Development and Application of Laboratory Tools To Predict Particle

Article pubs.acs.org/OPRD

Development and Application of Laboratory Tools To Predict Particle Properties upon Scale-Up in Agitated Filter-Dryers David am Ende,†,§ Melissa Birch,‡ Steven J. Brenek,† and Mark T. Maloney*,† †

Pharmaceutical Sciences, Pfizer Inc., Eastern Point Road, Groton, Connecticut 06340, United States Pharmaceutical Sciences, Pfizer Inc., Ramsgate Road, Sandwich, Kent CT13 9NJ, U.K.

‡

ABSTRACT: Agitated filter-dryers (AFDs) are commonly used for performing both filtration and drying operations in the manufacture of active pharmaceutical ingredients (APIs) and intermediates. Successful scale-up from the laboratory to manufacturing AFD equipment requires that physical properties specifications such as particle size be consistently met in addition to chemical purity specifications. Depending on the API−solvent system and equipment operational parameters, undesired attrition or agglomeration may occur, so an improved understanding of these phenomena upon scale-up is of key importance. In this paper, we describe recent advances in laboratory methods, based on material characterization methods common to drug product formulation development, to better assess the risk of agglomeration and attrition potential upon scaleup. These methods provide data to evaluate solid behavior, in both wet and dry states, associated with processing in an AFD. For agglomeration prediction, the application of mixer torque rheometry for measuring the propensity to form granules or agglomerates of API wet cake is described as well as how to categorize agglomeration risk based on the output of this testing. For measuring attrition propensity, the application of powder rheometry is described, and risk categories are proposed. For both testing methods, good agreement was seen between laboratory predictions and actual behavior upon scale-up. For compounds evaluated as high risk for attrition or agglomeration, alternate drying protocols are recommended to mitigate risk. In addition, progress on enhancing cycle times for difficult to dry materials is discussed.

1. INTRODUCTION

AFD. We also discuss mitigation strategies for compounds that are highly prone to either agglomeration or attrition. 1.1. Particle Attrition. Particle attrition, or breakage, can occur during agitation in an AFD due to the forces imparted on the particles from contact with other particles, the agitator blades, or the dryer walls. Excessive attrition can lead to a significant decrease in the average particle size and increase in fines generation, which may ultimately lead to out-ofspecification product. The increased level of fines can also lead to slow filtration and poor washing of subsequent lots if a specific product is campaigned on an AFD and a residual “heel” is carried over from lot to lot. Complete understanding of particle attrition behavior in an AFD, leading to the ability to predict particle size distributions as a function of time, would require knowledge of both the spatial particle dynamics and stress fields in the equipment as well as the mechanical properties of the specific solid of interest. In lieu of this comprehensive description, researchers have explored a number of approaches to understand the mechanism of particle attrition and to predict the extent of attrition during the processing of solids. The measurement of attrition in shear cells1,2 established that the extent of attrition (defined in terms of fines generation) depends strongly on the normal force and shear strain within the cell and varies greatly for different types of granular solids. Discrete element analysis (DEM) was used to simulate attrition of spherical particles in a

In the production of active pharmaceutical ingredients (APIs), the final drug substance and key intermediates are often isolated as crystalline solids. Considerable effort is applied to the design of crystallization processes that deliver these solids not only with the requisite chemical quality but also with physical properties (filterability, particle size, uniformity, etc.) suitable for isolation and downstream processing including drug product manufacture. However, the particle properties attained through carefully designed crystallizations may be compromised during the filtration and drying operations necessary to separate product solids from free solvent. In the pharmaceutical industry, the agitated filter-dryer (or AFD) is frequently used for both filtration and drying of crystalline intermediates and APIs. By combining these unit operations, AFDs provide many advantages including excellent containment since the filtered, wet cake does not need to be transferred to a separate dryer. Unfortunately, the use of agitation during drying can also lead to a variety of processing problems including (1) agglomeration or snow-balling of an overly wet or sticky cake, (2) attrition of particles as a result of grinding from the stress of the impeller blade or other particles, and (3) formation of hard, compacted heels at the filtration surface that can lead to slow filtration and nonfriable agglomerates that can cause batch nonuniformity. All of these phenomena have been difficult to predict based on tests in laboratory drying equipment. In this paper, we describe several laboratory techniques that we have developed to assess the likelihood and severity of agglomeration and attrition behavior when scaling up into an © 2013 American Chemical Society

Special Issue: Engineering Contributions to Chemical Process Development Received: March 27, 2013 Published: June 21, 2013 1345

dx.doi.org/10.1021/op400080x | Org. Process Res. Dev. 2013, 17, 1345−1358

Organic Process Research & Development

Article

Figure 1. Examples of types of agglomerates. Left to right: (1) “snow-balling” demonstrated with an API in 2-methyl THF, (2) tray dried cake hardening of solids, (3) an isolated agglomerate “chunk” believed to have formed during compression and squeezing of the wet cake by the agitator during filtration, (4) spherical agglomerates sieved from a batch, and (5) 120+ micrometer agglomerates formed during agitated filter drying (individual API particles approximately 5 μm) from methanol.

Figure 2. Contrasting wet granulation process with that of the agitated drying process leading to agglomeration or balling. Tools used in the development of granulation formulations have proven useful for improving our understanding and prediction of agglomeration potential during agitated drying.

shear cell,3 and particle fragmentation, rather than surface wear or chipping, was found to be the dominant mode of attrition. Experiments performed under agitated drying conditions indicated a competition between agglomeration and attrition phenomena with attrition dominating towards the end of the drying phase when the overall drying rate is low.4 In addition, needle-like crystals were found to be much more sensitive to attrition than were cubic crystals.5 The extent of attrition for needle-like crystals increased significantly with increasing agitator speed, while agitator speed had little effect on attrition for cubic crystals. A DEM-based computational model for attrition of spherical particles in an AFD provided good prediction of attrition behavior in a small-scale dryer for paracetamol.6 This approach, based on simulation estimates of the stress distribution in the dryer and measurement of particle attrition under controlled stress conditions in a shear cell, represents significant progress towards predicting attrition behavior in an AFD and will be of broader value when extended to pharmaceutical particles of more common geometry (such as needles or lathes). Because of the large number of intermediates and APIs that are under development, we require techniques that can quickly assess the propensity for attrition based on limited data. Recently, a laboratory method was developed by Lamberto and co-workers7 that assesses attrition sensitivity by subjecting particles to mechanical stresses that mimic conditions in an AFD. Compounds were then categorized in terms of attrition propensity based on the change in particle size distribution that occurred during testing. We used this test as a starting point for further development and analysis of an attrition assessment tool. 1.2. Particle Agglomeration. Agglomeration is a potential problem during processing in an agitated filter-dryer for both APIs and intermediates for several reasons. First, there is the potential impact on product quality such as variable or out of specification particle size distribution (PSD). This can lead to

variable input for formulation and potentially impact content uniformity across formulation blends or tablets. Second, agglomeration can lead to difficulty removing the batch from the processing equipment and can negatively impact cycle times. Agglomeration can also occur during the filtration stage on the AFD due to agitation and/or compression or squeezing of the cake. This can negatively impact washing efficiency especially if agglomerates were formed during compression. In extreme cases, severe agglomeration (“snowballing”) has been known to cause agitator damage due to the formation of large, nonfriable “boulders.” Several types of agglomeration are shown and further described in Figure 1. Agglomeration during drying is the process where smaller particles in the presence of residual solvent become cohesively bound during the process of agitation. As the free moisture evaporates, a viscous film around the particles, composed of dissolved API in a thin layer of solvent, forms, which potentially acts as a binding agent and increases the cohesive strength of the bulk material. As the residual solvent within the thin layer of liquid is finally evaporated away, the soluble API in the viscous film forms crystalline bridges that further strengthen the agglomerate.8 Known causes of agglomeration during drying include stirring when moisture content is too high, especially for certain solid−liquid systems (case 1), bridging of crystals due to dissolution/recrystallization in mixed solvent cases as the solvents are selectively removed during drying (case 2), and cake hardening due to a wide particle size distribution especially in the presence of excessive fines cementing and bridging particles together8 (case 3). There may be other modes of agglomeration, but these three will be the main focus of the discussion here. “Snowballing” is a type of agglomeration resulting from case 1 in which the material is too wet and tends to pack and ball-up as the agitator moves through the cake. From our experience, case 1 occurs most commonly in our scale-up operations and 1346



Article

the properties of the solid, such as particle size or shape, or changes to the solvent can lead to changes in this torque measurement. To illustrate the type of data obtained from MTR, we will present results for a well-characterized excipient with excellent granulation properties. Microcrystalline cellulose is a commonly used excipient and when added to a wet granulation improves bonding on tablet compression and reduces capping and friability of the tablet.16 The MTR torque profile is shown in Figure 3 for microcrystal-

causes the most serious issues with respect to quality and downstream processing. In the plant, we have typically relied upon visual observation during initial stages of stirring to see if balling is occurring. Well-trained operators can in many cases manage the drying process visually using a default drying protocol. In this way, if balling is observed, the agitator speed and height are manually adjusted to actively mitigate any issue. However, this does not always offer the best outcome and can lead to operator-to-operator or plant-to-plant variation. In some cases small agglomerates are formed anyway and may not be easily observed by the operator. It was recognized that a predictive tool was clearly needed to better assess the risk of agglomeration for specific material and solvent combinations prior to scale-up. In this way, alternative drying policies could be designed to minimize issues of agglomeration. For cases posing potential risk, an alternative drying method or protocol may be necessary to best preserve particle integrity (as compared to the standard vacuum drying with constantagitation protocol). Since wet granulation can be thought of as the reverse operation to drying, we sought to better understand the science of wet granulation and its associated lab tools so as to potentially better understand, predict, and avoid agglomeration during batch agitated drying (illustrated in Figure 2). In drug product formulation wet granulation is an important unit operation and is an example of particle design in which generating larger granules is the desired outcome.9 Specifically, wet granulation incorporates API in combination with excipients and binders to produce granules with desirable mechanical properties. By understanding the mechanism of wet granulation from drug formulation science, we should be able to better understand and ultimately control or prevent agglomeration from occurring in our drying processes within API manufacturing. Iveson describes granulation as comprising three distinct rate processes:10

Figure 3. Rheological torque profile for microcrystalline cellulose (Avicel PH102) versus moisture content on a wet basis: wet basis wt % = (100 × g solvent)/(g solvent + g dry solid).

• wetting and nucleation • consolidation and growth • attrition and breakage (of granules)

line cellulose with water added as a binder. There is clearly an increase in torque with increasing water content rising to a maximum and subsequently decreasing as a slurry is formed above 70% moisture content. The behavior observed in the mixer torque profile is consistent with the mechanism of liquid saturation assemblies.17 Thus at low moisture there are “discrete lens-shaped rings of liquid” formed where the particles are in contact referred to as the pendular state (see for example inset Figure 3). As the moisture content increases the continuous network of liquid, air, and solid forms the funicular state with a corresponding increase in torque on the mixer. As all the pores become filled with liquid in the capillary state the maximum torque is observed. Granule strength has been shown to correspond to maximum measured torque such that the higher the torque, the stronger the granule. Any further liquid results in the formation of a slurry with a corresponding decrease in torque. By using the mixer torque rheometer on APIs we can assess the tendency to form granules or agglomerates as a function of moisture content. The higher the torque readings, the higher will be the potential for agglomeration and the stronger will be the resulting granules that are formed. In general the MTR measurements depend on the instrument operating parameters such as impeller speed, duration of mixing, data collection time, etc., so it is important to establish a consistent set of operating conditions for comparing results.

For wet granulation, a powder blend typically starts dry and then is wetted with a liquid that needs to be distributed homogeneously through the powder. The wetting, spreading, and liquid distribution are important parameters in the wet granulation process. In API drying, the particles are already uniformly wetted as a result of the filtration and wash operations. Therefore, the rate processes common to agglomeration during API drying and DP granulation are associated with consolidation and growth. Consolidation is the process where the wet mass is compressed during agitation, squeezing out liquid or entrapped gas from the forming granule and potentially leading to coalescence of granules and agglomeration.11 Predicting a priori whether a wet mass will agglomerate or granulate based on an algorithm or pure component properties is not yet possible. With respect to consolidation Lister states that “it is very difficult to predict the effect of changing binder content, even qualitatively!”, so we are left with empirical methods to assess a material’s potential to agglomerate.11 One empirical method that we have studied for this purpose is the mixer torque rheometer. Rowe and co-workers have demonstrated that the rheological properties of wet powder masses can be successfully monitored via mixer torque rheometry (MTR).12−15 The measurement of torque required to shear a wet mass is directly related to the physical interactions of a given solid−liquid system. Changes to 1347



Article

The stirring mechanism does impart a significant level of compaction to the cake, and it has been shown that compaction can increase the apparent viscosity of wet particulate beds.18 Further, we are suggesting that fine particulate wet solids that exhibit high viscosities, as measured by MTR, are more likely to agglomerate during agitated filter drying.

2. EXPERIMENTAL METHODS 2.1. Attrition Testing. 2.1.1. Large Attrition Cell. Our first attrition testing rig was modeled after the laboratory attrition cell described by Lamberto and co-workers.7 An Ace Glass jacketed, cylindrical two-piece, fritted-bottom filter-reactor (150 mL) with a 50 mm i.d. agitation zone was outfitted with a Heidolph low speed, high torque agitator. The stainless steel impeller had a 47-mm diameter and four blades oriented with 45° pitch. Impellers were fabricated with two orientations so that the cake could either be smoothed (compressed) or plowed (lifted) during agitation. A normal force could be exerted on the agitating solids by placing a stack of steel washers onto a Teflon disk that was located directly on the surface of the solids. Experiments were typically run with 1 kg of weight, which is equivalent to the normal force that would be exerted by a bed height of 70 cm of solids of typical bulk density (0.5 g/mL). Experiments were carried out by charging adequate solids to fill the agitation zone (∼ 50 mL) and then agitating under controlled conditions. Initial attrition testing with this device was carried out using operating conditions similar to those of Lamberto: smoothing impeller with 10 mm clearance from bottom frit, 55 rpm, 60 min agitation time. These experiments, run with Pfizer proprietary compounds, provided baseline results that could be compared to available scale-up attrition data to assess the suitability of the test. In an effort to better understand the parameters influencing the performance of the device, a 1/2 fractional factorial experimental design with 3 center points design was executed. The following variables were examined: orientation of agitator (smooth or plow), normal force on particles (0.5−1.0 kg), agitation speed (35−75 rpm), distance of agitator from bottom frit (5−15 mm), and total agitation time (20−60 min.). The experimental response was defined as the comparison of the post-agitation particle size distribution to the initial size distribution, as measured by Sympatec Helos and represented by values for D[4,3], D[v,0.1], and D[v,0.9]. L-Threonine (Aldrich) was used as a model compound for this DOE, along with the subsequent FT4 DOE and testing in the 0.06 m2 AFD, because it has been shown to readily attrit due to its needle-like habit.5 The L-threonine sourced for our laboratory experiments had a mean particle diameter D[4,3] of ∼385 μm, a D[v,0.9] of 652 μm, a D[v,0.1] of 178 μm, and a bulk density of 0.78 g/cm3. 2.1.2. Attrition Cell Based on FT-4. In order to reduce the amount of material needed for testing, an alternate attrition testing procedure was investigated using the FT4 Powder Rheometer (Freeman Technology). For this procedure, the standard 25-mm bore-diameter rheometer cell was used with a 23.5-mm diameter agitator, which was fitted with a Teflon disk supporting a 90 or 160 g stainless steel sliding shaft weight in order to impart the same normal force/unit area used in the larger attrition cell (see Figure 4). Experiments were performed by charging ∼10 mL of dry material (the same starting material lot used for the large attrition cell) to the test cell and lowering the agitator, using

Figure 4. Photograph of FT4 attrition cell with 160 g agitator shaft weight and Teflon separation disk.

autoturn function, to a height of 2 mm from the cell bottom. To complete the first cycle, the agitator was started at a specified rpm for a specified time followed by retraction to the top of the cake to ensure homogeneity of the solids. Three additional cycles were performed at 4, 6, and 8 mm heights to complete testing. The particle size distribution of the material before and after testing was measured via Sympatec HELOS (Sympatec GmbH) and compared to determine the extent of breakage. A set of designed experiments were performed using the FT4 instrument to investigate the effect of the agitation parameters on the extent of attrition. The 2-level full factorial design with 4 center replicates initially considered only three parameters, agitator RPM (80−120 rpm), run time (12−36 min), and sample size (8−10 cc), but was subsequently expanding to include a 90 g normal force in addition to the original 160 g normal force. To explore this factor, eight additional design points were chosen to augment the first 12 runs. 2.1.3. Testing in 0.06 m2 Agitated Filter Dryer. The attrition behavior for L-threonine was studied in a 0.06 m2 Rosenmund agitated filter-dryer fitted with a 2 μm filter plate to help validate the laboratory attrition test protocols. Attrition trials with dry solids were run with an 8 kg charge of Lthreonine (AcceleDev Chemical) and an agitator speed of 10 rpm. The L-threonine sourced for the scale-up trials had a mean diameter of 443 μm, a D[v,0.9] of 883 μm, and a D[v,0.1] of 141 μm 2.2. Experimental for Agglomeration. A mixer torque rheometer (Caleva Model MTR-3, Dorset, England) was used for torque measurements. After calibration, the dry powder was charged to the sample bowl, and the torque measurement sequence started. The stirrer blades were paused, aliquots of solvent, typically 1−2 mL, were carefully added to the powder via syringe, and stirring restarted. This sequence was repeated until the maximum torque was reached or slurry was obtained. Most materials exhibit an increase in torque with increasing solvent content. Mean torque of the data at each aliquot is computed. The Mixer Torque Rheometer (MTR) consists of a small mixing chamber (available in full-bowl or half-bowl sizes) in the 1348



Article

Figure 5. Photograph of the Caleva Mixer Torque Rheometer . The front includes a stainless steel mixing bowl with torque arm in contact with the load cell. A safety enclosure ensures safe operation while the blades are stirring. Data acquisition is accomplished via computer. Dosing of the solvent can be done either manually with a syringe or via a syringe pump. (Photo courtesy of AC Compacting LLC, New Brunswick, NJ).

and to establish standard operating conditions that reliably and discriminately evaluate particle attrition in large scale agitated filter dryers. The particle size data from the DOE for this cell indicated a wide range of results for different operating conditions, ranging from no attrition to significant particle size reduction representing a 38% decrease in D[4,3] and a 29% decrease in D[v,0.9]. Analysis of the responses from this DOE identified 4 significant effects with respect to D[4,3] and D[v,0.1]: impeller type (plow vs smooth), time, impeller clearance, and an interaction between impeller type and clearance. Of these, effects related to impeller type and clearance are dominant, accounting for >80% of the data variability in D[4,3], D[v,0.1], and D[v,0.9] measurements. A summary of the analysis of variance for D[4,3] is given in Table 2, and variance results for D[v,0.1] data and D[v,0.9] are nearly

center of which are two horizontally mounted, intermeshing mixing blades. Attached to the side of the bowl is a horizontally mounted arm (10 cm) that rests on top of a load cell (Figure.5). The mixing bowl can also be covered with a small square cover to reduce evaporative losses with binder solutions containing solvents. The resistance of the mixing blades to movement through a powder/wet mass in the bowl generates a turning effect (torque) on the bowl which is measured by the load cell. The torque generated is directly related to the consistency of the wet powder mass in the bowl. The output of the load cell as a function of time is then processed using the associated software and PC. For example, the data shown in Figure 3 are for an experiment that started with 15 g of Avicel powder in the mixing bowl followed by 5-mL aliquots of HPLC-grade water. After each aliquot, the mixture was stirred for 60 s at 50 rpm to ensure homogeneity, then torque data was collected for 20 s.

Table 2. Analysis of Variance Summary for D[4,3] Data in Large Attrition Cell DOE

3. RESULTS AND DISCUSSION 3.1. Attrition. 3.1.1. Large Attrition Cell. Preliminary attrition data collected in the large cell for two Pfizer proprietary compounds are summarized in Table 1 along Table 1. Comparison of Large Attrition Cell Data Using Initial Protocol with Scale-Up Results for Two Pfizer Compounds % decrease in D[4,3] sample

compound A

compound B

large attrition cell 60 min scale-up

44 35 (138 kg)

48 38 (34 kg)

operating factors (effects)

% of total data variance

impeller type time impeller clearance 2-factor interaction: impeller type and clearance total model residual

51 9 24 7 91 9

identical with the exception that RPM has a significant, though minor impact on D[v,0.9]. As anticipated, attrition was highest with the smoothing impeller at low clearance. On the basis of this analysis of key effects, we only needed to consider impeller type and clearance and, to a lesser extent, agitation time when defining standard conditions for attrition testing, provided that we are most concerned with changes D[4,3] and D[v,0.1]. The final standard operating conditions chosen to assess relative particle breakage in the large attrition cell were 1 kg normal force, 55 rpm, smooth agitator type, 5 mm clearance, and 30 min agitation cycle. While normal force had no detectable impact on PSD in this study within the range studied, follow-up experiments with very low normal force (0.1 kg) showed minimal particle breakage, so a force of 1 kg was maintained as the standard. The output from this DOE afforded fundamental knowledge about the impact of operating

with corresponding scale-up data for these compounds when they were isolated in a pilot plant AFD. Results are expressed as % decrease in the average particle size, D[4,3], and show fairly close agreement between the lab testing and scale-up results. At that point, we had limited scale-up data for comparison, but this initial analysis suggested that the large cell test could provide a useful assessment of attrition risk for selected compounds. The initial positive results with the large attrition cell led us to perform a detailed experimental design (DOE) with that device, the goal of which was to create a fundamental understanding of the controlling variables as related to attrition 1349



Article

agitator blade at the bottom of the vessel except for 1 reciprocation per hour (raise agitator to top of cake and lower back to bottom while rotating) prior to sampling from the top hatch of the vessel using a sample thief. The second run (defined as intermittent agitation in Figures 6 and 7) used the same parameters with the exception that the cake was agitated only 10 min every hour followed by 2 blade reciprocations.

parameters on attrition testing results but more importantly provided us with a model that would allow us to tune the operating conditions of the device to achieve a level of attrition that matches behavior at full scale. 3.1.2. FT4 Attrition Cell. While the large cell testing can predict attrition risk, it requires a significant amount of material (∼25 g) and the testing protocol cannot be readily automated. As a result, we shifted our efforts toward the FT4 instrument, which only uses ∼5 g, to develop a long-term testing platform and initiated a DOE to understand the importance of the operating variables. Particle size data from the set of designed experiments using the FT4 attrition cell resulted in generally smaller distribution statistics under the conditions studied when compared to large attrition cell values but spanned a similar range. D[4,3] values varied from minor attrition (5% decrease) to 37% reduction, while D[v,0.9] varied from no attrition to a 24% reduction, and most notably D[v,0.1] was reduced between 33% and 93%. The statistical analysis results for the FT4 attrition cell were based on the consolidated data of 20 runs. Analysis of the particle size statistics showed that all 4 factors (RPM, time, sample volume, and normal force) were significant, with normal force as the most dominant factor, accounting for ∼50% of the data variability in D[4,3] and D[v,0.9] as summarized in Table 3. Similar results are shown in Table 3 for D[v,0.1], with the exception that the impact of normal force is less and RPM was found to have no significant effect.

Figure 6. D[4,3] versus number of agitator revolutions for dry powder charge of L-threonine using constant (▲) and intermittent (⧫) agitation.

Table 3. Analysis of Variance for Results from the FT4 TwoLevel Factorial Design % of total data variance operating factors (effects)

D[4,3]

D[v,0.1]

D[v,0.9]

RPM time sample volume normal force total model residual

7 17 13 56 93 7

not significant 15 21 38 74 26

9 10 11 49 79 21

Figure 7. D[v,0.1] versus number of agitator revolutions for dry powder charge of L-threonine using constant (▲) and intermittent (⧫) agitation.

Particle size data (D[4,3] and D[v,0.1]) for the two dry runs with continuous and intermittent agitation are summarized in Figures 6 and 7, with results normalized by total number of agitator revolutions. While data for these large scale trials are somewhat noisy, most likely due to the large batch size, sampling difficulties, and non-homogeneity of the batch, clear trends are evident. A sharp decrease in average particle size, as defined by D[4,3], occurs during the first ∼2000 revolutions, and a comparable total decrease in D[4,3] is seen during that period for both agitation protocols. For the continuous agitation experiment, which was extended until the total number of revolutions was ∼12,000, almost no additional decrease in average particle size is seen after 2000 revolutions. A sharp decrease in D[v,0.1], signifying the formation of small particles, is also seen for both agitation protocols. However, unlike the D[4,3] value, the D[v,0.1] continues to decrease during the extended stirring in the continuous agitation experiment, indicating that small particles continue to form via attrition throughout the course of the experiment. A comparison of attrition data for dry L-threonine processed in the two laboratory testing units and the pilot plant AFD is summarized in Table 4, while the complete particle size

Overall, the goal of the FT4 experiments was similar to those described for the large attrition cell (fundamental knowledge allowing for a tunable protocol) but additionally included the determination of operating condition to deliver particle size reduction comparable to that achieved in the large attrition cell under the “standard” operating conditions. The factor model from the FT4 DOE was extremely valuable for this exercise. We decided to fix the most influential factor (normal force) at 160 g and then use the model to predict the remaining factors that would deliver the desired D[4,3], D[v,0.1], and D[v,0.9] obtained from the large cell standard operating conditions. On the basis of that analysis, the standard operating conditions to assess relative particle breakage in the FT4 attrition cell were set at 160 g normal force, 80 rpm, 10 cc sample loading, and 12 min agitation cycle. 3.1.3. Pilot Plant AFD (0.06 m2) and Comparison. Once we chose preferred operating parameters for both laboratory-scale attrition testing systems based on agreement with available scale-up data, the next step was to confirm predictions from these tests using our model compound, L-threonine, in a pilot plant AFD (0.06 m2). Two attrition runs were performed using dry powder charges of L-threonine. The first used constant agitation for 10 h at 10 rpm followed by 5 h at 20 rpm with the 1350



Article

results, the FT4 test is able to provide a prediction for the extent of attrition during agitated drying in an AFD that, while not exact, gives a valuable assessment of the attrition risk. A large number of Pfizer proprietary compounds have been tested via the FT4 protocol, and the level of attrition risk for each compound was classified according to the categories defined in Table 5, which were modeled after those presented by Lamberto7 and are based on the decrease in the volumemean diameter (VMD, D[4,3]). Changes in the additional distribution characteristics, D[v,0.1] and D[v,0.9], are also assessed along with photomicrographic images, in order to better understand the nature of attrition (e.g., fracture of large particles vs formation of fines). To date, 17 Pfizer compounds have been tested with the FT4 protocol. Of those, 29% were classified as having a high attrition potential, 29% medium, and 42% low. We have pilot plant scale-up data for four of those compounds, and Tables 6 and 7 summarize those results along with the corresponding FT4 attrition assessment. Comparison of D[4,3] data in Table 6 shows close agreement between laboratory predictions for compounds A, C, and D and reasonable agreement for compound B. Negative values in the table represent cases where either no attrition has taken place or any attrition is masked by particle agglomeration. Further analysis of pre- and postattrition samples by photomicroscopy indicated that compound C is prone to agglomeration, while compound D appears to have no change in particle size during attrition testing or agitated drying. A similar comparison for the D[v,0.1] data in Table 7 indicates overall good agreement with laboratory predictions and scale-up results except for compound C. Of particular interest is the ability of the FT4 test to predict the large

Table 4. Comparison of L-Threonine Particle Size Data after Processing in the Large Laboratory Attrition Cell, the FT4 Cell, and the 0.06 m2 Pilot Plant AFDa % decrease large attrition cell std conditions FT4 attrition cell std conditions 0.06 m2 AFD trial

D[4,3]

D[v,0.1]

D[v,0.9]

18 24 26

49 61 43

9 17 25

a Initial particles size distributions: laboratory (D[4,3] = 385 μm, D[v,0.9] = 652 μm, D[v,0.1] = 178 μm); pilot plant (D[4,3] = 443 μm, D[v,0.9] = 883 μm, D[v,0.1] = 141μm).

distributions are shown in Figure 8. Because the initial particle size distribution of L-threonine used for lab testing was different from that of the material used in the pilot plant, the attrition data is presented as % change in the initial particle size after attrition testing. For this analysis, the particle size distribution used for the pilot plant test corresponds to agitation for 12,000 revolutions since the lab tests were designed to predict aggressive agitation. The results indicate good agreement between the laboratory results for D[4,3] and D[v,0,1] values and the resultant scale-up behavior, confirming the predictive value of the lab tests for L-threonine attrition with regard to both average particle size and fines formation. There is less consistency among the D[v,0.9] values, which may be due to the unusually large particles at this end of the distribution. 3.1.4. Attrition: Pfizer Proprietary Compounds. The material-sparing testing protocol using the FT4 powder rheometer has been adopted within Pfizer as the standard tool for assessing the potential for particle attrition during drying in an AFD. As illustrated with the threonine attrition

Figure 8. Particle size density and cumulative distributions of L-threonine postattrition from (1) FT4 attrition cell under standard test conditions (red curves), (2) large attrition cell under standard test conditions (blue curves), and (3) 0.06 m2 AFD trial after 12,000 revolutions (green curves). 1351



Article

Table 5. Attrition Risk Categories Based on % Decrease in Volume-Mean Diameter (VMD), D[4,3] after Testing via the FT4 Attrition Protocol

Table 6. Comparison of Laboratory Attrition Data with Scale-Up Data (Expressed as % Decrease in D[4,3]) for 4 Pfizer Compoundsa

a

sample

compound A

compound B

compound C

compound D

laboratory FT4 attrition test scale-up data

35 35 (138 kg)

58 38 (34 kg)

−14 −4 (18.5 kg)

6 3 (15 kg)

Negative values indicate an increase in particle size.

Table 7. Comparison of Laboratory Attrition Data with Scale-Up Data (Expressed as % Decrease in D[v,0.1]) for 4 Pfizer Compoundsa

a

sample

compound A

compound B

compound C

compound D

laboratory FT4 attrition test scale-up data

51 35 (138 kg)

88 86 (34 kg)

7 43 (18.5 kg)

9 4 (15 kg)

Negative values indicate an increase in particle size.

decrease in D[v,0.1] for compound B since this excessive formation of fines can cause slow filtrations, if a product is campaigned on an AFD and a residual “heel” is carried over from lot to lot, or can be detrimental to drug product manufacture. For compound C, the laboratory attrition test significantly underestimates the formation of fines, as characterized by the decrease in D[v,0.1] value. This may be due to the propensity for this compound to agglomerate and suggests that FT4 results for these types of compounds be carefully scrutinized. Once the attrition risk has been assessed for a given compound via FT4 testing, recommended processing conditions for drying at full scale, as in Table 5, can be made. From a cycle time perspective, continuous agitation during drying is usually preferred because it maximizes heat transfer between the solid particles and the filter surfaces. However, for compounds that are prone to attrition, reduced agitation protocols should be considered depending on the final particle size requirements. For compounds with potentially severe attrition, extensive drying in the absence of agitation may be preferred; this can often be achieved via extended blow through of nitrogen through the particle bed after filtration and washing. We are currently studying the impact that reduced agitation protocols have on drying cycle time. 3.2. Agglomeration. Compound E consisted of very fine particle plates (5 μm), and we observed in the pilot plant a strong tendency for this material to agglomerate during agitated filter drying. This material also retained a high moisture content after filtration (approx 60% wet basis or 1.4 g/g on dry basis). Data obtained from the mixer torque rheometer revealed high torque values when wetted with the wash solvent (in this case methanol) as shown in Figure 9. During drying the moisture content will of course decrease, but notice that in this case given the starting moisture content of 60% the torque will increase during drying and move into a sticky region of high torque and high agglomeration potential.

Figure 9. Mixer torque (rheological) profile overlay of compound E + methanol with Avicel (PH102)+ water. Peak Torque for Avicel is 1.2 N m. Using Avicel as a reference standard we conclude that compound E + methanol has high agglomeration potential. On the basis of our portfolio assessment of a sampling of compounds, relative guidelines were designated for high, medium, and low agglomeration potential.

Simply on the basis of height of the MTR profile in Figure 9 we had qualitative validation that high measured torque from the MTR corresponds to a likely or high potential for agglomeration. Although good consistency is obtained from run to run on a given instrument, we still needed a way to compare results across different operators, instruments, and sample bowl sizes. A straightforward way we found to compare results across instruments and methods is to use a reference 1352



Article

up. Conversely high torque measurements in lab resulted in higher incidents of agglomeration on scale. From this mapping we also designated potential cut-offs to assess risk. For example we designated 30% of peak Avicel as the break between medium and high potential for agglomeration and below 8% of peak Avicel to designate low potential for agglomeration. Peak torque (as % of peak Avicel) of 8−30% corresponds to medium agglomeration potential as shown in Table 8. As we tested compounds in our development portfolio, including those that had already been scaled-up as well as new compounds, we found that the agglomeration risk for these Pfizer materials (approximately 20) resulted in the following distribution: 30% high potential for agglomeration, 50% medium potential, 20% low potential for agglomeration. A number of APIs have been evaluated by this approach. On the basis of these data, we have concluded that the MTR as useful tool for assessing agglomeration risk in agitated filterdryers. 3.2.1. Agglomeration Potential vs Agglomeration Risk. The MTR experiment does not simulate agitated drying. It is simply a measure of agglomerating potential when a wet mass is stirred or agitated. To assess the risk of actual agglomeration, we must determine what the typical moisture content will be for the material following the typical pressure filtration and compare that with the torque versus moisture content curve. Some materials deliquor so well that the moisture content will be fairly low prior to drying, and although the material may have a medium agglomeration potential, it may actually have a low risk to agglomeration on scale-up because the moisture content is low enough prior to agitated filter drying. For example, in referring to Figure 9 for compound E in methanol, it is evident that low torque and low potential are obtained when the moisture content is 25% or less. In this way, we can reduce the risk of agglomeration by lowering the moisture content prior to agitation. For compound E we found that centrifugation was able to deliquor the cake close to 25−30% moisture content before being discharged as a wet flowable powder that did not agglomerate in an agitated pan dryer. (The cake resistance of compound E, α, was 1 × 1012 m/kg, which translated to extremely long filtration times in a standard filter, so centrifugation was preferred.) In this case, appropriate equipment selection helped to mitigate the risk of agglomeration because centrifugation significantly lowered the moisture content of the wetcake below the critical point. Had the filtration rate been manageable in the AFD, we would have mitigated the potential for agglomeration through an extended blow period (transition from deliquoring to partial drying with nitrogen passing through the cake) and intermittent agitation. The MTR data in combination with cake resistance and postfiltration moisture content are useful to quantify the potential for agglomeration and thus useful for assessing the risk of scale-up. This is illustrated in Figure 11 where agglomeration risk depends on both the torque measurement as well as the moisture content that the cake exhibits after filtration and the start of drying. Here the risk assessment is shown. For example, if the wetcake were to be agitated between 58% and 38% moisture content, agglomeration risk is at its highest for this compound. Improved deliquoring and blowing the cake to below 25% moisture content result in lower agglomeration risk once agitation is started. This was shown to be the case when the material was agitated in a pan dryer following discharge from centrifugation for compound E; agitation of the cake occurred

standard such as Avicel (PH102) and simply ratio the peak torque results. So in Figure 9, Avicel showed a peak torque of 1.2 N m, while compound E showed a peak torque of 0.94 N m or 82% of our reference Avicel. In this way we can compare data from different sample bowls, sample sizes, or configurations provided both Avicel and compound are run under the same test conditions. An important consideration in any predictive test is the quantity of material required. The standard whole bowl requires 15−20 g of powder to cover the blades of the mixer torque rheometer, while the half-bowl uses approximately 7−10 g of material. Although only half the quantity of material is used in the smaller bowl for the MTR test, the torque values are only slightly lower and have a more complex profile when 1-mL aliquots of solvent are used. One explanation for this observation with the small bowl is the higher percentage of material tending to stick to the walls and potential for material to form a solid bridge above the blades leading to possible lower torque values. Nevertheless, the peak torque values are still consistently reliable when the test parameters are kept constant. As illustrated in Figure 10, for

Figure 10. Reference standards of Avicel (PH102) wetted with HPLCgrade water during mixer torque rheometry experiment using whole bowl and half bowl. Half-bowl used 7.5 g of Avicel and 1 mL aliquots, while whole-bowl used 15 g of Avicel and 5 mL aliquots, 60 s stir time, 20 s log time, 50 rpm. The reference standards can be used to compare and assess on a relative basis the potential for agglomeration in agitated filter-dryer for compound + solvent combinations of interest. The amount of material needed for the test depends on the specific bulk volume since we typically add enough material to completely cover the blades within the bowl.

example, the comparison between whole and half test bowls, the maximum peak value is 1.15 and 0.94 N m, respectively, so the half-bowl is essentially 82% of the whole-bowl peak value. After establishing the protocol for the MTR, we proceeded to evaluate various APIs and intermediates by mixer torque rheometry where we also had scale-up history. In this way we could construct a semiquantitative mapping of MTR results with scale-up results. We obtained, from this mapping exercise, good correspondence from measured peak torque values in lab with the extent and severity of agglomeration observed on scale. Where there was low torque measured during lab tests corresponded well with fewer agglomeration issues on scale1353



Article

Table 8. Risk Categories for Agglomeration Based on MTR Resultsa

a

Relative torque values of greater than 30% of Avicel indicate a higher potential for agglomeration. In our laboratory, when the whole bowl is used the reference standard is 1.15 N m and when the half-bowl is used the reference standard is 0.94. In practice it is recommended to occasionally retest/reconfirm torque profiles for Avicel standard as a way to ensure the system is calibrated and performing as expected.

Figure 11. Mixer torque rheometry profile of compound E in methanol.

Figure 12. Rheological property (mixer torque rheometry) of compound F in the presence of n-butanol showed a much higher torque (sticky wet mass) as compared to compound F wetted with MTBE (methyl tert-butyl ether). Thus agglomeration potential is dramatically reduced or eliminated by selecting an appropriate wash solvent prior to agitated drying.

in the “Low Risk” region, resulting in a fine powder with no agglomeration observed. In summary, agglomeration risk of a solid + solvent combination is based on both the MTR torque profile and the moisture content at the end of filtration. Other risk factors include particle size distribution, the presence of fines, and the wash solvent composition. 3.2.2. Wash Solvent Selection. In the development of this agglomeration assessment tool we found, quite unexpectedly, dramatic differences in torque profiles when different solvents were used for the same compound. In one case, a compound exhibited peak torque values of 150% of Avicel when wetted with n-butanol, yet when the solvent was switched to MTBE it measured 10% of Avicel or low potential (Figure 12). On the basis of these data, MTBE was selected as the solvent of choice, and indeed no agglomeration was observed during the isolation of two 25-kg lots for this particular compound. A strong solvent effect was also seen with Avicel directly. Specifically, Avicel wetted with water exhibits high potential for agglomeration but if wetted with isopropyl alcohol instead exhibits very low potential (approximately 5% of water Avicel).

The solubility of Avicel in both of these solvents is very low. In a lab-scale agitated filter-dryer experiment it was demonstrated that agitating a water-wet cake of Avicel during drying resulted in lumpy agglomerates. Unlike the n-butanol/MTBE system described above, the Avicel system exhibits higher torque for the lower viscosity solvent (water), indicating that the agglomeration potential cannot be predicted simply on the basis of solvent viscosity. In Table 9 we summarize drying results using extended blow through (flowing nitrogen) for Avicel as a model compound. In the presence of water, we know it has high agglomeration potential. In this case we see that if agitation starts immediately at high moisture content, 50% wet, significant number of agglomerates are formed as measured by the 16 wt.% retained lumps after sieving. If the water-wet Avicel is partially blown dry with nitrogen prior to agitation, removing approximately 80% 1354



Article

Table 9. Effect of 3 Drying Modes Using Extended Blow Through (Flowing Nitrogen) with Water-Wet Avicel on wt % Agglomerates Formed and Retained on a 850 μm Sievea potential for agglomeration based on MTR moisture content (post filtration) drying mode

post drying: wt % lumps retained on 850 μm sieve % powder

Avicel/water

Avicel/water

Avicel/water

Avicel/isopropyl alcohol

high

high

high

low

50%

50 wt % at end of filtration 17 wt % at start of agitation extended blow through with N2 to 17% moisture content followed by constant agitation 5

50%

40%

extended blow through with N2 (static/no stirring) 4b

extended blow through with N2 (static/no stirring) 0.005

95

96

99.995

extended blow through with N2; constant agitation immediately after filtration 16 84

a

Column 1: constant agitation beginning with a 50% wet cake. Column 2: extended blow through with nitrogen (with no agitation) to reduce the moisture content about 17% then application of constant agitation. Column 3: extended blow through (i.e., convective nitrogen) with no agitation. The fourth column is isopropyl alcohol-wet Avicel being dried with convective nitrogen with no agitation. bInitially the discharged cake is comprised of large soft lumps but the lumps easily break up during gentle sieve operation so the 4% refers to the small granules remaining after sieveing operation.

Table 10. Common Modes of Agglomerate Formation in Agitated Filter-Dryers As Well As Mitigation Approaches for Each case 1

case 2

case 3

Known Contributors to Agglomeration in AFDs

Mitigation

moisture content too high during stirring; especially problematic for certain solid + solvent combinations that are prone to granulate as measured by mixer torque rheometery (requires agitation to push particles together) mixed solvent case; enrichment of one solvent can occur leading to dissolution, recrystallization, and bridging of crystals (may still require agitation to push particles together) wide particle size distribution with excess of fines generated from crystallization leads to cake hardening (does not require agitation and can happen in tray dryer; AFD is designed to avoid this by turning over cake)

consider a different wash solvent with less potential for agglomeration; determine critical moisture content for agglomeration (e.g., from MTR studies); static dry during constant rate period; intermittent agitation during final stages of drying consider additional wash to isolate as a single solvent; alternatively, tune the wash solvent composition to avoid enrichment of the solvent (having higher solubility of API); tune to the drying conditions − avoid enrichment mitigation is limited − modify or fix the crystallization to minimize the fines generation; consider Ostwald ripening during crystallization if necessary

of the moisture, agglomerate formation is reduced 3-fold to only 5%. Of interest is that static drying with convective nitrogen of the same wetcake resulted in nearly the same level of agglomerates (4%) as when the material was partially dried and then agitated. If this material were to be vacuum-dried instead, then agitation would be necessary at some point in the drying process to facilitate heat transfer. Otherwise, complete static vacuum drying would be very slow, so extended blow through with nitrogen followed by agitated drying would be preferred over static vacuum drying from a productivity perspective. In addition, some agitation is desired toward the end of drying as a means to reduce the potential for cake hardening. In the fourth column of Table 9 we show that if water is replaced with isopropyl alcohol as wash solvent, there was very little propensity to form agglomerates, even with static drying. From these data we conclude that for a solid + solvent combination that exhibits high agglomeration potential, extending the nitrogen blow before commencing agitation (i.e., blow drying from 50 wt % (LOD) down to 17 wt % (LOD)) significantly reduced the number of agglomerates formed in the batch from 16% retained to 5% retained. However, it did not completely eliminate the formation of all granules. A different wash solvent, which showed low agglomeration potential, was significantly better at preventing lump formation. So in practice, wash solvents should be screened that meet all the purification criteria as well as for low agglomeration potential. In this way the agitation protocol becomes less critical. If the wash solvent cannot be changed and it exhibits high agglomeration potential, then a prolonged nitrogen blow to reduce the moisture content and stickiness of the wet mass is preferred prior to agitation. Many other examples were seen from MTR testing where solvent type dramatically affected the rheology and stickiness of

the wet mass. Thus for isolation of API or intermediates, the MTR provides a good way to screen whether a wash solvent is likely to promote or mitigate agglomeration potential in AFDs. Table 10 is a general guide to agglomeration risk factors discussed in this paper as well as mitigation approaches to consider. 3.2.3. Additional Agglomeration Risk Factors. Even if a material shows low agglomeration potential by MTR there are at least two additional risk factors that can give rise to lumps, chunks, or agglomerates in the dry product: (1) a wide particle size distribution with excessive fines that can sometimes create cake hardening as well as exacerbate the formation of heels and (2) variation in solvent composition during the drying process due to mixed solvent washes or mixtures of residual solvent on the cake. Both of these risk factors are discussed in more detail below. 3.2.4. Cake Hardening. Cake hardening can lead to lumps in the batch and is observed to a greater extent when excessive fines are present. To test for this behaviour, a simple static drying experiment can be performed, e.g., blowing nitrogen through the wet cake until dryness and assessing the degree of cake hardening such as by sieving and measuring the mass percentage of agglomerates retained from the total mass of solids. We wanted to establish if we could significantly mitigate cake hardening by using agitation during drying. The idea is that agitation may prevent the hardening in the first place and could be used to essentially delump any agglomerates that formed. To examine this approach we had tested two different lots of L-threonine, one as received from the vendor and another that had undergone stirring in an AFD to test for attrition potential, leading to the generation of fines. Each lot was then used to generate two separate slurries that were filtered and subjected to two different drying modes: convective drying (flowing nitrogen) with agitation and convective drying 1355



Article

Table 11. Comparison of Two Lots of L-Threonine and Two Modes of Drying (Static and Agitated)a L-threonine

D[4,3] μm D[v,0.1] μm D[v,0.5] μm % less than 10 μm cake resistance (m/kg) filtration time (min) initial moisture content wt % (wet basis) static drying (via nitrogen blow through) agitated drying (with nitrogen blow through) MTR test with ethanol

(original)

L-threonine

347 108 307 0.76 4.93 × 108 5 min 20 powder no lumps; retained 0.5% lumps after sieving powder no lumps; retained 0.5% lumps after sieving low agglomeration potential

(fines)

330 50 293 4.76 1.8 × 1010 35 min 26 hard disk of crust formed on top layer; retained 8% lumps after sieving crust was broken up during agitation but hard lumps remained; retained 6% lumps after sieving low agglomeration potential

a

The two lots of L-threonine have different particle size distributions. Four experiments were run to evaluate the potential for forming cake-hardened lumps through static or agitated drying. The sieve used to screen the material after drying was a 850 μm sieve. For cake resistance and filtration time the slurries were prepared from 200 g of solid and 450 mL of ethanol, each filtered at 10 psi and 20 °C filtered on an Algochem laboratory filterdryer.

Figure 13. Batch distillation profile of water being enriched during drying of a 65%/35% isopropyl alcohol/water mixture.

generated in a way that could be considered a gentle “milling” process from the AFD (obtained from our attrition studies), and thus those particles with surface defects generated from dry grinding may cement differently than the distribution of particles generated from a crystallization process. Further investigation is needed to probe those differences as this was beyond the scope of the current work. From this current experimentation it is concluded that excessive fines will likely result in the formation of some cake hardened agglomerates that are not well predicted by MTR. Further agitation alone in the AFD may not be able to fully delump those agglomerates. The mitigation strategy recommended is to ensure good control of the crystallization process or develop effective ripening sequences and avoiding precipitates with excessive fines. Small amounts of cake hardening or heels can often be mitigated through the use of a comill to delump during discharge of the AFD. In general wide particle size distributions would have a tendency to agglomerate due to efficient packing of particles (similar reason as to why filtration will be slow) − better packing of particles leading to higher density cake − so it is not just the fines content but the width of the particle size distribution that is also important to consider. 3.2.5. Solvent Composition during Drying. If there is a solvent mixture present on the cake surface, one solvent may become enriched during drying. If the solid happens to more soluble in the enriched solvent, then dissolution can occur followed by recrystallization and bridging of crystals. This can lead to agglomeration as well and would not be predicted from

without agitation. The initial PSD parameters for these lots along with the drying results are shown in Table 11. Although the average particle size (D[4,3]) was similar for the two lots, there is a significant difference in the (D[v,0.1]) values (below 50 μm for the “fines” lot as compared to 108 for the original lot). In addition the lot with fines has 6 times more particles that are less than 10 μm. From Table 11 a few observations can be made. First, the Lthreonine with more fines resulted in significantly higher cake resistance and slower filtration than the original lot: 35 min versus 5 min. Long filtration times are an obvious indicator that fines are present with added potential for cake hardening and heel formation. Second, the additional fines result in a higher liquid hold-up and therefore a higher moisture content at the start of drying. The lot with fines also resulted in significantly more cake hardening regardless of whether it was static dried or agitated dried. Specifically the fines lot resulted in 6−8% retained lumps compared to 0.5% retained, so approximately 15 times more cake-hardened agglomerates were generated when fines were present. Interestingly, in the original lot a small fraction of cake-hardened lumps formed independent of the mode of drying; 0.5% lumps were retained on the sieve in both the static and agitated case. Either lot when wetted with ethanol (the solvent to be dried) resulted in low torque on the MTR and low agglomeration potential. In the case of excessive fines, the MTR did not appreciably help identify cake-hardening potential. It should be noted that the lot used with higher fraction of fines was 1356



Article

considered high risk, 50% were considered medium risk for agglomeration, and 20% were considered low risk for agglomeration as tested. These were tested using current wash solvents. Risks can be lowered in some cases by replacing a wash solvent with one exhibiting lower agglomeration potential. Agglomeration risk includes not only the potential as measured from the mixer torque rheometry but also the particle size distribution (fines can lead to cake hardening) and the moisture content (postfiltration and prior to stirring). Agitation that occurs when the moisture content is in the region of high agglomeration potential will likely result in “snow-balling” and agglomerates being formed. For compounds with high agglomeration potential but with agitation avoided until entering the low risk region, the number of agglomerates formed was significantly reduced but not completely eliminated. Wide particle size distributions dramatically increase the risk of agglomerate formation and cake hardening. This is attributed to smaller particles filling the voids and acting as cement bridges between particles. It should be recognized that the isolation and drying operations cannot rescue a poorly designed or poorly controlled crystallization. In fact, a large percentage of fines from a poor crystallization will slow the filtration, extend drying times, tend to cement particles together into agglomerates or chunks, and may cause filter blinding despite the best efforts to mitigate during isolation and drying. Where agitation needs to be minimized to minimize agglomeration risk, we used convective drying with nitrogen (through the cake from top to bottom) to provide heat and mass transport of solvent out of the cake as a means to reduce the moisture content to lower risk regions. In this way, convective drying provides heat transfer in the absence of agitation. Once the moisture is sufficiently reduced, agitation can be commenced while maintaining a low risk of agglomeration.

MTR testing. In these cases, both solvents should be tested independently to probe sensitivity to agglomeration in the event enrichment occurs. Figure 13 shows the example of a distillation profile for isopropyl alcohol and water mixed solvent starting at 65% IPO and 35% water at 200 mbar. It approximately simulates how the mixed solvent becomes enriched with water as the isopropyl alcohol is selectively removed during the initial phases of drying. Compounds more soluble in water then could potentially redissolve and recrystallize in the dryer. The mitigation strategy here is to wash with additional solvent to dry out of a single solvent. If this is not possible, the wash composition may be tuned to arrive at the desirable side of the azeotrope. In some cases, drying conditions may be adjusted to minimize enrichment either through changing the pressure or temperature or switching to convective or vacuum drying modes.

■

CONCLUSIONS A material-sparing, automated laboratory method for the assessment of attrition risk during drying in AFDs was developed by applying the technique originated by Lamberto7 to a modified FT4 rheometer cell. Assessment protocols were established by measuring the impact of testing variables on the extent of attrition and comparing the predictions to existing scale-up data. Evaluation of a model compound, L-threonine, indicated close agreement between the FT4 and Lamberto methods and satisfactory assessment of attrition risk upon scale-up to a pilot plant AFD, supporting the application of this predictive tool. Application of the FT4 test to a large number of Pfizer proprietary compounds indicated a wide range of attrition behavior, and categories were established to define attrition risk. Scale-up data available for a subset of these compounds further confirmed the predictive ability of the FT4 test. Ultimately, the results from the attrition risk assessments can be used to select agitation protocols during drying that provide a balance between drying cycle time and minimizing attrition. An operational model based on a design of experiments was developed for the FT4 powder rheometer to understand the main factors affecting particle breakage and can be used to tune the operating conditions of the equipment to apply the attrition forces observed on scale by measure of particle size reduction. The tendency for agglomeration depends on the specific solid−solvent system. We have shown how mixer torque rheometry MTR can be used to quantitatively assess the potential for agglomeration in AFDs. High torque measurement from MTR for wet cakes showed a greater tendency to form agglomerates on scale-up during agitated drying conditions. Given the complexity of solid + solvent interactions and the difficulty to predict the effect on agglomeration, it was shown how wash solvents could be screened against the potential for agglomeration with a particular solid API or intermediate. This screening is useful for predicting the effect of stirring on scaleup for a wet cake in an AFD. It was shown that solvent + solid combinations that indicated very low potential for agglomeration via MTR testing had fewer agglomeration issues and in fact could be dried with little or no agitation provided the material does not contain a wide particle size distribution or excess of fines. We have also shown across a random sampling of compounds in our portfolio that approximately 30% were

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: mark.t.maloney@pfizer.com. Present Address §

Nalas Engineering Services, Inc., 85 Westbrook Road, Centerbrook, CT 06409. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS We gratefully acknowledge our Chemical Engineering co-op students who contributed significantly to our efforts: Eoin Condon (Purdue), Tim Salerno (Northeastern), Annmarie Uliano (Northeastern), and Mary Hesse (Ohio State University). We also acknowledge the contributions and support of our drug product formulation colleagues including Matthew Mullarney for valuable input on the development of the FT4 methodology and extensive support on solids testing, Walter Cook for development of the MTR as a tool for predicting agglomeration, and Dauda Ladipo and Weili Yu for facilitating particle size analysis, photomicroscopy, and SEM support. We also thank Michael St. Pierre, Dominic Salvagna, Jr., Joseph Hochdorfer, and Stephen E. Zelinsky for their assistance in executing the pilot plant attrition testing of Lthreonine. 1357



■

Article

ABBREVIATIONS AFD, agitated filter dryer; API, active pharmaceutical ingredient; DOE, design of experiments; DP, drug product; MTR, mixer torque rheometer; PSD, particle size distribution

■

REFERENCES

(1) Neil, A. U.; Bridgewater, J. Powder Technol. 1994, 80, 207. (2) Ghadiri, M.; Ning, Z.; Kenter, S. J.; Puik, E. Chem. Eng. Sci. 2000, 55, 5445. (3) Ning, Z.; Ghadiri, M. Chem. Eng. Sci. 2006, 61, 5991. (4) Lekhal, A.; Girard, K. P.; Brown, M. A.; Kiang, S.; Glasser, B. J.; Khinast, J. G. Powder Technol. 2003, 132, 119. (5) Lekhal, A.; Girard, K. P.; Brown, M. A.; Kiang, S.; Khinast, J. G.; Glasser, B. J. Int. J. Pharm. 2004, 270, 263. (6) Hare, C.; Ghadiri, M.; Dennehy, R. Chem. Eng. Sci. 2011, 66, 4757. (7) Lamberto, D. J.; Cohen, B.; Marencic, J.; Miranda, C.; Petrova, R.; Sierra, L. Chem. Eng. Sci. 2011, 66, 3868. (8) Mehos, G.; Kozicki, C. Chemical Engineering 2011, January issue, 46−49. (9) Hapgood, K. P.; Litster, J. D. Wet Granulation Processes. In Chemical Engineering in the Pharmaceutical Industry: R&D to Manufacturing; am Ende, D. J., Ed.; Wiley: New York, 2011; Chapter 39. (10) Iveson, S. M.; Litster, J. D.; Hapgood, K.; Ennis, B. J. Powder Technol. 2001, 117, 3. (11) Litster, J. and Ennis, B. The Science and Engineering of Granulation Processes; Kluwer Academic Publishers: Norwell, MA, 2004. (12) Rowe, R. C.; Sadeghnejad, G. R. Int. J. Pharm. 1987, 38, 227. (13) Parker, M. D.; York, P.; Rowe, R. C. Int. J. Pharm. 1990, 64, 207. (14) Hancock, B. C. Material Interaction and Surface Phenomena in Size Enlargement Processes, Thesis, School of Pharmacy, University of Bradford, 1991. (15) Hancock, B. C.; York, P.; Rowe, R. C. Int. J. Pharm. 1994, 102, 167. (16) Pharmaceutical Dosage Forms−Tablets; Liberman, H. A., Lachman, L., Schwartz, J. B., Eds.; Dekker: New York, 1989; Vol. 1. (17) Newitt, D. M.; Conway-Jones, J. M. Trans. Inst. Chem. Eng. 1958, 36, 422. (18) Karmakar, S.; Kushwaha, R. L. J. Terramech. 2007, 44, 197.

1358


Development and Application of Laboratory Tools To Predict Particle

Recommend Documents