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Quality-by-Design (QbD): Effects of Testing Parameters and Formulation Variables on the Segregation Tendency of Pharmaceutical Powder Measured by the ASTM D 6940-04 Segregation Tester
Quality-by-Design (QbD): Effects of Testing Parameters and Formulation Variables on the Segregation Tendency of Pharmaceutical Powder Measured by the ASTM D 6940-04 Segregation Tester LIN XIE,1 HUIQUAN WU,2 MEIYU SHEN,3 LARRY L. AUGSBURGER,1 ROBBE C. LYON,2 MANSOOR A. KHAN,2 AJAZ S. HUSSAIN,4 STEPHEN W. HOAG1 1
School of Pharmacy, University of Maryland, Baltimore, Maryland
2
Division of Product Quality Research (DPQR, HFD-940), Office of Testing and Research, Office of Pharmaceutical Science, CDER, FDA, White Oak Campus Life Science Building 64 Room 1080, 10903 New Hampshire Ave, Silver Spring, Maryland 20993 3
Division of Biometrics VI, Office of Biostatistics, Office of Translational Science, CDER, FDA, Silver Spring, Maryland
4
Office of Pharmaceutical Science, CDER, FDA, Silver Spring, Maryland
Received 27 September 2007; revised 14 December 2007; accepted 17 December 2007 Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/jps.21320
ABSTRACT: The objective of this study was to examine the effects of testing parameters and formulation variables on the segregation tendency of pharmaceutical powders measured by the ASTM D 6940-04 segregation tester using design of experiments (DOE) approaches. The test blends consisted of 4% aspirin (ASP) and 96% microcrystalline cellulose (MCC) with and without magnesium stearate (MgS). The segregation tendency of a blend was determined by measuring the last/first (L/F) ratio, the ratio of aspirin concentrations between the first and last samples discharged from the tester. A 22 factorial design was used to determine the effects of measurement parameters [amount of material loaded (W), number of segregation cycles] with number of replicates 6. ANOVA showed that W was a critical parameter for segregation testing. The L/F value deviated further from 1 (greater segregation tendency) with increasing W. A 23 full factorial design was used to assess the effects of formulation variables: grade of ASP (unmilled, milled), grade of MCC, and amount of lubricant, MgS. MLR and ANOVA showed that the grade of ASP was the main effect contributing to segregation tendency. Principal Component Regression Analysis established a correlation between L/F and the physical properties of the blend related to ASP and MCC, the ASP/MCC particle size ratio (PSR) and powder cohesion. The physical properties of the blend related to density and flow were not influenced by the grade of ASP and were not related to the segregation tendency of the blend. The direct relationship between L/F and PSR was determined by univariate analysis. Segregation tendency increased as the ASP to MCC particle size increased. This study highlighted critical test parameters for segregation testing and identified critical physical properties of the blends that influence segregation tendency. ß 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:4485–4497, 2008
The views and opinions expressed in this article are only of the authors, and do not necessarily reflect the views or policies of the FDA. Ajaz S. Hussain’s present address is Sandoz, Inc., 506 Carnegie Center, Suite 400, Princeton, NJ.
Correspondence to: Huiquan Wu (Telephone: 301-796-0022; Fax: 301-796-9816; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 97, 4485–4497 (2008) ß 2008 Wiley-Liss, Inc. and the American Pharmacists Association
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Keywords: quality-by-design; segregation; powder; segregation tendency; formulation; microcrystalline cellulose; design of experiments; multivariate data analysis; ANOVA; powder physical characterization
INTRODUCTION Segregation is a process through which a uniform powder blend becomes nonuniform, with regions of varying composition. Powder segregation is a ubiquitous phenomenon that exists in virtually all dry-powder-handling industries such as pharmaceutical, agriculture, and mining. Any process which causes relative particle movement can introduce powder segregation if the components have different flow characteristics. For freeflowing powders, segregation can occur when set in motion by a mixer, during handling after mixing, during material transfer (e.g., discharge from a hopper), during transport of stored blends. The primary particle properties that influence the segregation blends include: particle size, density, shape, and surface texture.1,2 Secondary factors include coefficient of friction, moisture, shape, and surface of the container, and the difference in resilience of the particles. On the other hand, for cohesive powders, which do not flow as well, segregation involves overcoming interparticulate bonding forces, for example, van der Waals, electrostatic, and hydrogen bonding. The primary mechanisms of segregation are: percolation (sifting or void filling),2–4 trajectory (rolling),3,5–8 fluidization,1,9 push-away effects,10 angle of repose, and stratification.11–13 A number of papers14–22 have been published previously that examined powder segregation and its effect using different experimental methods. Williams23 and Chowhan24 reviewed various methods to measure segregation. Over the past few years, quantitative studies on segregation tendency of a powder blend have received increased attention. For example, computer simulations were used to study the segregation of binary mixtures in a bladed mixer.25 Abatzoglou and Simard26 adapted a simple mathematical tool previously developed for the study of the behaviors of continuous chemical reactors with classical fluid flows, to evaluate the tendency of a granular mixture to segregate. They proposed an experimental tool to predict the tendency of segregation in binary granular mixtures. The segregation of powders during gravity flow through vertical pipes was investigated using
glass beads with different colors and sizes to visualize the state of segregation.27 The ASTM D 6940 segregation tester is a standard device for testing the segregation tendency of a powder;28 it uses powder flow to measure the segregation tendency of a powder blend. However, little has been published on the use of the ASTM D6940-04 segregation tester for pharmaceutical powder testing.29 To the best of our knowledge, there have been no published studies on the reliability and repeatability of this method for pharmaceutical powders. To bridge this knowledge gap from a measurement perspective,30 we need to address the following questions when testing for segregation: (1) Does the amount of materials loaded in the hopper affect the results? (2) How many times should the materials be cycled through the hoppers? (3) How many replicates are required to give a reliable evaluation? To date, little research has been published on the impact of bulk powder properties on segregation tendency. Thus, this research examines the relationship between segregation tendency and physical properties of the blend related to density [true density (Dt), bulk density (Db), tapped density (Dtap), Carr’s Index (CI)] and flow [angle of friction (AF), effective friction (AEF), and cohesion (Co) from shear cell testing31 and minimum orifice diameter32 (MOD)]. An in-depth understanding of powder behavior would be useful for predicting the performance of a powder blend in unit operations such as capsule filling and tablet compression. Design of experiments (DOE) and multivariate statistical data analysis are important tools recognized in recent regulatory documents such as the FDA’s Process Analytical Technology (PAT) Guidance33 and ICH Q8 Guideline.34 They are essential elements for pharmaceutical quality-bydesign concept. However, currently there is a lack of published case studies that illustrate the integration of these important tools for pharmaceutical development and manufacturing.35 The objectives of this study were to: (1) examine the utility of the ASTM D6940-04 segregation tester for pharmaceutical powder characterization, and (2) determine the relationships between segregation tendency and bulk powder properties.
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The model system selected contained 4% (w/w) aspirin (ASP) and 96% (w/w) microcrystalline cellulose (MCC). For the first objective, a DOE approach was used to examine the effect of the amount of material loaded into the hopper, number of segregation cycles, and number of replicates needed. Once a standard method was established based on the foregoing study, the impacts of varying particle size and various formulations on segregation potential were systematically studied using another DOE protocol.
EXPERIMENTAL Materials and Methods ASP was received from AnMar International Ltd (Aspirin, Lot # 98626, Crystals, 20–60 mesh, USP, Metuchen, NJ). Coarse particles were collected from those retained on a 35 mesh (500 mm) screen and fine particles were obtained by twice milling with a Comil1 (Model: 197S, Quadro Engineering, Inc., Waterloo, Canada). The mill was operated at 80 rpm with a number 200 washer. The mesh used was 2A019R014/19. The temperature of the processing environment was 20 0.58C, and the relative humidity was 40 5%. The particles that could pass through a 200 mesh (75 mm) sieve were collected and considered fine ASP particles for this study. MCC from FMC (Avicel1 PH200 Lot # M414C, and Avicel1 PH301, Lot # M445C, Mechanicsburg, PA) was used as received. Magnesium stearate (MgS) was obtained from Spectrum (NF/USP, Lot # RH0744, New Brunswick, NJ). Powder blends of 200 g were blended in the PK twin shell V-blender (4 quarts) (Patterson-Kelley Co., East Stroudsburg, PA), and powder blends of 400 g were blended in the PK twin shell V-blender (8 quart) (Patterson-Kelley Co.). The 200 and 400 g powder blends were mixed at a rotating speed 28 RPM for 30 min. If a lubricant was included, it was added after first mixing the drug and filler. MgS was blended for 30 min to achieve complete lubricant coverage. To minimize segregation prior to testing, the powder collected from the V-blender was carefully handled to minimize the free fall distance, and the powder blends were transferred into the top hopper of the segregation tester in a manner which minimize the power free-fall height. All mixing and transferring procedures were carried out by one person to avoid individual operator differences. DOI 10.1002/jps
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Segregation Tester The segregation tester shown in Figure 1 conforms to the ASTM D 6940-04 standard, and was custom built for this study (Pride Machining, Upper Falls, MD). The tester consists of three plexiglass hoppers and two aluminum stands, each equipped with a slide gate to control powder flow. The hoppers were assembled as shown in Figure 1. The mass flow hopper was on the top and funnel flow hopper at the bottom. Fifty-milliliter beakers were used to collect powder sample. The sample size collected was controlled by the volume of the guide cylinder. The dimensions of the three hoppers and guide cylinder are given in ASTM standard28 D6940-04. Minimal segregation should occur as the blend discharges the mass flow hopper. As the particles free fall into a heap in the lower hopper, various mechanisms can contribute to segregation based on the size, density, shape, and flow properties of the particles that make up the two different mixture components. Particles that are smaller, denser, rougher in shape and have a larger angle of repose tend to stay in the center of the heap. Particles that are larger, less dense, smoother in shape and have a smaller angle of repose tend to migrate to the base of the heap near the sides of the hopper. The blend is then discharged out of the lower hopper. Since the lower hopper is a funnel flow hopper, discharge occurs so that the mixture is separated based on the location in the lower hopper. The particles in the central core (from bottom to top) are discharged first followed by discharge (from top to bottom) of the particles
Figure 1. ASTM D 6940-04 Segregation tester (with the top slide gate open and the powder flow from the mass flow hopper down to the funnel flow hopper). Fifty microliter glass beakers were used as sample collector.
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located near the outer wall of the hopper. The difference between the first and the last samples can be used as an indicator of segregation potential, when a single-valued result is compared for different powder systems. In this work, segregation tendency is determined by calculating the last/first (L/F) ratio of drug concentrations, referred as the ratio of aspirin concentrations between the first and the last sample. The L/F ratio of drug concentrations is given by: L Drug concentration in the last sample ratio ¼ F Drug concentration in the first sample (1) An L/F ratio equal to or close to one means that there is little difference in drug concentration in the first and last sample, indicating that minimal segregation has occurred. This powder system is stable and more likely to maintain uniformity under free flowing conditions. The more the L/F ratio deviates from 1 the greater the redistribution of drug particles during flow. This redistribution indicates the tested powder system is unstable and the blend is prone to segregate.
numbered according to the order in which they were collected. Samples were collected after one segregation cycle and after five segregation cycles. The number of segregation cycles depended on the number of rotations of the hoppers. Instead of collecting samples from bottom hopper after powder fell from the top hopper to the bottom hopper, we rotated the positions of top and bottom hoppers with slide gates being closed. Then, the slide gate of top hopper was opened and the powders were allowed to flow from the top hopper to the bottom hopper. Repeating this rotation one more time completed one segregation cycle. Coning-and-quartering method was used to reduce the 20 g sample to 1.0–1.5 g. Three samples were measured for each beaker. All samples were placed in a 0.1 N NaOH solution and sonicated for 30 min to completely dissolve the aspirin. The suspension was centrifuged at 2000 rpm for 10 min; the supernatant was diluted with 0.1 N NaOH to suitable concentration for measurement by UV absorbance at 298 nm. The 0.1 N NaOH solution was used as a control. The standard curve was linear (R2 ¼ 0.999).
Segregation Testing During testing, the powder blend was carefully loaded into the top hopper with the slide gates closed. This careful loading was done in all cases the same way in order to maintain consistency. The slide gate of the top hopper was opened to allow the powder to flow from the top hopper to the bottom hopper (as shown in Fig. 1). If the bulk powder did not flow freely out of the hopper, a gentle tap was given to the hopper to initiate flow; this was done without noticeably disturbing the character of the powder bed. Only the PH 301 formulations needed taping to initiate and maintain flow; typically, between one and five light taps were used as needed. Thus, the authors feel these taps did not alter or bias the powder bed or our analysis of the data. Samples were collected from the lower hopper by placing the bottom of the beaker up against the guide cylinder of the lower hopper and opening the slide gate until the guide cylinder was full. Then the slide gate was closed and the beaker was lowered to allow the powder flow into the beaker; a typical sample weighed 20 g. This sample-collecting step was repeated until all the powder had been discharged from the lower hopper. The samples were collected in separate beakers and
Experimental Design The first part of this study was to assess the segregation tester performance. The factors examined were the amount of material loaded (200 and 400 g), the number of segregation cycles (1, 5), and number of replicates (for assessing measurement variability). The response variables were L/F values and SD (standard deviation) of L/F values. The experimental design listed in Table 1 was created using STATGRAPHICS1 5 PLUS (Manugistics, Inc., Rockville, MD). It consisted of six replicates of 22 factorial design. The order of the experiments was fully randomized to reduce systematic errors. The formulation included coarse ASP and coarse MCC (Avicel1 PH 200). The testing protocol for subsequent study was based on the results of this DOE study. In the second part of this study, the effects of formulation variables on the segregation tendency were examined using another DOE. The material loading was fixed at 400 g and one segregation cycle was used. Two levels for each of the three formulation components were included: coarse and fine particle size ASP, Avicel1 PH 200 and PH 301, 0% (w/w) and 0.5% (w/w) MgS. L/F value
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Table 1. Experiment Protocol for the 1st DOE and the L/F and SD Results of Each Experimental Run Run
Replicate
Material Loading (g)
Segregation Cycles
L/F
SD
1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 6 6 6 6
200 400 400 200 200 400 200 400 200 400 400 200 200 400 400 200 200 400 400 200 200 200 400 400
1 1 5 5 5 5 1 1 5 1 5 1 1 1 5 5 1 1 5 5 5 1 1 5
0.382 0.249 0.257 0.507 0.539 0.247 0.386 0.290 0.460 0.335 0.234 0.590 0.585 0.255 0.241 0.512 0.388 0.337 0.249 0.541 0.451 0.449 0.296 0.246
0.235 0.035 0.030 0.111 0.120 0.045 0.160 0.100 0.174 0.117 0.149 0.187 0.178 0.030 0.148 0.105 0.235 0.112 0.047 0.122 0.178 0.170 0.099 0.036
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
The SD was calculated based on triplicate results.
was the response variable. A 23 full factorial design was created as shown in Table 4. The derived variable, aspirin to MCC particle size ratio (PSR), was also listed in Table 4.
Physical Characterization of Bulk Powder Particle Size Analysis Particle size was measured by laser diffraction with a Malvern Mastersizer (Malvern, Inc., Worcestershire, UK). The dry powder feeder was operated at an air pressure 60 psi, and a sample size of 1.0–1.5 g. The volume based particle size analysis produced a mean particle size D[4,3] and a mode diameter. A microscope (Nikon, Eclipse ME 600, Image Systems, Inc., Columbia, MD) was used to take the pictures.
Density Measurements True density (Dt) values of neat materials were measured using a helium pycnometer (USP26/ NF21 <699>) (Helium Pycnometer 1305, MicroDOI 10.1002/jps
meritics Instument, Norcross, Georgia). The Dt of a blend was calculated from the formulation composition. Bulk density (Db) values were measured by using a Stampfvolumeter (USP26/ NF21 <616>). The tapped density (Dtap) values of powder blends were measured using a STAV 2003 Tap density tester (USP26/NF21 <616>). Carr’s compressibility index (CI) value was calculated using the formula (Carr, 1965): Dtap Db CI ¼ 100 (2) Dtap Generally a smaller CI value indicates better the powder flow. Minimum Orifice Diameter MOD was determined using a FlodexTM tester (Hanson Research Corporation, Chatsworth, CA). The sample size was 50 g. The smallest orifice that the bulk powder passed through and left an upside-down truncated cone instead of a cylinder shape rathole was used as the MOD. A smaller MOD value indicates better powder flow.
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Shear Cell Analysis
N the number of segregation cycles, and R the number of replicates for the 22 factorial design. The following statistical model was used:
An annular shear cell was used to determine the AF, angle of effective friction (AEF) and cohesion (Co). A detailed description of the shear cell and methods used in these studies are given by Ramachandruni and Hoag31 In brief, a powder bed height of 1 cm, a sample size of approximately 40–60 g of bulk powder and a lid with baffles was used for all tests. The tests were carried out using a constant rotating speed of 8 rpm.
yijk ¼ m þ ai þ bj þ g k þ ðabÞij þ "ijk i ¼ 1; 2
j ¼ 1; 2
(3)
k ¼ 1; 2; . . . ; 6
where yijk is the observation (either L/F value or SD) in the ith level of variable W and the jth level of variable N at the kth replicate, m is the overall mean, ai is the ith amount of materials loaded effect, bj is the jth number of segregation cycles effect, gkis the kth replication effect, (ab)ij is the two-way interaction between the ith amount of materials loaded effect and the jth segregation cycles effect, eijk is the random error. Replicate was treated as a block factor in our statistical model. Therefore, we were most interested in the interaction among variables within each block, the ‘‘ab’’ effect. Thus, in the final model selected, the two-way interaction terms ‘‘ag’’ and ‘‘bg’’ were not included. The ANOVA consisted of partitioning the total sum of squares into the following components:
Data Analysis Analysis of variance (ANOVA) for the two DOE protocols was done using SAS 9.1.3 software program (SAS Institute, Inc., Cary, NC). Multivariate statistical analysis, including multi-linear regression (MLR) and principal component regression (PCR), was done with Unscrambler 9.2 software (Camo Technologies, Woodbridge, NJ).
RESULTS AND DISCUSSION
SST ¼ SSW þ SSN þ SSR þ SSWN þ SSE
Statistical Analysis of the DOE Results for the Segregation Tester: Identifying Critical Testing Parameters
(4)
This model was used to analyze the six replicates of 22 factorial design, which examined how L/F and SD varied with W, N, and R. Results are shown in Tables 2(a) and 3(a). It showed that for both L/F and SD, the replicate variable was not
The L/F values and standard deviations (SD) from the 24 experimental runs were summarized in Table 1. Let W be the amount of material loaded,
Table 2. ANOVA Results on L/F Data for the 1st DOE Dependent
Source
Degree of Freedom
Mean Sum of Square
F-Value
p-Value
Weight Cycle Weightcycle Replicate
1 1 1 5
0.28 0.00 0.01 0.00
76.01 0.14 2.32 0.45
<0.0001 0.71 0.15 0.80
Weight Cycle Weightcycle
1 1 1
0.28 0.00 0.01
88.02 0.16 2.69
<0.0001 0.69 0.12
Hypothesis Type
(a) Relative importance L/F 3 L/F 3 L/F 3 L/F 3 (b) Interaction L/F 3 L/F 3 L/F 3 Dependent
Source
Degree of Freedom
Mean Sum of Square
F-Value
p-Value
(c) Main effects L/F
Weight
1
0.28
84.74
<0.0001
Name
Weight
Least Square Mean
Standard Error
0.49 0.27
0.017 0.017
(d) Calculate least squares mean L/F 200.00 L/F 400.00 JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 97, NO. 10, OCTOBER 2008
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Table 3. ANOVA Results on SD Data (of L/F) for the 1st DOE Dependent
(a) Relative importance SD 3 SD 3 SD 3 SD 3 (b) Interaction SD 3 SD 3 SD 3 (c) Main effects SD 1 SD 3 Name
Source
Degree of Freedom
Mean Sum of Square
F-Value
p-Value
Weight Cycle Weightcycle Replicate
1 1 1 5
0.03 0.00 0.00 0.00
20.75 0.99 0.95 1.31
0.0004 0.34 0.35 0.31
Weight Cycle Weightcycle
1 1 1
0.03 0.00 0.00
19.26 0.92 0.88
0.0003 0.35 0.36
Weight Weight
1 1
0.03 0.03
19.44 19.44
0.0002 0.0002
Hypothesis Type
Weight
(d) Calculate least squares mean SD 200 SD 400
statistically significant ( p < 0.05). Therefore, data can be pooled across replicates. Next, a two-factor ANOVA was performed on the pooled data across replicates using the following partition model: SST ¼ SSW þ SSN þ SSWN þ SSE
(5)
The results in Tables 2(b) and 3(b) showed that for both L/F and SD, variable N and interaction between variable W and variable N were not statistically significant. Therefore, only the variable W entered to the main effect model: SST ¼ SSW þ SSE
(6)
The results in Tables 2(c) and 3(c) showed that for both L/F and SD, variable W was statistically significant. Finally, the least squares mean was calculated for each level of the weight variable. As shown in Tables 2(d) and 3(d), compared to the 200 g loading, the 400 g loading generated L/F values deviating further from 1.0 with smaller SD values.
Selecting Testing Protocol for Maximizing Test Sensitivity As discussed above, variable replicate was not statistically significant at the 0.05 level. This suggested that the experimental conditions were well controlled and run-to-run variability was not DOI 10.1002/jps
Least Square Mean
Standard Error
0.15 0.08
0.012 0.012
statistically significant. This is not to say that replicate experiments are not needed as replicates helped to confirm the repeatability of test conditions and avoid gross errors. For a given formulation the segregation tendency was assumed to be constant, dictated by the physical properties of formulation components. For the aspirin formulations studied, as the amount of material loaded increased, the L/F and SD values decreased. This indicates that the response (L/F value) depends on the specific test conditions. Because certain test conditions (such as larger amount of materials loaded and different segregation cycles) resulted in smaller L/F values, test parameters could be adjusted to achieve greater sensitivity and should be standardized for data to be comparable. In our study, the 400 g loading was equivalent to the full occupancy of the inner hopper. The 400 g loading produced smaller L/F values indicating more material increased the sensitivity and the 400 g loading had a standard deviation less than the 200 g loading (Tab. 1). For the segregation tester, the shape of the powder pile was an essential factor affecting particle segregation. With the 200 g sample, less powder was available to form a pile for rolling segregation to occur and the pile’s shape was less repeatable. These results showed that on average, the measured L/F value for 200 g loading was 1.5–2.1 times greater than the L/F values for 400 g loading, depending on the number of segregation cycles.
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Effects of Formulation Variables on L/F Values To examine how formulation variables affected the segregation tendency, we conducted a 23 factorial design, which included the eight formulations listed in Table 4. Using the test protocol established from the first DOE study (400 g loading and one segregation cycle), we measured the L/F values for those eight formulations. The data were analyzed with MLR and ANOVA (Tab. 5). For MLR, the following relationship between the formulation components (including two-way interactive terms) and the response factor, L/F was evaluated
tendency is almost completely dependent on the properties of the ASP and not greatly influenced by the properties of the MCC or the lubrication level. This same analysis was applied to the SD of the L/F as the dependent variable and the results are presented in Table 6. As determined for L/F, only the grade of ASP was a significant factor ( p < 0.05) in contributing to the SD. Finally, the least squares mean was calculated for each ASP grade. As shown in Tables 5(c) and 6(c), compared to the blends with fine ASP, the blends with coarse ASP generated larger segregation tendency (L/F values further from 1.0) and greater SD values.
y ¼ b0 þ b1 x1 þ b2 x2 þ b3 x3 þ b12 x1 x2 þ b13 x1 x3 þ b23 x2 x3
(7)
where y is L/F, b0 is the intercept term, x1 is the grade of ASP (coded 1 for fine and þ1 for coarse), x2 is the grade of MCC (coded 1 for fine and þ1 for coarse), and x3 is lubricant level (LL) of MgS (coded 1 for 0% and þ1 for 0.5%). The ANOVA analysis used the following partition model: SST ¼ SSMCC þ SSASP þ SSLL þ SSMCCASP þ SSASPLL þ SSMCCLL þ SSE
(8)
As shown in Table 5(a), only the grade of ASP was significant ( p < 0.05). A second analysis was performed using the terms with the three lowest p values (ASP, ASPMCC, and MCCLL) and the results are shown in Table 5(b). As determined by the previous analysis, only the grade of ASP was a significant factor ( p < 0.05). The two-way interactive term, ASPMCC, was nearly significant ( p ¼ 0.051). This indicates that the segregation
Physical Characterization of Bulk Powders and Blends The shape and particle size of coarse (before milling) and fine ASP (after milling) particles were shown in Figure 2a and b. The coarse particles were smooth and elongated (high aspect ratio). After milling, the ASP particles rough and compact (low aspect ratio). Particle size results are listed in Table 7. The ASP/MCC PSR ranged from 0.354 to 8.716 as shown in Table 4. Results of other physical properties of the blends are shown in Table 4. The physical properties of the blend related to density (Dt, Db, Dtap) and flow (CI, MOD, AF, AEF) were primarily dependent on the type of MCC (PH200 or PH301) and not related to the grade of ASP. This was not unexpected since the filler represents 96% of the blend. Except for the MOD values, these parameters showed a dependence on the amount of lubricant, especially Dtap and CI. In contrast, the blend flow property, Co, was strongly
Table 4. Experimental Protocol for the 2nd DOE, Segregation Results, and Physical Properties of Blends
Blend F1 F2 F3 F4 F5 F6 F7 F8
ASP (4%) Fine Coarse Fine Coarse Fine Coarse Fine Coarse
LL L/F, PSR Dt Db Dtap MOD AF AEF MCC (%) Mean (Std) (ASP/MCC) (g/cm3) (g/cm3) (g/cm3) CI mm (Degree) (Degree) Co PH200 PH200 PH301 PH301 PH200 PH200 PH301 PH301
0 0 0 0 0.5 0.5 0.5 0.5
0.749 0.328 0.978 0.305 0.874 0.479 0.900 0.341
(0.049) (0.144) (0.040) (0.171) (0.031) (0.174) (0.032) (0.134)
0.354 3.330 0.926 8.716 0.354 3.330 0.926 8.716
1.514 1.514 2.464 2.464 1.506 1.506 2.451 2.451
0.377 0.379 0.394 0.382 0.445 0.449 0.463 0.459
0.508 0.510 0.594 0.590 0.542 0.541 0.666 0.660
34.6 34.5 50.7 54.4 21.8 20.5 43.9 43.7
4 4 20 20 4 4 20 18
40.14 41.64 43.14 42.74 37.38 37.93 39.17 38.72
40.60 41.75 43.79 43.13 37.68 36.97 40.12 39.29
0.24 0.06 0.27 0.16 0.14 0.00 0.44 0.26
PSR, ASP/MCC particle size ratio value based on mode size mean; PH200, Avicel1 PH200; PH301, Avicel1 PH301; LL, MgS lubricant level; Dt, true density; Db, bulk density; Dtap, tapped density; CI, Carr’s compressibility index; MOD, minimum orifice diameter; AF, angle of friction tested by shear cell analysis; AEF, angle of effective friction tested by shear cell analysis; Co, cohesion of powder by shear cell analysis. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 97, NO. 10, OCTOBER 2008
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Table 5. ANOVA Results From L/F Data for the 2nd DOE Dependent
Hypothesis Type
Source
Degree of Freedom
Mean Sum of Square
F-Value
1 1 1 1 1 1
0.00 0.52 0.02 0.01 0.01 0.00
1.14 541.6 22.3 7.07 13.1 2.53
0.48 0.03 0.13 0.23 0.17 0.36
1 1 1
0.52 0.02 0.01
184.5 7.61 4.45
0.0002 0.05 0.10
(a) Relative importance L/F 3 MCC L/F 3 ASP L/F 3 ASPMCC L/F 3 LL L/F 3 MCCLL L/F 3 ASPLL (b) Terms with greatest importance L/F 3 ASP L/F 3 ASPMCC L/F 3 MCCLL Name
ASP
p-Value
Least Squares Mean
Standard Error
0.36 0.88
0.049 0.049
(c) Calculate least squares mean L/F Coarse L/F Fine
trend: they have higher Co than those of PH 301 batches without lubricant.
dependent on the grade of ASP, although the results for lubricated blends were inconsistent. For example, all batches with fine ASP have a higher Co than those with coarse ASP. No matter lubricant is added or not, the batches with PH 301 always have higher Co than those with PH 200. However, compared to the batches without lubricant, batches with lubricant display a complicated manner for the Co. The PH 200 batches with lubricant have smaller Co than those of PH 200 batches without lubricant. The PH 301 batches with lubricant have, however, an opposite
Relationships between L/F and the Physical Properties of the Blends As discussed previously by Wu et al.,36 it is important and necessary to bring the product and process knowledge into the multivariate data analysis domain so that knowledge from different perspectives may be bridged. In this work,
Table 6. ANOVA Results From SD Data for the 2nd DOE Dependent
(a) Relative importance SD 3 SD 3 SD 3 SD 3 SD 3 SD 3 (b) Main effects SD 3 SD 3 SD 3 Name
Source
Degree of Freedom
Mean Sum of Square
F-Value
MCC ASP MCCASP LL MCCLL ASPLL
1 1 1 1 1 1
0.00 0.03 0.00 0.00 0.00 0.00
0.07 37.42 0.00 0.18 0.55 0.06
0.83 0.10 0.96 0.74 0.59 0.85
MCC ASP LL
1 1 1
0.00 0.03 0.00
0.18 92.78 0.46
0.69 0.0007 0.54
Hypothesis Type
ASP
(c) Calculate least squares mean SD Coarse SD Fine DOI 10.1002/jps
p-Value
Least Squares Mean
Standard Error
0.16 0.04
0.01 0.01
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Figure 2. Microscopy images of aspirin particles (a) before and (b) after milling.
attempt was made to link the multivariate data analysis results with the powder’s physical properties from a pharmaceutical perspective, as demonstrated below. Principal Component Regression (PCR) Modeling PCR analysis was conducted to determine quantitative relationships between the physical properties for various formulations (the X-variables) and segregation tendency (L/F values, the Yvariable). The dependent X-variables (the physical properties for various formulations) were normalized by auto-scaling. The PCR model generated shows that the first three principal components, PC1, PC2, and PC3, explained 0 %, 3%, and 81% of the Y-variance, respectively. Consequently the X-variables with large loading coefficients in PC3 were the primary contributors
to the variability in L/F. As shown in Figure 3, the largest coefficients of the X-variables associated with PC3 were for PSR (0.83) and Co (0.54). The rest PC3 coefficients of the X-variables were small ( 0.12). This is consistent with the MLR results that showed that L/F is dependent on the properties of ASP and not on the properties of MCC. As shown in Table 4, the PSR and Co vary with the grade of ASP. The rest of the physical properties of the blends were only dependent on the type of MCC.
Table 7. Volume Based Mean Particle Size D(4,3) of Raw Materials (n ¼ 3, the Number in Parenthesis Is Standard Deviation) Particle Size (mm) ASP (fine) ASP (coarse) PH200 PH301
D(4,3), Mean (Std) 62 516 191 72
(1) (19) (5) (1)
Mode Size, Mean (Std) 75 706 212 81
(5) (16) (3) (1)
Figure 3. PCR model to link segregation tendency with physical properties for various formulations. PC1, PC2, and PC3 coefficients of X-loadings for model based on all nine physical properties
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With three PCs, this PCR calibration model had a R2 of 0.846, a slope of 0.846 and a RMSEC of 0.105; its cross-validation model had a R2 of 0.486, a slope of 0.733 and a RMSEP of 0.214. A PCR model using only the two physical properties, PSR and Co, produced better results than the model using all nine properties. As shown in Figure 4, with two PCs, the PCR calibration model had a R2 of 0.857, a slope of 0.857 and a RMSEC of 0.101; its cross-validation model had a R2 of 0.727, a slope of 0.865 and a RMSEP of 0.146. Comparison of Multivariate Approach and Univariate Approach As shown in Figure 5a, L/F values were inversely proportional to PSR. The tendency of the blends to segregate increased as the ASP/MCC PSR increased. The SD of the L/F values also increased with increasing PSR (Figure 5b). Various univariate models were used to relate the PSR data to L/F, as shown in Table 8. A power law model provided the best fit based on the largest R2 value. It can be readily appreciated that compared to the multivariate approach using PCR, the univariate approach generated a lower R2 value for the calibration model. The performance difference between univariate approach and multivariate approach demonstrated in this study was in good agreement with the publication by Wu et al.37 A univariate approach relating L/F to only the grade of ASP leads to an incomplete model. This was supported by the multivariate approaches. The MLR results indicated that the grade of ASP was the primary factor influencing segregation, but the interaction between ASP and MCC may play a
Figure 5. Dependency of segregation tendency on ASP/MCC particle size ratio (PSR) for the 2nd DOE study. (a) L/F mean versus PSR, the curve represents the best fit using a power model; (b) SD (L/F) versus PSR.
minor role. Using PCR analysis, the L/F correlated well with PSR and Co, blend properties related to both ASP and MCC, and not with flow properties of the blend that were dominated by MCC.
CONCLUSIONS In this work, a segregation tester conforming to the ASTM D 6940-04 was built, and evaluated for pharmaceutical powder segregation tendency characterization using a DOE approach. In the Table 8. Univariate Regression Models: Dependence of PSR (x) on L/F (y)
Figure 4. Predicted L/F versus measured L/F. PCR model is based on PSR and Co only. Solid line is the regression line for calibration model (&); dotted line is the regression line for leave-one-out cross-validation results (*). DOI 10.1002/jps
Model Type
Model Equation
R2
Linear Logarithm Power Exponential
y ¼ 0.0685x þ 0.846 y ¼ 0.1886Ln(x) þ 0.722 y ¼ 0.6717x0.3388 y ¼ 0.8405e0.1237x
0.707 0.760 0.801 0.752
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first study design, it was found that the amount of material loaded was a critical variable for the segregation tester. The larger the amount of material loaded, the smaller was the L/F value with a smaller standard deviation. For the same number of segregation cycles, the ST values for 200 g material loaded were on average 1.6–2.04 times that of the 400 g loading. The standard deviation in segregation testing is ranging from 0.030 to 0.235 for our study, depending on the formulation and testing conditions. The second study design addressed the effect of formulation on segregation tendency of the blends. MLR and ANOVA relating L/F to the formulation variables (ASP grade, MCC grade, and level of lubrication), showed that ASP grade was the main effect and has additional contributions from the ASPMCC interaction. The relationship between segregation and physical properties of the blend was evaluated by PCR analysis. A correlation was established between L/F and the blend properties related to ASP and MCC, the ASP/MCC PSR and powder cohesion. The physical properties of the blend related to density (Dt, Db, Dtap) and flow (CI, MOD, AF, AEF) were not influenced by the ASP grade and were not related to the segregation tendency of the blend. The direct relationship between L/F and PSR was determined by univariate analysis. Segregation tendency increased as the ASP to MCC particle size increased. In summary, this work demonstrated the importance of identifying critical test parameters to ensure the reliability and repeatability of segregation testing. It also elaborated on the possibility of integrating experimental design approach and multivariate statistical data analysis methodologies for establishing a relationship between the physical characterization data of bulk powder and the segregation tendency data of powder formulations. This knowledge may help formulation scientists and process engineers to design a formulation and manufacturing process in a way that minimizes segregation.
ACKNOWLEDGMENTS This work was financially supported by the FDA Center for Drug Evaluation and Research (CDER) Regulatory Science and Review (RSR) Grant RSR 04-16 and the Consortium for Industrial Pharmaceutical Education and Training (CIPET). The authors would like to thank Mr. Edward Strickland for helping to design and construct the seg-
regation tester, Dr. Stella G Machado and Dr. Yi Tsong at DBVI/OB/OTS/CDER/FDA for statistical guidance and support, Mr. Christopher D. Ellison at DPQR/OTR/OPS/CDER/FDA and Dr. James Prescott at Jenike & Johanson for helpful discussions.
REFERENCES 1. Chowan ZT. 1995. Segregation of particulate solids, part I. Pharm Technol 19:56–70. 2. Williams JC. 1976. Continuous mixing of solids—A review. Powder Technol 15:237–243. 3. Savage SB, Lun CKK. 1988. Particle size segregation in inclined chute flow of dry cohesionless granular solids. J Fluid Mech 189:311–335. 4. Samadani A, Pradhan A, Kudrolli A. 1999. Size segregation of granular matter in silo discharges. Phys Rev E 60:7203–7209. 5. Dolgunin VN, Kudy AN, Ukolov AA. 1998. Development of the model of segregation of particles undergoing granular flow down an inclined chute. Powder Technol 96:211–218. 6. Dolgunin VN, Ukolov AA. 1995. Segregation modeling of rapid powder gravity flow. Powder Technol 83:95–103. 7. Khahar DV, McCarthy JJ, Ottino JM. 1999. Mixing and segregation of granular materials in chute flows. Chaos 9:594–610. 8. Drahun JA, Bridgwater J. 1983. The mechanisms of free surface segregation. Powder Technol 36:39–53. 9. Olivieri G, Marzocchella A, Salatino P. 2004. Segregation of fluidized binary mixtures of granular solids. AIChE J 50:3095–3106. 10. Salter GF, Farnish RJ, Bradley MSA, Burnett AJ. 2000. Segregation of binary mixtures of particles during the filling of a two-dimensional representation of a hopper. Proc IMechE Part E 214:197–208. 11. Makse HA, Havlin S, King PR, Stanley HE. 1997. Spontaneous stratification in granular mixtures. Nat (Lond) 386:379–382. 12. Cizeau P, Makse HA, Stanley HE. 1999. Mechanisms of granular spontaneous stratification and segregation in two-dimensional silos. Phys Rev E 59:4408–4420. 13. Makse HA. 1997. Stratification instability in granular flows. Phys Rev E 56:7008–7016. 14. Duffy SP, Puri VM. 2002. Primary segregation shear cell for size-segregation analysis of binary mixtures. KONA 20:196–207. 15. Staniforth JN, Rees JE, Kayes JB. 1981. Relation between mixing time and segregation of ordered mixes. J Pharm Pharmacol 33:175–176. 16. Stanforth JN, Rees JE. 1983. Segregation of vibrated powder mixes containing different concentrations of fine potassium chloride and tablet excipients. J Pharm Pharmacol 35:549–554.
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17. Carson JW, Royal TA, Goodwill DJ. 1986. Understanding and eliminating particle segregation problems. Bulk Solids Handling 6:139–144. 18. Johanson JR. 1996. Predicting segregation of bimodal particel mixtures using the flow properties of bulk solids. Pharm Technol 20:46–57. 19. Wong LW, Pilpel N. 1990. Effect of particle shape on the mixing of powders. J Pharm Pharmacol 42:1–6. 20. Alexander A, Roddy M, Brone D, Michaels J, Muzzio FJ. 2000. A method to quantitatively describe powder segregation during discharge from vessels. Pharm Technol, Tableting & Granulation Yearbook, p. 6–21. 21. Venables HJ, Wells JI. 2001. Powder mixing. Drug Dev Ind Pharm 27:599–612. 22. Swaminathan V, Kildsig DO. 2002. Polydisperse powder mixtures: Effect of particle size and shape on mixture stability. Drug Dev Ind Pharm 28:41–48. 23. Williams JC. 1976. The segregation of particulate materials. A review. Powder Technol 15:245–251. 24. Chowan ZT. 1995. Segregation of particulate solids, part II. Pharm Technol 19:80–94. 25. Zhou Y, YU A, Bridgwater J. 2003. Segregation of binary mixture of particles in a bladed mixer. J Chem Technol Biotechnol 78:187–193. 26. Abatzoglou N, Simard JS. 2005. Prediction of segregation tendency occurrence in dry particulate pharmaceutical mixtures: Development of a mathematical tool adapted for granular systems application. Pharm Dev Technol 10:59–70. 27. Liss ED, Conway SL, Zega JA, Glasser BJ. 2004. Segregation of powders during gravity flow through vertical pipes. Pharm Technol Feb:78–96. 28. ASTM International. 2004. D6940-04 standard practice for measuring shifting segregation tendencies of bulk solids. West Conshohocken, PA: ASTM International. 29. Prescott JK, Ramsey PJ. 2000. Bench scale segregation tests as a predictor of blend sampling bias.
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AAPS PharmSci 2: Abstract 235 (2000). Available at http://www.aapspharmaceutica.com/search/abstract_ view.asp?idU2065 & ctU00Abstracts. Wu H, Heilweil EJ, Hussain AS, Khan MA. 2007. Process analytical technology (PAT): Effects of instrumental and compositional variables on terahertz spectral data quality to characterize pharmaceutical materials and tablets. Int J Pharm 343: 148–158. Ramachandruni H, Hoag SW. 2001. Design and validation of an annular shear cell for pharmaceutical powder testing. J Pharm Sci 90:531–540. Brittan HG. 1995. Physical characterization of pharmaceutical solids. New York: Marcel Dekker, Inc. FDA. 2004. Guidance for industry, PAT—A framework for innovative pharmaceutical development, manufacturing, and quality assurance. Available at http://www/fda.gov/cder/guidance/64 19fnl.pdf. FDA/ICH. 2006. Guidance for industry. Q8 pharmaceutical development. Available at http://www. fda.gov/cder/guidance/6746fnl.pdf. Wu H, Hussain AS. 2005. Integration of multivariate statistics and design of experiments to identify critical process variables for pharmaceutical process analytical technology (PAT) applications. In: American Statistical Association 2005 Proceedings of joint statistical meetings, Minneapolis, Minnesota August 7–11, 2005. CD-ROM, Mira Digital Publishing. Wu H, Hussain AS, Khan MA. 2007. Process control perspective for process analytical technology: Integration of chemical engineering practice into semiconductor and pharmaceutical industries. Chem Eng Commun 194:760–779. Wu H, Heilweil EJ, Hussain AS, Khan MA. 2007. Process analytical technology (PAT): Quantification approaches in terahertz spectroscopy for pharmaceutical application. J Pharm Sci 97:958–971.
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School of Pharmacy, University of Maryland, Baltimore, Maryland
2
Division of Product Quality Research (DPQR, HFD-940), Office of Testing and Research, Office of Pharmaceutical Science, CDER, FDA, White Oak Campus Life Science Building 64 Room 1080, 10903 New Hampshire Ave, Silver Spring, Maryland 20993 3
Division of Biometrics VI, Office of Biostatistics, Office of Translational Science, CDER, FDA, Silver Spring, Maryland
4
Office of Pharmaceutical Science, CDER, FDA, Silver Spring, Maryland
Received 27 September 2007; revised 14 December 2007; accepted 17 December 2007 Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/jps.21320
ABSTRACT: The objective of this study was to examine the effects of testing parameters and formulation variables on the segregation tendency of pharmaceutical powders measured by the ASTM D 6940-04 segregation tester using design of experiments (DOE) approaches. The test blends consisted of 4% aspirin (ASP) and 96% microcrystalline cellulose (MCC) with and without magnesium stearate (MgS). The segregation tendency of a blend was determined by measuring the last/first (L/F) ratio, the ratio of aspirin concentrations between the first and last samples discharged from the tester. A 22 factorial design was used to determine the effects of measurement parameters [amount of material loaded (W), number of segregation cycles] with number of replicates 6. ANOVA showed that W was a critical parameter for segregation testing. The L/F value deviated further from 1 (greater segregation tendency) with increasing W. A 23 full factorial design was used to assess the effects of formulation variables: grade of ASP (unmilled, milled), grade of MCC, and amount of lubricant, MgS. MLR and ANOVA showed that the grade of ASP was the main effect contributing to segregation tendency. Principal Component Regression Analysis established a correlation between L/F and the physical properties of the blend related to ASP and MCC, the ASP/MCC particle size ratio (PSR) and powder cohesion. The physical properties of the blend related to density and flow were not influenced by the grade of ASP and were not related to the segregation tendency of the blend. The direct relationship between L/F and PSR was determined by univariate analysis. Segregation tendency increased as the ASP to MCC particle size increased. This study highlighted critical test parameters for segregation testing and identified critical physical properties of the blends that influence segregation tendency. ß 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:4485–4497, 2008
The views and opinions expressed in this article are only of the authors, and do not necessarily reflect the views or policies of the FDA. Ajaz S. Hussain’s present address is Sandoz, Inc., 506 Carnegie Center, Suite 400, Princeton, NJ.
Correspondence to: Huiquan Wu (Telephone: 301-796-0022; Fax: 301-796-9816; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 97, 4485–4497 (2008) ß 2008 Wiley-Liss, Inc. and the American Pharmacists Association
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Keywords: quality-by-design; segregation; powder; segregation tendency; formulation; microcrystalline cellulose; design of experiments; multivariate data analysis; ANOVA; powder physical characterization
INTRODUCTION Segregation is a process through which a uniform powder blend becomes nonuniform, with regions of varying composition. Powder segregation is a ubiquitous phenomenon that exists in virtually all dry-powder-handling industries such as pharmaceutical, agriculture, and mining. Any process which causes relative particle movement can introduce powder segregation if the components have different flow characteristics. For freeflowing powders, segregation can occur when set in motion by a mixer, during handling after mixing, during material transfer (e.g., discharge from a hopper), during transport of stored blends. The primary particle properties that influence the segregation blends include: particle size, density, shape, and surface texture.1,2 Secondary factors include coefficient of friction, moisture, shape, and surface of the container, and the difference in resilience of the particles. On the other hand, for cohesive powders, which do not flow as well, segregation involves overcoming interparticulate bonding forces, for example, van der Waals, electrostatic, and hydrogen bonding. The primary mechanisms of segregation are: percolation (sifting or void filling),2–4 trajectory (rolling),3,5–8 fluidization,1,9 push-away effects,10 angle of repose, and stratification.11–13 A number of papers14–22 have been published previously that examined powder segregation and its effect using different experimental methods. Williams23 and Chowhan24 reviewed various methods to measure segregation. Over the past few years, quantitative studies on segregation tendency of a powder blend have received increased attention. For example, computer simulations were used to study the segregation of binary mixtures in a bladed mixer.25 Abatzoglou and Simard26 adapted a simple mathematical tool previously developed for the study of the behaviors of continuous chemical reactors with classical fluid flows, to evaluate the tendency of a granular mixture to segregate. They proposed an experimental tool to predict the tendency of segregation in binary granular mixtures. The segregation of powders during gravity flow through vertical pipes was investigated using
glass beads with different colors and sizes to visualize the state of segregation.27 The ASTM D 6940 segregation tester is a standard device for testing the segregation tendency of a powder;28 it uses powder flow to measure the segregation tendency of a powder blend. However, little has been published on the use of the ASTM D6940-04 segregation tester for pharmaceutical powder testing.29 To the best of our knowledge, there have been no published studies on the reliability and repeatability of this method for pharmaceutical powders. To bridge this knowledge gap from a measurement perspective,30 we need to address the following questions when testing for segregation: (1) Does the amount of materials loaded in the hopper affect the results? (2) How many times should the materials be cycled through the hoppers? (3) How many replicates are required to give a reliable evaluation? To date, little research has been published on the impact of bulk powder properties on segregation tendency. Thus, this research examines the relationship between segregation tendency and physical properties of the blend related to density [true density (Dt), bulk density (Db), tapped density (Dtap), Carr’s Index (CI)] and flow [angle of friction (AF), effective friction (AEF), and cohesion (Co) from shear cell testing31 and minimum orifice diameter32 (MOD)]. An in-depth understanding of powder behavior would be useful for predicting the performance of a powder blend in unit operations such as capsule filling and tablet compression. Design of experiments (DOE) and multivariate statistical data analysis are important tools recognized in recent regulatory documents such as the FDA’s Process Analytical Technology (PAT) Guidance33 and ICH Q8 Guideline.34 They are essential elements for pharmaceutical quality-bydesign concept. However, currently there is a lack of published case studies that illustrate the integration of these important tools for pharmaceutical development and manufacturing.35 The objectives of this study were to: (1) examine the utility of the ASTM D6940-04 segregation tester for pharmaceutical powder characterization, and (2) determine the relationships between segregation tendency and bulk powder properties.
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The model system selected contained 4% (w/w) aspirin (ASP) and 96% (w/w) microcrystalline cellulose (MCC). For the first objective, a DOE approach was used to examine the effect of the amount of material loaded into the hopper, number of segregation cycles, and number of replicates needed. Once a standard method was established based on the foregoing study, the impacts of varying particle size and various formulations on segregation potential were systematically studied using another DOE protocol.
EXPERIMENTAL Materials and Methods ASP was received from AnMar International Ltd (Aspirin, Lot # 98626, Crystals, 20–60 mesh, USP, Metuchen, NJ). Coarse particles were collected from those retained on a 35 mesh (500 mm) screen and fine particles were obtained by twice milling with a Comil1 (Model: 197S, Quadro Engineering, Inc., Waterloo, Canada). The mill was operated at 80 rpm with a number 200 washer. The mesh used was 2A019R014/19. The temperature of the processing environment was 20 0.58C, and the relative humidity was 40 5%. The particles that could pass through a 200 mesh (75 mm) sieve were collected and considered fine ASP particles for this study. MCC from FMC (Avicel1 PH200 Lot # M414C, and Avicel1 PH301, Lot # M445C, Mechanicsburg, PA) was used as received. Magnesium stearate (MgS) was obtained from Spectrum (NF/USP, Lot # RH0744, New Brunswick, NJ). Powder blends of 200 g were blended in the PK twin shell V-blender (4 quarts) (Patterson-Kelley Co., East Stroudsburg, PA), and powder blends of 400 g were blended in the PK twin shell V-blender (8 quart) (Patterson-Kelley Co.). The 200 and 400 g powder blends were mixed at a rotating speed 28 RPM for 30 min. If a lubricant was included, it was added after first mixing the drug and filler. MgS was blended for 30 min to achieve complete lubricant coverage. To minimize segregation prior to testing, the powder collected from the V-blender was carefully handled to minimize the free fall distance, and the powder blends were transferred into the top hopper of the segregation tester in a manner which minimize the power free-fall height. All mixing and transferring procedures were carried out by one person to avoid individual operator differences. DOI 10.1002/jps
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Segregation Tester The segregation tester shown in Figure 1 conforms to the ASTM D 6940-04 standard, and was custom built for this study (Pride Machining, Upper Falls, MD). The tester consists of three plexiglass hoppers and two aluminum stands, each equipped with a slide gate to control powder flow. The hoppers were assembled as shown in Figure 1. The mass flow hopper was on the top and funnel flow hopper at the bottom. Fifty-milliliter beakers were used to collect powder sample. The sample size collected was controlled by the volume of the guide cylinder. The dimensions of the three hoppers and guide cylinder are given in ASTM standard28 D6940-04. Minimal segregation should occur as the blend discharges the mass flow hopper. As the particles free fall into a heap in the lower hopper, various mechanisms can contribute to segregation based on the size, density, shape, and flow properties of the particles that make up the two different mixture components. Particles that are smaller, denser, rougher in shape and have a larger angle of repose tend to stay in the center of the heap. Particles that are larger, less dense, smoother in shape and have a smaller angle of repose tend to migrate to the base of the heap near the sides of the hopper. The blend is then discharged out of the lower hopper. Since the lower hopper is a funnel flow hopper, discharge occurs so that the mixture is separated based on the location in the lower hopper. The particles in the central core (from bottom to top) are discharged first followed by discharge (from top to bottom) of the particles
Figure 1. ASTM D 6940-04 Segregation tester (with the top slide gate open and the powder flow from the mass flow hopper down to the funnel flow hopper). Fifty microliter glass beakers were used as sample collector.
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located near the outer wall of the hopper. The difference between the first and the last samples can be used as an indicator of segregation potential, when a single-valued result is compared for different powder systems. In this work, segregation tendency is determined by calculating the last/first (L/F) ratio of drug concentrations, referred as the ratio of aspirin concentrations between the first and the last sample. The L/F ratio of drug concentrations is given by: L Drug concentration in the last sample ratio ¼ F Drug concentration in the first sample (1) An L/F ratio equal to or close to one means that there is little difference in drug concentration in the first and last sample, indicating that minimal segregation has occurred. This powder system is stable and more likely to maintain uniformity under free flowing conditions. The more the L/F ratio deviates from 1 the greater the redistribution of drug particles during flow. This redistribution indicates the tested powder system is unstable and the blend is prone to segregate.
numbered according to the order in which they were collected. Samples were collected after one segregation cycle and after five segregation cycles. The number of segregation cycles depended on the number of rotations of the hoppers. Instead of collecting samples from bottom hopper after powder fell from the top hopper to the bottom hopper, we rotated the positions of top and bottom hoppers with slide gates being closed. Then, the slide gate of top hopper was opened and the powders were allowed to flow from the top hopper to the bottom hopper. Repeating this rotation one more time completed one segregation cycle. Coning-and-quartering method was used to reduce the 20 g sample to 1.0–1.5 g. Three samples were measured for each beaker. All samples were placed in a 0.1 N NaOH solution and sonicated for 30 min to completely dissolve the aspirin. The suspension was centrifuged at 2000 rpm for 10 min; the supernatant was diluted with 0.1 N NaOH to suitable concentration for measurement by UV absorbance at 298 nm. The 0.1 N NaOH solution was used as a control. The standard curve was linear (R2 ¼ 0.999).
Segregation Testing During testing, the powder blend was carefully loaded into the top hopper with the slide gates closed. This careful loading was done in all cases the same way in order to maintain consistency. The slide gate of the top hopper was opened to allow the powder to flow from the top hopper to the bottom hopper (as shown in Fig. 1). If the bulk powder did not flow freely out of the hopper, a gentle tap was given to the hopper to initiate flow; this was done without noticeably disturbing the character of the powder bed. Only the PH 301 formulations needed taping to initiate and maintain flow; typically, between one and five light taps were used as needed. Thus, the authors feel these taps did not alter or bias the powder bed or our analysis of the data. Samples were collected from the lower hopper by placing the bottom of the beaker up against the guide cylinder of the lower hopper and opening the slide gate until the guide cylinder was full. Then the slide gate was closed and the beaker was lowered to allow the powder flow into the beaker; a typical sample weighed 20 g. This sample-collecting step was repeated until all the powder had been discharged from the lower hopper. The samples were collected in separate beakers and
Experimental Design The first part of this study was to assess the segregation tester performance. The factors examined were the amount of material loaded (200 and 400 g), the number of segregation cycles (1, 5), and number of replicates (for assessing measurement variability). The response variables were L/F values and SD (standard deviation) of L/F values. The experimental design listed in Table 1 was created using STATGRAPHICS1 5 PLUS (Manugistics, Inc., Rockville, MD). It consisted of six replicates of 22 factorial design. The order of the experiments was fully randomized to reduce systematic errors. The formulation included coarse ASP and coarse MCC (Avicel1 PH 200). The testing protocol for subsequent study was based on the results of this DOE study. In the second part of this study, the effects of formulation variables on the segregation tendency were examined using another DOE. The material loading was fixed at 400 g and one segregation cycle was used. Two levels for each of the three formulation components were included: coarse and fine particle size ASP, Avicel1 PH 200 and PH 301, 0% (w/w) and 0.5% (w/w) MgS. L/F value
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Table 1. Experiment Protocol for the 1st DOE and the L/F and SD Results of Each Experimental Run Run
Replicate
Material Loading (g)
Segregation Cycles
L/F
SD
1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 6 6 6 6
200 400 400 200 200 400 200 400 200 400 400 200 200 400 400 200 200 400 400 200 200 200 400 400
1 1 5 5 5 5 1 1 5 1 5 1 1 1 5 5 1 1 5 5 5 1 1 5
0.382 0.249 0.257 0.507 0.539 0.247 0.386 0.290 0.460 0.335 0.234 0.590 0.585 0.255 0.241 0.512 0.388 0.337 0.249 0.541 0.451 0.449 0.296 0.246
0.235 0.035 0.030 0.111 0.120 0.045 0.160 0.100 0.174 0.117 0.149 0.187 0.178 0.030 0.148 0.105 0.235 0.112 0.047 0.122 0.178 0.170 0.099 0.036
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
The SD was calculated based on triplicate results.
was the response variable. A 23 full factorial design was created as shown in Table 4. The derived variable, aspirin to MCC particle size ratio (PSR), was also listed in Table 4.
Physical Characterization of Bulk Powder Particle Size Analysis Particle size was measured by laser diffraction with a Malvern Mastersizer (Malvern, Inc., Worcestershire, UK). The dry powder feeder was operated at an air pressure 60 psi, and a sample size of 1.0–1.5 g. The volume based particle size analysis produced a mean particle size D[4,3] and a mode diameter. A microscope (Nikon, Eclipse ME 600, Image Systems, Inc., Columbia, MD) was used to take the pictures.
Density Measurements True density (Dt) values of neat materials were measured using a helium pycnometer (USP26/ NF21 <699>) (Helium Pycnometer 1305, MicroDOI 10.1002/jps
meritics Instument, Norcross, Georgia). The Dt of a blend was calculated from the formulation composition. Bulk density (Db) values were measured by using a Stampfvolumeter (USP26/ NF21 <616>). The tapped density (Dtap) values of powder blends were measured using a STAV 2003 Tap density tester (USP26/NF21 <616>). Carr’s compressibility index (CI) value was calculated using the formula (Carr, 1965): Dtap Db CI ¼ 100 (2) Dtap Generally a smaller CI value indicates better the powder flow. Minimum Orifice Diameter MOD was determined using a FlodexTM tester (Hanson Research Corporation, Chatsworth, CA). The sample size was 50 g. The smallest orifice that the bulk powder passed through and left an upside-down truncated cone instead of a cylinder shape rathole was used as the MOD. A smaller MOD value indicates better powder flow.
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Shear Cell Analysis
N the number of segregation cycles, and R the number of replicates for the 22 factorial design. The following statistical model was used:
An annular shear cell was used to determine the AF, angle of effective friction (AEF) and cohesion (Co). A detailed description of the shear cell and methods used in these studies are given by Ramachandruni and Hoag31 In brief, a powder bed height of 1 cm, a sample size of approximately 40–60 g of bulk powder and a lid with baffles was used for all tests. The tests were carried out using a constant rotating speed of 8 rpm.
yijk ¼ m þ ai þ bj þ g k þ ðabÞij þ "ijk i ¼ 1; 2
j ¼ 1; 2
(3)
k ¼ 1; 2; . . . ; 6
where yijk is the observation (either L/F value or SD) in the ith level of variable W and the jth level of variable N at the kth replicate, m is the overall mean, ai is the ith amount of materials loaded effect, bj is the jth number of segregation cycles effect, gkis the kth replication effect, (ab)ij is the two-way interaction between the ith amount of materials loaded effect and the jth segregation cycles effect, eijk is the random error. Replicate was treated as a block factor in our statistical model. Therefore, we were most interested in the interaction among variables within each block, the ‘‘ab’’ effect. Thus, in the final model selected, the two-way interaction terms ‘‘ag’’ and ‘‘bg’’ were not included. The ANOVA consisted of partitioning the total sum of squares into the following components:
Data Analysis Analysis of variance (ANOVA) for the two DOE protocols was done using SAS 9.1.3 software program (SAS Institute, Inc., Cary, NC). Multivariate statistical analysis, including multi-linear regression (MLR) and principal component regression (PCR), was done with Unscrambler 9.2 software (Camo Technologies, Woodbridge, NJ).
RESULTS AND DISCUSSION
SST ¼ SSW þ SSN þ SSR þ SSWN þ SSE
Statistical Analysis of the DOE Results for the Segregation Tester: Identifying Critical Testing Parameters
(4)
This model was used to analyze the six replicates of 22 factorial design, which examined how L/F and SD varied with W, N, and R. Results are shown in Tables 2(a) and 3(a). It showed that for both L/F and SD, the replicate variable was not
The L/F values and standard deviations (SD) from the 24 experimental runs were summarized in Table 1. Let W be the amount of material loaded,
Table 2. ANOVA Results on L/F Data for the 1st DOE Dependent
Source
Degree of Freedom
Mean Sum of Square
F-Value
p-Value
Weight Cycle Weightcycle Replicate
1 1 1 5
0.28 0.00 0.01 0.00
76.01 0.14 2.32 0.45
<0.0001 0.71 0.15 0.80
Weight Cycle Weightcycle
1 1 1
0.28 0.00 0.01
88.02 0.16 2.69
<0.0001 0.69 0.12
Hypothesis Type
(a) Relative importance L/F 3 L/F 3 L/F 3 L/F 3 (b) Interaction L/F 3 L/F 3 L/F 3 Dependent
Source
Degree of Freedom
Mean Sum of Square
F-Value
p-Value
(c) Main effects L/F
Weight
1
0.28
84.74
<0.0001
Name
Weight
Least Square Mean
Standard Error
0.49 0.27
0.017 0.017
(d) Calculate least squares mean L/F 200.00 L/F 400.00 JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 97, NO. 10, OCTOBER 2008
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Table 3. ANOVA Results on SD Data (of L/F) for the 1st DOE Dependent
(a) Relative importance SD 3 SD 3 SD 3 SD 3 (b) Interaction SD 3 SD 3 SD 3 (c) Main effects SD 1 SD 3 Name
Source
Degree of Freedom
Mean Sum of Square
F-Value
p-Value
Weight Cycle Weightcycle Replicate
1 1 1 5
0.03 0.00 0.00 0.00
20.75 0.99 0.95 1.31
0.0004 0.34 0.35 0.31
Weight Cycle Weightcycle
1 1 1
0.03 0.00 0.00
19.26 0.92 0.88
0.0003 0.35 0.36
Weight Weight
1 1
0.03 0.03
19.44 19.44
0.0002 0.0002
Hypothesis Type
Weight
(d) Calculate least squares mean SD 200 SD 400
statistically significant ( p < 0.05). Therefore, data can be pooled across replicates. Next, a two-factor ANOVA was performed on the pooled data across replicates using the following partition model: SST ¼ SSW þ SSN þ SSWN þ SSE
(5)
The results in Tables 2(b) and 3(b) showed that for both L/F and SD, variable N and interaction between variable W and variable N were not statistically significant. Therefore, only the variable W entered to the main effect model: SST ¼ SSW þ SSE
(6)
The results in Tables 2(c) and 3(c) showed that for both L/F and SD, variable W was statistically significant. Finally, the least squares mean was calculated for each level of the weight variable. As shown in Tables 2(d) and 3(d), compared to the 200 g loading, the 400 g loading generated L/F values deviating further from 1.0 with smaller SD values.
Selecting Testing Protocol for Maximizing Test Sensitivity As discussed above, variable replicate was not statistically significant at the 0.05 level. This suggested that the experimental conditions were well controlled and run-to-run variability was not DOI 10.1002/jps
Least Square Mean
Standard Error
0.15 0.08
0.012 0.012
statistically significant. This is not to say that replicate experiments are not needed as replicates helped to confirm the repeatability of test conditions and avoid gross errors. For a given formulation the segregation tendency was assumed to be constant, dictated by the physical properties of formulation components. For the aspirin formulations studied, as the amount of material loaded increased, the L/F and SD values decreased. This indicates that the response (L/F value) depends on the specific test conditions. Because certain test conditions (such as larger amount of materials loaded and different segregation cycles) resulted in smaller L/F values, test parameters could be adjusted to achieve greater sensitivity and should be standardized for data to be comparable. In our study, the 400 g loading was equivalent to the full occupancy of the inner hopper. The 400 g loading produced smaller L/F values indicating more material increased the sensitivity and the 400 g loading had a standard deviation less than the 200 g loading (Tab. 1). For the segregation tester, the shape of the powder pile was an essential factor affecting particle segregation. With the 200 g sample, less powder was available to form a pile for rolling segregation to occur and the pile’s shape was less repeatable. These results showed that on average, the measured L/F value for 200 g loading was 1.5–2.1 times greater than the L/F values for 400 g loading, depending on the number of segregation cycles.
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Effects of Formulation Variables on L/F Values To examine how formulation variables affected the segregation tendency, we conducted a 23 factorial design, which included the eight formulations listed in Table 4. Using the test protocol established from the first DOE study (400 g loading and one segregation cycle), we measured the L/F values for those eight formulations. The data were analyzed with MLR and ANOVA (Tab. 5). For MLR, the following relationship between the formulation components (including two-way interactive terms) and the response factor, L/F was evaluated
tendency is almost completely dependent on the properties of the ASP and not greatly influenced by the properties of the MCC or the lubrication level. This same analysis was applied to the SD of the L/F as the dependent variable and the results are presented in Table 6. As determined for L/F, only the grade of ASP was a significant factor ( p < 0.05) in contributing to the SD. Finally, the least squares mean was calculated for each ASP grade. As shown in Tables 5(c) and 6(c), compared to the blends with fine ASP, the blends with coarse ASP generated larger segregation tendency (L/F values further from 1.0) and greater SD values.
y ¼ b0 þ b1 x1 þ b2 x2 þ b3 x3 þ b12 x1 x2 þ b13 x1 x3 þ b23 x2 x3
(7)
where y is L/F, b0 is the intercept term, x1 is the grade of ASP (coded 1 for fine and þ1 for coarse), x2 is the grade of MCC (coded 1 for fine and þ1 for coarse), and x3 is lubricant level (LL) of MgS (coded 1 for 0% and þ1 for 0.5%). The ANOVA analysis used the following partition model: SST ¼ SSMCC þ SSASP þ SSLL þ SSMCCASP þ SSASPLL þ SSMCCLL þ SSE
(8)
As shown in Table 5(a), only the grade of ASP was significant ( p < 0.05). A second analysis was performed using the terms with the three lowest p values (ASP, ASPMCC, and MCCLL) and the results are shown in Table 5(b). As determined by the previous analysis, only the grade of ASP was a significant factor ( p < 0.05). The two-way interactive term, ASPMCC, was nearly significant ( p ¼ 0.051). This indicates that the segregation
Physical Characterization of Bulk Powders and Blends The shape and particle size of coarse (before milling) and fine ASP (after milling) particles were shown in Figure 2a and b. The coarse particles were smooth and elongated (high aspect ratio). After milling, the ASP particles rough and compact (low aspect ratio). Particle size results are listed in Table 7. The ASP/MCC PSR ranged from 0.354 to 8.716 as shown in Table 4. Results of other physical properties of the blends are shown in Table 4. The physical properties of the blend related to density (Dt, Db, Dtap) and flow (CI, MOD, AF, AEF) were primarily dependent on the type of MCC (PH200 or PH301) and not related to the grade of ASP. This was not unexpected since the filler represents 96% of the blend. Except for the MOD values, these parameters showed a dependence on the amount of lubricant, especially Dtap and CI. In contrast, the blend flow property, Co, was strongly
Table 4. Experimental Protocol for the 2nd DOE, Segregation Results, and Physical Properties of Blends
Blend F1 F2 F3 F4 F5 F6 F7 F8
ASP (4%) Fine Coarse Fine Coarse Fine Coarse Fine Coarse
LL L/F, PSR Dt Db Dtap MOD AF AEF MCC (%) Mean (Std) (ASP/MCC) (g/cm3) (g/cm3) (g/cm3) CI mm (Degree) (Degree) Co PH200 PH200 PH301 PH301 PH200 PH200 PH301 PH301
0 0 0 0 0.5 0.5 0.5 0.5
0.749 0.328 0.978 0.305 0.874 0.479 0.900 0.341
(0.049) (0.144) (0.040) (0.171) (0.031) (0.174) (0.032) (0.134)
0.354 3.330 0.926 8.716 0.354 3.330 0.926 8.716
1.514 1.514 2.464 2.464 1.506 1.506 2.451 2.451
0.377 0.379 0.394 0.382 0.445 0.449 0.463 0.459
0.508 0.510 0.594 0.590 0.542 0.541 0.666 0.660
34.6 34.5 50.7 54.4 21.8 20.5 43.9 43.7
4 4 20 20 4 4 20 18
40.14 41.64 43.14 42.74 37.38 37.93 39.17 38.72
40.60 41.75 43.79 43.13 37.68 36.97 40.12 39.29
0.24 0.06 0.27 0.16 0.14 0.00 0.44 0.26
PSR, ASP/MCC particle size ratio value based on mode size mean; PH200, Avicel1 PH200; PH301, Avicel1 PH301; LL, MgS lubricant level; Dt, true density; Db, bulk density; Dtap, tapped density; CI, Carr’s compressibility index; MOD, minimum orifice diameter; AF, angle of friction tested by shear cell analysis; AEF, angle of effective friction tested by shear cell analysis; Co, cohesion of powder by shear cell analysis. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 97, NO. 10, OCTOBER 2008
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Table 5. ANOVA Results From L/F Data for the 2nd DOE Dependent
Hypothesis Type
Source
Degree of Freedom
Mean Sum of Square
F-Value
1 1 1 1 1 1
0.00 0.52 0.02 0.01 0.01 0.00
1.14 541.6 22.3 7.07 13.1 2.53
0.48 0.03 0.13 0.23 0.17 0.36
1 1 1
0.52 0.02 0.01
184.5 7.61 4.45
0.0002 0.05 0.10
(a) Relative importance L/F 3 MCC L/F 3 ASP L/F 3 ASPMCC L/F 3 LL L/F 3 MCCLL L/F 3 ASPLL (b) Terms with greatest importance L/F 3 ASP L/F 3 ASPMCC L/F 3 MCCLL Name
ASP
p-Value
Least Squares Mean
Standard Error
0.36 0.88
0.049 0.049
(c) Calculate least squares mean L/F Coarse L/F Fine
trend: they have higher Co than those of PH 301 batches without lubricant.
dependent on the grade of ASP, although the results for lubricated blends were inconsistent. For example, all batches with fine ASP have a higher Co than those with coarse ASP. No matter lubricant is added or not, the batches with PH 301 always have higher Co than those with PH 200. However, compared to the batches without lubricant, batches with lubricant display a complicated manner for the Co. The PH 200 batches with lubricant have smaller Co than those of PH 200 batches without lubricant. The PH 301 batches with lubricant have, however, an opposite
Relationships between L/F and the Physical Properties of the Blends As discussed previously by Wu et al.,36 it is important and necessary to bring the product and process knowledge into the multivariate data analysis domain so that knowledge from different perspectives may be bridged. In this work,
Table 6. ANOVA Results From SD Data for the 2nd DOE Dependent
(a) Relative importance SD 3 SD 3 SD 3 SD 3 SD 3 SD 3 (b) Main effects SD 3 SD 3 SD 3 Name
Source
Degree of Freedom
Mean Sum of Square
F-Value
MCC ASP MCCASP LL MCCLL ASPLL
1 1 1 1 1 1
0.00 0.03 0.00 0.00 0.00 0.00
0.07 37.42 0.00 0.18 0.55 0.06
0.83 0.10 0.96 0.74 0.59 0.85
MCC ASP LL
1 1 1
0.00 0.03 0.00
0.18 92.78 0.46
0.69 0.0007 0.54
Hypothesis Type
ASP
(c) Calculate least squares mean SD Coarse SD Fine DOI 10.1002/jps
p-Value
Least Squares Mean
Standard Error
0.16 0.04
0.01 0.01
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Figure 2. Microscopy images of aspirin particles (a) before and (b) after milling.
attempt was made to link the multivariate data analysis results with the powder’s physical properties from a pharmaceutical perspective, as demonstrated below. Principal Component Regression (PCR) Modeling PCR analysis was conducted to determine quantitative relationships between the physical properties for various formulations (the X-variables) and segregation tendency (L/F values, the Yvariable). The dependent X-variables (the physical properties for various formulations) were normalized by auto-scaling. The PCR model generated shows that the first three principal components, PC1, PC2, and PC3, explained 0 %, 3%, and 81% of the Y-variance, respectively. Consequently the X-variables with large loading coefficients in PC3 were the primary contributors
to the variability in L/F. As shown in Figure 3, the largest coefficients of the X-variables associated with PC3 were for PSR (0.83) and Co (0.54). The rest PC3 coefficients of the X-variables were small ( 0.12). This is consistent with the MLR results that showed that L/F is dependent on the properties of ASP and not on the properties of MCC. As shown in Table 4, the PSR and Co vary with the grade of ASP. The rest of the physical properties of the blends were only dependent on the type of MCC.
Table 7. Volume Based Mean Particle Size D(4,3) of Raw Materials (n ¼ 3, the Number in Parenthesis Is Standard Deviation) Particle Size (mm) ASP (fine) ASP (coarse) PH200 PH301
D(4,3), Mean (Std) 62 516 191 72
(1) (19) (5) (1)
Mode Size, Mean (Std) 75 706 212 81
(5) (16) (3) (1)
Figure 3. PCR model to link segregation tendency with physical properties for various formulations. PC1, PC2, and PC3 coefficients of X-loadings for model based on all nine physical properties
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4495
With three PCs, this PCR calibration model had a R2 of 0.846, a slope of 0.846 and a RMSEC of 0.105; its cross-validation model had a R2 of 0.486, a slope of 0.733 and a RMSEP of 0.214. A PCR model using only the two physical properties, PSR and Co, produced better results than the model using all nine properties. As shown in Figure 4, with two PCs, the PCR calibration model had a R2 of 0.857, a slope of 0.857 and a RMSEC of 0.101; its cross-validation model had a R2 of 0.727, a slope of 0.865 and a RMSEP of 0.146. Comparison of Multivariate Approach and Univariate Approach As shown in Figure 5a, L/F values were inversely proportional to PSR. The tendency of the blends to segregate increased as the ASP/MCC PSR increased. The SD of the L/F values also increased with increasing PSR (Figure 5b). Various univariate models were used to relate the PSR data to L/F, as shown in Table 8. A power law model provided the best fit based on the largest R2 value. It can be readily appreciated that compared to the multivariate approach using PCR, the univariate approach generated a lower R2 value for the calibration model. The performance difference between univariate approach and multivariate approach demonstrated in this study was in good agreement with the publication by Wu et al.37 A univariate approach relating L/F to only the grade of ASP leads to an incomplete model. This was supported by the multivariate approaches. The MLR results indicated that the grade of ASP was the primary factor influencing segregation, but the interaction between ASP and MCC may play a
Figure 5. Dependency of segregation tendency on ASP/MCC particle size ratio (PSR) for the 2nd DOE study. (a) L/F mean versus PSR, the curve represents the best fit using a power model; (b) SD (L/F) versus PSR.
minor role. Using PCR analysis, the L/F correlated well with PSR and Co, blend properties related to both ASP and MCC, and not with flow properties of the blend that were dominated by MCC.
CONCLUSIONS In this work, a segregation tester conforming to the ASTM D 6940-04 was built, and evaluated for pharmaceutical powder segregation tendency characterization using a DOE approach. In the Table 8. Univariate Regression Models: Dependence of PSR (x) on L/F (y)
Figure 4. Predicted L/F versus measured L/F. PCR model is based on PSR and Co only. Solid line is the regression line for calibration model (&); dotted line is the regression line for leave-one-out cross-validation results (*). DOI 10.1002/jps
Model Type
Model Equation
R2
Linear Logarithm Power Exponential
y ¼ 0.0685x þ 0.846 y ¼ 0.1886Ln(x) þ 0.722 y ¼ 0.6717x0.3388 y ¼ 0.8405e0.1237x
0.707 0.760 0.801 0.752
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first study design, it was found that the amount of material loaded was a critical variable for the segregation tester. The larger the amount of material loaded, the smaller was the L/F value with a smaller standard deviation. For the same number of segregation cycles, the ST values for 200 g material loaded were on average 1.6–2.04 times that of the 400 g loading. The standard deviation in segregation testing is ranging from 0.030 to 0.235 for our study, depending on the formulation and testing conditions. The second study design addressed the effect of formulation on segregation tendency of the blends. MLR and ANOVA relating L/F to the formulation variables (ASP grade, MCC grade, and level of lubrication), showed that ASP grade was the main effect and has additional contributions from the ASPMCC interaction. The relationship between segregation and physical properties of the blend was evaluated by PCR analysis. A correlation was established between L/F and the blend properties related to ASP and MCC, the ASP/MCC PSR and powder cohesion. The physical properties of the blend related to density (Dt, Db, Dtap) and flow (CI, MOD, AF, AEF) were not influenced by the ASP grade and were not related to the segregation tendency of the blend. The direct relationship between L/F and PSR was determined by univariate analysis. Segregation tendency increased as the ASP to MCC particle size increased. In summary, this work demonstrated the importance of identifying critical test parameters to ensure the reliability and repeatability of segregation testing. It also elaborated on the possibility of integrating experimental design approach and multivariate statistical data analysis methodologies for establishing a relationship between the physical characterization data of bulk powder and the segregation tendency data of powder formulations. This knowledge may help formulation scientists and process engineers to design a formulation and manufacturing process in a way that minimizes segregation.
ACKNOWLEDGMENTS This work was financially supported by the FDA Center for Drug Evaluation and Research (CDER) Regulatory Science and Review (RSR) Grant RSR 04-16 and the Consortium for Industrial Pharmaceutical Education and Training (CIPET). The authors would like to thank Mr. Edward Strickland for helping to design and construct the seg-
regation tester, Dr. Stella G Machado and Dr. Yi Tsong at DBVI/OB/OTS/CDER/FDA for statistical guidance and support, Mr. Christopher D. Ellison at DPQR/OTR/OPS/CDER/FDA and Dr. James Prescott at Jenike & Johanson for helpful discussions.
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