketone (MIBK) m-xylene

acetone methylene chloride hexane cyclohexane heptane 1,4-dioxane n-butyl acetate o-xylene

method detailed in the Experimental Section. In each of these solutions, butan-2-ol was spiked at 0.1% v/v. For levels from 0.0005 to 0.1%, response factors (RFs) and RRFs were established through experimentation, and RFs were populated into the software quantitation method, thus establishing a calibration table (Table 2). RF calculations used by Chemstation software for quantification are as follows:

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RESULTS AND DISCUSSION The first step in the development of the automated headspace GC workflow was to establish RRFs for all of the solvents of interest. The use of internal standard calibration was employed, and butan-2-ol was selected because it is readily available in high purity, elutes in a clear section of the chromatogram with no interference from the other solvents determined by the method, and is not commonly used in chemical batch processing. In order to determine the RRFs and evaluate the performance of the system, standards were prepared at concentrations of 0.1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001% v/v in DMAc and analyzed using the headspace GC

(amount ratio)x =

amount(x) amount(ISTD)

(response ratio)x =

area(x) area(ISTD)

RFx = C

(amount ratio)x (response ratio)x DOI: 10.1021/acs.oprd.5b00367 Org. Process Res. Dev. XXXX, XXX, XXX−XXX

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Table 2. RRFs for the 25 Solvents Considered in This Method at Each Concentration, Including Precision over the Range relative response factors (Chemstation RFx) solvent

0.10%

0.05%

0.01%

0.005%

0.001%

0.0005%

mean

% RSD

methanol ethanol i-propanol t-butanol ethyl acetate i-propyl acetate n-butanol toluene p-xylene diethyl ether acetonitrile MTBE n-propanol THF methyl-cyclohexane MIBK m-xylene acetone methylene chloride hexane cyclohexane heptane 1,4-dioxane n-butyl acetate o-xylene

0.866 0.789 0.748 0.509 0.409 0.409 1.584 0.346 0.572 0.072 0.627 0.094 1.000 0.211 0.089 0.635 0.578 0.255 1.254 0.043 0.064 0.069 1.067 0.889 0.690

0.864 0.790 0.745 0.508 0.401 0.402 1.589 0.344 0.568 0.072 0.635 0.093 1.001 0.212 0.089 0.631 0.577 0.251 1.243 0.042 0.063 0.068 1.058 0.881 0.692

0.894 0.811 0.761 0.514 0.407 0.406 1.636 0.346 0.577 0.071 0.626 0.091 0.999 0.206 0.087 0.626 0.569 0.249 1.226 0.042 0.062 0.067 1.043 0.860 0.677

0.855 0.800 0.740 0.502 0.395 0.394 1.579 0.337 0.555 0.073 0.642 0.093 1.010 0.209 0.088 0.625 0.574 0.251 1.241 0.042 0.063 0.068 1.057 0.862 0.682

0.822 0.839 0.740 0.508 0.397 0.398 1.620 0.332 0.546 0.072 0.632 0.092 0.942 0.195 0.087 0.606 0.559 0.246 1.194 0.041 0.061 0.066 1.027 0.729 0.648

0.759 0.819 0.716 0.493 0.379 0.382 1.506 0.323 0.527 0.073 0.609 0.092 0.874 0.186 0.087 0.590 0.554 0.252 1.244 0.042 0.063 0.066 1.063 0.774 0.657

0.843 0.808 0.741 0.506 0.398 0.399 1.586 0.338 0.558 0.072 0.628 0.093 0.971 0.203 0.088 0.619 0.568 0.251 1.234 0.042 0.063 0.067 1.053 0.833 0.674

5.59 2.36 2.01 1.43 2.71 2.48 2.84 2.71 3.35 1.12 1.76 1.26 5.50 5.19 1.18 2.80 1.72 1.16 1.71 0.82 1.52 1.88 1.42 7.87 2.66

Table 3. Solvent Validation Data, Including Linearity, Precision, and Sensitivitya linearity

precision (% RSD)

solvent

range (% w/w)

correlation coefficient

methanol ethanol i-propanol t-butanol ethyl acetate i-propyl acetate n-butanol toluene p-xylene diethyl ether acetonitrile MTBE n-propanol THF methyl cyclohexane MIBK m-xylene acetone methylene chloride hexane cyclohexane heptane 1,4-dioxane n-butyl acetate o-xylene

0.016−1.6 0.016−1.6 0.016−1.6 0.008−1.6 0.009−1.8 0.009−1.7 0.016−1.6 0.009−1.7 0.009−1.7 0.007−1.4 0.008−1.6 0.007−1.5 0.008−1.6 0.009−1.8 0.008−1.5 0.008−1.6 0.009−1.7 0.008−1.6 0.013−2.7 0.007−1.3 0.008−1.6 0.007−1.4 0.010−2.1 0.009−1.8 0.009−1.8

0.99999 0.99999 0.99999 1.00000 0.99990 0.99993 0.99999 0.99999 0.99998 0.99999 0.99996 0.99999 1.00000 0.99999 0.99999 0.99999 1.00000 0.99996 0.99998 0.99997 0.99996 0.99997 0.99998 0.99999 0.99999

0.10% v/v STD 0.0005% v/v STD 1.22 0.80 0.67 0.66 1.84 1.70 0.99 1.14 0.71 3.36 1.70 3.04 0.11 2.48 2.74 0.84 0.42 2.28 2.00 2.43 2.47 2.44 1.25 0.69 0.46

2.09 7.40 3.38 2.22 1.77 1.98 2.59 2.03 1.78 1.90 3.25 1.52 4.33 2.03 1.66 1.16 0.74 3.01 4.29 2.67 2.58 2.80 2.34 4.39 1.75

limit of detection (% w/w) limit of quantification (% w/w) 0.008 0.008 0.008 0.002 0.002 0.002 0.008 0.002 0.002 0.001 0.002 0.001 0.002 0.002 0.002 0.002 0.002 0.002 0.003 0.001 0.002 0.001 0.002 0.002 0.002

0.016 0.016 0.016 0.008 0.009 0.009 0.016 0.009 0.009 0.007 0.008 0.007 0.008 0.009 0.008 0.008 0.009 0.008 0.013 0.007 0.008 0.007 0.010 0.009 0.009

a

Limit of detection and limit of quantification values are based on the concentration of the lowest standard that achieved signal to noise ratios of at least 3:1 and 10:1, respectively.

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Comparison between Direct Injection and Headspace GC. The use of headspace GC in determination of common process solvents in pharmaceutical products has long been used as a direct alternative to liquid injection GC.6 The comparison between the two techniques in the presence of a 0.1% v/v solvent spike in 50 mg/mL API matrices is documented in Table 5. The residual solvents analysis of two API samples using headspace GC and direct liquid injection GC analysis is shown to achieve good agreement. The experimental data demonstrates that the automated headspace GC system produces accurate and precise results. Good recoveries from API matrices are observed with typical values of ±5% of theoretical. The comparison of API samples at 50 mg/mL in DMAc spiked with 0.1% v/v solvents analyzed by headspace GC also shows very good agreement to the well-established direct injection GC method. The % difference observed for all solvents at 0.1% v/v spike is 0.9999 over a typical range of 0.01−1.5% w/w. This covers the range of residual solvent typically observed in pharmaceutical samples and regulatory limits documented in ICH Q3C Impurities Guideline for Residual Solvents.

where x is the solvent of interest and ISTD is the internal standard. Note that Chemstation RFs (RFx), as defined above, are referred to as RRFs in this article. The % RSD for the RRFs across the range is acceptable for all solvents and demonstrates that the RRF is statistically constant and that the method is precise for the determination of all solvents across the concentration range. Method Validation. For each solvent, the RRF was calculated in terms of % w/w vs internal standard % w/w. Evaluation of the data from the standard sets revealed acceptable linearity, with a correlation coefficient greater than 0.999. Each standard concentration was injected four times, and the relative standard deviation was calculated. The limit of detection (LOD) and the limit of quantification (LOQ) have been proven experimentally for all solvents in groups 1−3. The signal/noise ratio, as calculated by the Agilent Chemstation software, is >10 for the LOQ and >3 for the LOD (Table 3). The method has been proven to be linear over a range that is typical for the levels of residual solvents that can be observed in pharmaceutical compounds. The precision data show acceptable % RSD for all solvents at the two levels tested. Active Pharmaceutical Ingredient Matrix Effects. To examine the effect of the matrix, a 0.01% v/v spike of each solvent was added to the API at a concentration of 50 mg/mL. The recovery of this solvent spike was calculated (Table 4). Successful recovery of the low-level solvent spike was observed from two different API compounds (proprietary Merck Sharp and Dohme development molecules).

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Table 4. Solvent Recovery from Two API Matrices at 50 mg/ mL Sample Concentration Using Headspace GC solvent methanol ethanol i-propanol t-butanol ethyl acetate i-propyl acetate n-butanol toluene p-xylene diethyl ether acetonitrile MTBE n-propanol THF methyl cyclohexane MIBK m-xylene acetone methylene chloride hexane cyclohexane heptane 1,4-dioxane n-butyl acetate o-xylene

recovery (%) from API 1 recovery (%) from API 2 96.0 96.7 98.0 98.6 96.9 86.8a 99.9 98.1 98.8 95.7 96.2 97.4 98.4 96.6 98.2 98.0 97.3 98.3 99.6 96.7 98.2 99.7 98.0 98.5 98.7

CONCLUSIONS

In this project, the biggest hurdle to overcome was the development of suitable software not only to manage the logging of samples and sample queue but also to enable suitable data checks to ensure that the system is performing to the specified limits. Software from instrument vendors is typically very inflexible when it comes to instrument control and oftentimes does not include the automation features required for the modern high-throughput laboratory. Walk-up or open access software is readily available for techniques such as mass spectrometry,7,8 where utilization of expensive equipment relies on queue management, but it is not typically available for techniques such as gas chromatography. Consequently, the current laboratory environment is filled with software from many small programmers providing bespoke solutions to individual problems.9 Ideally, as needs for customized software control of instrumentation continues to grow, generalized software control systems and strategies capable of controlling a variety of instrument types will become the norm. At the present time, such overarching software capabilities are not present and the ability of typical process and analytical chemists to create customized solutions is also limited. Consequently, we contracted a developer to create the software required to run our envisioned device. This software solution has provided fully open access solvent analysis to scientists with limited GC experience. The software negates the requirement for specific chromatographic data system training and enables significant reduction in analysis cycle time compared to that of traditional external standard approaches. This automated system determines comparable accuracy of data in approximately 30 min, compared to around 240 min when manually preparing, analyzing, and processing data. This automated walk-up nature of the system allows nonanalytical staff to analyze samples with minimal user training. As external standards of solvents do not need to be prepared, prior knowledge of which solvents are contained in the sample is not required. The system will identify and report results from all 25 specified solvents in a single injection. As

97.4 97.5 94.9 100.7 100.8 101.9 99.5 100.9 100.5 92.5 94.7 95.6 96.5 94.5 98.2 97.4 97.8 96.1 94.6 94.8 96.8 99.8 96.0 98.1 97.6

a

The recovery of i-propyl acetate (IPAc) from API is low in sample API 1. This API sample contained high levels of IPAc, introducing errors in the recovery determination of the low-level spike. E

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Table 5. Comparison between 0.1% v/v Solvents Spiked into Two API Sample and Analyzed by Direct Injection and Headspace GC API 1

API 2

solvent

direct injection GC (% w/w)

headspace GC (% w/w)

% difference

direct injection GC (% w/w)

headspace GC (% w/w)

% difference

methanol ethanol i-propanol t-butanol ethyl acetate i-propyl acetate n-butanol toluene p-xylene diethyl ether acetonitrile MTBE n-propanol THF methyl cyclohexane MIBK m-xylene acetone methylene chloride hexane cyclohexane heptane 1,4-dioxane n-butyl acetate o-xylene

0.1486 0.1499 0.1486 0.1487 0.1725 1.3958 0.1592 0.1653 0.1660 0.1295 0.1531 0.1405 0.1540 0.1831 0.1462 0.1537 0.1647 0.1204 0.2051 0.0961 0.1169 0.1221 0.1586 0.1329 0.1313

0.1520 0.1530 0.1540 0.1530 0.1750 1.4220 0.1620 0.1700 0.1710 0.1350 0.1510 0.1440 0.1580 0.1830 0.1510 0.1570 0.1680 0.1220 0.2070 0.1000 0.1200 0.1280 0.1590 0.1360 0.1360

2.3 2.0 3.6 2.9 1.4 1.9 1.7 2.8 3.0 4.2 1.4 2.5 2.6 0.1 3.2 2.1 2.0 1.3 0.9 4.0 2.6 4.7 0.3 2.3 3.5

0.1516 0.1492 0.2162 0.1499 0.1653 0.1616 0.1472 0.1615 0.1661 0.1330 0.1640 0.1440 0.1580 0.1900 0.1540 0.1590 0.1720 0.1180 0.2820 0.0970 0.1170 0.1060 0.1540 0.1340 0.1330

0.1500 0.1500 0.2280 0.1520 0.1770 0.1730 0.1570 0.1700 0.1690 0.1304 0.1644 0.1389 0.1653 0.1790 0.1459 0.1517 0.1669 0.1154 0.2621 0.0955 0.1141 0.0998 0.1525 0.1309 0.1301

1.1 0.5 5.3 1.4 6.8 6.8 6.4 5.1 1.7 2.0 0.2 3.6 4.5 6.0 5.4 4.7 3.0 2.2 7.3 1.6 2.5 6.0 1.0 2.3 2.2

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ACKNOWLEDGMENTS The authors are grateful to the Merck Research Laboratories New Technologies Review & Licensing Committee (MRL NTRLC) for providing funding for this project and to Chris Welch for valuable input and review of this manuscript.

such, much less solvent is required for quantitative analysis, resulting in time, material, and labor savings for the laboratory. In the development phase of this project, an internal standardization approach using RRF calibration was chosen over an external standardization approach using stored RF calibration. This approach was deemed to be more suitable10 for our workflow as it mitigated a few risks: using internal standardization, sample size variation is not so critical. Although not evaluated here, it is accepted that any changes in detector response will also be compensated by the internal standard, which is desirable as it negates the need for the instrument to be frequently recalibrated. This approach provides confidence to the user that the system is operating in a satisfactory manner for each single analysis. The chromatography data software allows for controls over the internal standard area to be set up as part of the quantitation method. If the internal standard area does not fall within specified limits, then a warning is issued, highlighting a potential bad injection or instrument issues. This functionality might indicate that the internal standard has not been prepared correctly, thus preventing the user from using incorrect quantification results.

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REFERENCES

(1) Kumar, N.; Gow, J. G. J. Chromatogr. A 1994, 667, 235−240. (2) Chen, T. K.; Phillips, J. G.; Durr, W. J. Chromatogr. A 1998, 811, 145−150. (3) Impurities: Guideline for Residual Solvents Q3C(R5); ICH: Geneva, Switzerland, 2011. (4) ⟨467⟩ Residual Solvents, USP38(2)-NF33; U.S. Pharmacopeia: Rockville, MD; p 309. (5) Sharma-Singh, G.; Brown, D. A.; Pullen, F. S.; Wright, A. G. J. Chromatogr. A 2000, 888, 219−227. (6) Snow, N. H.; Slack, G. C. TrAC, Trends Anal. Chem. 2002, 21, 608−617. (7) Fang, A. S.; Miao, X.; Tidswell, P. W.; Towle; Goetzinger, W. K.; Kyranos, J. N. Mass Spectrom. Rev. 2008, 27, 20−34. (8) Taylor, L.; Johnson, R.; Raso, R. J. Am. Soc. Mass Spectrom. 1995, 6, 387−393. (9) Hu, C.; Liu, Y. Quality Control in Pharmaceutical: Residual Solvents Testing and Analysis. Wide Spectra of Quality Control 1990, 183−210. (10) Rome, K.; McIntyre, A. Chromatography Today, May/June 2012, 52−55.

AUTHOR INFORMATION

Corresponding Authors

*(S.E.H.) E-mail: [email protected]. *(M.D.R.) E-mail: [email protected]. Notes

The authors declare no competing financial interest. F

DOI: 10.1021/acs.oprd.5b00367 Org. Process Res. Dev. XXXX, XXX, XXX−XXX