Risk Considerations on Developing a Continuous Crystallization

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Risk Considerations on Developing a Continuous Crystallization System for Carbamazepine Xiaochuan Yang, David Acevedo, Adil Mohammad, Naresh Pavurala, Huiquan Wu, Alex L. Brayton, Ryan A. Shaw, Mark J. Goldman, Fan He, Shuaili Li, Robert Joseph Fisher, Thomas F. O'Connor, and Celia N. Cruz Org. Process Res. Dev., Just Accepted Manuscript • DOI: 10.1021/acs.oprd.7b00130 • Publication Date (Web): 15 Jun 2017 Downloaded from http://pubs.acs.org on June 18, 2017

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Organic Process Research & Development

Risk Considerations on Developing a Continuous Crystallization System for Carbamazepine Xiaochuan Yang, David Acevedo, Adil Mohammad, Naresh Pavurala, Huiquan Wu, Alex L. Brayton, Ryan A. Shaw, Mark J. Goldman, Fan He, Shuaili Li, Robert J. Fisher, Thomas F. O’Connor, Celia N. Cruz Office of Pharmaceutical Quality, CDER, FDA 10903 New Hampshire Avenue, Silver Spring, Maryland 20993-0002 Corresponding Author Email: [email protected] Note: This publication only reflects the views of the authors and should not be construed to represent FDA’s views or policies.

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Table of Contents

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Abstract Continuous manufacturing (CM) is an emerging technology in the pharmaceutical manufacturing sector and the understanding of the impact on product quality is currently evolving. As the final purification and isolation step, crystallization has a significant impact on the final physicochemical properties of drug substance and is considered a critical process step in achieving the continuous manufacturing of drug substances. Although many publications previously focused on various innovative techniques to continuously make crystals with desired properties, engineering difficulties such as system design, automation, and integration with Process Analytical Technology (PAT) tools have not been thoroughly discussed. Here, we focus on how to develop a continuous crystallization system, from the perspective of process engineering, and the related risk considerations on product quality. Specifically, we designed and built an automated two-stage Mixed Suspension Mixed Product Removal (MSMPR) crystallization platform for a model compound (Carbamazepine, CBZ) that exhibits multiple polymorphs. The crystallization process includes integration of PAT tools (on-line Raman microscopy and Focused Beam Reflectance Microscopy, FBRM) for real-time monitoring. A series of case studies were done to evaluate the performance of the continuous system and PAT tools. Specifically, the drawing schemes, slurry transport, and variations on process variables are considered as the three key risk areas for continuous crystallization process development. Our proof of concept continuous crystallization system uses feedback/feedforward controls to achieve constant levels in crystallizers, a centralized automation program coded in LabVIEW, and PAT monitoring for polymorphs and particle size distribution (Raman and FBRM). To the best of our knowledge, this is also the first study on continuous crystallization of Carbamazepine for form III and its polymorphic transition (between form II and form III). Keywords: Continuous crystallization, carbamazepine, polymorphism, process analytical technology (PAT), risk assessment

1. Introduction The pharmaceutical industry nowadays is undergoing a shift from batch to continuous manufacturing1-3. While an end-to-end integration process from synthesis to final dosage forms still remains challenging, continuous manufacturing of drug product part was demonstrated successfully with commercial production of Orkambi tablets by Vertex Pharmaceuticals4,5 and Prezista tablets by Janssen Pharmaceutica6. As for an integrated line on continuous manufacturing of drug substance part, no commercial manufacturing line has been reported yet and there are still several roadblocks to be solved. One major roadblock is a continuous crystallization step, though several advances have been made in flow chemistry for pharmaceutical drug substances7-9. Crystallization is a vital step in drug substance manufacturing process, which not only purifies APIs, but also solidifies APIs from liquid phase for downstream processing. Research efforts on continuous crystallization have increased significantly over the past few years. Currently, there are two popular approaches to achieve continuous crystallization. One approach is cascade of MSMPR crystallizers, where one or more well-stirred crystallizers are connected in sequence for multiple-stage operations10-14. This design is suitable for systems that require longer residence times, but its strong agitation and nonPage 3 of 33



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uniform temperature profile may cause problems for particle size and polymorphism control. The other approach is tubular crystallizers15-19, which are suitable for faster kinetics and better temperature control. However, tubular crystallizers are more prone to have crystal fouling, clogging, settling issues, and poor mixing, which are all hurdles for industrial implementation20. In addition to these two common setups, there are several novel crystallization approaches that potentially can be used for continuous crystallization, such as microfluidics18, impinging jet20, film crystallization22, electro-spraying23, nanospraying24, and crystallization within porous media25-27. Research efforts mentioned above mainly focus on novel technologies or methodologies with little discussion from the perspective of process robustness and product quality. In this paper, we would like to emphasize the importance of several scientific and engineering considerations from the perspective of quality risk management, specifically what are the potential quality risks when developing a two-stage MSMPR continuous crystallization system, and how to minimize these risks through process design, monitoring, and control. To demonstrate these concepts, a laboratory scale continuous crystallization platform is presented as a proof of concept for the development and implementation of automation, monitoring, and control strategies.

2. Experimental section 2.1. Materials Carbamazepine (CBZ) was chosen as the model compound throughout the various studies performed in this work. CBZ possesses four polymorphs (I, II, III and IV)28. Polymorph III is the most stable form under room temperature. The commercial CBZ purchased from Sigma Aldrich was tested by X-Ray Powder Diffraction (XRPD), and the results showed polymorph III. Ethanol 200 proof (Decon Laboratories) was used as solvent throughout this work. The solubility of CBZ in ethanol at 295.80 K is 2.2 g/ml ethanol. Deionized water (Millipore System) was used as antisolvent for the polymorphic transformation experiments. Air was used for level indicator and went through an ethanol buffer bottle to get saturated with ethanol first before entering the system. 2.2. Experimental setup

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Figure 1. Process and instrumentation diagram for continuous crystallizer.

The two-stage MSMPR system and associated instrumentation is shown in Figure 1; the equipment contained is described in Table 1. Feed solution from the feed tank (V 1) is fed through the feed pump (P 1) into crystallizer 1 (Cr 1). Similarly, slurry may be removed from crystallizer 1 by the intermediate pump (P 2) and sent to crystallizer 2 (Cr 2) before being removed by the product pump (P 3) to a product tank (V 2). In addition, anti-solvent from the anti-solvent tank (V 3) may be pumped to either crystallizer 1 or crystallizer 2 via the dosing pump (P 4), not shown in the figure. Crystallizer temperature, crystallizer stirring speed, and anti-solvent flow are controlled by Mettler Toledo’s EasyMax 402 System. The temperature in the crystallizer was measured using a Pt100 thermocouple. The system uses a Peltier device for cooling and electric system for heating the water that passes through the jacket system. Pumps P 1 through P 3 are controlled manually through dials on the pump housing or automatically through the control system described as below. Table 1. Continuous crystallizer equipment description.

Number

Name(s)

Description

CR 1

Crystallizer 1

A thermostated, stirred tank (500ml) produced by Mettler Toledo as part of the EasyMax 402 Synthesis Workstation. EasyMax 402 allows for temperature and impeller speed control.

CR 2

Crystallizer 2

A thermostated, stirred tank (500ml) produced by Mettler Toledo as part of the EasyMax 402 Synthesis Workstation. EasyMax 402 allows for temperature and impeller speed control.

P1 P2 P3

Pump 1, Feed Pump Pump 2, Intermediate Pump Pump 3, Product Pump

A peristaltic pump produced by Ismatec as part of the Reglo Analog line (ISM829). A peristaltic pump produced by Ismatec as part of the Reglo Analog line (ISM829). A peristaltic pump produced by Ismatec as part of the Reglo Analog line (ISM829). Page 5 of 33



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P4

Pump 4, AntiSolvent Pump, Dosing Pump

A 50 mL (alternatively 10 mL) syringe pump produced by Mettler Toledo as an accessory to EasyMax 402.

V1

Feed Tank

A bottle (which may be used with or without a sealed top) that feeds Crystallizer 1 via the feed pump.

V2

Product Tank

V3

Anti-Solvent Tank

A bottle (which may be used with or without a sealed top) that draws from Crystallizer 2 via the product pump. A bottle (which may be used with or without a sealed top) that feeds Crystallizer 1 or Crystallizer 2 via the anti-solvent pump.

2.3. Automation control system The feedback and feedforward control system for the experimental setup is briefly discussed in this section. For detailed engineering documentation, see Supplement. The control strategy for flow and level control system can also be seen in Figure 1: (1) (2) (3)

Computer control of peristaltic pumps P 1, P 2, and P 3 via FC01, FC02, and FC03 Level indication in Cr 1 and Cr 2 Level control of Cr 1 and Cr 2 via manipulation of P 1 and P 3.

In this system total process throughput is established by the flow controller on P 2 (FC02), level in Cr 1 is manipulated via FC01, and level in Cr 2 is manipulated via FC03. This configuration ensures level disturbances do not propagate between crystallizers. This software front-end for the flow and level control system has been implemented via LabVIEW, a development environment by National Instruments. 2.3.1. Pump Flow Control The analog pumps are controlled using LabVIEW via a data acquisition unit to allow communication between the pumps and LabVIEW. To facilitate this communication, custom circuitry has been designed, tested and constructed. This circuitry allows LabVIEW to send the following signals to the pumps: • • • •

Remote condition (manual or computer control) Run status (on or off) Pump direction (forward or reverse) Pump speed

A LabVIEW program has been constructed and a user-interface for pump flow control has been designed. The pump calibration has been implemented in the LabVIEW pump control. 2.3.2. Level Indication and Control

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Figure 2. Schematic of bubbler level indication

Figure 2 illustrates the bubbler level indication scheme for each of the crystallizer tanks. A bubbler operates by sending a stream of air to the base of a liquid containing tank. The pressure difference between the gas in the bubbler and the headspace of the tank gives an indication of the liquid level, via the following equation, where Δ𝑃𝑃 is the differential pressure, 𝜌𝜌 is the liquid density, 𝑔𝑔 is the force due to gravity, and ℎ is the height. Δ𝑃𝑃 = 𝜌𝜌𝜌𝜌ℎ ⇒ ℎ =

Δ𝑃𝑃 𝜌𝜌𝜌𝜌

In order for the differential pressure signal to be successfully integrated into LabVIEW, it is filtered and amplified via a strain gauge transmitter. The filtered and amplified signal from transmitter is sent to LabVIEW via the data acquisition unit. Using this signal, LabVIEW adjusts the pump flowrates via a PI controller to maintain the level at its desired set point. 2.4. Semi-continuous filtration setup The semi-continuous filtration device filters in a traditional batch fashion, but does so intermittently. The designed filtration system targets a collection frequency of one residence time. To achieve this collection frequency, the filtration system has two parallel vacuum filter setups; while one of these setups is actively collecting sample, the other setup is being harvested and reestablished. At the end of the collection period, the setups are switched via two valves controlled by the control system implemented in LabVIEW. This system is illustrated in Figure 3. This type of setup is inspired by industrial practices in catalytic bed reactors or pressure swing adsorption 29.

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Figure 3. Periodic batch filtration system schematic.

2.5. Off-line characterization The particle size distribution (PSD) and the polymorphic form of samples obtained throughout the various case studies were determined using off-line characterization techniques. The PSD was measured using a Sympatec HELOS laser diffractometer with a RODOS disperser. An optimal concentration of 0.1% was setup as the trigger condition and a feed rate of 50% and 1 bar. The HELOS sensor can detect particles in the range of 0.1µm to 875µm. X-ray powder diffraction (XRPD) was used to identify the polymorph obtained throughout the continuous process. The CBZ polymorphs form II and III (form III is more stable than form II), can be easily identify through their XRPD spectra. Each polymorph has characteristic peaks at distinct values of 2θ27.The XRD was performed by a Bruker D8 Advance (Bruker AXS, Madison, Wisconsin) diffractometer equipped with the LYNXEYE scintillating detector and Cu Kα (lambda = 1.5405 Å) using a voltage of 40 kV and an anode current of 40 mA. Before measurement, the instrument functionality was checked using corundum as an external standard. About 500 mg of sample was placed in the sample holder and three replicate diffractograms of each sample were collected over 2θ range of 4-40o with an increment of 0.00251 at 0.2 s per step (7181 total steps). Sample holder was rotated during run to minimize preferred orientation and to get average diffractogram of the sample. The XRPD operation, data collection, and data analysis were achieved through Diffract.Suite (V2.2). 2.6. Process Analytical Technology (PAT) tools Ensuring that a process stays within a state of control is crucial to achieving consistent quality. Interrogating the quality of the materials in process via process monitoring and PAT tools is a key element of establishing a state of control. Raman spectrophotometer and Focused Beam Reflectance Measurement (Mettler Toledo, FBRM) probes were chosen as PAT tools to monitor the process and the product quality. Figure 4 illustrates the final setup of the whole continuous crystallization system: a twoPage 8 of 33


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stage continuous crystallization unit with engineered flow and level control, implemented downstream semi-continuous filtration, and integrated PAT tools.

Raman

FBRM

Cr2

Cr1

Figure 4. A photo of the continuous crystallization system. The electronics of the automation control system are mostly mounted on the back rack. In the Easymax system, the Raman and FBRM probes were located in the Cr 2 (the left crystallizer). Contrary to Figure 1, the flow direction is from right to left.

2.6.1.

Focused Beam Reflectance Measurement (FBRM)

FBRM is based on a large sample of 2D projections of the particles with random rotations30,31. A count of these events as a function of the chord length yields a chord length distribution (CLD), which can be correlated to particle size distribution in real time. However, significant factors can impact this correlation such as crystal shape and orientation33,34. Because the particles are scanned in one dimension as they flow past the probe window, the CLD count is biased toward lower size ranges as the shape tends away from ideal spherical. For needle like crystals or rod-like crystals the dominant chord length will be closer to its minor axis length, and as a result, FBRM is not very sensitive to axial growth of particles32. Thus, without detailed knowledge of particle shape, CLD cannot be mapped onto a crystal size distribution with sufficient confidence31. The system must also be properly calibrated for accuracy. The CLD can, however, track how one dimensional size and count change during a process33,34. The data collected can be trended over time. Focusing on the qualitative analysis, it can capture events such as nucleation, crystal growth, agglomeration and breakage though tracking of CLD evolution12,35. Therefore, the utility of FBRM as a quantitative PAT tool for this system needs to be demonstrated with increased efforts in calibration and correlation development for the particulate system. The FBRM was installed and integrated with iControl, allowing for simultaneous collection and display of both the parameters of EasyMax operation (i.e. temperature andstir speed) with FBRM data. With FBRM as a PAT, trends related to particle size distribution can be tracked real-time to better understand, and eventually optimize, and control the crystallization system. Page 9 of 33



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2.6.2.

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Raman spectroscopy

Raman spectroscopy is used to distinguish polymorphs in the system. The Raman analyzer used is the Raman Rxn2 system by the Kaiser Optical Systems, Inc.: a four channel analyzer that operates sequentially allowing both fast analysis per channel and programmable channel interrogation. It supports both off-line analysis and on-line analysis mode with a 785 nm laser beam. The initialization of Raman requires about 1 hour warming up time. Proper coverage of the system such as aluminum foils is required because the Raman probe is sensitive to visible light.

3.

Results and discussions

Unlike many other unit operations in pharmaceutical manufacturing process, continuous crystallization (excluding some novel methods mentioned in the introduction) requires handling a mixture of both solid and liquid phases. The two-phase flow creates many challenges to process robustness, such as clogging in tubing and settling of large particles in low-velocity flow zones. These process failure modes, if not properly addressed, may affect product quality. Here, as shown in Figure 5, we will discuss these potential process risks and our related findings in the following sections.

Figure 5. Ishikawa diagram that shows possible causes for process failure in a continuous crystallization process.

To achieve a robust continuous crystallization process, we have identified six areas of focus during process development: (1) Drawing scheme; (2) Slurry transport; (3) Automation system for level control; (4) PAT tools; (5) Materials; (6) Process variables. Based on our experience, Area 1, 2, and 6 should be considered as the key risk areas for continuous crystallization process design, and are discussed in detail in the following sections. Without appropriate considerations on these three areas, a fully functional Page 10 of 33


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integrated system is hardly possible. Meanwhile, Area 3, 4, and 5 are crucial additions to the system to ensure that the system is operated in a monitored state-of-control. Considerations for Area 3 have been described in the above experimental setup section and in the supplement. In addition, regarding Area 4, the details of the PAT method development and validation will be discussed in a separate manuscript. For Area 5, more studies will be conducted on the effect of seeded crystals (different polymorphs and size distributions) on the process dynamics of continuous crystallization later. 3.1. PAT tools 3.1.1. Focused Beam Reflectance Measurement (FBRM) A series of batch experiments were conducted with the FBRM to determine the sensitivity and precision of the method. Measurements were collected and saved every 5 seconds for the primary distribution mode. First, a solution of carbamazepine (CBZ) was kept at 20 °C in CR 2 and stirred at constant stir speed of 300rpm. The FBRM was tested at different locations. No significant difference was observed at different locations, as long as the probe tip was sufficiently submerged. The impact of stir speed on the FBRM was evaluated by varying the stir speed between 100 to 500 rpm in CR 2 shown in Figure 6. Under these saturated conditions, there is no crystal growth, i.e. the crystal size distribution should be almost constant over time (Ostwald ripening and crystal breakage due to agitation may exist). From the experiment, it is clear that the FBRM is quite responsive to changes in stir speed. As explained above, the FBRM measures the numbers of particles per rotation of the laser beam. Therefore, the number of particles observed by the FBRM varies as the mixing behavior in the vessel changes. This can be observed clearly when significant step changes are applied; a significant decrease in counts for all bin ranges is observed when the stir speed decreases to 100rpm. Low stir speeds increases the probability of settling of crystals in the system, which results in a significant decrease in particle counts. The use of FBRM as a tool to evaluate the impact of stir speed on homogeneity of the system has been presented in literature36. Nonetheless, this is a qualitative observation for a fixed volume, solid concentration and crystal size distribution. The interpretation of the changes observed, in terms of particle size and impact on product quality will be evaluated with additional development.

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Figure 6. Stir speed experimental results as presented by FBRM raw data. The purple line is the stir speed. Other lines for counts represent different ranges of chord sizes.

3.1.2. Raman Form II and form III CBZ were made in house and tested with off-line Raman (Figure 7). The characteristic peaks of the two polymorphs of CBZ correspond closely to the peak locations that have been characterized in literature37,38. This is especially useful for monitoring the transition from form III to form II when anti-solvent (water) is added to the system. The peak area ratio of the peak at 1050 cm-1 to the peak at 1027 cm-1 is used to distinguish form II and form III38,39, as shown in Figure 7. In addition, form II also has two small characteristic peaks at 257 cm-1 and 391 cm-1. The peak ratio will be used throughout this work since the two small characteristic peaks (i.e. 257 and 391cm-1) are at the end of the spectrum where the measurement noise increases.

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Pure CBZ Samples Form II Form III Intensity

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3200

2700

2200

1700

1200

700

200

Wavenumber(cm-1) Figure 7. Off-line Raman spectrum of CBZ forms II and form III powders. The dashed squares highlight the characteristic peaks.

3.1.3. Case study: Polymorph transition in a single-stage MSMPR setup To evaluate the ability of the system to monitor process status and detect disturbances in real-time with the PAT tools (specifically polymorphic change from form III to form II in this case study), an experimental run using anti-solvent in a single-stage MSMPR setup is conducted. Raman and FBRM were implemented into the crystallizer to monitor real-time particle counts and polymorph transition from form III to form II. The system started at 20 oC with form III partially dissolved in ethanol (a total concentration of 4.0 g/100 ml). The system was stirred under 300 rpm and kept at 20 oC. After the system reached a steady state according to FBRM data (after ~ 40 mins), both the inlet (only water, no CBZ ethanol solution) and outlet pumps were initiated simultaneously. The flowrate of water (anti-solvent) in and the slurry out were set to the same target to achieve a residence time of 30 minutes (the crystallizer runs at a set volume of 400 ml and thus the flowrate is 13.3 ml/min). The process was monitored by FBRM as shown in Figure 8.

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3500

300

Continuous

3000

200

2000 150 1500 100

1000 Total counts

500

Mean Sqrwt (µm)

250

2500 Counts (#/s)

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

Mean SqrWt 0

0 20

40

60 Time (min)

80

Figure 8. FBRM trends of counts in various ranges during adding anti-solvent to the system.

The experiment included two residence times to ensure full transformation of the polymorphs in the vessel. Samples from the product crystals after one residence time, product crystals after two residence times and crystals in the crystallizer itself after two residence times were collected, filtered and denoted as product 1, product 2, and Cr 2. The Raman spectra were also monitored during the process, three of which are shown in Supplement. After one residence time (~ 01:20:00 in Figure 9), some obvious changes can be observed for the characteristic peaks. After two residence times (~ 1:50:00 in Figure 9), a change of peak area ratio near 1040 cm-1, between 1050 cm-1 and 1027 cm-1 was clearly seen. When form II is formed in the system, it is expected that the peak at 1027 cm-1 would increase and 1050 cm-1 decrease, which is clearly observed in Figure 9; in addition, the characteristic peaks of form II at 257 cm-1 and 391 cm-1 increased. However, it should be noted that for polymorphic systems like CBZ which lacks major characteristic peak differences, selection of PAT tool with adequate sensitivity is crucial in CM process development. If needed, signal processing or multivariate analysis should be considered to aid detection and monitoring.

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Continuous

Form II Form III

Figure 9. Dynamic profile obtained for characteristic peaks for CBZ form III and II during a continuous MSMPR operation. Blue (1050 cm-1, form III); green (1027 cm-1, form II); red (257 cm-1, form II); light pink (391 cm-1, form II). Y-axis is peak height. Continuous operation and anti-solvent addition initiated after 40 min (red dashed line).

The off-line analysis of product 1, product 2, and Cr 2 was conducted to further characterize the properties and completeness of the polymorph transition. From the XRPD patterns in Figure 10, we can see that both product 1 and product 2 and Cr 2 are mainly form II crystals as shown from the characteristic peaks at 2θ values of 8.7 and 13.328. There are some small peaks in the XRPD pattern of product 1 (one residence time) indicating existence of some form III crystals. Figure 11 shows that the PSD of crystals (product 2) becomes broader after the anti-solvent addition. It is possible that the antisolvent addition created supersaturation very fast to generate a large amount of nuclei or small crystals and therefore the PSD tends to be broader, or due to that the shape of form II is needle-like (CBZ crystals has been well characterized in literature and it has been observed from microscopy that form II results in needle-like crystals40,41. It should be noted that both FBRM and Raman methods will be developed and validated in later studies to better monitor the process dynamics with full details.

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Form II

CBZ CR2

CBZ PROD 2

CBZ PROD 1

Form III

Intensity 5

10

15

20

25

30 35 2θ (degree)

40

45

50

55

Figure 10. XRPD results of product1, product2 and Cr2 comparing with pure form II and form III samples.

3.5

Density distribution q3*

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3.0 2.5 2.0 1.5 1.0 0.5 0.0 1

5

10

50

100

500

particle size / µm

Figure 11. The comparison of the particle size distribution of sampling drawn before (red) and after adding antisolvent (blue, product 2).

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3.2. Drawing scheme for intermediate crystal collection In continuous crystallization, crystals should be collected during the process, without disturbing the state of control (steady state in some cases). Therefore, the design for collecting the crystals in continuous crystallization should be carefully considered. For example, some questions should be asked: (1) where in the crystallizers should the crystals be collected? (2) Should the drawing be continuous or periodic? (3) How should the liquid level in the crystallizers be controlled? Regarding the collecting methodologies, there are several common setups12,14,36. One example is continuous skimming of the top layer of the suspension, as shown in Figure 12 (Left). This setup requires that the pump handle both slurry and air, and that level control is achieved by the position of the tubing end. However, in terms of the number and particle size of crystals, material from the top layer may not be representative of the bulk suspension, due to settling of larger crystals. A single-stage continuous crystallization system was used to test different drawing schemes from the top (See Supplement). For each test, crystallization was run for approximately one residence time (~ 30 minutes), so the collection tank has the same amount of suspension as the crystallizer tank. The collection efficiency was defined by percentage as: (weight of crystals in collection tank)/(weight of crystals in crystallizer tank); results are shown in Table 2. By visual inspection, the suspension in the crystallizer tank appeared uniform, indicating that settling of large crystals may not be an issue during the experiment. To further investigate settling, the stirring speed was increased from 300 to 500 in the second experiment, but no improvement on collection efficiency was observed. The uniformity of the suspension was further studied using FBRM as qualitative tool and will be addressed in later sections (section 3.1.3). The result suggests that settling of large crystals was not the cause here for low collection efficiency.

Figure 12. Schematic for skimming (left) and periodic collection (right) experiments. Table 2. Collection efficiency by skimming at different stirring speeds.

RPM 300 500 RPM 500 RPM 500

collection efficiency by continuous skimming 22% 19% collection efficiency by periodic drawing 49% collection efficiency by bottom drawing 73%

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Periodic drawing is another widely-used setup as shown in Figure 12 (Right)12,42,43. In a periodic drawing scheme (or alternatively intermittent, cyclic or pulsatile), the liquid level is allowed to rise above that of the draw tube in the skimming scheme, with normal inlet flow rate with pneumatic system disengaged. After a set amount of time or volume, the pneumatic system is engaged by re-introduction of seal to the tank. In a relative short amount of time, the liquid is transferred via the pressure gradient and reduced back to the level of the draw tube, at which point the seal on the tank is disengaged. The additional 15% of the tank volume or ~13% of the total overflow volume is similar to those cited in literature for labscale continuous crystallization system44. At 500 RPM, the periodic drawing (49%) is able to collect more than twice the amount of crystal solid as that by skimming. This could be due to the draw tube being more submerged in the tank at the onset of the drawing by the pneumatic system (location), or the longer duration it has in collecting the suspension (dynamics). However, it was observed that the mean particle size in the collection tank was slightly smaller than that in the crystallizer tank (See Supplement). In addition, during non-flow periods in periodic drawing, the liquid residue evaporates to crystallize solid CBZ at the tubing end. After accumulating over a sufficiently long period, the solid may clog the flow or fall out as product, affecting the process robustness or product quality in the tank. The third configuration evaluated was the “bottom draw” scheme (Figure 13a), where a metal guide tube is inserted along the side to the bottom of the tank. The experiment performed with continuous drawing led to a collection efficiency of 73%, and almost identical particle size distributions (PSD) in the two tanks as shown in Figure 13b. This represented a significant improvement from skimming and periodic draws by capturing the majority of solid crystals and removing inefficiencies of drawing over an inconsistent flow. In addition, the efficiency was improved by the reduction of clogging of the outlet tube (addressed later in this article). The “bottom draw” was selected for the final material collection design, and integrated with the flow and level control. (a)

(b) Density distribution q3*

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1.2 1.0 0.8 0.6 0.4 0.2 0.0 1

5

10

50

100

500

particle size / µm

Figure 13.(a) Schematic of bottom draw by metal guide and (b) particle size distribution in collection tank (red square) and crystallizer (blue circle).

Further improvement could be performed in the design of the continuous system by using gravity in favor of the continuous flow. Bottom draw by gravity is a drawing scheme which can be continuous or periodic. Unlike the previous three methods, this setup normally does not require pumping or pressure difference to transfer slurry. Instead, the slurry naturally flows into the second crystallizer by gravity without energy Page 18 of 33


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consumption. This setup was not studied since it requires customized glassware, which is not congruent with the existing experimental apparatus. An interesting question to consider is whether a representative (i.e. entire PSD) collection is strictly necessary to mitigate product quality risk. If the material balance is achieved and the PSD of crystals reaches a dynamic equilibrium within the crystallizers, the crystallization may be in a state of control and run continuously. An example of this may be a drawing scheme that consistently pulls larger crystals, but allows nucleation and small crystal growth to occur simultaneously. In this case, process yield and efficiency will be maximized. However, achieving this may require complex design to only extract large crystals and comprehensive understanding towards crystallization kinetics (or even process modeling/simulation tools). Also, any potential disruptions in flow that would allow a different population of crystal particle size to exit the vessel would need to be avoided. Nonetheless, the main attribute of an efficient collect scheme is the consistent performance throughout the continuous operation with respect to particle properties of interest. 3.3. Slurry transport After drawing the crystals from the vessels, the next question here is: how to handle the slurry transport to another stage or filtration? Slurry transport may encounter several issues. Clogging and settling of solids are major concerns since they may fully stop flow in the tubing, force the process variables out of design space, and require the whole system to shut down. Furthermore, the heat loss to the environment can cause either additional crystal growth during transfer and therefore clogging/settling in the tubing, or crystals to dissolve and thus lower yields for the process. In this section, we will discuss possible solutions to these risks. Maintaining consistent flow of the suspension and preventing clogging is a major challenge to the robust operation of continuous crystallization. The crystal sizes obtained in these experiments are mostly 10-300 μm, while the tubing used is 2.49 mm ID. However, the connection fittings reduce the inner diameter to approximate 1 mm. Fouling occurs generally at these connection points, causing a buildup of solid. Once particulate flow is obstructed, solid plug forms, further restricting flow and creating of air bubbles downstream. To mitigate this risk, possible solutions are to remove unnecessary fittings, enlarge tubing inner diameters, increase flow velocity, selecting material of construction of tubing inner walls, etc. However, larger tubing inner diameters will decrease the flow velocity and cause settling (another failure mode) of particles if the total flowrate is kept constant. The system was improved via process design, by removing unnecessary connections, and reducing clogging risk without increasing settling risk. After the system upgrades, in a 2-hour-run continuous crystallization experiment, only one clogging event was observed which was easily addressed with manual tools within 2 minutes. Overall, clogging can be a serious problem for process safety and product quality. Prevention via process design and control of process parameters and step by step instructions on handling clogging events should be a key consideration in the control strategy45. Another potentially process risk factor is settling of crystals. Settling can be seen clearly through the transparent tubing via crystal solids flow at the bottom of the tubing (Figure 14), creating an observable gradient. Several potential risks to quality due to crystal settling can be: (1) risk to material uniformity, (2) risk of degradation of material adhered to the walls, (3) perturbations in the transfer lines can initiate a Page 19 of 33



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bolus of slurry concentrated in crystal solids, and a potential for clogs downstream. The nature of the flow is a function of factors such as velocity, solid loading, particle size, and physical properties of the system46. These physical phenomena should be sufficiently understood in order to avoid settling problems. Fluid dynamics modeling and heuristic equations can be used for the design and optimization of slurry transport47,48. The minimum transport velocity (i.e. for heterogeneous suspensions) in horizontal tubing can be calculated by the modified Durand equation47,48: 𝑑𝑑𝑝𝑝 𝑣𝑣horizontal = 𝐹𝐹[2𝑔𝑔(𝑠𝑠 − 1)𝐷𝐷]( )1/6 𝐷𝐷

Where F is an empirical constant that varies between 0.4 and 1.5 (the maximum value of 1.5 was used in the calculation), and D is the tubing diameter, s is the ratio of particle and fluid densities (1310 kg/m3 for CBZ and 789 kg/m3 for ethanol), dp is the particle diameter (100 μm was used in the calculation), g is gravity constant 9.8 m/s2. Therefore, 𝑣𝑣horizontal

1

100 ∗ 10−6 6 1310 = 1.5 ∗ �2 ∗ 9.8 ∗ � − 1� ∗ 2.49 ∗ 10−3 � � � = 0.0283 𝑚𝑚/𝑠𝑠 2.49 ∗ 10−3 789

In addition, the minimum transport velocity (i.e. for heterogeneous suspensions) in vertical tubing is calculated by the following equation 47,48: 𝑣𝑣vertical

𝑔𝑔(𝑠𝑠 − 1)𝑑𝑑𝑝𝑝 2 = , 𝑓𝑓𝑓𝑓𝑓𝑓 𝑅𝑅𝑅𝑅𝑝𝑝 < 1.4 18𝜈𝜈 𝑅𝑅𝑅𝑅𝑝𝑝 =

𝑑𝑑𝑝𝑝 𝑣𝑣vertical 𝜈𝜈

Where Rep is the particle Reynolds number, and 𝜈𝜈 is the fluid kinematic viscosity (=viscosity/density). A value of 0.001095 Pa*s was used for the viscosity of ethanol in the calculation. So the minimum vertical transport velocity is:

𝑣𝑣vertical =

1310 9.8 ∗ � 789 − 1� (100 ∗ 10−6 )2 0.001095 18 ∗ � 789 �

= 0.00259

𝑅𝑅𝑅𝑅𝑝𝑝 = 0.187 < 1.4

𝑚𝑚 𝑠𝑠

The actual velocity should be designed to be larger than both minimum transport velocities 𝑣𝑣horizontal and 𝑣𝑣vertical. In our case, the actual velocity in the tubing is: 𝑣𝑣actual =

13.3 𝑚𝑚𝑚𝑚/𝑚𝑚𝑚𝑚𝑚𝑚 13.3 ∗ 10−6 /60 = = 0.0452 𝑚𝑚/𝑠𝑠 2.49 ∗ 10−3 2 𝜋𝜋𝑟𝑟 2 𝜋𝜋( ) 2 Page 20 of 33


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Figure 14. The solid stream visible at the bottom of the tubing during transfer.

Finally, process risk due to slurry transport concerns heat loss to the environment. While the slurry temperature is well controlled in the crystallizers, there may be significant cooling during transport between thermostat vessels. This can exacerbate the issues of clogging and settling, if a stream cools sufficiently to promote growth on the tubing inner wall. The best solution is to keep the transport distances as short as possible. In addition, insulation in form of a fiber glass wrap was implemented for the transfer line between the feed tank and CR 1, as this was the transition with the greatest temperature gradient (e.g. feed solution T = 50 °C). Due to loss of visual access to the connections, other methods of detecting clogging are also suggested to be implemented (e.g. periodic checks and verification at the end of the run). 3.4. Process variables Potential risks from process variables, such as temperature and flowrate, should be considered. Robust control of process variables is critical to avoid process induced disturbances, which may result in significant variations in the product quality. For example, an increase on the temperature, can affect the solute concentration and crystal size distribution by dissolving fine crystals in the system49. Therefore, it is important to choose an appropriate temperature control strategy. Commercially available external temperature baths with programmed simple loops to control the process temperature can be connected to the crystallizer with jackets, which allows for efficient controllability. In our work, the temperature and the agitation speed were controlled using the programmed control algorithm built within the iControl platform. This software allows for implementation of cooling and stir speed ramps in the EasyMax platform (refer to section 2.2). The minimum temperature suggested by the manufacturer for the configuration developed in this system is -10 °C. The temperature controller in the EasyMax platform maintained the temperature within ±0.2 °C at the operating conditions studied. Other Page 21 of 33



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cooling mediums such as cryostat or silicone oil should be considered if the desired operating temperature is out of the suggested range. Variations in temperature may affect the nucleation and crystal growth kinetics, and thus alter the critical quality attributes of the product crystals during process. For our study, the effect of introduced disturbances in temperature on the product quality will be evaluated in later studies. Nonetheless, responsive control of process variables, such as temperature, will enable faster response in the case of disturbances that may occur during the continuous crystallization process. The agitation speed is also a potential risk factor since it impacts the mixing conditions of the process; variations in the agitation speed can affect the crystal growth and withdrawal of crystals, among others. The isokinetic withdrawal of the suspension from the vessel is critical for achieving consistent crystal properties. A homogeneous suspension should be achieved to avoid any settling of crystals in the system. The settling of crystals in the crystallizer may result in variations on the crystal growth since fine crystals have a higher probability to growth. Deposition of crystals at the bottom could also cause aggregation or agglomeration of particles. Hence, it is important to determine an agitation speed region in which internal particle classification effects in the operating volume are minimized. A series of batch cooling experiments were performed in which a saturated solution was cooled down from 40 to 5°C at 0.5°C per minute and 300 rpm. For a minimum of 2 hour holding period or until equilibrium was reached, the position and agitation speed was varied following the methodology described by Hou et al36. The agitation speed was varied among 150, 300, and 450 rpm. The volume was fixed to 400 ml since it is the operating volume used in the continuous crystallization process. Figure 15 shows the average CLDs obtained at both the top and the bottom of the crystallizer.

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counts (#/s, No Wt)

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counts (#/s, No Wt)

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1

10 100 chord length (µm)

1000

Figure 15. Comparison between the CLD of suspensions withdrawn from the top or bottom of the crystallizer at different agitation speeds: (a) 150, (b) 300, and (c) 450rpm.

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The particle counts changes significantly throughout the entire chord length between the top and the bottom of the crystallizer when the agitation speed was set to 150rpm. The results indicate that significant settling of particles occurred at this condition. However, little differences was observed between the top and the bottom CLDs for the scenarios in which the agitation speed was set to 300 and 450 rpm, as observed in Figure 15b-c. The agitation speeds between 300 to 450 rpm demonstrated sufficient mixing to avoid differences on the CLDs. Although, the approximated power input per unit volume for 300 to 450 rpm is 0.05-0.2 kW per m3, the Reynolds number for stirred vessels was approximately 3x103 for 300 rpm and 104 for 450 rpm, indicating that the flow patterns were both in the turbulent region45. Therefore, agitation speed is a potential risk factor that should be well studied and controlled, since it can affect product quality (e.g. particle size) and process efficiency. Another potential risk factor is the working volume (or the suspension level). The suspension level can change due to variations on the flow rate due to clogging, pump malfunction, or flow of air, among others. Hence, an efficient control strategy that could react to changes on the suspension level needs to be implemented to avoid variations that will affect the product properties. The development requires the design and implementation of a level indicator, efficient tuning of the pumps, and a software for automation. Hydrostatic, radar, and ultrasonic level indicators, among others, can be implemented for continuous monitoring. The indicator should be capable of providing accurate monitoring for suspensions of high solid concentration. For this work, a bubbler was used as level indicator for the controller (refer to section 2.3.2). Tuning of the pumps and level control is necessary for the development of an efficient control framework. For our work, a level controller (feedback and feedforward controls) was developed and implemented as described in section 2 and supplement material. The performance of controller was improved by tuning the PID control loops via open-loop studies. The characteristic gain, time constant and dead time can be evaluated from the dynamic response as a result of the step increase. A set of tuning rules, such as Ziegler-Nichols, can be used to estimate appropriate tuning parameters for the PID controllers. As Figure 16 illustrates, the controller is satisfactory in keeping the level around 105 ml (±5 ml) with setting the P2 at the set point (7.5 mL/min) and varying the P1 in real time through feedback control. Further fine tuning of controller parameters may be able to further reduce the level oscillations. Overall, process variables such as agitation speed, suspension level, and vessel temperature can have significant impact in product quality, often with a multivariate relationship. Therefore, implementing a control strategy to monitor and reduce variability in process parameters will be the key to achieving consistent quality of the crystals and process efficiency.

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Figure 16. Closed loop response of level controller around its set point. The pump flowrate is shown in the top figure and the level measurement and set point (105ml) are shown in the bottom figure.

3.5. Case study: Robustness of continuous platform To evaluate the robustness of the system, long-run experiments of continuous crystallization were performed on the two-stage MSMPR system. The results of two long-run experiments are discussed here. Specifically, we picked a scenario without clogging events (Experiment 1) and another one with a clogging event (Experiment 2) to demonstrate the monitoring ability of PAT tools. It should be noted that we continue to optimize our system as discussed above. Currently we have seen relatively low occurrence of clogging in the system. Since only one FBRM and one Raman mobile station were available, the FBRM was placed into the first crystallizer for particle size distribution, and the Raman probe was put into the second crystallizer for polymorphism monitoring. The first crystallizer is mainly for primary/secondary nucleation and the second crystallizer is mainly for crystal growth, so monitoring the particle size distribution in Cr 1 can better capture process dynamics and disturbances. Due to the proximity to final product collection, monitoring the polymorphs in Cr 2 was selected as a good preliminary surrogate for monitoring product polymorphic forms. Polymorphism change during transport in the tubing from Cr 2 to filtration (~10 seconds) is low likelihood. The system was pre-charged with 4.0 g/100ml CBZ ethanol solution in both crystallizers. The P2 flowrate from Cr 1 (400 ml) to Cr 2 (400 ml) is 13.3 ml/min for a residence time of 30 minutes each stage (in total, 2-3 hours was planned for an initial experiment, and normally the system need 2-4 residence times to reach a relatively steady state). The P1 and P3 flowrate were controlled by the automation system as described above to maintain the liquid levels of Cr 1 and Cr 2. Both crystallizers were stirred at 300 rpm. Cr 1 temperature was set at 15 °C and Cr 2 temperature was set at 5 °C. To initiate nucleation, form III crystals (in-house made) were seeded. FBRM and Raman data on two continuous run experiments with the same conditions were collected). Due to the limited volume of the feed solution, the longest run time achieved so far is 2 hours and 17 minutes for Experiment 2 (Figure 17). However, we anticipate it can be run continuously for a much longer period. Page 25 of 33



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The FBRM data can be used for qualitative analysis of the dynamic behavior for the continuous process. In Experiment 1, the FBRM data (Figure 17a) seems to indicate that the system achieved a relatively steady operation at about ~20 minutes after seeding. However, in Experiment 2, there was a clogging in tubing between Cr 1 and Cr 2, for about 10 minutes (around 45minutes) as shown in Figure 17b). The system was able to detect the clogging event, which caused significant crystal number growth in Cr 1 as the FBRM trends show. After the clogging was detected and resolved, the numbers of crystals started to decrease gradually. It took approximately two residence times (~ 80 minutes) for the system to return to a relatively stable operation after the 10-minute clog event. However, the square weighted mean crystal size obtained for Experiments 1 and 2 is close to 200µm which shows that the clogging event has a small impact on the crystal size after it reaches a relatively stable state, as characterized by process conditions (temperature, stirring rate, residence time, etc.).

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5000

300

(a)

4500

200

3000 2500

150

2000 100

1500 1000

Total counts

500

Mean SqrWt

50 0

0 0 5000

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40 Time (min)

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(b)

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RS

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250 200

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Counts (#/s)

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50

500 0

0 30

50

70

90 110 Time (min)

130

150

Figure 17. FBRM data showing dynamic profile and relatively steady (RS) operation for case studies where (a) no clogging and (b) one clogging event occurred.

The Raman Spectra (Figure 18) in both experiments suggest form III consistently, and the offline XRPD analysis also confirmed all products were form III. In addition, the large variations in Experiment 2 Raman spectra also suggest the clogging event and its development as discussed above. In both experiments, during a fixed input volume (200 ml) of feed solution, yield was calculated as the ratio between the weight of the crystals collected and the amount of CBZ in the 200 ml feed solution supposed to crystallize at 5 °C (CBZ solubility at 5 °C = 16.1 ± 0.2 g/kg solvent): 𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌 =

crystals collected during input of 200 ml feed solution amount of CBZ in 200 ml feed solution supposed to crystallize at 5 °C Page 27 of 33



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Experiment 1’s yield is 44.1% and Experiment 2’s yield is 45.0%. Given the short residence time (totally 60 mins), the results seem reasonable compared to multiple-stage continuous crystallization process without recycle35. For the purpose of a bench-top two-stage continuous crystallizer to be used for demonstrating control strategy concepts, ~ 45% yield is considered adequate. However, continuous crystallization in industry may have additional stages and/or recycle streams to increase yield. As the number of stages increases, the residence time will be increased and thus the total yield will be increased. As a result, the solid density in the slurry will be higher at the last few stages and the material transport would need to address more challenges to avoid potential clogs.

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(a)

(b)

Figure 18. Real-time Raman spectra obtained for studies where (a) no clogging and (b) one clogging event occurred.

This case study illustrated that by mitigating the risks through the above considerations, we were able to run the continuous crystallization for a period of time. Our LabVIEW-based feedforward and feedback control system was able to maintain a relatively steady operation for the crystallizers. In addition, the PAT tools (Raman and FBRM) successfully detected when there was a clogging event in the system.

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4. Conclusion Continuous manufacturing, as an emerging technology in the pharmaceutical industry, presents many challenges for process and product development. It is essential to understand and consider the risks and how they can be mitigated via process dynamics understanding and process analytics tools capable of detecting changes at a relevant timescale. In this work, a proof of concept two-stage MSMPR continuous crystallization system was developed with integration of two PAT tools (Raman and FBRM) to monitor the process in real time. This research scale system can be used to evaluate concepts of control strategy development and process risks. Currently, the system is successfully demonstrated to perform two-stage cooling crystallization of CBZ. Preliminary studies indicate that a level control system with bottom draw suspension removal configuration and alternating filtration set up can provide a basis of operation. However, the current setup requires further optimization, particularly around defining characteristic run times to study system dynamics and reproducibility of process start up. Issues with clogging and particle settling have been mostly addressed and are detectable by the PAT. Next steps includes process variation studies, definition of system deliverables for product quality (form, particle size, morphology), method development and validation for PAT, in order to evaluation more advanced control strategies, e.g. automatic system for statistically detecting clogging issues, robustness of level control, and modeling/simulation applications to predict system behaviors.

Acknowledgement We appreciate the collaboration and funding from FDA-MIT David Koch Practice School Internship Program, and the help on instrumentation from Mettler Toledo. Supporting Information The supplement information provides detailed description on the tuning and development of the control system. The calibration procedure of the bubbler is explained in details. Also, figures showing more details of the experimental setup are provided. References 1. Mascia, S., Heider, P.L., Zhang, H., Lakerveld, R., Benyahia, B., Barton, P.I., Braatz, R.D., Cooney, C.L., Evans, J.M.B., Jamison, T.F., Jensen, K.V., Myerson, A.S., Trout, B.L. Angew. Chem. Int. Ed. 2013, 52,12359-12363. 2. Lee, S.L., O'Connor, T.F., Yang, X., Cruz, C.N., Chatterjee,S., Madurawe, R.D., Moore, C.M.V., Yu, L.X., Woodcock, J. J. Pharm. Innov. 2015, 10, 1-9. 3. Byrn, S., Futran, M., Thomas, H., Jayjock, E., Maron, N., Meyer, R.F., Myerson, A.S., Thien, M.P. Trout, B.L. J. Pharm. Sci. 2015. 104(3), 792-802. 4. Vertex Case Study. [cited April 4th, http://www.gea.com/global/en/stories/vertex-case-study.jsp.

2016;

Available

from:

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5. Smith, D.E. Quality by Design (QbD) for the Continuous Manufacturing of Solid Oral Dosage Forms. April 4th, 2016]; Available from: http://pqri.org/wp-content/uploads/2015/11/. 6. FDA Approves Tablet Production on Janssen Continuous Manufacturing Line. April 13th, 2016]; Available from: http://www.pharmtech.com/fda-approves-tablet-production-janssen-continuousmanufacturing-line. 7. Pimparkar, K., Yeng, B.,Goodell, J.R., Martin, V.I., Lee,WH., Proco, J.A., Beeler, A.B., Jensen, K.F. J. Flow Chem. 2011, 2, 53-55. 8. Goodell, J.R., McMullen, J.P., Zaborenko, N., Maloney, J.R., Chuan-Xing, H., Jensen, K.F., Porco Jr., J.A., Beeler, A.B. J. Org. Chem. 2009, 74, 6169-6180. 9. Heider, P.L., Born, S.C., Basak, S., Benyahia, B., Lakerveld, R., Zhang, H., Hogan, R., Buchbinder, L., Wolfe, A., Mascia, S., Evans, J.M.B., Jamison, T.F., Jensen, K.F. Org. Process Res. Dev. 2014, 18, 402-409. 10. Wong, S.Y., Tatusko, A.P., Trout, B.L., Myerson, A.S. Cryst. Growth Des. 2012. 12(11), 5701-5707. 11. Quon, J.L., Zhang, H., Alvarez, A., Evans, J., Myerson, A.S., Trout, B.L. Cryst. Growth Des. 2012. 12(6), 3036-3044. 12. Peña, R., Nagy, Z.K. Cryst. Growth Des. 2015, 15(9), 4225-4236. 13. Zhang, H., Lakerveld, R., Heider, P.L., Tao, M., Su, M., Testa, C.J., D'Antonio, A.N., Barton, P.L., Braatz, R.D., Trout, B.L., Myerson, A.S., Jensen, K.F., Evans, J.M.B. Cryst. Growth Des. 2014, 14(5), 2148-2157. 14. Yang, Y., Song, L., Nagy, Z.K. Cryst. Growth Des. 2015, 15(12), 5839-5848. 15. Wong, S.Y., Cui, Y., Myerson, A.S. Cryst. Growth Des. 2013, 13(6), 2514-2521. 16. Alvarez, A.J., Myerson, A.S. Cryst. Growth Des. 2010.,10(5), 2219-2228. 17. Lawton, S., Steele, G., Shering, P. Org. Process Res. Dev. 2009, 13(6), 1357-1363. 18. Zhang, S., Ferte, N., Candoni, N., Veesler, S. Org. Process Res. Dev. 2015, 19(12), 1837-1841. 19. Yang, H., Yu, X., Raval, V., Makkawi, Y., Florence, A. Cryst. Growth Des. 2015, 16, 875-886. 20. Majumder, A., Nagy, Z.K. Cryst. Growth Des. 2015, 15(3), 1129-1140. 21. Liu, W.J., Ma, C.Y., Wang, X.Z. Procedia Eng. 2015, 102, 499-507. 22. Yazdanpanah, N., Ferguson, S.T., Myerson, A.S., Trout, B.L. Cryst. Growth Des. 2016, 16(1), 285296. 23. Wang, M., Rutledge, G.C., Myerson, A.S., Trout, B.L. J. Pharm. Sci. 2012, 101(3), 1178-1188. Page 31 of 33



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Risk Considerations on Developing a Continuous Crystallization

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