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Application of Online NIR for Process Control during the Manufacture of Sitagliptin George Zhou, Shane T. Grosser, Lei Sun, Gabriel Graffius, Ganeshwar Prasad, Aaron Moment, Angela Spartalis, Paul Fernandez, John P Higgins, Busolo Wabuyele, and Cindy Starbuck Org. Process Res. Dev., Just Accepted Manuscript • DOI: 10.1021/acs.oprd.5b00409 • Publication Date (Web): 12 Feb 2016 Downloaded from http://pubs.acs.org on February 13, 2016

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Organic Process Research & Development

Application of Online NIR for Process Control during the Manufacture of Sitagliptin George Zhou*, Shane Grosser, Lei Sun, Gabriel Graffius, Ganeshwar Prasad, Aaron Moment, Angela Spartalis, Paul Fernandez, John Higgins, Busolo Wabuyele, Cindy Starbuck Global Science, Technology and Commercialization Merck Sharpe and Dohme Corporation P.O. Box 2000, Rahway, NJ, 07065, U.S.A. *To whom correspondence should be addressed. E-mail: [email protected]

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Table of Contents

Use Online NIR to Enable Robust Process Control of Sitagliptin Crystallization


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Abstract The transamination chemistry-based process of Sitagliptin is a through process which challenges the crystallization of API in a batch stream composed of multiple components. Risk assessmentbased DoE studies of PSD and crystallization showed that the final API PSD strongly depended on the seeding-point temperature which in turn relied on the solution composition. To determine the solution composition, NIR methods had been developed with Partial Least Squares (PLS) on spectra of simulated process samples whose compositions were made by spiking each pure component, either Sitagliptin free base (FB), water, IPA, DMSO or IPAc to the process stream according to a DoE. Additional update to the PLS models was made by incorporating the matrix difference between simulated samples in lab and factory batches. Overall, at temperature 20-35 °C NIR models provided a standard error of prediction (SEP) less than 0.23 wt% for FB in 10.56 - 32.91 wt%, 0.22 wt% for DMSO in 3.77 - 19.18 wt%, 0.32 wt% for IPAc in 0.00 - 5.70 wt%, and 0.23 wt% for water in 11.20 - 28.58 wt%. After passing the performance qualification, these online NIR methods were successfully established and applied for the online analysis of production batches for compositions prior to the seeding point of Sitagliptin crystallization.

Key words: NIR, crystallization, inline, online, concentration measurement, seeding-point


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Introduction Sitagliptin, an oral antihyperglycemic of the dipeptidyl peptidase-4 (DPP-4) inhibitor, is the key component of medications JANUVIA and JANUMET currently available to patients. It was initially manufactured via the 1st generation process of hydrogenation.1 With the advances of biocatalysis, Sitagliptin API could also be produced with innovative transamination chemistry. 2 To better meet the patient needs and ensure the reliable supply of Sitagliptin, the 2nd generation process via transamination has been developed. It is a new, green, direct through process commercially viable without the isolation of Sitagliptin free base (FB). However, this approach inherently involves solutions of multiple co-existing components including residual by-products or impurities or solvents, posing an enormous challenge to the purification step. As the key pure step, crystallization process rejects these undesired multiple components, playing a critical role in the successful commercialization of this new process.

To address this challenge, a quality-by-design (QbD) methodology was implemented. Since particle size distribution (PSD) of the isolated Sitagliptin API would affect the physical properties, and may influence other properties such as bio-availability and stability of API, risk assessment-based DoE studies of PSD as well as crystallization were carried out. These DoE studies indicated that the final API PSD strongly depends on the seeding-point temperature, which in turn relies on the solution composition. Thus, it is imperative to accurately measure the composition in real-time for a through process stream, enabling a robust crystallization process within the process design space as well as reduction of cycle time.

For the 2nd generation process of Sitagliptin, the concentrations of FB, IPA, water, IPAc and DMSO are being used to determine the charge of phosphoric acid (used to convert FB into Sitagliptin) and adjust water/IPA ratio to ensure a robust seeding point which could result in a


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smooth crystallization process. Despite the use of seed, inaccurate FB concentration and ratio of water/IPA in the crystallization step could result in two abnormal scenarios, (1) an over-saturated condition leading to unwanted, spontaneous crystallization, and (2) an under-saturated condition causing dissolution of seeds and subsequent out of control crystallization for target PSD. In addition, overcharge or undercharge of phosphoric acid could result in yield loss and/or impair the subsequent crystallization steps.

Accurate real-time measurement of this composition,

especially the amount of FB and the ratio of water and IPA, is desirable for a robust crystallization process.

Traditionally free base concentration was measured by taking samples for off-line HPLC analysis, water level by Karl Fisher, and DMSO and IPAc by GC analyses. However, the turnaround time for these laboratory analyses is typically 2 to 4 hours. In addition, these offline techniques require handling of concentrated, volatile solutions at room temperature, posing difficulties to achieve accurate measurement of each concentration. Even though the individual offline in-process analysis procedure can be optimized, the sampling-induced uncertainty could impact the overall accuracy of the off-line methods.

Near infrared (NIR) spectroscopy has gained wide acceptance for online analyses of chemical properties and physical parameters in the chemical, food, and pharmaceutical industries7-13,15-17 because of its advantages including rapid, nondestructive analysis, minimal sample preparation, robustness and regulatory suitability. For example, Ge et al. developed a noninvasive shortwavelength NIR method for monitoring cell density in a fermentation process.7 Zhou et al. established a NIR method for the determination of API composition at seeding-point in a crystallization process composed of mostly the API and one solvent .10 Rodrigues et al. used FT-


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NIR for API content assay in organic solvent media.12 Acetaminophen in low-dose pharmaceutical syrup was determined by Ziemons et al. with NIR.13 Schaefer et al. applied on-line near infrared spectroscopy to control an industrial seeded API crystallization.17 These applications indicate that NIR is attractive for the online measurement of solution concentration. In this study, a NIR method has been developed to determine if the spectra are suitable for analysis (free from crystals and bubbles), and then measure the concentration of each component simultaneously. Sources of variation that may affect the accuracy of the NIR measurement, including temperature, sources of materials, instrument stability, presence of bubbles, and fluctuation of flow in the recycle loop have been examined. In order to build accurate and robust NIR models, special attention has been paid to (a) establish reliable outlier detection limits, (b) achieve robust and reliable offline reference methods, and (c) optimize the procedures to sample solutions at the production line.

Experimental Experimental Design, Materials and Sample Preparation Due to the complexity of the process stream matrix, a constrained mixture design (Table 1) was performed to cover key sources of variation to build robust NIR methods. The matrix impact from process stream was incorporated by spiking individual components to the process batch solution. Simulated process samples were prepared through addition of either Sitagliptin free base, water, IPA, DMSO or IPAc to the process stream with known compositions. The order and amount of sequential addition of each component during each run was based on the mixture design. In each run, the process stream was pumped through a recirculation loop within which a NIR flow cell was incorporated and NIR spectra were collected.


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Temperature variation was incorporated by carrying out the experiments between two targeted values, 20 and 35 oC. Therefore, at each composition spectra were collected while the batch temperature was varied.

Thus, by adding additional components according to Table 1 to the process stream, NIR spectra were collected, covering wide ranges of variation of each component composition as well as temperature. Five runs (Batches 1 to 5) based on such a design with each batch going through 8 vertexes have been completed. For the reference values used to build the calibration models, the initial composition of process stream was determined by reference methods of HPLC, GC and KF, and the compositions of subsequent samples were determined based on the weight of all components after each addition.

Table 1. Constrained Mixture Design for Method Development

Vertex Vertex Vertex Vertex Center Vertex Vertex Interior

ID 1 2 3 4 5 6 7 8

Free Base wt%

Water %wt

IPA&imp wt%

DMSO wt%

IPAc wt%

15.0 28.0 30.0 15.0 21.0 18.0 17.0 25.0

28.0 25.0 19.0 16.0 21.0 14.0 23.0 17.0

41.0 45.0 47.0 46.0 47.0 52.0 51.0 44.0

16.0 1.0 3.0 18.0 8.0 14.0 5.0 11.0

0.0 1.0 1.0 5.0 3.0 2.0 4.0 3.0

Temp ( 20 20 20 20 20 20 20 20

o

C) 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0

Total wt% 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

During the initial calibration stage, experiments were carried out in a 500 ml jacketed vessel fitted with a thermometer, an agitator and a water bath. The flow-through cell (Precision, Boston, MA) with 4.0 mm path length via fiber bundles was incorporated in a recycling loop in which sample solutions were pumped at 200 ml/min. At a commercial site this NIR flowcell was installed in a production line as illustrated in Figure 1.


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Figure 1. Schematic of NIR measurement with flow-through cell in a recirculation loop.

NIR Measurement Transmission spectra were obtained with an Antaris EX FT-NIR spectrophotometer (Thermo Fischer Scientific, Waltham, MA) equipped with a flow-through cell. The analyzer was enclosed in a Class I, Div I explosion-proof box with a Vortex cooler controlling the internal temperature. An optical path length of 4.0 mm of the transmission flow through cell and fiber-optic bundles were used to keep the nominal absorbance below 1.2 and provide a long pathlength to good signal. Each spectrum was the average of 64 scans over the range of 4000 to 10000 cm-1. A reference spectrum of polystyrene internal reference was collected for every sample spectrum. The software package RESULT (Ver. 3.3.474 by Thermo Fisher Scientific) was used to collect the spectra. At production site, the NIR measurement was realized in SIPAT (Simatic SIPAT by Siemens, Nuremberg, Germany) after passing two additional outlier detection methods based on X-Residual (Res_XVal)and Hotelling T2.

During the method development stage, the flow was adjusted to the optimum with a positive displacement pump without the presence of bubbles in the recycle stream. The visual ACS Paragon Plus Environment

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appearance of the recycle stream in contact with the NIR flow cell was determined through a sight glass in the recycle loop. During production, however, a stop-flow measurement was implemented when NIR reading was taken for process record.

HPLC Sampling and Analysis The off-line HPLC analysis was performed using an Agilent 1100 series (Santa Clara, CA, USA) HPLC equipped with a variable wavelength detector. Each sample was weighed and diluted in duplicate to a target concentration of approximately 0.3 mg/mL with 50/50 acetonitrile/water (0.1% v/v H3PO4). A Zorbax Eclipse Plus C18 (Agilent, 1.8 µm, 50 x 4.6 mm) column was utilized. The mobile phase consisted of (A) 0.1% aqueous H3PO4 and (B) acetonitrile using a gradient elution of 10 – 95% B at 0 – 5 min, hold 95 % B at 5 – 6 min, and re-equilibrate at initial conditions for 2 min. The column temperature was held at 25 °C with UV detection at 210 nm. The method conditions were validated for specificity, linearity, precision, robustness, reproducibility and limit of detection of the analyte. Two 5 µL injections of each preparation were obtained.

KF Sampling and Analysis Water content was measured with a KF Titrino 701 (Metrohm, Switzerland) volumetric Karl Fischer instrument using Hydranal Composite 2 (~ 2 mgH2O/mL) purchased from Sigma-Aldrich (St. Louis, MO, USA) as titrant with methanol as the working medium. The extraction period of 60 s was used to ensure equilibration of the titration. The sample size was varied to target consumption of half of the 20 mL burette at the amperometric endpoint. The end-point criterion was a drift stabilization of 15 µgH2O/min. The measurement was performed manually with a gas tight syringe in duplicate.


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GC Sampling and Analysis The residual solvent analysis was performed with an Agilent (Santa Clara, CA, USA) 6890 series GC. The system was equipped with a split/splitless injection inlet, electronic pressure control, and a 7683 autosampler; ATLAS software was used for instrument control and data analysis. Samples were diluted in duplicate to be within the quantitative range of the GC method (0.0005 %v/v to 0.1% v/v) in methanol and the result was calculated relative to a standard solution. Two injections of each sample were made onto a recessed gooseneck liner packed with deactivated wool (Restek, No. 22409). The chromatographic conditions were: a Rtx-624 (Restek, Bellefone, PA, USA) or a DB-624 (Agilent) capillary column of 20 m x 0.18 mm i.d. and 1.0 µm film thickness, He constant flow of 1.3 mL/min, inlet temperature = 180 °C, injection volume: 1 µL (split flow, 75:1), temperature program: initial temperature held for 1.5 min, then a 25 °C/min ramp to 90 °C followed by a 35 °C/min ramp to 200 °C (held for 0.5 min). A flame ionization detector (FID) was used for peak detection and quantitation with the ionization oven set to 240 °C. The method conditions were validated for specificity, linearity, precision, robustness, reproducibility and limit of detection of each analyte.

Data Analysis Quantitative calibration models were built with Partial Least Squares (PLS) regression in TQ Analyst software (Ver. 8.3.125 by Thermo Fisher Scientific) as well as Unscrambler chemometrics software package (ver. 9.8) from Camo Inc., Norway. During the calibration step, full cross validation (leave-one-out) was applied to the calibration data set and the standard error of cross validation (SECV) was obtained for each calibration model. In the prediction step, the standard error of prediction (SEP) was calculated by predicting an independent prediction set with a calibration model. In addition, spectral data pretreatment, 2nd derivative and mean centering, were also applied.


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m

) ∑ (ci SECV =

2

m

) ∑ (ci

− ci )

i =1

SEP =

m

2

− ci )

i =1

m −1

In these two equations, ci and cˆi are the actual and estimated or predicted concentrations, respectively, while m is the number of samples in the calibration or prediction set.

Outlier detection limit tests, X-residual and Hotelling T2, were performed on the Unscrambler chemometrics software package (ver. 9.8). When implemented at commercial scale, all calibration models were realized in Unscrambler.

Results and Discussion Specificity and Calibration Sitagliptin free base, water, IPA, DMSO and IPAc have different absorption bands in this NIR region as illustrated in Figure 2. For example, the bands near 6500 cm-1 corresponds to Sitagliptin free base. Based on these specific features, several ranges mainly related to each component were investigated. The first region focuses on the 6587-7502 cm-1 absorption band for water; the second region focuses on the 6364-6653 cm-1 band for Sitagliptin free base; the third region focuses on the 5411-6045 cm-1 band for DMSO; the fourth region focuses on the 5426-6002 cm-1 band for IPAc. Meanwhile, the optimum spectral region for each component was further verified by evaluating the performance of numerous calibration models of each spectral region. In addition, the optimum number of factors was determined when the model built with this number of factors resulted in reasonable low SECV. Due to the complexity of the matrix and overlapping peaks of different components, partial least squares (PLS) regression has been used to provide the necessary specificity for quantitative analysis3,4,14. Spectral ACS Paragon Plus Environment

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pretreatment may be used to improve the quality of the spectra and thus improve the performance of calibration models. Spectral pretreatment of 2nd derivative with 13 data points averaging, and mean centering were chosen. These have been used in all of the calibration models described below.

Water (after subtraction 12.5%(v/v) water in IPA) 0.33 Pure FB c IPAc 0.32 DMSO IPA 0.31

Water

0.30 0.29

Absorbance

Absorbance

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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.28 0.27 0.26

IPA

0.25 0.24 0.23 0.22

Free Base (FB) 0.21 0.20

IPAc

0.19

DMSO 7500 7500

7000 7000

6500 6500

Wavenumber Wavenumbers (cm-1) (cm-1) Wavenumber (cm-1)

6000 6000

5500 5500

Figure 2. NIR Spectra between 5400-7600 cm-1 of Sitagliptin FB, Water, IPA, DMSO and IPAc.

When used at the production line, measurements of process stream composition are to be made in temperature range of 20 to 35 °C. Thus, to build NIR calibration models for use within the temperature range, temperature variation needs to be incorporated into the calibration model. One approach is to develop a single general model based on spectra collected at different temperatures. The resulting model will account for the temperature effect and therefore, can be used for predictions at different temperatures within the same temperature range. In Table 2


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some representative NIR calibration models built with experiments at different temperatures and their prediction results were summarized. This indicates that the calibration models built with different temperatures can predict the process stream composition at temperatures within the range 20 to 35 °C.

DMSO

Water

Free Base

Table 2. Calibration models and the prediction for experiments at different temperatures.

IPAc

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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Experiments in calibration 2, 3, 4, 5 1, 3, 4, 5 1, 2, 4, 5 1, 2, 3, 5 1, 2, 3, 4 2, 3, 4, 5 1, 3, 4, 5 1, 2, 4, 5 1, 2, 3, 5 1, 2, 3, 4 2, 3, 4, 5 1, 3, 4, 5 1, 2, 4, 5 1, 2, 3, 5 1, 2, 3, 4 2, 3, 4, 5 1, 3, 4, 5 1, 2, 4, 5 1, 2, 3, 5 1, 2, 3, 4

Calibration model Conc. Spectral region T oC range (wt%) (cm-1) 10.56 – 32.91 20-35 6364 - 6653 10.56 – 32.91 20-35 6364 - 6653 10.56 – 32.91 20-35 6364 - 6653 10.56 – 32.91 20-35 6364 - 6653 10.56 – 32.91 20-35 6364 - 6653 14.81 – 28.58 20-35 6649 - 6897 14.81 – 28.58 20-35 6649 - 6897 14.81 – 28.58 20-35 6649 - 6897 14.81 – 28.58 20-35 6649 - 6897 14.81 – 28.58 20-35 6649 - 6897 3.77 – 19.18 20-35 5411 - 6045 3.77 – 19.18 20-35 5411 - 6045 3.77 – 19.18 20-35 5411 - 6045 3.77 – 19.18 20-35 5411 - 6045 3.77 – 19.18 20-35 5411 - 6045 0.17 – 5.70 20-35 5426 - 6002 0.17 – 5.70 20-35 5426 - 6002 0.17 – 5.70 20-35 5426 - 6002 0.17 – 5.70 20-35 5426 - 6002 0.17 – 5.70 20-35 5426 - 6002

No. of factors 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

SECV Wt% 0.35 0.19 0.20 0.20 0.20 0.24 0.18 0.16 0.18 0.19 0.11 0.20 0.17 0.19 0.20 0.50 0.27 0.27 0.26 0.27

Prediction Experiment SEP In prediction Wt% 1 0.20 2 0.27 3 0.22 0.23 4 0.22 5 0.25 1 0.29 2 0.16 3 0.29 0.23 4 0.21 5 0.15 1 0.34 2 0.14 3 0.24 0.22 4 0.17 5 0.16 1 0.32 2 0.29 3 0.32 0.32 4 0.37 5 0.33

The calibration models' accuracy, range and linearity were evaluated by predicting spectra of other batches not used in the calibration model. The accuracy of methods can be evaluated with the prediction results as SEPs in Table 2. For example, the FB prediction has an overall SEP of 0.23 wt%. Similarly, those of water, DMSO and IPAc are 0.23 wt%, 0.22 wt% and 0.32 wt% respectively. In the end, the final PLS calibration models for production evaluation were established by using samples from all five runs in the leave-one-out cross validation.


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(b)

(a)

0.8 1.0

Factory/Online

Factory/Online

0.6

Lab/Offline

0.4 0.5

Lab/Offline

Residual

Residual

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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.0

0.2 0.0 -0.2 -0.4 -0.6

-0.5

-0.8 1

50

100

150 200 Observation Order

250

300

1

20

40

60

80 100 120 140 160 180 200 220 240 260 280 300 Observation Order

Figure 3. Residual plots of water level between NIR predictions and offline KF analysis over samples from lab calibration runs and factory batches: (a) before NIR model update, and (b) after NIR model update

However, when the NIR models built with the simulated solutions were applied to the factory batches, a process matrix effect was observed. For example, in Figure 3a when the water model was used to predict water level in lab and factory samples, a large positive bias of prediction was present for factory samples. To achieve the robust performance for long term production, therefore, new calibration models of free base, water, DMSO, and IPAc were re-constructed with PLS regression by incorporating spectra of full-scale factory batches. The matrix effect on the prediction of water concentration was corrected as shown in Figure 3b. In addition, for the water model, further analysis revealed that a slightly wider spectral region 6588 – 7502 cm-1 and incorporating 5 PLS factors helped to improve its performance.

As a result, NIR models with good linearity and accuracy have also been established for the updated models together with their corresponding parameters and performance. Table 3 shows the correlation coefficient (R) of linear fit of the predicted component concentration versus the actual concentration of the validation standard samples. They all demonstrate the good linearity ACS Paragon Plus Environment

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of these methods. FError! Reference source not found.igure 4 shows the graphical results of the Sitagliptin FB, water, DMSO and IPAc calibration models, respectively. Overall, the NIR calibration models built with PLS yielded a SECV of 0.20 wt% for FB (Table 3), 0.20 wt% for water, 0.22 wt% for DMSO and 0.26 wt% for IPAc.

Table 3. Summary of NIR methods for Sitagliptin Freebase, Water, IPA, DMSO and IPAc Model Parameter -1

Spectral range(s) (cm ) Spectral Preprocessing Number of calibration samples Calibration range (wt%) Number of PLS factors Calibration correlation coefficient (R) Slope of actual concentration vs. NIR prediction linear regression RMSEC (wt%)

Sitagliptin Freebase 6364 - 6653 2nd SG Deriv. 13pts 310 10.56 – 32.91 4

Water

DMSO

IPAc

6588 – 7502 2nd SG Deriv. 13pts 310 11.20 – 28.58 5

5411 - 6045 2nd SG Deriv. 13pts 310 3.77 – 19.18 4

5426 - 6002 2nd SG Deriv. 13pts 310 0.00 – 5.70 4

1.00

1.00

1.00

0.99

1.00

1.00

1.00

0.98

0.20

0.22

0.22

0.26


15


(a)

(b) 28

Y = aX + b b: 0.0173 a: 0.9992 r ² 0.9991

30 28

Water wt% by NIR Prediction

Free Base wt% by NIR Prediction

32

26 24 22 20 18 16 14 12

Y = aX + b b: 0.0415 a 0.9978 r ² 0.9969

26 24 22 20 18 16 14 12

10 10

15

20

25

30

12

14

Free Base wt% by Weight/Reference

16

18

20

22

24

26

28

Water wt% by Weight/Reference

(c) (a)

(b) (d) 6

20

Y = aX + b b: 0.0361 a: 0.9973 r ² 0.9979

18 16

IPAc wt% by NIR Prediction

DMSO wt% by NIR Prediction

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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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14 12 10 8 6 4

5

Y = aX + b b: 0.0529 a: 0.9795 r ² 0.9794

4

3

2

1

0 4

6

8

10

12

14

16

DMSO wt% by Weight/Reference

18

20

0

1

2

3

4

5

6

IPAc wt% by Weight/Reference

Figure 4. Correlation plots of (a) actual Sitagliptin FB concentration versus predicted Sitagliptin FB, (b) actual water concentration versus predicted water, (c) actual DMSO concentration versus predicted DMSO, and (d) actual IPAc concentration versus predicted IPAc.

Method Validation The equivalency of NIR methods to the off-line in-process methods of FB, water, DMSO and IPAc was established. Three consecutive factory batches were used to perform the performance qualification. A paired t-test was used to determine the equivalency of the free base, water, and DMSO methods. For the IPAc NIR method, however, paired t-test is not applicable to IPAc because its concentration in the batch is usually near zero. Instead, method equivalency for IPAc is established when the differences between NIR and off-line GC method are less than 0.96 wt%


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(3xSEP). Table 4 shows the results of the method equivalency. Each batch has three samples and each value is the average of triplicate measurement. All the methods passed the equivalency criteria.

Robustness of the methods was assessed by predicting four component compositions for three different batches which inherited process variations including those of impurity profiles from the upstream process. All calibration models demonstrated robustness to these non-deliberate variations as demonstrated by the acceptable results obtained for the performance qualification tests illustrated in Table 4.

Table 4. Method equivalency results for the four updated NIR methods. Batch: sample name 0000068181: 03-14 112329 0000068181: 03-14 152351 0000068181: 03-14 161934 0000068666: 03-16 224848 0000068666: 03-17 033821 0000068666: 03-17 044753 0000069562: 04-01 060327 0000069562: 04-01 114144 0000069562: 04-01 124116

Free Base (wt%)

Water (wt%)

DMSO (wt%)

IPAc (wt%)

NIR

HPLC

NIR

KF

NIR

GC

NIR

GC

27.4

27.2

11.7

12.1

9.1

9.1

0.1

0

24.1

23.6

23.4

23.1

8.1

8.1

0.1

0

21.5

21.6

21.1

20.8

7.3

7.1

0

0

26.7

26.4

12.2

12.3

8.5

8.6

0

0

23.4

23.3

23.4

23.4

7.6

7.4

0.1

0

21.6

21.6

21.6

21.9

7

6.8

0

0

26.7

26.4

12

12.4

9.5

9.6

0.1

0

23.1

23.3

24.5

24.5

8.3

8.2

0.1

0

21.4

21.8

22.8

22.6

7.8

7.6

0.1

0

SEP (wt%)

0.29

0.28

0.15

0.09

P-Value of paired-t

0.416

0.76

0.17

n/a

Pass or fail (< 0.05)

Pass

Pass

Pass

n/a

Pass: (IPAc) ∆ < 0.96 wt%

n/a

Pass

Outlier Detection Limits ACS Paragon Plus Environment

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In order to prevent false reading of NIR methods, two outlier detection limit methods were implemented. Each spectrum has to pass these outlier tests before being predicted for composition of the batch solution. The first is Hotelling T2 (HT2) limit which checks the chemical and spectral information of incoming spectra against the standards in the library of a calibration model. The limit of this test has been set such a level that when a spectrum fails to pass the test, its solution either contains significant bubbles or interferents such as undissolved crystals. In this study the HT2 limit was calculated as a product of F-value of F-distribution and mean HT2 of all the spectra used in the calibration model. Since 310 spectra were used in the calibration model, the HT2 outlier detection limit at 99% confidence level is 26.8 which is the product of F-value (6.72) and average HT2 (3.99). The F-value at 99% confidence level (or probability level 0.01) of F-distribution with degrees of freedom I at 1 and freedom II at 310 is 6.72. For example, Figure 5 shows the HT2 values of some spectra of different solution. All clear solutions have a low HT2, passing the HT2 outlier limit and being separated from those of solutions containing bubbles.

90 80 70

2

60

Hotelling T

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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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50 40

Outlier detection limit

30 20

Clean Solution Containing Bubbles

10 0 0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Residual of FB (|NIR-LC| wt%)

Figure 5. Hotelling T2 values of solutions with or without bubbles, plotted against the difference between NIR prediction and offline LC method for each solution. ACS Paragon Plus Environment

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Another outlier detection method (Res_XVal) is based on spectral residual of X-values. Res_XVal is related to the spectral information (X-axis) of incoming spectra against the standards in the library of a calibration model. Similarly, the limit of this test has been set at a level at which a “normal” spectrum passes the test while others of solutions containing either significant amount of bubbles or interferents such as undissolved crystals would fail the test. An outlier detection limit 3.16E-9 for Res_XVal has been calculated based on the product of F-value of F-distribution and mean XVal residual (4.70E-10) of all the spectra used in the calibration model. The F-value at 99% confidence level (or probability level 0.01) of F-distribution with degrees of freedom I at 1 and freedom II at 310 is 6.72. Figure 6 shows the Res_XVal values of some spectra of different solution. All spectra of clear solutions (either from lab or production) have a low Res_XVal, passing the Res_XVal outlier limit and being separated from those of solutions containing bubbles.

1.0e-8 Lab-1 Lab-2 Production-1 Production-2 Production-3 Production-4 Prod-w. bubbles

8.0e-9

Residual X-Value

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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


6.0e-9

4.0e-9

Outlier detection limit 2.0e-9

0.0 0

2

4

6

8

10

12

14

16

18

20

22

24

Sample order

Figure 6. Res_XVal values of solutions with or without bubbles, plotted against the arbitrary sample order of solutions.


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Page 20 of 23

Online Application During Production When applying the NIR methods during production, batch solutions were pumped through a recirculation loop where a flow-through cell was inserted. After passing the outlier detection limit tests, each spectrum will be used to predict the concentrations of all components, Sitagliptin free base, water, DMSO, and IPAc simultaneously. Figure 7 illustrates a full course of continuous, online measurement of a batch process. In this process NIR methods were used to predict the initial composition where FB and water are 26.2 wt% and 16.7 wt% respectively (DMSO and IPAc are not shown). Based on this composition, additional amount of water and IPA was calculated toward the target of process operation, and added to the batch accordingly. If the reading of batch composition is still out of the targeted range, additional adjustment would be needed until reaching the desired composition range. In the end a final reading of batch composition such as 21.3 wt% FB and 20.4 wt% water was taken for downstream process. This batch composition would then be put into a formula to calculate the seeding-point temperature for the downstream crystallization process. It was based on the proper control of this critical process parameter, seeding-point temperature, that the process proceeded smoothly, producing the API product with desired PSD and purity.


20

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28 27

Sitagliptin Free Base

26 25 24

21 20 19 18 17

Water

Add IPA

22

Add water

23 Start NIR loop/measurement

Components (wt%)

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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


16 15 0.0

0.5

1.0

1.5

2.0

2.5

Time (hour)

Figure 7. NIR readings (FB and water) for a batch process before and after the adjustment with water and IPA during a production batch.

Conclusions Online NIR methods were successfully established for the analysis of commercial scale batches for compositions (FB, water, DMSO and IPAc) prior to the seeding point of crystallization of Sitagliptin. The NIR calibration models built by PLS with 2nd derivative pretreatment in the spectral range of each component performed quite well when applied to the production batches in the temperature range from 20 to 35 oC. In addition the NIR measurement is enhanced by two outlier detection methods, preventing potential false prediction of batch solutions containing bubbles or undissolved solids. The measurement of real-time batch composition with NIR has increased the productivity and brought flexibility in solvent composition from upstream, enabling the implementation of a control strategy based on the optimal seeding-point temperature. ACS Paragon Plus Environment

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Page 22 of 23

Acknowledgments We would like to express our appreciation to Marguerite Mohan, Naijun Wu, Fan Zhang-Plasket and Chuck Miller for helpful discussions and support.

References 1. Hansen, K.B.; Hsiao, Y.; Xu, F.; Rivera, N.; Clausen, A.; Kubryk, M.; Krska, S.; Rosner, T.; Simmons, B.; Balsells, J.; Ikemoto, N.; Sun, Y.; Spindler, F.; Malan, C.; Grabowski, E. J. J.; Armstrong, J. D. III. J. AM. CHEM. SOC. 2009, 131, 8798–8804. 2. Savile, C.K.; Janey, J. M.; Mundorff, E. C.; Moore, J. C.; Tam, S.; Jarvis, W. R.; Colbeck, J. C.; Krebber, A.; Fleitz, F. J.; Brands, J.; Devine, P. N.; Huisman, G. W.; Hughes, G.J.

Science 2010, 329, 305. 3. Beebe, K. H.; Kowalski, B. R. Anal. Chem. 1987, 59, 1007A-1017A. 4. Martens, H.; Naes, T.M. Multivariate Calibration, John Wiley and Sons: New York, 1989. 5. Hazen, K. H.; Arnold, M. A.; Small, G. W. Appl. Spectrosc. 1994, 48(4), 477-483. 6. Wülfert, F.; Kok, W.T.; Smilde, A.K. Anal. Chem. 1998, 70, 1761–1767. 7. Ge, Z.; Cavinato, A.G.; Callis, J. B. Anal. Chem. 1994, 66, 1354-1362. 8. Ge, Z.; Buchanan, B.; Timmermans, J.; DeTora, D.; Ellison, D.; Wyvratt, J. Process Control

and Quality 1999, Vol. 11, 4, 277. 9. Arnold, S. A.; Gaensakoo, R.; Harvey, L. M.; McNeil, B. Biotechnol. Bioeng. 2002, 80, 405– 413 10. Zhou, G. X.; Ge, Z.; Dorwart, J.; Izzo, B.; Kukura, J.; Bicker, G.; Wyvratt, J. J. Pharm. Sci.

2003, 92 (5), 1058-1065. 11. Roggo, Y.; Chalus, P.; Maurer, L.; Lema-Martinez, C.; Edmond, A.; Jent, N. J. Pharm.

Biomed. Anal. 2007, 44, 683–700. 12. Rodrigues, L.O.; Cardoso, J.P.; Menezes, J.C. Talanta 2008, 75, 1203–1207


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13. Ziemons, E.; Mantanus, J.; Lebrun, P.; Rozet, E.; Evrard, B.; Hubert, P. J. Pharm. Biomed.

Anal. 2010, 53, 510–516 14. Xiaobo, Z.; Jiewen, Z.; Povey, M.J.W.; Holmes, M.; Hanpin, M. Anal. Chim. Acta 2010, 667, 14–32 15. Chen, Z.; Lovett, D.; Morris, J. J. Process Control 2011, 21, 1467–1482 16. Jamrogiewicz, M. J. Pharm. Biomed. Anal. 2012, 66, 1–10. 17. Schaefer, C.; Lecomte, C.; Clicq, D.; Merschaert, A.; Norrant, E.; Fotiadu, F. J. Pharm.

Biomed. Anal. 2013, 83, 194-201.


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Application of On-Line NIR for Process Control ... - ACS Publications

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