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Identifying sources of batch to batch variation in processability
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Powder Technology 183 (2008) 213 – 219 www.elsevier.com/locate/powtec
Identifying sources of batch to batch variation in processability Arne Hagsten a,b , Crilles Casper Larsen b , Jørn Møller Sonnergaard a , Jukka Rantanen a , Lars Hovgaard a,⁎ a
Department of Pharmaceutics and Analytical Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, Denmark b Process Development and Maintenance, Ferring International Center SA, St-Prex, Switzerland Received 11 December 2006; received in revised form 14 June 2007; accepted 23 July 2007 Available online 15 August 2007
Abstract The source of variation in the full scale processability of 131 batches of the active pharmaceutical ingredient 5-aminosalicylic acid was investigated. The variation in processability, seen in the amount of granulation liquid needed for extrusion, was found to be related to a difference in the combined effects of particle size and packing behaviour. Interactions of particle size, specific surface areas and packing behaviour caused the variation in the individual variables to be mistakenly considered unimportant. Instead, multivariate analysis had to be introduced in order to realise their effect. The combination of the packing related compressed density and the 90% percentile diameter of the volume distribution provided especially good separation of the batches according to processability. Low-pressure compression in combination with particle size data, measured by laser diffraction, was found to be a quick, powerful and relevant tool for powder characterisation. The combination allows quick screening of many batches, thereby aiding rational selection of representative samples for further investigations. The results strongly support use of multivariate analysis for investigation of sources of batch to batch variation in processability. © 2007 Elsevier B.V. All rights reserved. Keywords: Batch to batch variation; Processability; Extrusion; Laser diffraction; Low-pressure compression; Multivariate data analysis
1. Introduction To secure the quality of pharmaceuticals, tight control and knowledge of the batch to batch variation in raw materials is of paramount importance. Hence, it is of continued interest to develop and evaluate the methods of powder characterisation to serve this purpose. Chemical tests are necessary in order to secure a low level of impurities with a safe product as a result. However, small levels of impurities can also alter the surface properties of a material [1]. The pharmacopoeial monographs on active pharmaceutical ingredients (APIs) primarily include tests with emphasis on chemical identity and purity. Since the content of impurities in APIs is required to be as low as possible, the absolute contents of individual and relevant impurities may be hard to quantify. Variations in measurable ⁎ Corresponding author. Lars Hovgaard, Department of Pharmaceutics and Analytical Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, Universitetsparken 2, DK-2100, Denmark. E-mail address: [email protected] (L. Hovgaard). 0032-5910/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.powtec.2007.07.042
physical properties of raw materials are typically easier to interpret and relate to processing. Ideally, the chosen physical tests should cover potential and relevant effects caused by all occurring impurities. Extensive and thorough quantification of the physical properties of the API is of particular importance in the manufacture of high load formulations. With the Process Analytical Technology (PAT) guideline, the development and the implementation of new innovative technologies in the manufacturing of pharmaceuticals are encouraged [2]. The aim of the PAT guideline is to enhance the understanding and control of the manufacturing processes, with a higher degree of confidence in the quality of the end product as a consequence. The overall principles of the PAT guideline are included in the recent ICH Q8 guideline [3] on pharmaceutical development. The ICH Q8 guideline defines the design space as the multidimensional combination and interaction of input variables and quality related process parameters. Process understanding obtained by the information and knowledge gained from pharmaceutical development studies and manufacturing experience forms the foundation of
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the design space. Establishment of a large design space is attractive, as changes made within the design space are not subject to regulatory assessment. In the pharmaceutical industry the most important purposes of agglomeration processes are to increase flowability of poorly flowing materials, to lock a certain mixing state, to reduce dust hazards and to control dissolution of the active ingredient from the final product. Wet granulation by extrusion involves forcing a wetted powder mass through a number of orifices, with more or less coherent strings as the immediate product. These strings are then processed into round pellets or dried and ground into short cylindrical granules. When extruding insoluble material with a liquid continuous phase, the wetted material can be regarded as a concentrated suspension, i.e. a paste. The rheology of the paste is important in relation to extrusion processes. The rheological behaviour of the paste is again influenced by the characteristics of its constituents. The extrudability of a material is affected by particle size, packing, surface chemistry, aggregation, etc. [4]. The impact of deliberate manipulations in particle size, particle size distribution and packing behaviour on the extrudability of a material is well described [5–7]. The challenges involved in the characterisation of powders vary between different materials and material grades. Precise size analysis of needle shaped particles is especially cumbersome. For such particles, digital image analysis (DIA) of microscopy images would normally be the method of choice. However, DIA is laborious and not without challenges. During development of a DIA method, careful attention must thus be paid to the sample preparation, the method settings, the algorithms used for selection of particles and the choice of size characteristics [8–10]. As DIA is made on two-dimensional images, it only provides measures of the length and width of needle shaped particles. Since flat needle shaped particles tend to rest on their largest surface [8], the thickness of the needles, which may be different from their width, will not be obtained. Therefore, the effect of small variations between batches in particle shape, expressed as the ratio between the particle dimensions, may be hard to detect by even DIA. Particle size analysis by laser diffraction are quick and easy to perform. Therefore, the laser diffraction technique is a popular alternative to DIA. However, a major disadvantage of the laser diffraction technique is a strong influence of particle shape on the results [11,12]. Even though clear interpretation of size distribution data measured by the laser diffraction on non-spherical particles may be complicated, laser diffraction nonetheless provides particle size related information, which allows quick evaluation of the similarity of batches. In a previous paper from our research group, a simple and low sample consuming method for evaluation of the packing density of powders was described [13]. Compression profiles were obtained under low pressures using a die and a flat-faced punch fitted on a TA-XT2 Texture analyser (Stable Micro Systems Ltd., Godalming, UK). Data derived from the compression profile acquired in the low pressure range (b1 MPa) was found useful for the determination of several packing related parameters. Among these, especially the
specific density of the powders at 0.2 MPa (d0.2) was found useful. This parameter was very easy to determine, while at the same time being correlated to tapped densities measured by traditional tapping volumetry. The d0.2 enabled good differentiation of not only different materials but also of different batches of the same material grade of 5-aminosalicylic acid (5ASA). The d0.2 has been found to be sensitive to particle size and shape [14]. Hence, for a given material the discriminative power of d0.2 might be compromised if the interaction of packing with particle size and shape is neglected. In this work, samples of 137 full scale batches of an API, 5ASA, are investigated. 131 of the batches have been processed by extrusion. In the subsequent steps, the extruded strings are dried and a ground into smaller cylindrical granules. Even though the 5-ASA raw material batches appear very similar, the batches differ in their liquid requirement for extrusion. In this work the liquid requirement is defined as the amount of liquid needed to avoid clogging of the extruder and damaging the extruder screen. Until now, it has not been possible to identify the source of difference in the liquid requirement for successful extrusion of the 5-ASA batches. In order to identify the source of the variation in processability of the 5-ASA batches, compressed densities, particle size distributions and specific surface areas of the batches will be measured and evaluated individually and by use of multivariate analysis. 2. Materials and methods 2.1. Materials Samples from 137 500 kg full scale batches of 5-ASA were supplied by Syntese A/S (Copenhagen, Denmark). The batches were divided into six groups: A.1 (39), A.2 (10), B.1 (74), B.2 (6), C (4), D (4). A, B, C and D describe the overall qualities of the batches. The suffixes, 1 or 2, relate to the processing history of the batches. The C and D batches are development batches produced in full scale. The numbers in parenthesises refer to the number of each batch type enrolled in the study. The batches have been granulated by extrusion using a fixed amount of liquid according to the specified quality of the batch (A or B). Generally, the A batches have been granulated using 23.5% of granulation liquid, while the B batches have been granulated using 27.5% of granulation liquid. One C batch has been granulated using 28.5% of granulation liquid, while one D batch has been granulated using 29.5% of granulation liquid in order to be extruded successfully. The remaining development batches have not been processed. 5-ASA is very slightly soluble in the granulation liquid (b 10 g/L). 2.2. Methods For determination of compressed densities, ∼50 mg 5-ASA samples were compressed on a TA-XT2 Texture Analyser (Stable Micro Systems Ltd., Godalming, UK) fitted with a 6 mm die and a flat-faced punch. The texture analyser was equipped with a 5 kg load cell and the test speed was fixed at 0.5 mm/s. The exact weights of the compressed powder plugs
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were determined after compression. The compressed density at 0.2 MPa, d0.2, was interpolated from the compression profiles. The repeatability of the measurements was evaluated by performing three replicates of the compressed density measurements on four different batches (two B.1 batches, one B.2 batch and one C batch). The remaining batches were tested without replicates. Particle size distributions were measured by laser diffraction on a Malvern Mastersizer S equipped with a 300 F lens and an MSX 64 dry powder feeder unit (Malvern Instruments Ltd., Malvern, UK). The air pressure was fixed at 0.3 MPa. The repeatability of the laser diffraction measurements was evaluated on one batch by performing three replicates. The following parameters were derived from the particle size distributions: the 10% percentile, the median and the 90% percentile diameters of the volume distribution (d(v,0.1), d(v,0.5) and d(v,0.9)), the volume mean diameter (d[4,3]), the surface area mean diameter (d[3,2]) and the span. The span is calculated as (Eq. (1)): span ¼
dðv; 0:9Þ dðv; 0:1Þ dðv; 0:5Þ
ð1Þ
Specific surface areas were determined by air permeability according to the method described in section 2.9.14 of the European Pharmacopoeia [15]. SEM micrographs of one type A.1 and one type B.1 batch deviating much in d0.2 but having the same d(v,0.5) were acquired using a JSM 5200 scanning electron microscope (Jeol Ltd., Tokyo, Japan).
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standard deviation (RSD) of the d0.2 measurement on sample B.1, batch 2, is 1.7%, while the RSDs of the remaining measurements are below 1.0% (n = 3). The RSDs of the size distribution derived parameters are 2.6% for d[4,3], 0.9% for d[3,2], 0.4% for d(v,0.1), 1.6% for d(v,0.5), 3.3% for d(v,0.9) and 2.1% for span (n = 3). High repeatability of the d0.2 and size distribution derived parameters is required, especially if the variation within the individual parameters is expected to be low. 3.2. Univariate approach The 5-ASA batches consist of needle shaped crystals. The two SEM micrographs shown in Fig. 1 are of a type A.1 and B.1 batch having the approximate same d(v,0.5), 10.8 μm, but much deviating in their d0.2s. The A.1 batch has a d0.2 of 0.622 g/cm3, while the d0.2 of the B.1 batch is 0.564 g/cm3. This corresponds to a 10% difference in volume of the two batches at a pressure of 0.2 MPa. The samples appear identical and it is not possible to identify the cause of the variation in packing behaviour from the micrographs visually. The median diameter of the volume distribution, d(v,0.5), is commonly encountered as an important characteristic of a particle system. The d(v,0.5)s of the A.1 batches appear to be lower than the d(v,0.5)s of the B.1 batches (p = 0.01) (Fig. 2). However, the A.2 and B.2 batches have d(v,0.5)s, which are clearly higher than for the other batches (p b 0.001). Furthermore, the d(v,0.5)s of the A batches range from approximately
2.3. Data treatment Principal component analysis (PCA) was performed in SIMCA 10.5 software (Umetrics AB, Umeå, Sweden). Variables were mean centred and scaled to unit variance. Analysis of covariance (ANCOVA) was done in Statistica '98 edition, release 5.1 (Statsoft Inc., Tulsa, USA). 3. Results and discussion 3.1. Repeatability As discussed in our previous work [13], the repeatability of d0.2 measurements is generally high. This is confirmed by the d0.2s determined on the four 5-ASA samples, where the compression test was repeated (Table 1). Hence, the relative Table 1 Results of repeated compressed density (d0.2) measurements with their related standard deviations (SD) and relative standard deviations (RSD) (n = 3) Code
d0.2, g/cm3
SD, g/cm3
RSD, %
B.1, batch 1 B.1, batch 2 B.2, batch 1 C, batch 1
0.568 0.562 0.565 0.505
±0.001 ±0.010 ±0.004 ±0.004
±0.2 ±1.7 ±0.6 ±0.8
See Materials for an explanation of codes.
Fig. 1. SEM micrographs of a A.1 and a B.1 type 5-ASA batch. Both batches have median diameters of approximately 10.8 μm, but very different compressed densities (0.622 and 0.564 g/cm3, respectively).
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10 to 14 μm. This range surrounds all the d(v,0.5)s of the B batches. Therefore, the differences in processability cannot be explained by variations in d(v,0.5) alone. The span is a measure of the width of the size distribution. A.1 batches are seen to have high spans, while B.1, B.2 and A.2 batches have spans of approximately the same level (Fig. 2). The spans of the development batches, C and D, are lower than the rest. A systematic variation in the spans of the different batches appears to be present. However, as A.2 batches cannot be separated from the B.1 or B.2 batches statistically (p = 0.18 and 0.14), span does not explain the variation in processing behaviour sufficiently. As the overall particle size of a powder decreases, the specific surface area of the powder can be expected to increase accordingly. Similar specific surface areas are seen for the A.1 and B.1 batches (Fig. 2). The specific surface areas of B.2 batches and the A.2 batches in particular appear to be low. Nevertheless, it is not possible to find a direct relation of the specific surface areas to processability. The general similarity of the specific surface areas of the 5-ASA batches confirms the similarity in size seen in the size characteristics measured by laser diffraction. The d0.2 allows good separation of the A.1 and B.1 batches (p b 0.001) (Fig. 2). If only the A.1 and B.1 batches had been analysed, d0.2 could be concluded to be able to differentiate the variation in processing behaviour sufficiently. Thus, with only a few outliers the B.1 batches have lower values of d0.2 than the A.1 batches. However, the low d0.2 of the A.2 batches show that variations in d0.2 alone do not explain the batch to batch variations in processability. 3.3. Multivariate approach The above discussion of the relationship of d0.2, size distribution derived parameters and specific surface areas to processability displays the inadequacies and potential pitfalls of studying variables individually when confronted with a multivariate problem. When evaluated individually, each characteristic is rejected one by one, as none is able to explain the variation in processing sufficiently. Furthermore, if the A.2 and B.2 batches had not been analysed, the variations in span or d0.2 could wrongly have been believed to be solely responsible for the differences in processability. PCA reveals a rough grouping of the 5-ASA batches according to their quality (Fig. 3). The first principal component (PC1) accounts for 60.3% of the variation while the second principal component (PC2) accounts for 26.2%. PC1 mainly describes variation in particle size, while PC2 mainly describes variation in span and d0.2 (Figs. 3 and 4). The A.1 point cluster is aligned towards d0.2 while the B.1 point cluster is aligned
Fig. 2. Size parameters, spans, specific surface areas and compressed densities for different qualities of 5-ASA. Batches are sorted according to quality and type (A.1, A.2, B.1, B.2, C and D). Generally, A batches require 23.5% of granulation liquid, while B batches require 27.5%. One C and one D batch have been processed using 28.5 and 29.5% of granulation liquid, respectively. See Methods for an explanation of the size parameters and span.
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Table 2 Discriminative power of compressed density (d0.2) alone and in combination with size related parameters Co-variable
A/B A.1/B.1 A.2/B.1 A.1/B.2 A.1/B.2 A.1/A.2 B.1/B.2 B/C B/D
Fig. 3. PC1 and PC2 scores calculated by PCA on the material properties shown in the loadings plot (Fig. 4) for batches of 5-ASA. Generally, A batches require 23.5% of granulation liquid, while B batches require 27.5%. One C and one D batch have been processed using 28.5 and 29.5% of granulation liquid, respectively.
against it. Hence, d0.2 should be able to separate the A.1 and B.1 batches, while span would cause some overlapping. The A.2 and B.2 clusters are more difficult to separate from each other, although the majority of the A.2 batches appear to be of a larger particle size than the B.2 batches. These observations could also be made from Fig. 2. However, it can now be seen that a combination of PC1 and PC2 scores allows the most effective separation of the A.1 and B.1 clusters (Fig. 3). Accordingly, it might be possible to improve the discriminative power of d0.2 if these interactions are taken into account. Since particle size and span appear to influence d0.2, the discriminative power of d0.2 in combination with those has been tested by analysis of covariance (ANCOVA). Table 2 shows values of the statistical probability (p) calculated by ANCOVA run on pairs of different qualities and types of batches. As only one C and one D batch have been processed, separation of the A and B batches is of primary interest. The combination of d0.2 and either d[4,3], d(v,0.5) or d(v,0.9) enables statistically
Fig. 4. Loadings of PC1 and PC2 calculated by PCA on measured material properties. See Methods for an explanation of the size parameters and span.
None
d[4,3]
d[3,2]
d(v,0.1)
d(v,0.5)
d(v,0.9)
Span
b0.001 b0.001 ns b0.001 ns b0.001 ns 0.02 b0.001
b0.001 b0.001 b0.001 b0.001 0.03 b0.001 ns 0.004 b0.001
b0.001 b0.001 b0.001 ns 0.02 ns b0.001 ns 0.04
b0.001 b0.001 b0.001 ns ns ns 0.002 0.02 0.04
b0.001 b0.001 b0.001 0.03 0.02 ns 0.001 ns 0.01
b0.001 b0.001 b0.001 b0.001 0.002 0.001 ns b0.001 b0.001
b0.001 b0.001 ns b0.001 0.04 b0.001 ns 0.003 b0.001
Probabilities (p), are calculated by a t-test for d0.2 alone and by ANCOVA for the combinations of d0.2 with particle size distribution derived variables. Not significant results on a 0.05 significance level are reported as ns. A/B refers to a test of the A and B quality batches as a whole, A.1/B.1 refers to a test of the A.1 and B.1 types, while A.1/B.2 refers to a test of the A.1 and B.2 types, etc. Groups showing differences in processability are marked in bold. See materials for an explanation of codes and methods for an explanation of the size parameters and span.
significant separation of all groups of A (A.1 and A.2) and B (B.1 and B.2) batches. The best differentiation of the batches is made with d(v,0.9) as co-variable. Hence, the combination of d0.2 and d(v,0.9) enables separation of nearly all combinations of A and B batches on a 0.001 significance level, while the separation of A.2 and B.2 batches is made with a p of 0.002. The best separation of the development batches (C and D) from the B batches is also made using d(v,0.9). However, it should be emphasised that only one C and one D batch has been processed. Hence, the processability of the remaining C and D batches could be different. The combination of d0.2 and d(v,0.9) reflects the differences in processability well (Fig. 5). Batches aligned towards the
Fig. 5. Compressed densities, d0.2, plotted against 90% percentile diameters of the volume distributions, d(v,0.9), for 5-ASA batches. Generally, A batches require 23.5% of granulation liquid (low level), while B batches require 27.5% (medium level). One C and D batch have been processed using 28.5% and 29.5% of granulation liquid (high level), respectively. Lines separating the batches according to the granulation liquid requirement of the majority are added for clarification.
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lower left corner require high amounts of granulation liquid, while batches aligned towards the upper right corner require less. The sensitivity of d0.2 to particle size across a seemingly narrow particle size range may be surprising. However, the d(v,0.9)s of the B batches range from 32 to 44 μm (Fig. 2). Hence, the relative variation is large. Nevertheless, the effect of a larger particle size on the granulation liquid requirement is countered by looser packing, while the effect of a smaller particle size is countered by denser packing. Variation in the degree of interaction between particle size and packing behaviour is the source of the differences in processability observed for the A and B batches. Hence, a B batch of a specific particle size will typically pack less densely than an A batch with the same particle size (Fig. 5). The interaction of particle size and packing causes a constantly low liquid requirement for A batches and a medium liquid requirement for B batches. The variation between the A, B and processed C and D batches shows a high d(v,0.9) in combination with a high d0.2 results in a low liquid requirement. The opposite is true for a batch with a combination of a low d(v,0.9) and a low d0.2. As only one C and one D batch have been processed, prediction of the behaviour of the remaining ones should be done with caution. Nevertheless, the unprocessed C and D batches, situated in the high zone (Fig. 5), would be expected to require a high amount of granulation liquid. Accordingly, the C and D batches in the medium zone would be expected to require an amount of granulation fluid at the same level as the B batches. The relation of d(v,0.9) and d0.2 to the granulation liquid requirement is reasonable. The following explanation is suggested. Since a B batch typically packs more loosely than an A batch at a given pressure (e.g. 0.2 MPa), a B batch will require more liquid in order to lower the resistance of the powder towards compression. The addition of liquid facilitates reduction of the particle–particle interactions [16]. Hence, a B batch requires more liquid to lower particle–particle interactions sufficiently in order for the wetted material to become saturated at the same pressures. For a given quality of 5-ASA (A or B), increased particle size is associated with looser packing. Furthermore, an increased particle size is associated with a lower specific surface area and fewer contact points between particles. Thus, relatively less liquid is needed to counter the particle–particle interactions in powders containing larger particles. For an individual quality of 5-ASA (A or B) the effects of particle size and packing cancel out. Therefore, 5-ASA batches of a certain quality tend to require the same amount of liquid irrespective of their specific particle size or packing behaviour. A.1 batches typically have larger spans than B.1 batches (Fig. 2). Accordingly, much of the variation in d0.2 and span is described in the same principal component, PC2 (Fig. 4). Hence, the variation in packing behaviour between A.1 and B.1 batches could be related to the spans of their particle size distributions. However, it is not possible from laser diffraction deduced size distribution data to conclude whether the differences in span are due to variations in particle shape, aggregates and surface properties or wider particle size distributions. Since packing densities can be increased by
mixing different sized particles [17], an effect of the spans on the packing behaviour of 5-ASA powders would be reasonable. Other more precise methods of measuring the size of needle shaped particles could possibly be able to distinguish the different qualities of 5-ASA directly. However, the packing behaviour can be expected to include variations in particle– particle interactions caused by not only differences in particle size distributions or particle shape but also in surface properties and aggregation. Therefore, the combination of low-pressure compression and laser diffraction data should be able to provide a relevant estimate of the overall bulk behaviour of a given material. The ease and speed of these methods allow efficient screening of many batches, thereby allowing rational selection of representative batches for further studies. Even though unable to fully differentiate 5-ASA batches according to their processability, the d0.2 generally possesses good discriminative power. For other materials, different lowpressure compression derived parameters could possibly be more suited. Procedures to extract packing related information from low-pressure compression data are described in detail by Sørensen et al. [13]. 4. Conclusions A systematic change in the combined effect of particle size and packing behaviour was the source of the 5-aminosalicylic acid (5-ASA) batch to batch variation in processability. Since particle size and packing behaviour interacts, realisation of their effect required the use of multivariate analysis. The combination of compressed density determinations and particle size measurements by laser diffraction is well suited for identification of packing and particle size related batch to batch variation in processability. Although the methods might not provide a direct explanation for the variation shown, they enable quick screening of many batches. Representative batches can then be chosen for further studies. In case of the 5-ASA batches, the variation in processability can be fully countered by adjustments of the granulation liquid level. For other processes and materials where this is not an option, such variation could be critical. Batches with mean particle sizes and packing densities near the maximum acceptance limit of the raw material specification could process very differently than similar sized batches with low packing densities. This is problematic if the specification has been set on studies with batches, where e.g. a low particle size was continually associated with loose packing. Therefore, it is recommended that interactions of particle size and packing behaviour are studied and preferably quantified as part of the establishment of new raw material specifications. In order to avoid neglecting the effect of critical batch properties, the use of multivariate statistics in processability investigations is strongly recommended. Acknowledgements The authors acknowledge Ferring Pharmaceuticals A/S (Copenhagen, Denmark) and the Drug Research Academy (Copenhagen, Denmark) for financial support.
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Powder Technology 183 (2008) 213 – 219 www.elsevier.com/locate/powtec
Identifying sources of batch to batch variation in processability Arne Hagsten a,b , Crilles Casper Larsen b , Jørn Møller Sonnergaard a , Jukka Rantanen a , Lars Hovgaard a,⁎ a
Department of Pharmaceutics and Analytical Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, Denmark b Process Development and Maintenance, Ferring International Center SA, St-Prex, Switzerland Received 11 December 2006; received in revised form 14 June 2007; accepted 23 July 2007 Available online 15 August 2007
Abstract The source of variation in the full scale processability of 131 batches of the active pharmaceutical ingredient 5-aminosalicylic acid was investigated. The variation in processability, seen in the amount of granulation liquid needed for extrusion, was found to be related to a difference in the combined effects of particle size and packing behaviour. Interactions of particle size, specific surface areas and packing behaviour caused the variation in the individual variables to be mistakenly considered unimportant. Instead, multivariate analysis had to be introduced in order to realise their effect. The combination of the packing related compressed density and the 90% percentile diameter of the volume distribution provided especially good separation of the batches according to processability. Low-pressure compression in combination with particle size data, measured by laser diffraction, was found to be a quick, powerful and relevant tool for powder characterisation. The combination allows quick screening of many batches, thereby aiding rational selection of representative samples for further investigations. The results strongly support use of multivariate analysis for investigation of sources of batch to batch variation in processability. © 2007 Elsevier B.V. All rights reserved. Keywords: Batch to batch variation; Processability; Extrusion; Laser diffraction; Low-pressure compression; Multivariate data analysis
1. Introduction To secure the quality of pharmaceuticals, tight control and knowledge of the batch to batch variation in raw materials is of paramount importance. Hence, it is of continued interest to develop and evaluate the methods of powder characterisation to serve this purpose. Chemical tests are necessary in order to secure a low level of impurities with a safe product as a result. However, small levels of impurities can also alter the surface properties of a material [1]. The pharmacopoeial monographs on active pharmaceutical ingredients (APIs) primarily include tests with emphasis on chemical identity and purity. Since the content of impurities in APIs is required to be as low as possible, the absolute contents of individual and relevant impurities may be hard to quantify. Variations in measurable ⁎ Corresponding author. Lars Hovgaard, Department of Pharmaceutics and Analytical Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, Universitetsparken 2, DK-2100, Denmark. E-mail address: [email protected] (L. Hovgaard). 0032-5910/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.powtec.2007.07.042
physical properties of raw materials are typically easier to interpret and relate to processing. Ideally, the chosen physical tests should cover potential and relevant effects caused by all occurring impurities. Extensive and thorough quantification of the physical properties of the API is of particular importance in the manufacture of high load formulations. With the Process Analytical Technology (PAT) guideline, the development and the implementation of new innovative technologies in the manufacturing of pharmaceuticals are encouraged [2]. The aim of the PAT guideline is to enhance the understanding and control of the manufacturing processes, with a higher degree of confidence in the quality of the end product as a consequence. The overall principles of the PAT guideline are included in the recent ICH Q8 guideline [3] on pharmaceutical development. The ICH Q8 guideline defines the design space as the multidimensional combination and interaction of input variables and quality related process parameters. Process understanding obtained by the information and knowledge gained from pharmaceutical development studies and manufacturing experience forms the foundation of
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the design space. Establishment of a large design space is attractive, as changes made within the design space are not subject to regulatory assessment. In the pharmaceutical industry the most important purposes of agglomeration processes are to increase flowability of poorly flowing materials, to lock a certain mixing state, to reduce dust hazards and to control dissolution of the active ingredient from the final product. Wet granulation by extrusion involves forcing a wetted powder mass through a number of orifices, with more or less coherent strings as the immediate product. These strings are then processed into round pellets or dried and ground into short cylindrical granules. When extruding insoluble material with a liquid continuous phase, the wetted material can be regarded as a concentrated suspension, i.e. a paste. The rheology of the paste is important in relation to extrusion processes. The rheological behaviour of the paste is again influenced by the characteristics of its constituents. The extrudability of a material is affected by particle size, packing, surface chemistry, aggregation, etc. [4]. The impact of deliberate manipulations in particle size, particle size distribution and packing behaviour on the extrudability of a material is well described [5–7]. The challenges involved in the characterisation of powders vary between different materials and material grades. Precise size analysis of needle shaped particles is especially cumbersome. For such particles, digital image analysis (DIA) of microscopy images would normally be the method of choice. However, DIA is laborious and not without challenges. During development of a DIA method, careful attention must thus be paid to the sample preparation, the method settings, the algorithms used for selection of particles and the choice of size characteristics [8–10]. As DIA is made on two-dimensional images, it only provides measures of the length and width of needle shaped particles. Since flat needle shaped particles tend to rest on their largest surface [8], the thickness of the needles, which may be different from their width, will not be obtained. Therefore, the effect of small variations between batches in particle shape, expressed as the ratio between the particle dimensions, may be hard to detect by even DIA. Particle size analysis by laser diffraction are quick and easy to perform. Therefore, the laser diffraction technique is a popular alternative to DIA. However, a major disadvantage of the laser diffraction technique is a strong influence of particle shape on the results [11,12]. Even though clear interpretation of size distribution data measured by the laser diffraction on non-spherical particles may be complicated, laser diffraction nonetheless provides particle size related information, which allows quick evaluation of the similarity of batches. In a previous paper from our research group, a simple and low sample consuming method for evaluation of the packing density of powders was described [13]. Compression profiles were obtained under low pressures using a die and a flat-faced punch fitted on a TA-XT2 Texture analyser (Stable Micro Systems Ltd., Godalming, UK). Data derived from the compression profile acquired in the low pressure range (b1 MPa) was found useful for the determination of several packing related parameters. Among these, especially the
specific density of the powders at 0.2 MPa (d0.2) was found useful. This parameter was very easy to determine, while at the same time being correlated to tapped densities measured by traditional tapping volumetry. The d0.2 enabled good differentiation of not only different materials but also of different batches of the same material grade of 5-aminosalicylic acid (5ASA). The d0.2 has been found to be sensitive to particle size and shape [14]. Hence, for a given material the discriminative power of d0.2 might be compromised if the interaction of packing with particle size and shape is neglected. In this work, samples of 137 full scale batches of an API, 5ASA, are investigated. 131 of the batches have been processed by extrusion. In the subsequent steps, the extruded strings are dried and a ground into smaller cylindrical granules. Even though the 5-ASA raw material batches appear very similar, the batches differ in their liquid requirement for extrusion. In this work the liquid requirement is defined as the amount of liquid needed to avoid clogging of the extruder and damaging the extruder screen. Until now, it has not been possible to identify the source of difference in the liquid requirement for successful extrusion of the 5-ASA batches. In order to identify the source of the variation in processability of the 5-ASA batches, compressed densities, particle size distributions and specific surface areas of the batches will be measured and evaluated individually and by use of multivariate analysis. 2. Materials and methods 2.1. Materials Samples from 137 500 kg full scale batches of 5-ASA were supplied by Syntese A/S (Copenhagen, Denmark). The batches were divided into six groups: A.1 (39), A.2 (10), B.1 (74), B.2 (6), C (4), D (4). A, B, C and D describe the overall qualities of the batches. The suffixes, 1 or 2, relate to the processing history of the batches. The C and D batches are development batches produced in full scale. The numbers in parenthesises refer to the number of each batch type enrolled in the study. The batches have been granulated by extrusion using a fixed amount of liquid according to the specified quality of the batch (A or B). Generally, the A batches have been granulated using 23.5% of granulation liquid, while the B batches have been granulated using 27.5% of granulation liquid. One C batch has been granulated using 28.5% of granulation liquid, while one D batch has been granulated using 29.5% of granulation liquid in order to be extruded successfully. The remaining development batches have not been processed. 5-ASA is very slightly soluble in the granulation liquid (b 10 g/L). 2.2. Methods For determination of compressed densities, ∼50 mg 5-ASA samples were compressed on a TA-XT2 Texture Analyser (Stable Micro Systems Ltd., Godalming, UK) fitted with a 6 mm die and a flat-faced punch. The texture analyser was equipped with a 5 kg load cell and the test speed was fixed at 0.5 mm/s. The exact weights of the compressed powder plugs
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were determined after compression. The compressed density at 0.2 MPa, d0.2, was interpolated from the compression profiles. The repeatability of the measurements was evaluated by performing three replicates of the compressed density measurements on four different batches (two B.1 batches, one B.2 batch and one C batch). The remaining batches were tested without replicates. Particle size distributions were measured by laser diffraction on a Malvern Mastersizer S equipped with a 300 F lens and an MSX 64 dry powder feeder unit (Malvern Instruments Ltd., Malvern, UK). The air pressure was fixed at 0.3 MPa. The repeatability of the laser diffraction measurements was evaluated on one batch by performing three replicates. The following parameters were derived from the particle size distributions: the 10% percentile, the median and the 90% percentile diameters of the volume distribution (d(v,0.1), d(v,0.5) and d(v,0.9)), the volume mean diameter (d[4,3]), the surface area mean diameter (d[3,2]) and the span. The span is calculated as (Eq. (1)): span ¼
dðv; 0:9Þ dðv; 0:1Þ dðv; 0:5Þ
ð1Þ
Specific surface areas were determined by air permeability according to the method described in section 2.9.14 of the European Pharmacopoeia [15]. SEM micrographs of one type A.1 and one type B.1 batch deviating much in d0.2 but having the same d(v,0.5) were acquired using a JSM 5200 scanning electron microscope (Jeol Ltd., Tokyo, Japan).
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standard deviation (RSD) of the d0.2 measurement on sample B.1, batch 2, is 1.7%, while the RSDs of the remaining measurements are below 1.0% (n = 3). The RSDs of the size distribution derived parameters are 2.6% for d[4,3], 0.9% for d[3,2], 0.4% for d(v,0.1), 1.6% for d(v,0.5), 3.3% for d(v,0.9) and 2.1% for span (n = 3). High repeatability of the d0.2 and size distribution derived parameters is required, especially if the variation within the individual parameters is expected to be low. 3.2. Univariate approach The 5-ASA batches consist of needle shaped crystals. The two SEM micrographs shown in Fig. 1 are of a type A.1 and B.1 batch having the approximate same d(v,0.5), 10.8 μm, but much deviating in their d0.2s. The A.1 batch has a d0.2 of 0.622 g/cm3, while the d0.2 of the B.1 batch is 0.564 g/cm3. This corresponds to a 10% difference in volume of the two batches at a pressure of 0.2 MPa. The samples appear identical and it is not possible to identify the cause of the variation in packing behaviour from the micrographs visually. The median diameter of the volume distribution, d(v,0.5), is commonly encountered as an important characteristic of a particle system. The d(v,0.5)s of the A.1 batches appear to be lower than the d(v,0.5)s of the B.1 batches (p = 0.01) (Fig. 2). However, the A.2 and B.2 batches have d(v,0.5)s, which are clearly higher than for the other batches (p b 0.001). Furthermore, the d(v,0.5)s of the A batches range from approximately
2.3. Data treatment Principal component analysis (PCA) was performed in SIMCA 10.5 software (Umetrics AB, Umeå, Sweden). Variables were mean centred and scaled to unit variance. Analysis of covariance (ANCOVA) was done in Statistica '98 edition, release 5.1 (Statsoft Inc., Tulsa, USA). 3. Results and discussion 3.1. Repeatability As discussed in our previous work [13], the repeatability of d0.2 measurements is generally high. This is confirmed by the d0.2s determined on the four 5-ASA samples, where the compression test was repeated (Table 1). Hence, the relative Table 1 Results of repeated compressed density (d0.2) measurements with their related standard deviations (SD) and relative standard deviations (RSD) (n = 3) Code
d0.2, g/cm3
SD, g/cm3
RSD, %
B.1, batch 1 B.1, batch 2 B.2, batch 1 C, batch 1
0.568 0.562 0.565 0.505
±0.001 ±0.010 ±0.004 ±0.004
±0.2 ±1.7 ±0.6 ±0.8
See Materials for an explanation of codes.
Fig. 1. SEM micrographs of a A.1 and a B.1 type 5-ASA batch. Both batches have median diameters of approximately 10.8 μm, but very different compressed densities (0.622 and 0.564 g/cm3, respectively).
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10 to 14 μm. This range surrounds all the d(v,0.5)s of the B batches. Therefore, the differences in processability cannot be explained by variations in d(v,0.5) alone. The span is a measure of the width of the size distribution. A.1 batches are seen to have high spans, while B.1, B.2 and A.2 batches have spans of approximately the same level (Fig. 2). The spans of the development batches, C and D, are lower than the rest. A systematic variation in the spans of the different batches appears to be present. However, as A.2 batches cannot be separated from the B.1 or B.2 batches statistically (p = 0.18 and 0.14), span does not explain the variation in processing behaviour sufficiently. As the overall particle size of a powder decreases, the specific surface area of the powder can be expected to increase accordingly. Similar specific surface areas are seen for the A.1 and B.1 batches (Fig. 2). The specific surface areas of B.2 batches and the A.2 batches in particular appear to be low. Nevertheless, it is not possible to find a direct relation of the specific surface areas to processability. The general similarity of the specific surface areas of the 5-ASA batches confirms the similarity in size seen in the size characteristics measured by laser diffraction. The d0.2 allows good separation of the A.1 and B.1 batches (p b 0.001) (Fig. 2). If only the A.1 and B.1 batches had been analysed, d0.2 could be concluded to be able to differentiate the variation in processing behaviour sufficiently. Thus, with only a few outliers the B.1 batches have lower values of d0.2 than the A.1 batches. However, the low d0.2 of the A.2 batches show that variations in d0.2 alone do not explain the batch to batch variations in processability. 3.3. Multivariate approach The above discussion of the relationship of d0.2, size distribution derived parameters and specific surface areas to processability displays the inadequacies and potential pitfalls of studying variables individually when confronted with a multivariate problem. When evaluated individually, each characteristic is rejected one by one, as none is able to explain the variation in processing sufficiently. Furthermore, if the A.2 and B.2 batches had not been analysed, the variations in span or d0.2 could wrongly have been believed to be solely responsible for the differences in processability. PCA reveals a rough grouping of the 5-ASA batches according to their quality (Fig. 3). The first principal component (PC1) accounts for 60.3% of the variation while the second principal component (PC2) accounts for 26.2%. PC1 mainly describes variation in particle size, while PC2 mainly describes variation in span and d0.2 (Figs. 3 and 4). The A.1 point cluster is aligned towards d0.2 while the B.1 point cluster is aligned
Fig. 2. Size parameters, spans, specific surface areas and compressed densities for different qualities of 5-ASA. Batches are sorted according to quality and type (A.1, A.2, B.1, B.2, C and D). Generally, A batches require 23.5% of granulation liquid, while B batches require 27.5%. One C and one D batch have been processed using 28.5 and 29.5% of granulation liquid, respectively. See Methods for an explanation of the size parameters and span.
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Table 2 Discriminative power of compressed density (d0.2) alone and in combination with size related parameters Co-variable
A/B A.1/B.1 A.2/B.1 A.1/B.2 A.1/B.2 A.1/A.2 B.1/B.2 B/C B/D
Fig. 3. PC1 and PC2 scores calculated by PCA on the material properties shown in the loadings plot (Fig. 4) for batches of 5-ASA. Generally, A batches require 23.5% of granulation liquid, while B batches require 27.5%. One C and one D batch have been processed using 28.5 and 29.5% of granulation liquid, respectively.
against it. Hence, d0.2 should be able to separate the A.1 and B.1 batches, while span would cause some overlapping. The A.2 and B.2 clusters are more difficult to separate from each other, although the majority of the A.2 batches appear to be of a larger particle size than the B.2 batches. These observations could also be made from Fig. 2. However, it can now be seen that a combination of PC1 and PC2 scores allows the most effective separation of the A.1 and B.1 clusters (Fig. 3). Accordingly, it might be possible to improve the discriminative power of d0.2 if these interactions are taken into account. Since particle size and span appear to influence d0.2, the discriminative power of d0.2 in combination with those has been tested by analysis of covariance (ANCOVA). Table 2 shows values of the statistical probability (p) calculated by ANCOVA run on pairs of different qualities and types of batches. As only one C and one D batch have been processed, separation of the A and B batches is of primary interest. The combination of d0.2 and either d[4,3], d(v,0.5) or d(v,0.9) enables statistically
Fig. 4. Loadings of PC1 and PC2 calculated by PCA on measured material properties. See Methods for an explanation of the size parameters and span.
None
d[4,3]
d[3,2]
d(v,0.1)
d(v,0.5)
d(v,0.9)
Span
b0.001 b0.001 ns b0.001 ns b0.001 ns 0.02 b0.001
b0.001 b0.001 b0.001 b0.001 0.03 b0.001 ns 0.004 b0.001
b0.001 b0.001 b0.001 ns 0.02 ns b0.001 ns 0.04
b0.001 b0.001 b0.001 ns ns ns 0.002 0.02 0.04
b0.001 b0.001 b0.001 0.03 0.02 ns 0.001 ns 0.01
b0.001 b0.001 b0.001 b0.001 0.002 0.001 ns b0.001 b0.001
b0.001 b0.001 ns b0.001 0.04 b0.001 ns 0.003 b0.001
Probabilities (p), are calculated by a t-test for d0.2 alone and by ANCOVA for the combinations of d0.2 with particle size distribution derived variables. Not significant results on a 0.05 significance level are reported as ns. A/B refers to a test of the A and B quality batches as a whole, A.1/B.1 refers to a test of the A.1 and B.1 types, while A.1/B.2 refers to a test of the A.1 and B.2 types, etc. Groups showing differences in processability are marked in bold. See materials for an explanation of codes and methods for an explanation of the size parameters and span.
significant separation of all groups of A (A.1 and A.2) and B (B.1 and B.2) batches. The best differentiation of the batches is made with d(v,0.9) as co-variable. Hence, the combination of d0.2 and d(v,0.9) enables separation of nearly all combinations of A and B batches on a 0.001 significance level, while the separation of A.2 and B.2 batches is made with a p of 0.002. The best separation of the development batches (C and D) from the B batches is also made using d(v,0.9). However, it should be emphasised that only one C and one D batch has been processed. Hence, the processability of the remaining C and D batches could be different. The combination of d0.2 and d(v,0.9) reflects the differences in processability well (Fig. 5). Batches aligned towards the
Fig. 5. Compressed densities, d0.2, plotted against 90% percentile diameters of the volume distributions, d(v,0.9), for 5-ASA batches. Generally, A batches require 23.5% of granulation liquid (low level), while B batches require 27.5% (medium level). One C and D batch have been processed using 28.5% and 29.5% of granulation liquid (high level), respectively. Lines separating the batches according to the granulation liquid requirement of the majority are added for clarification.
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lower left corner require high amounts of granulation liquid, while batches aligned towards the upper right corner require less. The sensitivity of d0.2 to particle size across a seemingly narrow particle size range may be surprising. However, the d(v,0.9)s of the B batches range from 32 to 44 μm (Fig. 2). Hence, the relative variation is large. Nevertheless, the effect of a larger particle size on the granulation liquid requirement is countered by looser packing, while the effect of a smaller particle size is countered by denser packing. Variation in the degree of interaction between particle size and packing behaviour is the source of the differences in processability observed for the A and B batches. Hence, a B batch of a specific particle size will typically pack less densely than an A batch with the same particle size (Fig. 5). The interaction of particle size and packing causes a constantly low liquid requirement for A batches and a medium liquid requirement for B batches. The variation between the A, B and processed C and D batches shows a high d(v,0.9) in combination with a high d0.2 results in a low liquid requirement. The opposite is true for a batch with a combination of a low d(v,0.9) and a low d0.2. As only one C and one D batch have been processed, prediction of the behaviour of the remaining ones should be done with caution. Nevertheless, the unprocessed C and D batches, situated in the high zone (Fig. 5), would be expected to require a high amount of granulation liquid. Accordingly, the C and D batches in the medium zone would be expected to require an amount of granulation fluid at the same level as the B batches. The relation of d(v,0.9) and d0.2 to the granulation liquid requirement is reasonable. The following explanation is suggested. Since a B batch typically packs more loosely than an A batch at a given pressure (e.g. 0.2 MPa), a B batch will require more liquid in order to lower the resistance of the powder towards compression. The addition of liquid facilitates reduction of the particle–particle interactions [16]. Hence, a B batch requires more liquid to lower particle–particle interactions sufficiently in order for the wetted material to become saturated at the same pressures. For a given quality of 5-ASA (A or B), increased particle size is associated with looser packing. Furthermore, an increased particle size is associated with a lower specific surface area and fewer contact points between particles. Thus, relatively less liquid is needed to counter the particle–particle interactions in powders containing larger particles. For an individual quality of 5-ASA (A or B) the effects of particle size and packing cancel out. Therefore, 5-ASA batches of a certain quality tend to require the same amount of liquid irrespective of their specific particle size or packing behaviour. A.1 batches typically have larger spans than B.1 batches (Fig. 2). Accordingly, much of the variation in d0.2 and span is described in the same principal component, PC2 (Fig. 4). Hence, the variation in packing behaviour between A.1 and B.1 batches could be related to the spans of their particle size distributions. However, it is not possible from laser diffraction deduced size distribution data to conclude whether the differences in span are due to variations in particle shape, aggregates and surface properties or wider particle size distributions. Since packing densities can be increased by
mixing different sized particles [17], an effect of the spans on the packing behaviour of 5-ASA powders would be reasonable. Other more precise methods of measuring the size of needle shaped particles could possibly be able to distinguish the different qualities of 5-ASA directly. However, the packing behaviour can be expected to include variations in particle– particle interactions caused by not only differences in particle size distributions or particle shape but also in surface properties and aggregation. Therefore, the combination of low-pressure compression and laser diffraction data should be able to provide a relevant estimate of the overall bulk behaviour of a given material. The ease and speed of these methods allow efficient screening of many batches, thereby allowing rational selection of representative batches for further studies. Even though unable to fully differentiate 5-ASA batches according to their processability, the d0.2 generally possesses good discriminative power. For other materials, different lowpressure compression derived parameters could possibly be more suited. Procedures to extract packing related information from low-pressure compression data are described in detail by Sørensen et al. [13]. 4. Conclusions A systematic change in the combined effect of particle size and packing behaviour was the source of the 5-aminosalicylic acid (5-ASA) batch to batch variation in processability. Since particle size and packing behaviour interacts, realisation of their effect required the use of multivariate analysis. The combination of compressed density determinations and particle size measurements by laser diffraction is well suited for identification of packing and particle size related batch to batch variation in processability. Although the methods might not provide a direct explanation for the variation shown, they enable quick screening of many batches. Representative batches can then be chosen for further studies. In case of the 5-ASA batches, the variation in processability can be fully countered by adjustments of the granulation liquid level. For other processes and materials where this is not an option, such variation could be critical. Batches with mean particle sizes and packing densities near the maximum acceptance limit of the raw material specification could process very differently than similar sized batches with low packing densities. This is problematic if the specification has been set on studies with batches, where e.g. a low particle size was continually associated with loose packing. Therefore, it is recommended that interactions of particle size and packing behaviour are studied and preferably quantified as part of the establishment of new raw material specifications. In order to avoid neglecting the effect of critical batch properties, the use of multivariate statistics in processability investigations is strongly recommended. Acknowledgements The authors acknowledge Ferring Pharmaceuticals A/S (Copenhagen, Denmark) and the Drug Research Academy (Copenhagen, Denmark) for financial support.
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