Real-Time Monitoring and Control of Critical Process Impurities during

Article pubs.acs.org/OPRD

Real-Time Monitoring and Control of Critical Process Impurities during the Manufacture of Fostamatinib Disodium Richard Hart, Adam Herring, Gareth P. Howell,* Ben McKeever-Abbas, Nicholas Pedge, and Robert Woodward Chemical Development, AstraZeneca, Etherow Building, Charter Way, Macclesfield, SK10 2NA, United Kingdom ABSTRACT: Process analytical technology (PAT) was used to probe, monitor, and control the formation of process impurities during the synthesis of a pharmaceutical intermediate at 600 kg input scale. An accurate determination of end of reaction (EoR) was vital in limiting side-products, and a novel inline UV/vis-based approach was used where more traditional EoR techniques had failed.

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INTRODUCTION Fostamatinib disodium 1 (Figure 1) is a SYK tyrosine kinase inhibitor under investigation for the treatment of rheumatoid

Figure 1. Fostamatinib disodium.

Figure 2. Competing demethylation.

arthritis and other indications.1,2 In preparation for commercialisation, a 3 tonne manufacturing campaign was conducted in 2012−13. A key transformation in the synthetic sequence is the SNAr coupling between pyrimidine 2 and aniline 3 shown in Scheme 1.3 This process, carried out at high temperatures, can be accompanied by demethylation of either of the Ar-OMe groups in 4 leading to the formation of phenol side products 5 and 6 as shown in Figure 2. These side products are of critical importance since they are not effectively removed during

downstream processing and can have negative impact on the overall purity of fostamatinib disodium 1. A detailed understanding of the process parameters affecting the formation of 5 and 6 and a reliable control method were required to ensure successful delivery of drug substance within strict purity parameters. This article describes the investigations carried out and the procedures developed prior to the successful manufacture of 5 tonnes of intermediate 4.

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RESULTS AND DISCUSSION The SNAr process shown in Scheme 1 was initially investigated using conventional offline HPLC in order to gain an assessment of the reaction kinetics (Figure 3). The following observations gave rise to concerns about the large scale operability of the process: (i) The reaction rate is highly sensitive towards temperature in the range 115−125 °C with reaction times varying from 5 to 15 h. The reaction does not proceed at an industrially useful rate below 115 °C, and the impurity profile (levels of 5 and 6) is unacceptable above 125 °C. A typical reaction time is 8 h, and sampling had previously been carried out at 1 h intervals beginning at 5 h when operating at >100 kg scale. Uncertainties regarding batch temperature control and heat-up times at the

Scheme 1. Formation of intermediate 4

Received: January 7, 2015

© XXXX American Chemical Society

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DOI: 10.1021/acs.oprd.5b00008 Org. Process Res. Dev. XXXX, XXX, XXX−XXX

Organic Process Research & Development

Article

Figure 3. Initial kinetic studies.

Figure 4. Typical UV/vis profile of the process.

proposed 600 kg scale gave serious concerns about processing time and EoR analysis. (ii) Conversion of pyrimidine 2 to intermediate 4 only occurs after an induction period of typically 30 min. The observed sigmoidal data curve is a clear indication that the reaction pathway is not fully understood and increases the risk of batch variation or failure. (iii) Mass balance (full recovery of start materials and products) is only achieved towards the beginning or end of reaction (incomplete mass recovery is observed between 20% and 80% conversion). As with the observation above, this is a clear indication of incomplete process understanding. (iv) Formation of the phenol side products 5 and 6 is observed towards the end of reaction and continues at roughly uniform rate after conversion is complete. In order to meet API quality criteria, downstream impurity-tracking experiments

indicated levels of phenols 5 and 6 should be not more than 1.3% (HPLC peak area), meaning that accurate determination of EoR is vital. This factor is further complicated since repeated reaction sampling of this process (for offline analysis) has been shown previously to lead to problems with fouling of the sampling loop at large scale resulting in inconsistent analyses. Inline analysis, or PAT, was chosen as a viable approach to further investigating (and controlling) the observations above. A recent review of PAT implementation in API development highlights some of the key drivers, technologies, and potential applications across the drug substance process flow.4 The standout feature of PAT is the ability to take representative measurements under challenging conditions such as at low temperature or high pressure and, as in the application described in this paper, high temperature, heterogeneous, and in the presence of an unstable chemical intermediate. Through B



Article

Figure 5. Principal component analysis of UV/vis data.

Figure 6. (Top) three component system showing relative concentrations of start materials (red, blue) and product (green) versus time; (bottom) residuals versus time.

the process; however, it was decided that the latter offered greater sensitivity towards determination of EoR and was simpler to apply at the large scale. A typical reaction UV/vis data set can be seen in Figure 4. The data were truncated between 240 and 450 nm and preprocessed using a Savitzky−Golay first derivative followed by standard normal variate (SNV) and mean-centering.5 Principal component analysis (PCA) reveals a two component model is sufficient to capture ∼99% of the spectral variation in the UV/vis data sets as shown in Figure 5. The PC1 scores reveal an induction period and sigmoidal profile as observed with the off-line HPLC data shown in Figure 3. The PC2 scores represent a transient source of variation, which in terms of the

the use of PAT, data and understanding attained in the laboratory environment can be validated as the process is scaled up. In this respect PAT can play a large part in derisking internal clinical manufacture or Technology Transfer to a third party through real-time monitoring or post mortem data review. As the reaction in question is heterogeneous, attenuated total reflectance (ATR) sampling was deemed to be a robust and reliable way to make the process measurement by minimizing the risk of probe fouling due to heterogeneity (solids). Since ATR technology is selective to the solution phase, this was also beneficial to studying reaction kinetics for modeling purposes. Mid-IR and UV/vis were both found to be suitable to monitor C



Article

Figure 7. (Top) four component system showing relative concentrations of start materials (red, blue), intermediate (turquoise), and product (green) versus time; (bottom) residuals versus time.

spectral variance captured (∼6%) is unlikely to be modeling nonlinearity in the spectral data and thought to be a true chemical or physical process. To extract chemically meaningful relative concentration profiles, multivariate curve resolution−alternating least squares (MCR-ALS) was applied.6 MCR-ALS has been proposed and extensively used to resolve multiple pure spectra and concentrations of the components present in unknown mixtures with little or no a priori information. The spectra were treated with a Savitzky−Golay first derivative to remove the variance contribution from the baseline offset, forgoing the MCR-ALS spectral non-negativity constraint. Assessment of the resulting models was based on the recovered concentration profiles owing to the difficulty of interpreting the recovered first derivative UV/vis spectra. A three-component model (Figure 6) left significant structure in the model concentration residuals suggesting the presence of a fourth UV active chemical component in the reaction (Figure 7). This was also observed in mid-IR data sets collected in parallel. Similar UV/vis derived relative concentration profiles were produced in 23 subsequent experiments giving confidence the intermediate species is a genuine chemical species. A working model for the reaction pathway is shown in Scheme 2. It is proposed that the process is autocatalytic wrt hydrogen chloride with an induction period being observed whilst sufficient hydrogen chloride is accumulated in the system. The fourth reaction component indicated by the analysis above (intermediate X) is most-likely a tetrahedral adduct of pyrimidine 2 and aniline 3 and is clearly unstable to the attempted (aqueous) HPLC analyses causing erroneous kinetic analysis and incomplete mass balance. This hypothesis was supported by the observations that addition of hydrogen chloride at the start of the reaction reduced the observed

Scheme 2. A proposed reaction pathway for the process

induction period (with no improvement in the eventual purity of 4) whilst addition of base effectively halted the reaction completely. A three factor PLS1 model was developed for real-time execution using the PC1 scores profile as the regressand since they followed a similar trajectory to that of the off-line HPLC data. The inline technique described above proved to be a reliable tool for determining EoR without continual sampling for offline analysis, negating the risk of erroneous analysis caused by fouling of the sampling loop. Although the model described is only qualitative, accurate EoR can be assumed at the point where the gradient of the predicted PC1 scores approach zero. D



Article

Verification of the tool was achieved during the first manufacturing (600 kg input) batch using a setup as shown in Figure 8; the real-time data are provided in Figure 9. The

identified until after EoR had been passed, greatly increasing the risk of batch failure due to elevated levels of phenols 5 and 6. In total, more than five tonnes of intermediate 4 were produced with an average yield of 86%. The levels of phenols 5 and 6 were controlled at

Real-Time Monitoring and Control of Critical Process Impurities during

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