High-Shear Rotor–Stator Wet Milling for Drug Substances - American

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High-Shear Rotor−Stator Wet Milling for Drug Substances: Expanding Capability with Improved Scalability Allison Harter,§ Luke Schenck,*,‡ Ivan Lee,‡ and Aaron Cote‡ ‡

Chemical Process Development and Commercialization, Merck & Co. Inc., 126 East Lincoln Highway, Rahway, New Jersey 07065, United States § Merck & Co. Inc., 770 Sumneytown Pike, West Point, Pennsylvania 19846, United States ABSTRACT: Rotor−stator wet mills are commonly used in the pharmaceutical industry to reduce particle size and normalize API physical properties as a means to facilitate downstream drug product operations and/or achieve targeted in vivo product performance. Wet milling is robust, relatively easy to use, broadly applicable, and offers both financial and API physical property advantages over dry milling. Historical rotor−stator wet mill technologies are generally capable of achieving particles sizes down to ∼25 μm. Newer high-shear wet mills allow for a reduction of particle size down to ∼10−15 μm. In addition to the improved particle size reduction, recent wet mill designs better maintain geometric consistency across the product line, thereby providing enhanced scalability. A traditional scale-up approach for wet milling involved maintaining the tip speed of the rotor (assuming constant shear gap and thus constant shear rate) and would generally allow comparable terminal particle size (that near steadystate particle size where particle size reduction drastically slows) across scales. In order to predict the milling time upon scale-up the number of passes, or batch turnover, through the mill was kept constant. However, this prediction of the required milling time was often less successful than the prediction of the terminal particle size. Studies presented here confirmed the importance of maintaining constant rotor tip speed across scales to achieve the predicted terminal particle size and identified the importance of additional parameters to address particle breakage kinetics to allow prediction of the required milling time to achieve the target particle size. Additional aspects of hydrodynamics, shear rates, and equipment properties were assessed as part of these scale-up model optimization efforts. Specific processing parameters evaluated included flow rate, API slurry solids concentration, and starting particle size distribution. Ultimately, the Slot Event Model was developed to incorporate the critical geometric parameters by considering the frequency and the probability of a slot event. In addition to applying the revised model across scales, further model verification was achieved by evaluating custom rotor−stator mill heads. Studies with these custom mill heads provided insight into the importance of mill efficiency and slot events. This, in turn, allowed for more accurate scale-up of not only the terminal particle size but also the milling time required to achieve the target particle size. The success of the optimized model reduces the reliance on in-process controls or at-line testing for determining the end point of milling.

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INTRODUCTION A key deliverable of the drug substance development process is to achieve an active pharmaceutical ingredient (API) having physical properties that are both consistent and amenable to drug product formulation processes. While API processes can be defined and carefully controlled to ensure the appropriate physical properties are achieved, numerous parameters can present a risk to long-term robustness and physical property consistency. These risks can result from process changes such as subtle variations in impurity profiles or solvent compositions that can affect nucleation and growth kinetics. The risks can also result from equipment changes such as mixing conditions or the equipment and process parameters across the isolation steps that can bring about particle breakage or granulation. As such, terminal milling stages have long been a route to ensure physical property control, and deliver smaller particle size API that can be important for improved drug product dissolution, tablet content uniformity, and powder compactability. A long-standing precedent for terminal milling generally included a form of dry milling (pin milling, jet milling, etc.). While scalable and relatively available through the drug substance supplier network, these operations present several disadvantages. They involve an additional operational step © 2013 American Chemical Society

(with commensurate labor and overhead), can introduce additional industrial hygiene containment costs,1 and can result in yield losses at a minimum of ∼1% and in some instances appreciably higher at production scale. While the financial drivers may present the biggest case for avoiding dry milling, potential disorder in the milled crystalline material2−4 that is driven by the shear and tensile strength stresses associated with dry milling5 can be another driver to avoid dry milling. Millinginduced disorder can span a spectrum from the formation of second phase amorphous material, to the exposure of higher energy crystal faces,6 to lattice disorder or conformational defects.5,7 This disorder, or bulk exposure of higher energy surfaces, can have detrimental impacts on API physical properties including flow, dissolution, and stability, among others. Additionally, the nature of the defects of milled material is complex, with the comparability of these milling induced defects to crystallization induced defects (i.e., those brought Special Issue: Engineering Contributions to Chemical Process Development Received: May 1, 2013 Published: September 12, 2013 1335

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Figure 1. Microscopy of three developmental compounds and aspirin used in the milling experiments in addition to the time zero mean volume and the slurry concentration and solvent composition.

size.14 A detailed application of this methodology to predicting particle breakage and to inform scale-up to that end has yet to be published. Some authors indicate scale-up procedures for high shear rotor−stator mixers should proceed on the basis of constant rotor tip speed while maintaining constant shear gap (RPM × diameter),18,24 with others suggesting that in the turbulent regime, scale-up should be based on constant energy dissipation rate per unit mass (RPM3 × diameter2) and at the same time maintain geometrical similarity.25,26 It was also recognized that the rotor−stator design is important in impacting the maximum shear stress and hydrodynamics, with key components including the clearance between the rotor and stator (or shear gap), and the width of slots, in addition to rotor diameter.23 In practice, scaling of pharmaceutical milling operations based on constant tip speed (assuming similar shear gaps) more often than not results in equivalent terminal particle size. While using the constant tip speed approach, applying the concept of batch turnovers in recycle mode achieves a reasonable approximation of the time required to achieve the desired particle size for a given batch size.8,23 Batch turnovers is a means to represent the number of passes through the mill, as calculated by

about via melt quenching, or other forms of process induced disorder) is generally unknown.5 The use of high shear rotor−stator mixers as wet mills presents a route to avoid the dry milling disadvantages of both the cost and milling induced disorder. Wet milling has a longstanding use in the pharmaceutical and other industries. The previous lower limit for average particle size achieved via rotor−stator wet milling has been in the range of ∼30 μm,8 while recent equipment evolution has meant that consistently achieving the 10−15 μm range is possible.9 The next step in implementing the newer version of these wet mills has been to evaluate scale-up capabilities. The scale-up of high shear rotor−stator mixers has been evaluated in other industrial applications, where the units are utilized for process intensification. While the most prominent of this work focuses on liquid−liquid emulsification,10−14 solid−liquid suspension including particle size reduction15 and chemical reactions have been explored, including several patented operations.16−21 There have been commensurate research efforts to characterize rotor−stator systems across these applications. Recent efforts have focused on the use of test reactions, computational fluid dynamics, and experimental fluid dynamics to achieve improved understanding of mixing processes,12,13 including multiple phase mixing.22,23 Development efforts to this end have focused on power input10,11 with energy dissipation rates proposed as a reasonable scale-up parameter for emulsions to match dispersed-phase particle

batch turnover = 1336

flow rate through mill*time volume of slurry being milled

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Figure 2. Schematic showing the key dimensions of the rotor−stator mill tooling geometry incorporated into scale-up considerations.

Quadro Ytron HV0 was used to study several experimental compounds to determine the extent of breakage that could be achieved at 70 m/s.9 The work presented here used both the lab/small pilot scale Quadro Ytron HV0 and the production/ larger pilot scale Quadro Ytron HV1 to look at the wet milling scale-up of several compounds. Several types of mill heads, tip speeds, and flow rates were studied to gain a more fundamental insight into the milling process from an equipment perspective. Mill heads had narrow slot widths with varying numbers of slots in the rotor and stator and varying heights of the slots. The standard mill head was modified to have more slots in both the rotor and stator and, in a different mill head, to have longer slots in the rotor. The rationale behind these customizations to further verify the proposed Slot Event Model for scale-up is discussed later in this study. Six dimensions of the mill head geometry are discussed as related to the Slot Event Model. These dimensions are shown in Figure 2. Particle Size Distribution. A Microtrac S3500 laser diffraction particle size analyzer was used to analyze samples. Most samples were vacuum filtered prior to analysis. Each sample was dispersed in Isopar G with 0.25% lecithin or in water, depending on the solubility of the compound. Isopar G or water was also used as the circulating fluid, respectively. Samples were sonicated appropriately (generally 30 s) before data collection. The results are reported as volume distributions.

However, there stands room for further optimization of this scale-up approach. When the milling time cannot be predicted, milling past the desired particle size can result in undesirable generation of fine particles and the unnecessarily extended processing time cycles. To this point, it is not clear which parameters are most important to incorporate into scale-up models to afford added capability to predict the kinetics of milling. The work presented here focused on applying mill geometry as the means to build a more robust and predictive empirical scale-up model. This work sought to verify the model with experimental data across lab, pilot, and production scale.

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MATERIALS AND METHODS Materials. Aspirin and three additional development compounds (Figure 1) were used in these experiments. The compounds were chosen for their varying particle morphologies. Compound A forms block-shaped crystals. Compound B is also block-shaped and had previously proven to be difficult to mill. Compound C presents as rod-shaped crystals and has a narrow, target particle size distribution which is challenging for scale-up. Compounds were milled in typical solvent compositions and slurry weight concentrations from the end of crystallization. Equipment. The rotor−stator homogenizers/wet mills used for this work were from the Quadro Ytron HV series that can operate at tip speeds up to 70 m/s, appreciably higher than existing development and commercial-scale wet mills. The 1337

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Figure 3. Graph of flow rate through the standard Quadro Ytron (a) HV0 and (b) HV1 mill head vs tip speed of the mill.

Experimental Procedure. The lab-scale setup consisted of a jacketed vessel with overhead stirring to ensure consistent mixing and solids suspension throughout the runs. A recirculation loop was connected to the bottom valve and was piped through the wet mill followed by a sampling valve and then a subsurface return to the vessel. The natural draw of the mill was used as the flow rate for most laboratory experiments, with Figure 3a showing the natural draw of the Quadro Ytron HV0 as a function of mill speed. At the HV0 scale, the effect of the flow rate on mill performance was studied. These studies involved a peristaltic pump placed upstream from the mill. The mill could not pull through the peristaltic pump at tip speeds, resulting in natural draws appreciably higher than the pump setting, and allowed the use of flow rates less than the natural draw, while not starve feeding the mill. These flow rates were important to fully understand in order to calculate the total batch turnovers. For the pilot-scale trials using the Quadro Ytron HV1, a pump was used upstream of the mill. Due to the scale of the HV1 trials, the pump was needed to prime the mill and sustain flow through the mill for recirculation back to the tank. At the pilot scale it was more challenging to determine the precise natural draw of the mill. However, a range of pump speeds was evaluated at each of three mill tip speeds. During these trials, it was observed that the flow rate through the mill did not change by more than 5 liters per minute (LPM) as a function of the pump setting. The following figure 3b shows these flow rates observed at the given mill tip speeds. A postcrystallization slurry was added to the vessel for the lab-scale trials on the Quadro Ytron HV0. At the beginning of each milling cycle, the bottom valve of the vessel was opened, and the mill was turned on to the desired tip speed. The mill generates a significant amount of heat that could not be removed just through the vessel jacket at this small scale. When the temperature of the slurry had risen several degrees (the degree of the tolerable temperature rise was based on the compound’s temperature-dependent solubility in the given solvent system so as to limit any particle annealing), the mill was turned off and the bottom valve closed to allow the temperature of the slurry to decrease via the cool jacketed vessel. Milling cycles were continued in this way until the

desired amount of batch turnovers was reached. Samples were taken between the milling cycles to analyze for particle size to generate the milling profile. Larger scale evaluations using Quadro Ytron HV1 afforded much better control of the energy input from the mills. There was generally a 2−3 °C temperature rise across the mill, but the batch temperature did not increase over the course of the runs. This way the milling process did not need to be stopped to allow for batch temperature control. At pilot scale an appropriately sized pump, recycle loop, and sampling setup were used as what was employed in the lab-scale trials. A lowshear disc flow pump was used for initial pilot-scale experiments to ensure the pump was not resulting in additional particle breakage and influencing the capacity to predict the scale-up of breakage kinetics. Once it was determined that pump-induced breakage was not implicating prediction of milling kinetics, the more standard centrifugal pumps were used. As outlined in greater detail in the Results and Discussion, this series of mills incorporates geometrically consistent dimensions across scales such that the max shear rate at a given tip speed is maintained. In order to further ensure the hydrodynamic shear stress is consistent across scales, the appropriate flow rate for the HV1 was set by attempting to achieve consistent slot velocity (see below for the calculation of slot velocity).

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RESULTS AND DISCUSSION Until this work, scale-up of wet milling had generally been accomplished by maintaining the same tip speed and performing an equivalent number of batch turnovers with limited success. Several potential hypotheses were postulated for why this model was not always effective for scale-up and sought to explain discrepancies on the grounds of the system or equipment variability. Prior to using the Quadro mills, a concerted effort was taken using compound C by more closely controlling these suspected sources of variability in an attempt to gain insight into wet milling scale-up. It is recognized that particle size reduction during wet milling is dependent on the hydrodynamic stresses as well as the mechanical properties of the starting particles. The primary source for the hydrodynamic stress is the tip speed of the rotor, 1338

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Note the mills used in this study are historical mills at three different scales and not the Quadro mills described above. The impacts of flow rate, starting particle size, and slurry concentration were explored using this same historical lab-scale mill to determine if any of these parameters might explain the discrepancies between results at various scales as observed in Figure 4. These results are shown in Figure 5. A curve describing the relationship between volumetric flow rate and tip speed (similar to Figure 3 for the Quadro) was calculated for this mill as well. Lower flow rate (half the flow rate of the natural draw, 5.5 LPM vs 11 LPM) led to a slightly smaller particle size. These results do highlight that maintaining a reasonably consistent slot velocity is considered a best practice. The trial evaluating starting particle sizes as shown in Figure 3b was in line with previous experience and indicated that the mill was able to normalize the input particle size variability and provide a very similar particle size at the end of milling. The final trial involved an evaluation of slurry solids concentration, using slurries at 170 and 30 g/L solids concentration. Figure 5c) showed that this did not seem to have an appreciable effect on the terminal particle size achieved, or the number of turnovers to achieve the terminal particle size. Slurry concentration studies with other compounds showed similar results. While both the flow rate, starting particle size, and slurry concentration can result in minor differences in the terminal particle size achieved or the number of turnovers to achieve the terminal particle size, the differences are not large enough to explain the discrepancies observed in Figure 4 as potential reasons why the constant tip speed and turnover model fell short on predicting milling scale-up performance. As an aside, the results evaluating slurry concentration suggest that the particle−particle effects are somewhat less important than shear or particle-wall effectsthough this was not explored further. These were important results that allow for running the lab scale trials at lower slurry concentrations to reduce the total material required in order to predict performance for production or pilot scale wet milling trials. With a scale-up study using milling best practices complete and experimental data demonstrating the lesser effects of starting particle size, flow rate and slurry concentration, focus was shifted to the geometrical design of the mill head as the critical parameter. Engstrom (2011) suggested there was a geometrical component to scale-up, incorporating the size of the rotor tooth for a calculation of collision area at each scale.29 In addition to rotor tooth size, there are other dimensions which can be examined. Several dimensions of the rotor−stator mill head geometry were considered for key scale-up factors as they impact the hydrodynamic stresses. Six dimensions were determined to be crucial in the design of the rotor−stator mill head in terms of their impact on the maximum shear stress and hydrodynamics: number of slots (or teeth), height of slot (or tooth), width of slot, thickness of slot (or tooth), vertical distance between the rotor and stator, and horizontal distance between the rotor and stator (Shown in Figure 2). The Quadro Ytron series of mills maintains consistent horizontal gap between the rotor and stator (shear gap), width of the slot, and thickness of the slot across scales. This would suggest that the maximum shear stress across scales should largely be consistent with these units, provided tip speed is

which was held equivalent across all scales. Regarding the mechanical properties of the materials, for this initial study the same starting batch of material was used for each scale. The authors note that slight differences did result in the starting particle size (as seen in Figure 4 at zero turnovers) due to

Figure 4. MV (μm) vs the number of batch turnovers for a batch of compound C milled at lab, pilot, and production scales at 17 m/s tip speed using historical mills prior to Quadro milling studies.

extended mixing times as the batch was subdivided and held while milling proceeded using the various equipment. These slight differences in particle size are thought to be insignificant since the shear from the agitator that resulted in these differences is minimal compared to the shear generated from the wet mill. Most importantly, the starting materials had the same aspect ratiowhich in the past was speculated as a potential source of variability thought to compromise the scaleup approach of using equivalent tip speed and batch turnovers. Herein particles having similar PSD but different aspect ratio could have milled differently, where shorter, thicker particles mill less effectively. Thus, changes in the crystal growth across scales could lead to different milling performance even if the mills were operating identically. Regarding the hydrodynamic stresses beyond those dictated by the rotor tip speed, it was thought that potentially the slot velocity, which is a ratio of the flow rate to the open area in the mill head, could be a source of variability across scales.27,28 The slot velocity was calculated by the ratio of the volumetric flow rate through the mill to the open area in the mill head. This open area consists of the open area in the slots of the rotor plus the open area in the bypass through the vertical gap between the rotor and stator: An attempt was made to achieve consistent slot velocity across the various equipment scales. This involved accounting for the open area of the mill head (defined as the area between the rotor and stator through which the slurry can flow, including the area within the slots and the area in the vertical gap between the rotor and stator) and adjusting the flow rate accordingly. Despite these added controls to attempt to reduce sources of variability and increase the predictive capability of the batch turnover scale-up model, Figure 4 shows that the time to reach the target MV (volumetric mean of the particle size distribution) could not be predicted from one scale to another.

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Figure 5. Particle size vs batch turnover profiles using the historical lab scale mill at 23 m/s, exploring additional process parameters thought to potentially impact milling performance, with (a) showing the effect of flow rate, (b) showing the effect of starting particle size, and (c) showing the effect of slurry concentration.

made for the impact of these design components on the frequency and probability of slot events. These efforts resulted in the proposal of a Slot Event Model, with an underlying assumption (later confirmed) that the slots within the rotor are responsible for the breakage of the particles. The frequency is the number of times a particle will have the opportunity to enter a slot. It is described by the number of the slots, factoring in the batch turnovers. The number of batch turnovers is incorporated as it normalizes the varying flow rates and batch volumes across scales.

consistent. However, the number of slots in the rotor and stator, the vertical gap between the rotor and stator, and height of the slot all increase with the size of the mill. It was felt that by fixing the slow velocity would give further assurance that the shear stress would be consistent. It was then hypothesized that the variation of these dimensional components resulted in differences in flow patterns that could explain discrepancies in the kinetics of breakage upon scale-up. When considering the implications for differences in the flow patterns, additional attention was paid to the potential impact of flow through the slots since several researches have shown via CFD that the highest shear stresses exist within these slots.14,17,24,27 It was felt that calculations from purely a geometric standpoint could be

Frequency of slot event = number of batch turnovers*number of slots in rotor 1340

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batch turnovers at laboratory scale is used to calculate with the number of batch turnovers at pilot scale which is shown as the model prediction. The experimental pilot-scale data is also shown on the graph, matching well with the predicted data. If the historical model was valid, which only uses turnovers needed at lab scale to predict pilot-scale behavior, the experimental HV1 data would be expected to overlay the HV0 data. However, as seen in Figure 7, the slot event model prediction for the HV1 calculated from the HV0 data is much more predictive of the number of batch turnovers needed upon scale-up. In order to further verify the key assumptions of the model, custom lab-scale (HV0) mill heads were fabricated, incorporating changes to various aspects of the mill head geometry. In one instance, the height of the slots was increased by 50% to increase the probability of a slot event. In a separate mill head, the number of slots was increased by 66% for both the rotors and stators to increase both the probability and frequency of a slot event. In both cases, the diameter of the mill head and mill housing of the standard HV0 were not modified. Increased probability and/or frequency, according to the Slot Event Model, should increase the rate of breakage. Additionally, the increased number of slots on the rotor and stator could be used together (both rotor and stator with more slots) or individually for two different configurations (standard rotor with increased slot stator or increased slot stator with standard rotor). This would provide insight into whether it is the rotor or stator playing a key role in particle breakage. Compound A was used for the initial studies with the modified mill heads. Increasing the height of the slots did increase the rate of breakage as shown in Figure 8. Transforming the data into the Slot Event Model shows that the Slot Event Model holds well for a change in slot height. The second custom mill head showed that increasing the number of slots also increased the rate of breakage. Furthermore, it was determined that the number of slots in the rotor is the important variable in the Slot Event Model. The number of slots in the stator had a much more limited effect of the rate of breakage when normalizing for the increased flow rate due to the increased number of slots in the stator. The Slot Event Model also correctly represents the data from the increased-slot mill head. Figure 9 shows milling profiles both in terms of batch turnovers and slot events. As the milling profile shows, to reach 20 μm with the standard rotor/stator takes about 50 batch turnovers, but the rate of milling is increased with a greater number of rotor slots and only about 25 batch turnovers needed to reach 20 μm. Overall, the modified mill heads were able to verify the correct components were included in the Slot Event Model. The modified mill heads also allowed verification of the Slot Event Model with an additional compound where material and resources were not available to scale up to the Quadro Ytron HV1. Compound C has a rodlike morphology. It was milled on the Quadro Ytron HV0 using the standard mill head and the modified mill head with more slots in both the rotor and stator. As expected (Figure 10), the modified mill head mills the compound more quickly due to the greater number of slot

This frequency must then be adjusted with a probability component, recognizing the opportunity for slurry to bypass the slots as it flows through the gap between the top of the rotor teeth and the stator rather than passing through the slot.17,27 The probability of a slot event thus factors in the variable vertical gap between the rotor and stator, the number of slots and the height of the slots (with both of these incorporated in the area of the slots). The total number of slot events is then the product of the slot event frequency and probability. Number of slot events = Frequency*Probability

When comparing the number of slot events required to reach a desired particle size, the number of slot events can be predicted from one scale to another. This is in contrast to looking at the milling profile in terms of batch turnovers. Figure 6 shows a

Figure 6. MV of compound A milled with the HV0 and HV1 is shown vs slot events.

milling profile where the x-axis has been transformed from batch turnovers to slot events. The respective numbers of slot events required on the HV0 and the HV1 for a given tip speed are shown to be relatively equivalent. Knowing the geometry of the mill heads at both scales, the number of slot events required at the laboratory scale can be used to calculate the milling time required at the pilot or commercial scale.

The slot event model was verified with three compounds at the pilot scale. Figure 7 shows the milling profiles for three compounds with batch turnovers on the x-axis. The number of 1341

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Figure 7. Milling profiles for (a) compound A, (b) compound B, and (c) aspirin. The red squares show the actual HV0 experimental results. The closed blue squares show the actual HV1 results, indicating that maintaining slot velocity and scaling the number of turnovers does not explain the kinetics of the milling process. The open blue squares are the slot event model predictions based on the HV0 data and show good prediction of the milling kinetics.

(•) Results confirmed that, by maintaining constant tip speed, in general, comparable terminal particle sizes could be achieved across scales. (•) Consideration for geometrical differences of the mill heads was determined to be the important factor for scale-up of milling kinetics, and a Slot Event Model was developed to account for the changes in geometry across the Quadro Ytron series. (•) The purely empirical Slot Event Model was found to predict the milling time at scale on the basis of laboratory data and knowledge of the geometry of the mill heads at both scales. (•) In addition to verification through scale-up efforts, the model was successfully applied to predict the results of using modified rotor−stator orientations at the laboratory scale.

events. The Slot Event Model reasonably predicts the performance of the milling of compound C from one mill head to the other.

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CONCLUSIONS

(•) Efforts confirmed limitations in applying the constant tip speed and equivalent batch turnover scale-up approacheven when attempting to use equivalent starting material and accounting for constant slot velocity. (•) The effects of flow rate, starting particle size, and concentration were examined, with results showing that each of these each can impact the kinetics and terminal particle size, but their effects were not large enough to account for discrepancies seen across scales using historical wet milling equipment. 1342

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Figure 8. Batch turnovers and slot events for standard and increased length rotor−stator for compound A.

Figure 9. Batch turnovers and slot events for standard rotor−stator and increased slot rotor and/or stator combinations for compound A.

Figure 10. Batch turnovers and slot events for standard and increased slot rotor−stator for compound C.

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(24) Atiemo-Obeng, V.; Calabrese, R. Rotor-Stator Mixing Devices. In Handbook of Industrial Mixing: Science and Practice; Paul, E., Atiemo-Obeng, V., Kresta, S., Eds.; Wiley: Hoboken, NJ, 2004; pp 479−505. (25) Utomo, A.; Baker, M.; Pacek, A. Chem. Eng. Res. Des. 2009, 87, 533−542. (26) Roux, J.; Mayade, T. Powder Technol. 1999, 105, 237−242. (27) Calabrese, R.; Francis, M.; Kevala, K.; Mishra, V.; Padron, G.; Phongikaroon, S.; Proceedings of 3rd World Congress on Emulsions: Applied Colloids Surfactants, Lyon, France, September 24-27, 2002. (28) Yang, M.; Calabrese, R. Mixing 23, Mayan Riviera, Mexico, June 17−23, 2012. (29) Engstrom, J.; Wang, C.; Chen, W.; Lai, C.; Sweeney, J.; Erdemir, J.; Pedro, A. AIChE Annual Meeting, Minneapolis, MN, 2011.

(•) Trials with the custom rotor and stator designs showed the importance of the rotor geometry was greater than that of the stator geometry in impacting milling performance.

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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 We thank Quadro Engineering (http://www.quadroytron.com) for their help in generating these models and for providing detailed geometric data and the modified mill heads. We also thank our Unit Operations Lab, especially Elizabeth Fisher, Dave Lamberto, and Ming Yue, for their help setting up the labscale unit, and our Multiple Scale Operations pilot-plant staff, especially Joe Kukura and Dave Lashen, for their help setting up the Quadro Ytron HV1 to meet our objectives evaluating scale-up.

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REFERENCES

(1) Seibert, K.; Collins, P.; Fisher, E. Milling Operation in the Pharmaceutical Industry. In Chemical Engineering in the Pharmaceutical Industry; am Ende, D., Ed.; Wiley: Hoboken, 2010; pp 365−378. (2) Chamarthy, S.; Pinal, R. Eng. Aspects. 2008, 331, 68−75. (3) Otte, A.; Carvajal, M. J. Pharm. Sci. 2010, 100, 1793−1804. (4) Descamps, M.; Willart, J.; Dudognon, E.; Caron, V. J. Pharm. Sci. 2007, 96, 1398−1407. (5) Luisi, B.; Medek, A.; Liu, Z.; Mudunuri, P.; Moutlton, B. J. Pharm. Sci. 2012, 101, 1475−1485. (6) Gamble, J.; Leane, M.; Olusanmi, D.; Tobyn, M.; Supuk, E.; Khoo, J.; Naderi, M. Int. J. Pharm. 2012, 422, 238−244. (7) Clawson, J.; Kennedy-Gabb, S.; Lee, A.; Copley, R. J. Pharm. Sci. 2011, 100, 4302−4316. (8) Lee, I.; Variankaval, N.; Lindemann, C.; Starbuck, C. Am. Pharm. Rev. 2004, 7, 120−128. (9) Lee, I.; Ostrosky, J.; Walker, M.; Sirota, E.; Schenck, L. AAPS Annual Meeting, Washington, DC,, 2011. (10) Kowalski, A. Chem. Eng. Process. 2009, 48, 581−585. (11) Cooke, M.; Rodgers, T.; Kowalski, A. AIChE J. 2012, 58, 1683− 1692. (12) Adler-Nissen, J.; Mason, S.; Jacobsen, C. Food Bioprod. Process. 2004, 82, 311−319. (13) Gringras, J.; Tanguy, P.; Mariotti, S.; Chaverot, P. Chem. Eng. Process. 2005, 44, 979−986. (14) Hall, S.; Pacek, A.; Cooke, M.; Kowalski, A.; Rothman, D. Mixing 23, Mayan Riviera, Mexico, June 17−23, 2012. (15) Shelley, S. Chem. Eng. Process. 2007, 103, 7−12. (16) Chu, G.; Song, Y.; Yang, J.; Chen, J.; Chen, H.; Chen, J. Chem Eng. J. 2007, 218, 191−196. (17) Xu, S.; Shi, J.; Cheng, Q.; Li, W.; Zhang, J. Chem. Eng. Sci. 2013, 87, 111−121. (18) Bourne, J.; Garcia-Rosas, J. Chem. Eng. Res. Dev. 1986, 64, 11− 17. (19) Zhang, J.; Xu, S.; Li, W. Chem. Eng. Processing: Process Intensif. 2012, 57−58, 25−41. (20) Ozcan-Taskin, N.; Padron, G.; Voelkel, A. Chem. Eng. Res. Dev. 2009, 87, 468−473. (21) Padron, G.; Eagles, W.; Ozcan-Taskin, G.; McLeod, G.; Xie, L. J. Dispersion Sci. Technol. 2008, 29, 580−586. (22) Bałdyga, J.; Jasińska, M.; Malik, K. Mixing 23, Mayan Riviera, Mexico, June 17−23, 2012. (23) Balddyga, J.; Orciuch, W.; Makowski, L.; Malik, K.; OzcanTaskn, G.; Eagles, W.; Padron, G. Ind. Eng. Chem. Res. 2008, 47, 3652−3663. 1344

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