Impact of Process Parameters on the Grinding ... - ACS Publications

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On the Impact of Process Parameters to the Grinding Limit in High Shear Wet Milling Carla Vanesa Luciani Org. Process Res. Dev., Just Accepted Manuscript • Publication Date (Web): 05 Sep 2018 Downloaded from http://pubs.acs.org on September 5, 2018

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Organic Process Research & Development

On the Impact of Process Parameters to the Grinding Limit in High Shear Wet Milling Carla V. Luciani* Small Molecule Design and Development, Lilly Research Laboratories, Eli Lilly & Co., 1400 West Raymond Street, Indianapolis, Indiana 46221, United States e-mail: [email protected]

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KEYWORDS: Milling, Pharmaceuticals, Particle Size, High-Shear Rotor-Stator Wet Mill, Grinding Limit.

ABSTRACT

Knowledge of the impact of process parameters on the minimum achievable (critical) particle size below which breakage is no longer observed for high shear rotor stator wet milling (HSWM) operations is vital to design and optimize milling processes of active pharmaceutical ingredients. The grinding limit is a result of a balance between material properties and the energy imparted to particles during the milling processes. In turn, the energy imparted to particles depends on rotation rate, generator geometry, mill configuration, flow rate, etc. In this communication, a master curve was constructed by normalizing critical particle size curves obtained at different cumulative breakage energies, using a shift factor that can be determined with minimal experimentation.

INTRODUCTION Both dry and wet milling processes are often used to produce active pharmaceutical ingredients (APIs) with acceptable and reproducible particle size and flow properties.1, 2, 3, 4, 5 In spite of its relatively broad use in the pharmaceutical industry, the design and optimization of high-shear rotor-stator wet milling (HSWM) operations is still highly empirical. Some scaleup factors have been reported. For recirculation configurations, the number of batch turnovers Nbt, defined as the ratio between the elapsed milling time and the average residence time of particles in the feed


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tank, is traditionally used to scale-up/down milling time. Additionally, parameters such as rotor •

tip speed Sr, average shear rate γ av , shear frequency fSh, and shear number NSh are used to scaleup/down mill rotation rate.6,7,8 The definitions of the aforementioned scale-up factors are as follows:

S r = ωπDr •

γ av =

Sr δ

f Sh = ωN s N r •

N Sh = f Sh γ av

(1)

(2)

(3) (4)

where ω is the rotation speed, Dr and Ds are the rotor and stator diameters, respectively, δ is the horizontal gap distance between the rotor and stator (δ=(Ds-Dr)/2), and Nr and Ns are the number of slots in the rotor and stator (accounting for rows), respectively. Harter et al.6 defined the number of slot events (Nse) to correct the number of batch turnovers by the detailed geometry of rotor and stator. Engstrom et al.9 introduced a normalized energy imparted to particles during milling (E*) as a scale-up parameter for HSWM processes. The normalized energy imparted to particles uses rotor and stator geometry, rotation rate, flow rate and a reference experiment for normalization purposes. In a previous article,10 we discussed the use of a population balance equation (PBE) to scale up HSWM processes. Austin’s model11,12 was adopted to describe breakage rate and distribution function. In our original article,10 a minimum achievable particle size ic below which particle breakage was no longer observed was adopted. It should be noticed that ic is different than the smaller particle size of the milled population (iN) since particles smaller than ic may either enter


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as feed or may be the result of breakage of larger particles by chipping. It is known that lattice imperfections generated during a grinding process lead to particle breakage. The process of generating defects continuous until the available elastic energy is large enough to fulfill the integral energy balance, thus inhibiting further breakage (i.e., only plastic deformation takes place).13 Due to its importance, grinding limits have been investigated in the literature for a variety of mills (e.g., media mill,13,14,15, planetary ball mill,16 and high-speed elliptical rotormixer17). For mills that operate in the sub-micron size range, both breakage and agglomerations affect the apparent grinding limit. As far as the author is aware, correlations between operating conditions and grinding limits for HSWM processes are not available. Operating at the grinding limit is a quite robust condition (flat) as compared to the transitional regions (significant slope) of the milling curve for HSWM processes (see Figure 1). If other mechanisms such as agglomeration are not affecting the final stages of the grinding process, it is expected that a master curve could help practitioners answer very important questions that arise when designing new HSWM processes: (i)

Is the existing milling setup adequate to meet particle size specifications? There are limited mills and generator sets available at the manufacturing sites. Practitioners need to define at early stages of the development cycle and with very limited information if any of available mills are suitable.

(ii)

How should milling conditions be adapted to meet different desired target particle sizes? If a given setup is suitable, practitioner need to define what specific conditions will be used to ensure target particle size/sizes are produced.

Figure 1 shows the impact of shear number on milling curves for a typical pharmaceutical compound using a HSWM process. Exposing an API to increasing shear numbers (by increasing


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mill rotation rate, reducing horizontal gap between rotor and stator, increasing the number of teeth available in rotor and stator, etc.) have a significant impact on the grinding limit (i.e., size where the milling curve reaches the plateau). Such an impact is not linear, as the milling curves start overlapping as the shear number increases (Figure 1).

Figure 1. Impact of shear number on milling curves for a typical pharmaceutical ingredient.

This work focuses on the impact of process parameters on the grinding limit of HSWM processes. In this communication, we constructed a master curve for several materials capable of describing the relationship between the critical particle size achievable in a HSWM operation and the cumulative energy imparted to particles during the grinding process. EXPERIMENTAL DATA In this study, we used historical laboratory scale data for six pharmaceutically relevant material. Experiments used a inline high-shear rotor-stator mixer Magic Lab, fitted with three generators. API type, rotation rate, generator type, process scale, milling configuration, suspension medium,


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and processing temperature were varied as shown in Table 1. While some minor setup differences can be found across the historical experiments, some general characteristics are summarized. A typical setup contained a feed vessel (glass jacketed vessel of 500 mL to 1 L fitted with a thermocouple and overhead agitation), a peristaltic pump, the HSWM, and a second receiver vessel (single pass configuration) or a recirculation loop (recirculation configuration), as shown in Figure 2. In all our experiments, flowrate and milling tip speed were decoupled. The set point for agitation rate in the feed tank ensured proper slurry suspension. When present, the recirculation loop was built of standard PTFE tubing. The feed vessel jacket was connected to a chiller to control temperature. For recirculation configurations, excessive heat generation by the high-shear mixer was prevented by periodically stopping the pump/mill. Allowed temperature limits were set based on solubility data.

Figure 2. Schematic representation of single pass and recirculation modes for HSWM. Adapted with permission from Luciani et al. Modeling-Aided Scale-Up of High-Shear Rotor–Stator Wet Milling for Pharmaceutical Applications. Org. Proc. Res. Dev. 2015, 19(5), 582–589.


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For each material, milling curves were determined by pulling samples at different milling times (number of batch turnovers for recirculation mode and number of passes for single pass mode). Particle size distributions were determined by laser diffraction, applying vortexing and sonication prior to particle size determination. Critical particle size at each milling condition was determined by ensuring the milling curve reached a plateau and additional milling time did not induce any further particle size reduction. At the final stages of the milling process, it is expected that the largest particle of the distribution (x100) should be a good indication of ic. Unfortunately, slight disturbances during the laser diffraction measurements are known to significantly affect x100. Therefore, for practical purposes, x90 was adopted as the experimental indication of ic. In addition to the materials listed in Table 1, additional data from literature was used to compare with the master curve proposed in this work.14,18


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Table 1. Description of historical data used to build a master curve. Material

A

Suspension Medium

Scale (g API)

Temperature (° C)

Generator Type

Rotation rate (rpm)

HSWM Configuration

Isopropyl acetate

40

0

Medium 4M

12,800 25,400

Recirculation

Ethanol/Methanol /Water

40

15

Fine 6F

15,000 26,000

Recirculation and single pass

MIBK/Heptane

75

20

Fine 6F

20,000 25,400

Recirculation

Agglomerates, x90=760 µm

B

Plate-like, x90=160-500 µm

C

Rod-like, x90=530 µm


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Material

D

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Suspension Medium

Scale (g API)

Temperature (° C)

Generator Type

Rotation rate (rpm)

HSWM Configuration

Acetone

40

30

Fine 6F

10,500 25,380

Recirculation

1-Butanol/Water

30

0-22

Fine 6F

11,000 26,000

Recirculation

Acetonitrile/THF

80

10

Fine 6F

3,200 – 25,400

Recirculation

Plate-like, x90=250 µm

E


F



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MASTER CURVE CONSTRUCTION The construction of master curves is relatively common in engineering fields (e.g., fluid viscosity, interrelationship between frequency-loading time and temperature for viscoelastic properties, etc.). By analyzing historical data, our group observed that a common pattern emerged when depicting critical particle size achievable with a HSWM as function of a specific cumulative energy (Ec) imparted to particles during the milling process, defined as follows: Ec =

Nbt mp

෍

(6)

NSh,j × τj

j=generator

for generator j, NSh,j and τj are the shear number and average residence time of particles, respectively. The average residence time is calculated as the ratio between the active milling volume of a given generator and the circulation flow rate. The number of batch turnovers Nbt accounts for the average number of times a particle is exposed to the high shear environment. The mass of particles mp takes into account differences in the process scale (i.e., as the scale increases using the same mill and conditions, the total imparted energy per particle is reduced).


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160 140

Material C Material E

120

Material D Material F

100

ic (µm)

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80 60 40 20 0 0

5E+09

1E+10

Ec

1.5E+10

2E+10

2.5E+10

(s-1 g-1)

Figure 3. Impact of cumulative energy on critical particle size. Lines were included to guide the eye only.

Figure 3 compares the most dramatic effects of Ec on ic. While the location of the curves varies for each material, the pattern can be normalized. For ic normalization, a relative critical particle size ic,r was defined, as follows:

ic,r =

ic,

(7)

ic,min

ic,min =

lim

Ec →Ec,max

(8)

ic

where ic,min is the critical particle size resulting when specific cumulative energy imparted to particles reaches the maximum for a given mill. For practical purposes, this value can be found


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by milling the material for long periods of time (Nbt increases) and/or increasing rotation rate (NSh increases). Based on Eqs. (7) and (8), the following horizontal asymptote should exist: lim

Ec →Ec,max

(9)

ic,r =1

The specific cumulative energy imparted to particles during the milling process was also normalized. To this purpose, we adopted the following normalization protocol: Ec Ec,ref

(10)

Eref = lim Ec

(11)

Ec,r =

ic,r →2

While somewhat arbitrary, the normalization was chosen to be consistent with the following equation to describe the following simple relationship between ic,r and Ec,r.

ic,r =

1 +1 Ec,r

(12)

According to Eq. (12), two parameters are needed to define the impact of milling conditions to a given system, ic,min and Ec,ref. From a practical perspective, the value of ic,min can be found experimentally, by ensuring long milling times at desired milling conditions. Ec,ref can be found from a few additional experiments at smaller cumulative energies, ensuring that the curvature of the master curve is captured properly. Particularly, Ec,ref is the value that minimizes the error between experimental and predicted values for measured ic,r.


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Figure 4. Normalized critical particle size vs normalized cumulative energy for different materials. Dashed line corresponds to mater curve in Eq. (12).

Figure 4 compares the proposed master curve to the experimental values for the systems shown in Table 1. Within the expected common cause variability for this type of experiments (e.g., laser diffraction error, sampling error, flow rate variability, temperature variability, slurry concentration variability, etc.), the proposed master curve was deemed acceptable. Based on these promising results, it is anticipated that Eq. (12) could also be valid for other mills/materials. It is important to notice that the larger deviations from the proposed master curve, observed for agglomerates (Material A) and rod-like particles (Material C) could be related to the fact that the value of x90 from laser diffraction was used an indication of the grinding limit. In cases where primary particles and agglomerate or particles with vastly different aspect ratio may coexist, the shape of the measured particle size distribution in general, and x90 in particular, may be quite misleading.


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Figure 5 compares the master curve proposed here with grinding limits obtained using media and ball mill experiments by Knieke et al.14 and Atashin et al.18, respectively. In those case, specific energies imparted to particles were adopted from the original articles. As before, an acceptable predictability of Eq. (12) was observed.

Figure 5. Normalized critical particle size vs normalized cumulative energy for different mills/materials. Dashed line corresponds to mater curve in Eq. (17).

CONCLUDING REMARKS In this work, a simple correlation between relative critical particle size and relative cumulative energy imparted to particles for HSWM was proposed that requires very limited but wellplanned experimentation. Due to the difficulty of building a complete curve at larger scales, the proposed master curve was only tested with experiments at small scale for HSWM processes. However, the fact that the approach works reasonably well for other mills is quite promising,


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indicating that may work at different scales and attrition processes. While the proposed correlation seems to be applicable to HSWM, media and ball milling, it may not describe processes with vastly different mechanism of breakage. It should also be noticed that the proposed master curve should be valid when residence time and mill rotation rate can be manipulated independently.

ACKNOWLEDGMENT The author wants to acknowledge the following scientists at Small Molecule Design and Development: Mrs. Molly Hess, Dr. Michael Lovette, Mr. Steven Myers, Dr. Lori Hilden, Dr. Moussa Boukerche, Dr. Derek Starkey, and Mrs. Rachel Hughes (historical milling experiments). The author would also like to thank Dr. Kevin Seibert for his valuable advice and Dr. Bret Huff and Dr. Paul Collins for their support. REFERENCES

1

Zhi Hui Loh, Asim Kumar Samanta, Paul Wan Sia Heng, Overview of milling techniques for improving the solubility of poorly water-soluble drugs. Asian J. Pharm. Sci. 2015, 255-274 2 Eleftherios Kougoulos, Ian Smales, Hugh M. Verrier. Towards Integrated Drug Substance and Drug Product Design for an Active Pharmaceutical Ingredient Using Particle Engineering, AAPS PharmSciTech 2011, 12(1), 287-294. 3 Eugene L. Parrott. Milling of Pharmaceutical Solids. J. Pharm. Sci. 1974, 63(6), 813-829. 4 Csilla Bartos, Piroska Szabó-Révész, Csaba Bartos, Gábor Katona, Orsolya Jójárt-Laczkovich, Rita Ambrus. The Effect of an Optimized Wet Milling Technology on the Crystallinity, Morphology and Dissolution Properties of Micro- and Nanosized Meloxicam. Molecules 2016, 21, 507, 1-11. 5 Onno de Vegt, Herman Vromans, Fried Faassen, Kees van der Voort Maarschalk. Milling of Organic Solids in a Jet Mill. Part 1: Determination of the Selection Function and Related Mechanical Material Properties. Part Part Syst. Charact. 2005, 22, 133-140.


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Harter, A.; Schenck, L.; Lee, I.; Cote, A. High-Shear Rotor–Stator Wet Milling for Drug Substances: Expanding Capability with Improved Scalability. Org. Process Res. Dev. 2013, 17, 1335-1344. 7 Lee, I.; Variankaval, N.; Lindemann, C.; Starbuck, C. Rotor-stator milling of APIs –empirical scaleup parameters and theoretical relationships between the morphology and breakage of crystals. Amer Pharm Rev 2004, 7, 120-123 and 128. 8 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. 9 Engstrom J. Wang, C.; Lai, C. Sweeney, J. Introduction of a new scaling approach for particle size reduction in toothed rotor-stator wet mills. Int. J. Pharm. 2013, 456, 261-268 10 Carla V. Luciani, Edward W. Conder, and Kevin D. Seibert. Modeling-Aided Scale-Up of High-Shear Rotor–Stator Wet Milling for Pharmaceutical Applications. Org. Proc. Res. Dev. 2015, 19(5), 582–589. 11 Austin, L.; Shoji, K.; Bhatia, V.; Jindal, V.; Savage, K.; Klimpel, R. Some Results on the Description of Size Reduction as a Rate Process in Various Mills. Ind. Eng. Chem. Process Des. Dev. 1976, 15, 187-196. 12 Austin, L.; Klimpel, R.; Luckie, P. Process Engineering of Size Reduction: Ball Mill. Society of Mining Engineers of the American Institute of Mining, Metallurgical, and Petroleum Engineers, 1984. 13 C. Knieke, M. Sommer, W. Peukert. Identifying the apparent and true grinding limit. Powder Technology 2009, 195, 25-30. 14 C. Knieke, S. Romeius, W. Peukert. Influence of Process Parameters on Breakage Kinetics and Grinding Limit at the Nanoscale. AIChE Journal 2011, 57(7), 1751-1758. 15 K. Ohenoja. Particle Size Distribution and Suspension Stability in Aqueous Submicron Grinding of CaCO3 and TiO2. Acta Univ Oul. 2014, C501. 16 Q. Q. Zhao, S. Yamada, G. Jimbo. The Mechanism and Grinding Limit of Planetary Ball Milling. KONA 1989, 7, 29-36. 17 J. Kano, H. Yabune, H. Mio, F. Saito. Grinding of talc particulates by high-speed rotor mixer. Advanced Powder Technol. 2001, 12(2), 207-214. 18 Atashin, S.; Wen, J.Z.; Varin, R.A. Optimizing milling energy for enhancement if solid-state magnesium sulfate (MgSO4) thermal extraction for permanent CO2 storage. RSC Adv. 2016, 6, 68860.


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