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1 This article was downloaded by: [Wyeth Research] On: 29 April 2009 Access details: Access Details: [subscription number ] Publisher Informa Healthcare Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Encyclopedia of Pharmaceutical Technology Publication details, including instructions for authors and subscription information: Crystallization: Particle Size Control Richard D. Braatz a ; Mitsuko Fujiwara a ; Thomas J. Wubben a ; Effendi Rusli a a Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, U.S.A. Online Publication Date: 02 October 2006 To cite this Section Braatz, Richard D., Fujiwara, Mitsuko, Wubben, Thomas J. and Rusli, Effendi(2006)'Crystallization: Particle Size Control',Encyclopedia of Pharmaceutical Technology,1:1, PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

2 Crystallization: Particle Size Control Richard D. Braatz Mitsuko Fujiwara Thomas J. Wubben Effendi Rusli Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, U.S.A. INTRODUCTION Crystallization is the main separation and purification step for the manufacturing of drug substances. The particle size distribution (PSD) obtained during crystallization is influenced by a combination of various mechanisms that occur during crystallization, such as nucleation, growth, aggregation, attrition, breakage, etc. Control of PSD during crystallization is critical to achieving the desired product properties. When the particle size cannot be consistently controlled during crystallization to meet the desired specifications, an extra processing step such as dry milling is required. [1] Seeded batch cooling and antisolvent crystallizations are the common types of crystallization used for specialty organic chemicals such as pharmaceuticals. Although seed crystals are usually added externally, in-situ generation of seeds through primary nucleation also has been studied. [2] The optimal recipe for the operation of batch crystallization to obtain the desired PSD can be determined by various approaches ranging from the empirical trial-and-error approach to the use of rigorous first-principles models. For efficient process development, systematic approaches are more desirable than trial-and-error-based approaches. [3] Systematic approaches, such as the first-principles [4 6] and direct design approaches [7 10] for the development of pharmaceutical crystallization processes are being increasingly applied in industry. In the first-principles approach, a model constructed from material and energy balances is used to optimize some function (e.g., mean crystal size) of the PSD. In contrast, the direct design approach involves the operation of crystallization process in the metastable zone by feedback control to follow a desired solution concentration profile. Both approaches to the control of batch crystallization have advantages and drawbacks. [11] Owing to the recent advances in in-situ sensor technology and reactor automation, there is a renewed interest in the direct design approach for batch pharmaceutical crystallization. This article describes the implementation of automation and sensor technology in particle size control during the batch pharmaceutical crystallization with emphasis on the direct design approach. First, product characterization for pharmaceutical crystals in terms of PSD is described. The PSD obtained during the crystallization step affects the efficiency of downstream operations such as filtration, drying, formulating, and product effectiveness such as bioavailability and shelf life. Thus, the control of PSD is an important objective during the operation of crystallization process. Applications of particle size sensors for the monitoring and control of particle size during crystallization are described. Next, the significance of metastable zone for the operation of crystallization process is explained, and an automated method for the determination of metastable zone is described. Most batch crystallization processes are operated within the metastable zone, a region bounded by the solubility curve and the metastable limit. Therefore, the first step to the development of batch crystallization process is usually the determination of the metastable zone. The size, shape, and solid-state phase of the product crystals are dependent on the supersaturation profile achieved during the crystallization process. Finally, the direct design approach for the development of cooling and antisolvent batch crystallization is described and its application to the particle size control is discussed. The direct design approach based on the concentration measurement involves feedback control to follow a user-specified setpoint supersaturation profile. This is in contrast to the traditional firstprinciples approach, which specifies the temperature profile as a function of time. In the direct design approach, the controller temperature setpoint is based on the concentration measurement rather than the time. The direct design approach has lower sensitivities of the crystal product quality to most process variations. PARTICLE SIZE DISTRIBUTION AND MEASUREMENT Most pharmaceuticals are produced in the particulate form, whether used in tablets or inhalers, or in 858 Encyclopedia of Pharmaceutical Technology DOI: /E-EPT Copyright # 2007 by Informa Healthcare USA, Inc. All rights reserved.

3 Crystallization: Particle Size Control 859 intermediate steps used to produce drug for use in solubilized forms such as gel caps. The processing properties of particles depend strongly on their size and shape. For example, the square crystals of table salt (sodium chloride) allow the crystals to easily flow out of a saltshaker. The uniform size of the crystals makes it easier to apply just the right amount of salt in cooking. The crystals are also the right size to flow through the holes in the top of the saltshaker. Larger crystals would not fit through the holes, while smaller crystals would stick together more easily, especially in a humid environment as that, which often occurs in kitchens during cooking. Figs. 1 and 2 show some pharmaceutical crystals produced in a laboratory at Merck. The crystals have a significantly different shape than that of the table salt. As such, the flowability and processability of such crystals are very different. For example, it is very time-consuming to filter the slurry containing the small needle-like crystals in Fig. 1. When the slurry is filtered, the variations in particle size result in the small particles filling in spaces between the larger crystals. This results in compacted crystals with no open spaces for the fluid to pass. Filtering such crystals requires very high pressures and long waiting times, which result in increased costs Fig. 1 Optical micrograph of Merck pharmaceutical crystals with poor filterability (200). Fig. 2 Optical micrograph of Merck pharmaceutical crystals with good filterability (200). during production. In contrast, the crystals in Fig. 2 are uniform in size and larger. Slurry containing these crystals filters quite easily. For particulate systems, the quality of the product is characterized in terms of a distribution of properties such as the PSD for crystals. The PSD of crystals can be obtained by sieving or imaging a large number of crystals using a microscope and measuring their characteristic lengths. The latter method can be automated using image analysis software. For particle size measurement during crystallization, it is more common to use a sensor that can measure the particle size characteristics of the crystals, with in-situ sensors being preferred over sensors that require sampling. In-situ sensors, such as those described next, allow the online monitoring of particle characteristics and control of PSD. One approach for particle size control using an in-situ sensor is fines dissolution. [12] The fines dissolution strategy is based on the temporarily heating of slurry to dissolve the smaller particles when a particle size sensor or turbidity sensor detects excessive nucleation of small particles. The slurry is heated until the particle count is reduced to a desired level during the crystallization process to minimize the production of small crystals, which are difficult to filter.

4 860 Crystallization: Particle Size Control Fines dissolution was used to produce the metastable glycine polymorph of the desired mean size during batch cooling crystallization. [13] PARTICLE MEASUREMENT SENSORS Sensors for particle size characterization used for crystallization include ultrasound attenuation measurement, [14] laser diffraction, [15] and laser backscattering, [5,8] commercially called focused beam reflectance measurement Õ (FBRM). Ultrasonic attenuation spectroscopy has been used to monitor the crystallization process parameters such as the crystal size distribution, concentration, and the onset of nucleation during batch crystallization of L-glutamic acid. [16] Off-line laser diffraction has been used to measure the crystal size distribution in the development of the crystallization process for a pharmaceutical intermediate. [17] FBRM is an in-situ sensor widely used in the pharmaceutical industry for real-time analysis of particle size characterization. The FBRM does not measure the particle size, but rather the chord length, which is related to the particle size and shape. FBRM uses a high velocity scanning infrared laser beam that emanates through the probe window inserted in the crystallizer (Fig. 3). When the laser beam hits a particle, it is reflected back to the probe. The length of the time for which a continuous signal is reflected back to the probe is multiplied by the velocity to determine the chord, which under ideal conditions is that distance where the laser crossed the particle. The chord length distribution is not the same as the PSD as shown in Fig. 4 for latex particles. For ideal particles such as ceramic beads or latex beads, which are spherical particles with good backscattering characteristics, the PSD can be deduced from the measured chord length distribution using inverse geometric modeling. [18 20] Most organic crystals used in pharmaceutical crystallization are non-ideal due to their complex shape and low refractive index. Thus for such systems it can be difficult to accurately calculate the PSD from FBRM measurement. Organic crystals are multifaceted and exhibit a variety of shapes such as plates, needles, rods, prismatic, etc. As the measured chord length is a function of the particle shape as well as size and concentration (Fig. 5), these various shapes need to be taken into account when trying to relate the chord length distribution to PSD. In addition, the refractive indices of most organic crystals are similar to the solvent used in crystallization, which results in poor backscattering characteristics, so that the estimated particle size calculated from the measured chord length by inverse geometric modeling will underestimate the actual particle size. Furthermore, some organic crystals have the tendency to aggregate or agglomerate, which Fig. 3 Schematic of FBRM sensor and a chord. complicates the measurement of particle size. Although the measurement of true PSD is difficult, the FBRM can be used to track changes (or trends) in particle size characteristics during crystallization. For example, the Fig. 4 Chord length distribution of latex particles suspended in water (- - -) and actual particle size distribution ( ). (From Ref. [24].)

5 Crystallization: Particle Size Control r r 1 r r Fig. 5 Theoretical chord length distribution for single crystal from orientation averaging. growth of crystals can be tracked by the square weighted median of chord lengths, a statistic found to be sensitive to the crystal length for many crystallizations. [7] FBRM is also used to detect primary or secondary nucleation and dissolution of crystals through changes in the particle counts. [21] For a proprietary pharmaceutical compound, the filtration resistance of crystals was correlated to the measured chord length distribution during crystallization. [22] Such a correlation can help detect operational problems during the batch and be used to determine the endpoint of the batch crystallization runs based on the FBRM measurement. In-process video microscopy, such as Particle Vision Measurement Õ, provides qualitative real-time information of the crystals such as the crystal shape and whether the crystals are aggregated during crystallization (Fig. 6). Although in-process images are not as clear as the offline microscope images, for some systems the video microscopy images have been analyzed to obtain the PSD. This has been done for the spherical particles such as gas bubbles and emulsion polymerization with offline processing of online images. [23,24] This method is not yet suitable for fast real-time analysis of PSD because few hundred images typically need to be processed to obtain a size distribution. In addition, the image processing for crystals is more complicated compared to the spherical particles because of their irregular shapes. However, trends in crystal shape and size information can be monitored using online images as demonstrated for the shape change associated with polymorphic transformation of L-glutamic acid using an online high-speed imaging system. [25] With a sampling loop, online optical microscope images can be obtained during crystallization. Online optical microscope images are clear enough that feedback control Fig. 6 Comparison of off-line optical microscopy image (A) of lactose crystals with in-situ PVM image (B) during process operation. of the crystal shape can be achieved during crystallization using a shape classification scheme. [26] PARTICLE SIZE CHARACTERISTICS When controlling the PSD during crystallization, some characteristics of the PSD of the product are often the focus of processing rather than exactly matching the full size distribution. These characteristics include moments of the PSD, number-mean crystal size, weight-mean crystal size, variance of the distribution function, and the coefficient of variation. Weight-mean crystal size is the most common PSD characteristic used in practice. For the case where the particles have

6 862 Crystallization: Particle Size Control a single characteristic size L, the weight-mean size is defined by the following equation: L w ¼ R 1 0 R 1 0 L 4 fðl; tþdl L 3 fðl; tþdl The number-mean crystal size is defined by L ¼ R 1 R0 1 0 LfðL; tþdl fðl; tþdl The variance of the distribution function, which quantifies the width of the distribution function, is defined by s 2 ¼ R 1 0 ðl LÞ 2 fðl; tþdl R 1 0 fðl; tþdl The coefficient of variation, which quantifies the width of the distribution function relative to its mean (sharpness of the PSD), is defined by c:v: ¼ s L The ith moment of the PSD is defined by m i ¼ Z 1 0 L i fðl; tþdl i ¼ 0; 1; 2;... The moments have physical meaning. The zeroth order moment m 0 is the total number of particles per unit volume (or mass, depending on the basis). The first-order moment m 1 is the total length of the particles per unit volume, with the particles lined up along the characteristic length. The second-order moment is proportional to the total surface area, and the third-order moment is proportional to the total volume. Many physical characteristics of the particles such as the numbermean crystal size, weight-mean crystal size, the variance of the distribution function, and the coefficient of variation also can be represented in terms of the lower order moments of the distribution. Several crystallization objectives have been recommended to favor downstream operations or product quality. One can maximize the number-mean or weight-mean crystal size, [27] maximize the final size of crystals grown from seed crystals, [28,29] or minimize the ratio of nucleated crystal mass to seed crystal mass. [6,30] Other particle size-related characteristics of product crystals that have been optimized during crystallization include the coefficient of variation [31] and the crystal shape. [32] Although the weight-mean crystal size is the most commonly used objective in optimal control studies, [6] the weight-mean crystal size is too insensitive to the number of small crystals [32] that can cause filtration problems when used as an objective to optimize the crystallization operations. METASTABLE ZONE AND SUPERSATURATION A model for the crystallization process must account for the differently sized particles distributed in the continuous phase. The population balance equation (PBE) is the material balance that accounts for the distribution in particle size, location, and other state variables, such as concentration and temperature. [33] Supersaturation (Dc ¼ c c ), which is the difference between the solution concentration c and the saturation concentration c, provides the driving force for the solute to leave the continuous phase and enter the crystal phase. The supersaturation profile achieved during the crystallization and the seed characteristics have a strong influence on the crystal size distribution. [34] The supersaturation profile determines the product property such as the particle size, morphology, and solid-state phase (polymorphic and enantiomeric purity). This is because the mechanisms associated with crystallization, such as nucleation and growth, are dependent on the extent of supersaturation. Many of the underlying principles of crystallization are the same for inorganic compounds as well as for the biochemicals and specialty organic chemicals such as those produced in the pharmaceutical industry. [35] The crystallization of organic compounds has additional complications compared to the inorganic crystallization studied extensively in the past. These complications include a much higher tendency toward polymorphism (multiple crystal forms), difficulty in predicting crystal morphology, temperature sensitivity, and increased cost of process development due to the limited supply of materials during early-stage design. The operating region for the majority of seeded batch and semibatch crystallizations, the widely used crystallizer operations for pharmaceuticals, is the metastable zone. The metastable zone is the area bounded by the solubility curve and the metastable limit (Fig. 7). The solubility curve is the saturation concentration plotted for a range of temperature and/or solvent-antisolvent ratio. The metastable limit is the point where nucleation is first observed when a solution becomes supersaturated by cooling or addition of antisolvent. The metastable limit is dependent on various operating conditions such as the rate of cooling or antisolvent addition, presence of seed and impurity, and mixing speed. [36] It is, therefore, best to determine the metastable zone under conditions expected to be similar to what will occur in the final batch crystallization recipe (e.g., only use cooling rates within the limit of what can be achieved by the jacketed cooling system). Operation of a crystallization process very close to the solubility curve (very low supersaturation) results in slow growth and long batch time. Operation beyond the metastable limit (high supersaturation) results in uncontrolled nucleation

7 Crystallization: Particle Size Control 863 Fig. 7 Schematic of the metastable zone, which is the region surrounded by the solubility curve and the metastable limit. This is the operating region for most seeded batch crystallization. leading to the production of many small crystals, which can result in long filtration times. For the fast growth of large crystals, some intermediate value of supersaturation is desired. Other factors such as mixing and impurities in the feed can also affect the product properties. It is known that impurities can shift the solubility curve (and, thereby, change the supersaturation) and affect the crystal morphology. Imperfect mixing can create pockets of higher supersaturation in the crystallizer. Mixing also affects such processes as aggregation, attrition, and breakage that can occur during crystallization. Fig. 8 Crystallization apparatus showing in-situ sensors (FBRM at upper right, PVM at upper left, FTIR spectrometer with ATR probe at left in the background). Antisolvent pump not shown. DETERMINATION OF METASTABLE ZONE Determination of metastable zone is usually the first step in the development of a batch crystallization process. Recent advances in in-process sensor technology enabled the determination of metastable zone to be carried out in an automated manner. [7,8,21] A typical apparatus and instrument setup for batch crystallization includes an FBRM, attenuated total reflection Fourier transform infrared (ATR FTIR) spectrometer, solvent pump(s), a jacketed vessel, and a thermocouple (Fig. 8). The metastable limit is detected by FBRM or any type of in-process turbidity sensor that can detect a sudden increase in particle numbers due to nucleation. The solubility curve is determined by measuring the equilibrium solution concentration at various temperatures or solvent-antisolvent ratios using ATR FTIR spectroscopy and an appropriate calibration model to relate IR spectra to the concentration. [37 40] The ATR probe creates an evanescent field that is generated when light is reflected due to a change in the density of the transmitting medium. This allows the external probe to be used to take a measurement as opposed to having the light pass through the sample. An ATR probe inserted into the crystallizer allows the measurement of IR spectra of the fluid in contact with the tip, with negligible signal from the crystals. The absorbances measured in the mid-ir range using ATR FTIR are usually linearly related to the solution concentration. ATR UV spectroscopy also has been used for in-situ solution concentration measurement. [41] In-situ Raman spectroscopy has been used for the monitoring of solute and solvent concentrations

8 864 Crystallization: Particle Size Control during antisolvent crystallization. [42] For some systems in-situ Raman spectroscopy can be used to measure the solid phase of the crystals, such as polymorphism. [43] The steps for metastable zone determination are outlined in Fig. 9. First, the IR spectra of known concentration, temperature, and solvent-antisolvent ratio are collected to build a model for relating IR spectra to solution concentration. The IR spectra are collected automatically by sequentially cooling and diluting the solution with solvent to cover a range of concentration and temperature that span the metastable zone (Fig. 10). The endpoints for solvent additions are based on data from the FBRM or a combination of userdefined target values and FBRM data, depending on the extent of the user s prior knowledge of the crystallization system. IR spectra are collected while the solution remains clear based on monitoring FBRM counts. During the cooling stage, the metastable limit can be detected by an increase in the total number of FBRM counts, which indicates nucleation. The metastable zone for an antisolvent system is determined similarly using two solvent pumps, one for the solvent and the other for the antisolvent, and sequentially adding the antisolvent and the solvent to cover a range of concentration and solvent-antisolvent ratio. The absorbances for a user-selected IR spectral frequency range and temperatures are used to build the calibration model relating IR spectra to the solution concentration (Fig. 11). Multivariate statistics, such Spectra collection; Metastable limit detection Fig. 10 Schematics for automatically collecting the IR spectra for constructing the calibration model for cooling and antisolvent crystallizations. Calibration model building as PLS and PCR, are used to build the calibration model. The solution concentration is calculated from the calibration model as follows: Solubility measurement Fig. 9 Steps for automated determination of metastable zone using ATR-FTIR and FBRM. While automatically collecting the IR spectra for calibration, the metastable limit is determined using FBRM. Then the model for relating the IR spectra to solution concentration is constructed using multivariate analysis such as principal component regression (PCR) or partial least squares (PLS). Using this model, the solubility curve can be obtained from the IR spectra of saturated slurry. c ¼ X w i a i þ w T T þ w o where c is the solution concentration, a i is the absorbance at frequency i, T is the temperature, and w i, w T, and w o are the coefficients of the model. It is typical to build calibration models for several frequency ranges and select the calibration that gives the smallest prediction intervals. [39] The calibration model is an extension of Beer s Law, which states that a linear relationship exists between the absorbance and concentration. Beer s Law holds for many dilute solutions and for some systems of more concentrated solutions

9 Crystallization: Particle Size Control 865 Fig. 11 The calibration model relating IR spectra to solution concentration. The multivariate model relates IR absorbances of a selected frequency range and temperature or solvent antisolvent ratio to solution concentration. for spectra in the mid-ir range. In the above model, the temperature T can be replaced with the solventantisolvent ratio (%S) for antisolvent crystallization. Alternatively, both T and %S can be included in the model for systems that will have changes in both the temperature and solvent-antisolvent ratio during crystallization. For some systems, using derivative spectra gives a better calibration because of the improved robustness to baseline drift or shift. Other methods, such as band ratioing or peak areas, have been used to build a calibration model instead of multivariate statistics, with judicious use of multivariate statistics resulting in models with more accurate predictions. Using the calibration model, the solubility can be estimated from IR spectra of the slurry equilibrated at various temperatures or solvent-antisolvent ratios (Fig. 12). The use of ATR FTIR spectroscopy is not a requirement for the determination of the metastable zone. If the goal is the determination of metastable zone or solubility curve alone, then there are less technically complicated methods, such as the gravimetric method for solubility measurement and observation by eye for detection of the metastable limit. Even for the automation of the system, ATR FTIR spectroscopy is not a requirement. Automated determination Fig. 12 The temperature profile for measuring the solubility curve. The solubility is determined from IR spectra of slurry equilibrated at various temperatures. of metastable zone can be done using FBRM or a turbidity probe alone. [21] The advantage of building a calibration model for concentration measurement using ATR FTIR spectroscopy during the determination of metastable zone is that it allows the monitoring and/or control of solution concentration during batch crystallization, which enables the rapid development of batch crystallization recipes, as discussed below. BATCH CRYSTALLIZATION PROCESS CONTROL A common approach in the pharmaceutical industry for the development of a batch crystallization process

10 866 Crystallization: Particle Size Control is to determine an operating profile (temperature profile or antisolvent addition profile) that gives crystals of acceptable properties by trial-and-error experimentation. With an increasing demand for faster process development, there is an increasing interest in automation and a high-throughput approach to crystallization, as well as a more systematic approach to the development of crystallization processes. [44] More efficient approaches to crystallization design than the empirical approach are: (i) first-principle design and (ii) direct design. FIRST-PRINCIPLES APPROACH The first-principles approach involves the determination of crystallization kinetics from a series of continuous, batch, or semibatch experiments. Once the solubility curve and kinetics are inserted into a firstprinciples model (consisting of crystal and solute mass conservation equations and possibly an energy conservation equation), the model is used to optimize a particle property such as the mean crystal size. To simplify the crystallization model, most batch crystallization optimization studies only consider nucleation and growth kinetics and ignore more complex kinetic phenomena such as aggregation. The optimal temperature or antisolvent addition rate profile for obtaining the desired particle property is calculated from the simplified crystallization kinetics. An example of optimal temperature profile calculated using a first-principle model for maximizing the mean crystal size for the unseeded crystallization of paracetamol (acetaminophen) is shown in Fig. 13. [45] The initial temperature drop generates seeds in situ. The optimal temperature Fig. 13 Optimal temperature profile calculated from a firstprinciple model for maximizing the mean crystal size for unseeded crystallization of paracetamol in water and the simulated change in mean crystal size during crystallization. profile then keeps the supersaturation nearly constant throughout the rest of the batch. The first-principles approach has been successfully applied to the particle size control for various pharmaceutical crystallization processes. First-principles design was used to optimize the coefficient of variation and the nucleated-to-seed mass ratio in batch cooling crystallization of a proprietary pharmaceutical. [4] Model-based optimization was applied to the batch cooling crystallization of paracetamol from ethanol to obtain a user-specified monomodal final PSD, [5] and to achieve a desired mean crystal size for a pharmaceutical compound by minimizing the batch crystallization time. [46] The successful implementation of first-principle design is dependent on the quality of the crystallization model used in the calculation of the optimal profile. If the model is not accurate or if there is a disturbance during crystallization, which changes the kinetics, then following the optimal profile does not yield crystals with the desired product characteristics. There is no point in identifying the extremely accurate kinetic parameters for a single crystallization batch when the kinetic parameters will vary from one batch to the next. It is therefore important to assess the effect of variations in the kinetic parameters on the performance of the control strategy. DIRECT DESIGN APPROACH The direct design approach uses feedback control to follow a setpoint based on a state measurement. States in a crystallizer include the temperature, the solution concentration, and the crystal size and shape distribution. An example of the direct design approach is to follow a supersaturation setpoint based on the concentration measurement. This approach was studied in the 1980s and early 1990s using a conductivity probe, densitometer, and refractometer as the sensor for online concentration measurement for KH 2 PO 4 (KDP), potash alum, and D-xylose, respectively. [47 49] Because the direct design approach does not require the derivation of the first-principle models and the associated determination of crystallization kinetics, it is conceptually simpler to carry out than the first-principles approach to crystallization design. In spite of this, wide industrial implementation of this approach did not take hold in the pharmaceutical industry. This was probably due to the limitations of sensors used for concentration measurement at the time and lack of transparency associated with the hardware/software implementation of this approach, which involves interfacing of various instrumentation for temperature and concentration measurement to one or more computers. There has been a resurgence of interest in the direct design approach due to the advances in sensor

11 Crystallization: Particle Size Control 867 technology and automation of lab reactors, making it easier to control various instrumentations through a computer interface. [7 10] Most current instruments are equipped with a computer interface, and software such as LabView, Microsoft Visual Basic, or Matlab can provide the graphical user interface for communication among the computers and control of various instruments such as a particle size sensor, concentration measurement sensor, dosing equipment, and temperature control. ATR FTIR spectroscopy overcomes many of the limitations of past sensors used for concentration measurements such as conductivity probe and densitometer. It is applicable to a variety of organic systems and can measure multiple solution concentrations in the multi-component systems often encountered in antisolvent crystallization. [4,50] It does not require an online sampling loop, which can cause operating problems such as clogging. [51] Commercially available automated lab reactors integrated to ATR FTIR spectroscopy are available. In the direct design approach, a desired supersaturation profile that falls between the solubility curve and the metastable limit of the system is followed based on feedback control of the concentration measurement. This is in contrast to the traditional first-principles approach, where a desired temperature profile or antisolvent addition rate profile is followed over time such as shown in Fig. 14. For a cooling crystallization, the direct design approach follows a setpoint profile that is solution concentration vs. temperature (or solventantisolvent ratio) as opposed to temperature (or addition rate) vs. time. Because the desired crystallizer temperature is determined from an in-situ solution concentration measurement, the batch time is not fixed. Rather, the batch time varies from batch to batch depending on the kinetics of the crystallization. As shown below, the direct design approach for antisolvent crystallization is implemented in a very similar manner. DIRECT DESIGN IMPLEMENTATION There are different approaches to implementing the feedback concentration control for the direct design. Various schemes to implement the concentration control for direct design are described in the literature for cooling and antisolvent crystallizations. [7 10,52] The basic steps are as follows: (i) the solution concentration is estimated from IR absorbances and temperature or solvent-antisolvent ratio using the calibration model that relates IR spectra to concentration and (ii) the temperature or antisolvent flow rate setpoint is calculated from the concentration, solubility curve, and the user-specified supersaturation setpoint. Fig. 15 shows an example of the direct design approach implemented for the isothermal antisolvent crystallization of acetaminophen (paracetamol) from acetone-water mixture. A constant relative supersaturation (Dc/c ) setpoint profile was followed. The flow rate setpoint of the antisolvent was calculated every minute based on the solution concentration measured using the IR spectra so that a setpoint supersaturation profile was followed. The change in solution concentration and antisolvent flow rate during the batch is shown in Fig. 16. After an initial start-up Fig. 14 Schematic block diagrams of the temperature (T) vs. time (t) profile from first principles approach (A) and of concentration (C) vs. temperature (T) profile from direct design (B). Fig. 15 Direct design approach using concentration measurement for seeded antisolvent crystallization of paracetamol (acetaminophen) from acetone-water mixture. The concentration-% solvent profile of the batch, the setpoint profile, and the solubility curve are shown. The setpoint followed is that of a constant relative supersaturation Dc/c ¼ 0.04 g/ml solventþantisolvent.

12 868 Crystallization: Particle Size Control Fig. 16 Change in solution concentration and antisolvent flow rate during the seeded antisolvent crystallization of paracetamol following a constant relative supersaturation. phase (time ¼ 0 7 min), the antisolvent addition rate increased as the crystallization progressed, because the increase in crystal surface area draws solute more quickly from solution. For time >7 min, the solution concentration decreased slowly in the beginning and somewhat more rapidly later in the batch. The FBRM counts were monitored every minute to detect the occurrence of secondary nucleation during the batch, which would be indicated by a large increase in the particle counts associated with the formation of large numbers of small crystals. As shown in lower line in Fig. 17, the setpoint profile followed in the direct design approach resulted in negligible secondary nucleation. Too large of a setpoint supersaturation results in a large increase in particle counts, which is indicative of significant secondary nucleation, as shown in the upper line in Fig. 17. The lower plot in Fig. 18 shows the increase in size uniformity and reduction in the number of small crystals that occurred when the direct design approach was used to minimize secondary nucleation. The direct design approach has been applied to various cooling and antisolvent crystallizations of pharmaceutical compounds to control supersaturation such that the secondary nucleation is minimized during the batch. [7,8,52] Batches can be run for several operating curves (supersaturation profiles) in the metastable zone [10,52] and with various agitation speeds and seed types, quantities, and sizes [9] to produce the desired product crystals. Experiments can be run in parallel for higher throughput using multiple small-automated reactors. The direct design approach based on the feedback control of concentration can also be coupled with a fines dissolution strategy to provide a finer tuning of particle size control during crystallization. Fig. 17 FBRM counts during seeded batch antisolvent crystallizations of paracetamol from water/acetone with secondary nucleation and with minimum secondary nucleation. Fig. 18 Microscopy images of seed and product crystals during seeded batch antisolvent crystallizations of paracetamol from water/acetone with secondary nucleation and with minimum secondary nucleation.

13 Crystallization: Particle Size Control 869 EFFECT OF DISTURBANCES During the operation of a crystallizer, disturbances occur that can affect the product properties. A common disturbance encountered in batch crystallization is a shift in kinetic rates or in the solubility curve due to impurities. Impurities also can affect sensor measurements and crystallization kinetics. With the firstprinciples approach, which utilizes a crystallization model, the success of producing crystals with the desired product characteristics is dependent on the quality of the model used in deriving the optimal profile. If disturbances occur during the run that affects the crystallization kinetics or if the condition in which the model is derived is sufficiently different from the operating condition, then the kinetic model may no longer describe the actual crystallization process during production. In these cases, crystals with the desired product property may not be produced even if the optimal temperature profile calculated from first-principles model is judiciously followed during the run. The optimum temperature trajectory, when calculated a priori, is unable to react to the unexpected disturbances that occur during the operation of the batch, no matter how accurate the parameter estimates. The kinetic parameters commonly vary in a real crystallization procedure, due to variations in the contaminant profiles in the feed. The direct design approach gives lower sensitivities to most practical disturbances and to variations in the nucleation and growth kinetics compared to the traditional first-principles approach that computes temperature vs. time or antisolvent rate vs. time profiles. [45] Simulation studies indicate that mean particle size is much less sensitive to variations in nucleation and growth kinetics for the direct design approach. This is because, in the direct design approach, the crystallizer can adjust to the variation in kinetics by varying the batch time. The direct design approach also provides a more constant yield in variable batch time under variations in the kinetics. Both the direct design and traditional first-principles approaches are sensitive to shifts in the solubility curve. The direct design approach is affected by disturbances that affect the ATR FTIR sensor measurement, such as an impurity that affects the IR spectral range used for calibration. This is expected because the successful implementation of the direct design approach is dependent on the accuracy of the concentration measurement. If the impurity is known and anticipated, this latter sensitivity can be greatly reduced by including the impurity (if anticipated) in the calibration model, or by selecting a spectral range when constructing the calibration curve in which the spectra of the solute and impurity do not overlap. The aforementioned reduced sensitivity to most common disturbances obtained by the direct design approach compared to the traditional first-principles approach does not imply that the use of first-principles models is inferior. Each approach has its advantages and disadvantages. Understanding these advantages and disadvantages is important in choosing the best approach for the design of an individual crystallization process. First-principles models have the advantages that model-building increases process understanding, and that simulation algorithms are becoming available, which utilize the kinetics model to investigate the effects of non-ideal mixing during scale-up. [53 55] Further, the reduced sensitivity to most common disturbances for the direct design approach is mostly due to the use of feedback control with a real-time solution concentration measurement. First-principles approaches for batch crystallization design and control can have similar insensitivity to disturbances by utilizing the real-time solution concentration measurement. CONCLUSIONS Crystallization from solution is an important operation for the production of pharmaceuticals due to its ability to provide high purity separations. The size distribution of the product obtained from the crystallization step is a critically important factor in the production of high quality products and for determining the efficiency of downstream operations. The crystallization process should be designed to produce the desired particle characteristics as well as to provide high purity separation. Seeded batch cooling and antisolvent crystallizations are the common types of crystallization processes used for pharmaceuticals. It is becoming increasingly common in pharmaceutical process development laboratories for a typical crystallization apparatus setup to include an in-situ sensor for particle size characterization, such as FBRM, and an in-situ sensor for solution concentration measurement, such as an ATR FTIR probe. These sensors enable the automated determination of the metastable zone, which is the first step in developing a crystallization process. During the crystallization process, these in-situ sensors are used to monitor the crystallization progress as well as to control the process through such strategies as fines dissolution and feedback control of concentration measurement. Batch crystallization process control using the firstprinciples and direct design approaches were discussed. The first-principles approach utilizes crystallization process models, which require the associated determination of crystallization kinetics. The optimal seed characteristics and/or supersaturation profile to obtain the desired product characteristics are then computed. The direct design approach involves feedback control of a state measurement, in this case the solution

14 870 Crystallization: Particle Size Control concentration, to follow a desired solution concentration profile. The direct design approach does not require the derivation of crystallization models and has lower sensitivities of the crystal product quality to most process variations. Our group has applied the direct design approach to optimize product properties other than particle size, such as polymorphism. Control of polymorphism is an important aspect during the crystallization of pharmaceutical compounds as different polymorph of the same drug compound can have different properties including particle shape and dissolution rate. [56 58] Trends in the crystallization process development in the pharmaceutical industry is to carry out measurements at a small scale in addition to utilizing automation and high throughput systems as exemplified by the use of automated metastable zone measurement for 1 ml samples. 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