In situ Monitoring of a Heterogeneous Etherification Reaction Using

Mar 25, 2014 - The model prediction results from the current spectrum are passed to the local KOSI OPC server within the HoloPro software. These resul...
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In situ Monitoring of a Heterogeneous Etherification Reaction Using Quantitative Raman Spectroscopy Richard J. Hart,* Nicholas I. Pedge, Alan R. Steven, and Kevin Sutcliffe Pharmaceutical Development, AstraZeneca, Silk Road Business Park, Macclesfield, SK10 2NA, United Kingdom ABSTRACT: The development and use of an in situ Raman spectroscopic method for the end-point detection of an etherification reaction, during a pilot plant scale manufacturing campaign, is described. Monitoring the reaction progress and minimising the level remaining of a chloropyrazine starting material at the reaction end-point were important, as the latter impacted a drug substance critical quality attribute (CQA). Furthermore, the etherification reaction required the use of a heterogeneous base (K2CO3), and therefore the time to reach reaction completion could be scale dependent, owing to differences in reactor geometry and mixing characteristics. To provide valuable process understanding about the rate and extent of reaction during scale-up, a quantitative method utilising in situ Raman spectroscopy was developed. A series of laboratory scale reactions were performed to provide the spectroscopic and off-line reference measurements (HPLC) required for calibration model development. A quantitative multivariate calibration (PLS2) was developed to allow the concentration of starting material and reaction product to be predicted during the final 30% of the reaction using in situ generated Raman spectra. The root-mean square error of prediction (RMSEP) values for the 5-factor PLS2 model were 0.2% w/w and 0.1% w/w for ether 1 and phenol 2 respectively. This method was transferred and implemented in the pilot plant to detect the reaction end-point for a number of batches. During the initial batches, it was demonstrated that the results obtained from the Raman calibration model were equivalent to results obtained by off-line HPLC analysis. OPC was used to transfer the predicted results to the pilot plant control system, thus allowing scientists to remotely view the reaction progress in real-time.

1. INTRODUCTION 1.1. PAT for Quality by Design. Process analytical technology (PAT) is one of the key tools to enable process understanding in the Quality by Design process to develop and manufacture pharmaceutical drug substance that consistently delivers product quality. Consistent with the FDA’s definition of PAT,1 a well understood and designed process can be monitored using an appropriate measurement system and subsequently controlled to ensure product quality. The measurement systems can manifest in several forms, from process parameters to analytical methods, all linked via process understanding to the critical quality attributes of the drug substance. The analytical measurement systems for process chemistry can vary, depending on the understanding required, the nature of the reaction being monitored, the kinetics and sampling frequency and also the scale at which the process is being run. Techniques such as MS, NMR and HPLC can provide detailed process understanding in early stages of development and are widely used, mostly off-line but increasingly using more at-line and on-line modes.2−4 Optical spectroscopic techniques can also be used as in-line measurement systems and offer some advantages over traditional off-line techniques for processes that are difficult to sample (heterogeneous, low temperature, unstable intermediates). They also provide continuous real-time monitoring and are much more amenable to use in large scale manufacturing.5,6 Whatever measurement system is used, the aim is clear, to gain process understanding and develop robust processes such that in-process measurements are only required where benefits can be realised, such as controlling critical process parameters and potentially reduce end product testing. Therefore, many of the measurement systems used in process development can often become embedded in the control strategy for a manufacturing process, © 2014 American Chemical Society

so it is important that the measurement system itself is reliable and robust. In-line spectroscopic measurements may require extensive calibration with off-line reference measurements and the use of chemometrics to develop quantitative results; however, for faster implementation during process development, the techniques can be frequently used in a qualitative mode by monitoring a single wavelength or spectral band to generate a trend plot, which can then be subsequently correlated to an off-line measurement such as purity or yield.7 One of the major benefits of continuous realtime measurements is that, combined with a well understood process, opportunities for remote monitoring and closed-loop feed-forward/back control can be realised. 1.2. Project Overview. The World Health Organization (WHO) projects that diabetes will be the seventh leading cause of death by 2030.8 Compound X, was being developed by AstraZeneca as an orally available, glucokinase activator for the treatment of type 2 diabetes (T2D) but recently failed to progress beyond its phase II clinical trial. Glucokinase is a hexokinase enzyme which promotes glycogen synthesis and glucose-sensitive insulin release, in the liver and pancreas, respectively, and thus plays a key role in glucose homeostasis.9−12 The synthesis of Compound X involved forging a bond between phenol 2 and chloropyrazine 3 so as to afford ether 1 (Figure 1). This reaction was performed in dimethyl sulfoxide and used potassium carbonate as base. The addition of one relative volume of water relative to the limiting reagent, phenol 2, Special Issue: Process Analytical Technologies (PAT) 14 Received: January 23, 2014 Published: March 25, 2014 196

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2.2. Laboratory Experiments for Raman Calibration Model Development. During previous laboratory development and pilot plant manufacturing campaigns, Raman spectroscopy and UV/vis spectroscopy were both successfully used to qualitatively follow the reaction time-course. Raman spectroscopy was eventually chosen to develop a quantitative calibration model owing to the superior spectral selectivity offered over that of the UV/vis option. Despite this, there are no purely selective Raman bands for phenol 2, chloropyrazine 3 or ether 1 in the Raman spectra, thus precluding simple univariate modelling. Owing to the difficulty of generating synthetic mixtures for calibration purposes, a set of experiments was designed to calibrate the Raman spectra using results obtained from off-line HPLC assay measurements of samples taken during the course of each experiment. A Kaiser Optical Systems, Rxn1 785 nm Raman System fitted with a MR probe head and a short-focus Raman immersion probe (12.7 mm diameter, 400 mm length, constructed from Hastelloy C276) was inserted into the reaction mixture. Raman spectra were acquired at 3 min intervals throughout the course of the experiment. The most dominant spectral interference that will influence the predictive ability of a multivariate calibration model is the amount of solvent charged. The in-house manufacturing tolerances allowed in GMP pilot plant manufactures are ±10% on solvent charges (cf. ±2% reagents, ±1% contributory raw materials), though in reality charging is expected to be achieved with much greater accuracy than this. A one-factor, three-level series of experiments using −5% v/v, 0% v/v, +5% v/v solvent (with respect to set point) at fixed DMSO:H2O ratio, were performed to introduce a level of robustness into the model. This produced levels of phenol 2 of 8.89, 8.53 and 8.17% w/w, respectively, at the end of the chloropyrazine 3 addition (ignoring any reaction with chloropyrazine 3 and the presence of potassium carbonate, which is largely insoluble). Two further laboratory reactions (user trials) were executed at the operating set points, measured using Raman spectroscopy and supplemented with off-line HPLC data for use as a model prediction set. Different batches of input materials were used to provide spectra with variable fluorescent backgrounds. This would allow a number of different baseline correction procedures to be assessed on the basis of their ability to remove the unwanted source of variation from the spectra arising from the fluorescent background. 2.3. The Use of Raman Spectroscopy in a Pilot Plant Environment. The scale up from laboratory development of the process and in situ Raman monitoring to the pilot plant was approximately 6000 fold;17 however, special consideration for operating Raman spectroscopy in a hazardous area was required, and this was addressed by conducting a specific AstraZeneca risk assessment. The current safety requirements for the use of a potential ignition source such as the laser used in the Raman spectrometer limits the maximum energy output to 35 mW,18 if no other engineering controls are in place. For most process applications, a maximum laser power of 35 mW would be inadequate, as it would take several minutes to acquire a Raman spectrum with a good signal-to-noise ratio comparable with spectra acquired in a laboratory. The main basis of safety identified from the specific AstraZeneca risk assessment was to ensure and maintain the complete removal of oxygen and to ensure the probe tip was always immersed in the reaction solvent whilst the Raman probe was in use. These minimised the risk of

Figure 1. Compound X reaction scheme.

was found to be important at lowering the reaction time, presumably by helping to solubilise the potassium carbonate.13 1.3. Requirement for in situ Process Monitoring of Etherification. Compound X was anticipated to be a high volume product for which reductions in batch cycle time could lead to appreciable savings on the cost of goods of the active pharmaceutical ingredient (API). For this reason, the product team was keen to explore opportunities to replace an off-line HPLC analysis used for in-process control (IPC) decisionmaking with a fully validated PAT method in the commercial operating environment. One such opportunity is the end of reaction IPC of the present etherification.14 As well as the time savings that could be exploited through the use of a PAT method to make a real-time assessment of the progress of the reaction, it would offer a number of other advantages. The reaction mixture is heterogeneous, on account of the use of potassium carbonate as base, leading to concerns of whether the sample taken for offline HPLC analysis was truly representative of the reaction mixture. Continuous monitoring would also allow the convenient assessment of how typical the reaction composition and concentrations were, and aid troubleshooting in the event that the end of reaction criterion was not smoothly achieved. Inline monitoring also provided information on the levels of excess chloropyrazine 3. Whilst this material is well retained in the mother liquors from the crystallisation of 1, its presence downstream in the process at the crude API stage favoured the crystallisation of an undesired polymorph of the API. As such, the chloropyrazine 3 was designated a critical quality attribute (CQA) at this stage of the process and an end of reaction criterion set at 1% w/w until further process understanding could be derived during process development.15

2. MATERIALS AND METHODS 2.1. Laboratory Procedure for Preparation of Ether 1.16 K2CO3 (s) (18.33 g of 325 mesh, 13.26 mmol) was added to a slurry of phenol 2 (12.00 g, 53.04 mmol) in DMSO (39.64 g) and water (12.00 g), that was being stirred (203 rpm) at 20 °C in an aluminium foil-wrapped, nitrogen-swept jacketed reactor (250 mL) equipped with Raman, UV/vis, and temperature probes and an overhead mechanical stirrer. The jacket temperature was raised to 60 °C, and the batch held for 2 h under these conditions. The batch temperature was then set to 55 °C, a solution of chloropyrazine 3 (11.01 g, 55.70 mmol) in DMSO (66.06 g including a line wash of 5.72 g) was added over 3.5 h, and sampling for off-line analysis was initiated. Samples were taken over 13 h from the start of the addition of the chloropyrazine 3 solution. The batch was periodically given a visual check to ensure that the slurry was well dispersed such that process samples were representative of the entire batch. In order to ensure that they were homogeneous, the samples were filtered in order to remove insoluble salts prior to their weighing and dilution with HPLC diluent. 197

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explosion and facilitated heat dissipation, respectively. It should be noted that, for applications using Raman spectroscopy with a laser power >35mW in a hazardous area, a basis of safety needs to be established following risk assessments and control measures that are appropriate for the user. At the time the experimental work described in this paper was undertaken, the equipment used by AstraZeneca was unique in that the dedicated PAT port in the reaction vessel was a bottomentry port and the Raman probe did not have any specific built-in safety features, other than a copper wire interlock embedded in the fibre-optic cable which automatically switches off the laser if the cable was inadvertently severed. More recently instrument manufacturers such as Kaiser Optical Instruments now manufacture process Raman probes that have built-in safety controls (such as a level sensor for top entry probes) that allow laser powers >35 mW. The specific AstraZeneca risk assessment procedure required two levels of control, such as engineering or procedural controls for each potential hazard identified that would allow a laser power >35 mW to be used. Consequently some of the controls implemented to address the basis of safety identified from the specific risk assessment were: • The use of Raman was approved only for the stated process steps. This stipulated in the process description exactly when the laser should be activated and deactivated. • Written instruction in manufacturing batch record for activating or deactivating the Raman laser. • Distributed Control System (DCS) sequence instruction to the operator when the laser could be activated. • Interlock of Raman laser with the inert flag in the Distributed Control System (DCS). ○ Without vessel inertion which is automatically confirmed by an inert flag in the DCS, the laser interlock would prevent the laser from being switched on. • Interlock of Raman laser with reaction vessel bottom run off valve (BROV) when spectrometer is connected. ○ This interlock system would prevent an operator from opening the BROV and emptying the contents of the vessel whilst the Raman laser is on. This is to ensure that the inert atmosphere is preserved and that the probe tip is always immersed in the reaction solvent when the laser is switched on. ○ DCS recipe instruction to manually deactivate laser before the BROV can be opened. There is also a written instruction in the batch sheet to deactivate the laser. • Spectrometer interlock cable must be physically disconnected from the interlock/DCS interface to activate the BROV, breaking the personnel protection circuit. The diagram in Figure 2 shows the location of the Raman system and the associated PC with respect to the process vessel. The Raman instrument was located in a small laboratory within the pilot plant. As this laboratory was not considered a process zone, it was possible to use a standard laboratory instrument. The Raman system was coupled to the process probe via a patch-bay within the laboratory (Figure 3). The process Raman immersion probe was inserted into the bottom of the reaction vessel via a dedicated port (Figure 4). 2.4. Transfer of Raman Calibration Model and OPC Communications. The Raman instrument used in the pilot plant was also a Kaiser Optical Systems (KOSI) Rxn1 785 nm Raman system as described previously in section 2.2. To provide

Figure 2. Diagram showing how the Raman instrument is connected to a “patch-panel” in the PAT laboratory (non-process area). The fibreoptic cables and interlock cable are run throughout the plant to enter the process environment. This allows the use of remote, laboratory instruments for PAT such as Raman and NIR spectroscopy.

spectra with similar intensity and signal-to-noise characteristics as the spectra acquired for calibration development, it was necessary to adjust the instrument acquisition parameters to compensate for the additional fibre connections, longer fibre lengths and probe construction. A series of instrument calibration steps was performed using a KOSI calibration accessory. The procedures performed were: (i) wavelength calibration of detector (illumination of process probe with a calibrated neon lamp); (ii) intensity calibration (illumination of process probe with a calibrated tungsten lamp); (iii) laser wavelength calibration (immersion of process probe in cyclohexane); (iv) system calibration check (immersion of process probe in cyclohexane). By performing these calibration steps using the process probe and optical-fibre configuration that would be used for process monitoring, the spectra obtained from each instrument should be standardised with respect to band positions and relative band intensities. A final, important step is to calculate an acquisition time factor between the two Raman systems. This was achieved by measuring the spectrum of neat isopropyl acetate on each system. This solvent was chosen because it was used for the solvent simulation in the pilot plant. The camera exposure time of the pilot plant instrument was adjusted to give an equivalent CCD pixel fill to that observed during the laboratory experiments. Owing to the longer acquisition time required, the rate of measurement was reduced to one spectrum every 4 min. The Kaiser Optical Systems HoloPro software provides a local OPC server (Object Linking and Embedding for Process Control). The model prediction results from the current spectrum are passed to the local KOSI OPC server within the HoloPro software. These results can then be queried using either a local or remote OPC client. An OPC data manager (Northern Dynamic OPC Gateway) was used to map the results from HoloPro OPC server to the DCS OPC server. This configuration required OPC tunneller software (Matrikon OPC Tunneller) to establish communication between the two PCs. This allowed the Raman model prediction results and associated statistics to be transferred to the DCS, where they could be viewed by the plant operator, as well as remotely by laboratory colleagues on a different AstraZeneca site, where the process development work had been undertaken. 198

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Figure 3. PAT “patch-bay” installed in the pilot plant at AstraZeneca R&D Charnwood. The photograph shows the two fibre-optic connections (illumination: black; collection: blue) and the laser interlock (yellow). The patch-bay also contained intrinsically safe circuits and the interface between the Raman laser interlock and the DCS.

Figure 4. This photograph shows the process Raman probe (red oval) installed into the bottom of the reaction vessel via a dedicated “PAT port” located to the right of the bottom run off valve, BROV (green oval).

3. RESULTS AND DISCUSSION

constant volume phase of the process, thus minimising the amount of unwanted variation in the calibration data. This approximates as the final 30% of conversion. As described in section 2.2, different batches of starting materials were used to introduce the variable fluorescent background. The variable fluorescent background that was present in different laboratory experiments is illustrated in Figure 5. As formation of ether 1 and consumption of phenol 2 are concomitant and linearly related through second-order kinetics, a

3.1. Calibration Model Development. From the five laboratory experiments described in section 2.2, a total of 47 Raman spectra with the associated reference information (phenol 2 and ether 1 concentrations determined by off-line HPLC analysis) were available for model development and testing. To attain a more robust local model for reaction end-point prediction, only reference data measured after the chloropyrazine 3 addition and the subsequent line wash was used for model development. This enabled the use of data collected during a 199

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Figure 5. Unprocessed Raman spectra prior to chloropyrazine 3 addition from each data set, exemplifying the variable nature of the fluorescent backgrounds between experiments.

Figure 6. PC1 vs PC2 scores plot pertaining to the proposed calibration (black squares) and validation (red circles) data set spectra. Highlighted in green are the additional spectra added to the calibration data set to improve model robustness.

regression purposes, leaving the regions in the ranges 290−449 cm−1 and 741−1649 cm−1 for model development. Principal components analysis of the calibration and validation Raman spectra (Figure 6) reveal that one of the prediction sets occupied a space in the PC1 vs PC2 scores plot beyond that of the calibration data, thus implying prediction of this validation set would be an extrapolation of any potential model. It was thought the spectral differences observed in this data set were caused by a minor difference in the DMSO/water ratio. Owing to the inadequacy of the existing calibration data set to capture this process variation, it was decided to augment the calibration set with the test set data. This would increase the robustness of the model when applied to new batches in the manufacturing campaign, but at the cost of generating overoptimistic model

single PLS2 model was chosen to simultaneously predict the concentration of both analytes. Spectral pre-processing was applied prior to model building to provide an accurate and robust calibration for future predictions. The data was initially truncated between 250 and 1750 cm−1 to exclude noisy regions of the spectrum or regions that are not useful for pre-processing or regression. A MATLAB script based on the Pearson19 baseline correction algorithm was applied to remove the irregular fluorescent baselines observed in the Raman spectra. The data was then normalised using the DMSO band centred at 670 cm−1 to remove any intensity differences caused by fluctuations in the Raman laser power. This band was subsequently removed from the data as it is not useful for 200

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prediction statistics that were calculated using data from only a single validation experiment. A 5-factor PLS2 model (using SIMCA P+ v12.01, Umetrics) yielded RMSEP values of 0.2% w/w and 0.1% w/w for ether 1 and phenol 2, respectively. It was envisaged that, as the project moved closer to commercial manufacture, the calibration set would be gradually augmented over time by including additional experimental data spanning the main sources of process variation such as temperature and stoichiometric ratios. 3.2. Results from Pilot Plant Manufacture. It was decided that a suitable end of reaction (EOR) would be based on the % w/w of ether 1 rather than the % w/w of phenol 2 due to the inherent sensitivity limitations of the Raman measurement. To this purpose, the theoretical ether 1 limit was calculated to be 14.7% w/w on the basis of 100% conversion from the input charges declared on the manufacturing batch sheet, and EOR was deemed acceptable on the basis of >99% of this theoretical value based on the chloropyrazine 3 CQA criterion discussed in section 1.3. The values predicted using the Raman calibration model (expressed as % w/w) were also used to calculate the amount of phenol 2 remaining (expressed as weight fraction, mirroring the off-line HPLC EOR calculation) and was displayed to allow plant operators to check the extent of conversion. The expression for calculating the amount of phenol 2 remaining is shown below.

Figure 7. Predicted Raman results for first pilot plant batch and corresponding off-line HPLC result at end of reaction. The reaction profiles from 0 to approximately 140 min correspond to the reagent addition phase. This was followed by a line wash, which resulted in a disturbance in the profile through dilution.

in the absence of this result. The predicted results for the second batch were qualitatively and quantitatively similar to those of batch 1. At the time the first process sample was extracted from batch 2, the predicted result obtained using the Raman method (PLS) was 14.9% w/w (see Figure 8). The mean result obtained using off-line HPLC method was 14.6% w/w.

calculated phenol 2 % phenol 2 {%w/w} = × 100% phenol 2 {%w/w} + ether 1 {%w/w}

The calibration model developed using data obtained from laboratory experiments was used to predict, in real-time, the concentration of ether 1 during the first batch manufactured in the pilot plant. To demonstrate that the results predicted using the in situ Raman method were equivalent to those that would be obtained using the off-line HPLC method, it was decided that, during the first pilot plant batch, a process sample would be taken for off-line HPLC analysis at a predefined time-point. The process sample was split into four laboratory samples that were individually assayed to determine a mean and 95% confidence interval. At the time the first process sample was extracted from batch 1, the predicted result obtained using the Raman method (PLS2) was 14.4% w/w (see Figure 7). The results obtained by the offline HPLC analysis were used to confirm that the Raman method had correctly predicted that the end-of-reaction criteria had been achieved. The mean result obtained using off-line HPLC method was 14.1% w/w with a confidence interval of 0.1% w/w. The offline HPLC results, in conjunction with the Mahalanobis distances and spectral residuals from the Raman model prediction statistics were evaluated to understand the validity of the predictions. The uncertainty of the results obtained using the off-line HPLC method was compared with the prediction error of the Raman model. On the basis of this comparison of the two sets of results, it was concluded that results predicted using the Raman method were equivalent to those obtained from the off-line HPLC method. After reviewing the results from batch 1, the project team and QA department agreed that the in situ Raman method could be used as the sole GMP in-process control for all subsequent batches. During batch 2, a process sample was taken for information purposes prior to quench/workup, but its analysis was off the critical path, and the reaction mixture was worked up

Figure 8. Predicted Raman results for second pilot plant batch and corresponding off-line HPLC result at end of reaction. The reaction profile from 0 to approximately 180 min corresponds to the reagent addition phase. This was followed by a line wash, which resulted in a disturbance in the profile through dilution.

The in situ Raman data indicated that the reactions were complete before the time-point stipulated in the process description and batch sheet when a sample would have be taken for off-line analysis. The results showed that the reaction progressed faster at this scale than was observed in the laboratory experiments and provided useful knowledge about the effect of improved mixing efficiency of the pilot plant scale reaction vessels. When moving to larger scale reaction vessels for commercial manufacture, the batch cycle-time could potentially be reduced by several hours through the use of real-time analysis to detect the reaction end-point. 201

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(5) De Smet, K.; van Dun, J.; Stokbroekx, B.; Spittaels, T.; Schroyen, C.; Van Broeck, P.; Lambrechts, J.; Van Cleuvenbergen, D.; Smout, G.; Dubois, J.; Horvath, A.; Verbraeken, J.; Cuypers, J. Org. Process Res. Dev. 2005, 9, 344−347. (6) Wiss, J.; Lanzlinger, M.; Wermuth, M. Org. Process Res. Dev. 2005, 9, 365−371. (7) Feth, M. P.; Rossen, K.; Burgard, A. Org. Process Res. Dev. 2013, 17, 282−293. (8) Global Status Report on Noncommunicable Diseases 2010, World Health Organization, 2011. (9) Iynedjian, P. B. Cell. Mol. Life Sci. 2009, 66, 27−42. (10) Kawai, S.; Mukai, T.; Mori, S.; Mikami, B.; Murata, K. J. Biosci. Bioeng. 2005, 99, 320−330. (11) Matschinsky, F. M. Nat. Rev. Drug Discovery 2009, 8, 399−416. (12) Pal, M. Curr. Med. Chem. 2009, 16, 3858−3874. (13) Under these conditions, the desired reaction most likely proceeds via a dianion, which arises from the complete deprotonation of the carboxylic acid of 2 and the partial deprotonation of its phenol. (14) The subject of the present contribution is one of a number of uses of PAT within the project, during both laboratory development and API manufacture. Other examples included the use of UV/vis to investigate the extent to which the reaction of the carboxylic acid of 2 with chloropyrazine 3 contributed to the formation of ether 1, calorimetry and NMR to investigate the degree of double deprotonation of phenol 2, IR to inform when the drying of ether 1 was complete, and Raman and UV/vis to study the arylation of ether 1 in the API step. (15) Before it was appreciated that the excess chloropyrazine 3 present at the end of the reaction could be efficiently removed using the crystallisation of ether 1, the criticality of this impurity had led to the inclusion of dedicated washes of the quenched reaction mixture with IPAC in order to remove it. This had a knock-on effect on batch cycle time and the process mass intensity. (16) Substances that are incompatible with DMSO have been implicated in a number of incidents where the heating of this solvent has resulted in an explosion or a thermal runaway. With this in mind, the reaction was hazard assessed by RC1 calorimetry, and thermal screening of the reaction mixture and the various process streams associated with the workup was undertaken prior to transfer to the pilot plant. These activities indicated no significant exothermic activity apart from prior to the addition of chloropyrazine 3 when the reaction mixture produced an exotherm and gas generation from ∼200 °C, and azeodrying the batch to dryness where the residue, when mixed with stainless steel containing 1% rust, produced an exotherm from ∼175 °C. For more information on substances that are incompatible with DMSO, see: Yang, X.-W.; Zhang, X.-Y.; Guo, Z.-C.; Bai, W.-S.; Hao, L.; Wei, H.-Y. Thermochim. Acta 2013, 559, 76−81. (17) Whilst the use of Raman was not established on a kilolab scale, after having been developed in the laboratory, previous manufactures of Compound X in the same pilot-plant facility and intermediary kilolab scale, which had relied wholly on off-line analysis, provided confidence in the outcome of the pilot-plant manufacture described herein. (18) Raman Products Technical Note, TN1800; Kaiser Optical Systems Inc.: Ann Arbor, MI, U.S.A. (19) Pearson, G. A. J. Magn. Reson. 1977, 27, 265−272. (20) Nagy, Z.K.; Pedge, N.; Baker, M.; Steele, G.; Supersaturation and direct nucleation control of an industrial pharmaceutical crystallisation process using a crystallisation process informatics system. Presented at ISIC18, 18th International Symposium on Industrial Crystallization, Zurich, Switzerland, 13−16 September, 2011.

4. CONCLUSIONS We have demonstrated that in situ Raman spectroscopy can be developed at 250 mL laboratory scale to monitor the profile of a heterogeneous reaction and to establish satisfactory reaction end-point criteria that ensure undesired impurities detrimental to downstream processing are reduced. Good process understanding, and the application of chemometrics allowed us to develop a quantitative calibration model which was successfully transferred to 1500 L pilot plant scale. The predicted concentration results obtained using the in situ Raman method were demonstrated to be equivalent to results obtained using offline HPLC. This allowed subsequent GMP batches to be released solely using the in situ Raman data. This application also highlights the difficulty of developing robust chemometric models early in the project development cycle, where processes may not be fixed until close to manufacture, leaving a limited number of data sets available for model development. The use of in-line analyses offers advantages in terms of improved process capability, by reducing variability from sampling and sample preparation for off-line or at-line analysis. Also, processing efficiency can be realised with PAT, with faster analyses and decision making, to optimise plant occupancy and reduce costs, these factors will become more significant as throughput and volumes increase. Development of PAT methods for the pilot plant has allowed us to explore using OPC to transfer data through to the pilot plant DCS; in this instance it allowed real-time reaction profiles to be monitored remotely by process chemists over 200 miles away. However, the more significant impact of feeding real-time analytical data into a DCS of an automated plant is that critical processing steps or parameters can be controlled through intelligent feed-back or feed-forward loops, as previously demonstrated by colleagues in AstraZeneca.20 In such circumstances one could envisage validating the PAT method with a feed-back or feed-forward loop to ensure consistent product quality and move away from traditional process validation procedures.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank the following colleagues at former AstraZeneca UK-sites at Avlon and Charnwood, Mike Baker, Phil Hopes, Craig Roberts, and Mandip Athwal for their ideas and support in the development of this PAT application and especially to Mike Baker for his input during the PAT infrastructure build in Pilot Plant 3 at Charnwood and for helping deliver practical solutions to AstraZeneca projects.

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REFERENCES

(1) Guidance for Industry, PAT a Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance; U.S. Department of Health and Human Services, Food and Drug Administration: Rockville, MD, U.S.A., September 2004. (2) Foley, D. A.; Wang, J.; Maranzano, B.; Zell, M. T.; Marquez, B. L.; Xiang, Y.; Reid, G. L. Anal. Chem. 2013, 85, 8928−8932. (3) Fabris, D. Mass Spectrom. Rev. 2005, 24, 30−54. (4) McCullough, B. J.; Bristow, T.; O’Connor, G.; Hopley, C. Rapid Commun. Mass Spectrom. 2011, 25, 1445−1451. 202

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