The Pharmaceutical Drying Unit Operation: An ... - ACS Publications

Form Conversion and Solvent Entrapment during API Drying. David J. Lamberto , Anthony Diaz-Santana , and George Zhou. Organic Process Research ...
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The Pharmaceutical Drying Unit Operation: An Industry Perspective on Advancing the Science and Development Approach for Scale-Up and Technology Transfer Edward W. Conder,† Andrew S. Cosbie,‡ John Gaertner,§ William Hicks,∥ Seth Huggins,‡ Claire S. MacLeod,∥ Brenda Remy,¶ Bing-Shiou Yang,# Joshua D. Engstrom,*,¶ David J. Lamberto,*,⊥ and Charles D. Papageorgiou*,▽ †

Small Molecule Design & Development, Eli Lilly & Co., Lilly Corporate Center, Indianapolis, Indiana 46285, United States Drug Substance Technologies, Process Development, Amgen Inc., 1 Amgen Center Drive, Thousand Oaks, California 91320, United States § Process Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, Illinois 60064, United States ∥ Pharmaceutical Development, AstraZeneca, Hulley Road, Macclesfield SK11 2NA, U.K. ¶ Drug Product Science & Technology, Pharmaceutical Development, Bristol-Myers Squibb Co., 1 Squibb Drive, New Brunswick, New Jersey 08901, United States # Material and Analytical Sciences, Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, Connecticut 06488, United States ⊥ Chemical Engineering R&D, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States ▽ Process Chemistry, Takeda Pharmaceuticals International Co., 40 Landsdowne Street, Cambridge, Massachusetts 02139, United States ‡

ABSTRACT: The drying unit operation is used extensively in the pharmaceutical industry, but often a lack of understanding of the impact of the drying process parameters on active pharmaceutical ingredient critical quality attributes can create challenges during development. A working group within the Enabling Technologies Consortium identified common challenges in pharmaceutical drying development, including material constraints for scale-up studies and transferring to different equipment types and sizes. This manuscript surveys current practices within the industry for drying development related to chemical and physical stability, drying kinetics, and powder properties and highlights common development gaps for improving drying development workflows within the industry. The ultimate goal is to encourage further fundamental research and technological advancements for improving the drying development workflow.

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INTRODUCTION In the pharmaceutical industry, the goal is to produce a drug product (DP) that is efficacious and safe for the patient in a cost-effective and timely manner. To this end, a target product profile (TPP) is created to define the critical quality attributes (CQAs) of the DP that then guide the determination of the active pharmaceutical ingredient (API) CQAs. For synthetically produced small molecular weight drug substances, in particular those formulated in oral solid dosage forms, the CQAs include aspects related to both purity and chemical and physical stability as well as those associated with powder properties, which can affect DP processing and performance. To ensure that the API CQAs are met, it is crucial to understand the impact of the processing parameters on these attributes. Typically, much time and effort are needed to develop an API manufacturing process to improve product purity and optimize the isolation steps to ensure proper form control is achieved and the required powder properties are met. Although a critical step of the synthesis compared to other unit operations, drying is often overlooked and not fully understood until later in the development cycle. This frequently occurs © 2017 American Chemical Society

because many of the issues are only discovered when manufacturing occurs at larger scale or the process is transferred to a different equipment train. Unfortunately, this approach carries high risk because the API drying step, which is normally the last step in the synthesis, can greatly impact the API CQAs. To mitigate this risk, it is important to have a firm understanding of the impacts of the drying process parameters on the API CQAs early in the development process. Development of an optimum drying protocol for an API involves having an in-depth understanding of the elements associated with the chemical and physical stability of the compound, the drying kinetics, and the physical properties of the isolated solids as illustrated in Figure 1. In the pharmaceutical industry, it is critical that all three elements are considered during processing including how they are impacted during scale-up and by the choice of equipment.1 Optimization of the processing parameters with focus on one element without considering the impact on the others can Received: December 1, 2016 Published: February 3, 2017 420

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wall and perhaps agitator are heated and the dryer is maintained under vacuum to allow solvent to evaporate (e.g., filter dryer, conical dryer, or rotary dryer).3f,6 Although it would be ideal to complete all development and manufacture work with one dryer type at a physically representative scale, it is normally not realistic or feasible to achieve this, as material is scarce and the choice of dryer and/or dryer type is often dictated by equipment availability at the particular processing facility as well as the stage of development. For example, wet-cakes at the lab scale are dried statically in tray dryers, whereas agitated dryers are only used upon scale-up into the kilo-lab and pilot plant. The Enabling Technologies Consortium (ETC)7 was formed to support improvements in the development and manufacturing of pharmaceuticals by addressing common development and manufacturing issues experienced throughout the industry in a precompetitive manner. Within the ETC, a drying working group (WG) composed of representatives from eight pharmaceutical companies was organized to focus on advancing the drying unit operation of APIs and intermediates. The aims of the ETC Drying WG are to “identify, evaluate, develop, and improve scientific tools and techniques for efficient development and manufacture of pharmaceuticals”.7a For the drying unit operation, this will be accomplished first by identifying current industry practices utilized in drying development and then by articulating and helping address ongoing knowledge and technology gaps that hinder development efficiency, scaleup predictability and success, as well as robustness. The goal of this manuscript is to provide a survey of the current state and to encourage future fundamental studies and technology development for the drying of pharmaceutical compounds. Current industry approaches to drying development for each of the three drying elements shown in Figure 1 will be discussed, and each section will include lab scale development approaches, relevant process analytical technology (PAT) and modeling tools, as well as scale-up considerations. The hope is that sharing the current industrial drying practices and highlighting the current gaps and shortcomings will stimulate technological and product innovations that will improve the way that drying is carried out within the pharmaceutical industry. Chemical and Physical Stability. When developing a drying process for an API or an intermediate, the chemical and physical stability of the compound must be understood to ensure that purity and form criteria are met; otherwise, batch failure may result, or reprocessing may be required, leading to significant development delays and added costs. Development studies to better understand the impact of drying process parameters on chemical and physical stability can greatly reduce this risk. Chemical stability of the API during drying is most often impacted by the drying temperature. To understand a compound’s degradation with temperature, thermal stability is typically assessed in a lab dryer by heating the compound in both the wet and dry state at several different temperatures for a duration exceeding the greatest potential drying time at scale. The highest reasonable dryer temperature, safely below the melting point or the highest temperature at which there is no significant degradation, is then selected for large-scale drying to apply the highest possible temperature driving force while avoiding compound degradation. To address compound-specific physical stability, a solid form phase map as a function of solvent/water composition and

Figure 1. Elements of API drying key to the development of an optimum drying protocol.

create unintended risk on the API CQAs. Often, changes that are beneficial for one are detrimental to another. For example, increasing the processing temperature or agitation rate to achieve faster drying kinetics can have a negative effect on maintaining product purity and desired physical attributes. Therefore, in addition to understanding each element, it is important to understand how each is interconnected with the other elements through the drying process parameters (e.g., temperature, agitation protocol, vacuum, dryer type) and how each is related to the API CQAs. Under ideal circumstances, the connections between the processing parameters and each drying element are fully understood and fed into the design so that the optimum drying protocol is achieved. Having this knowledge helps ensure that the drying protocol is robust and able to reliably deliver API with the desired attributes. It should be noted that the same elements presented in Figure 1 also apply to the drying of intermediates, but physical property control is typically less critical. Achieving the understanding needed to design and scale-up an optimum API drying protocol has many challenges. Typically, only limited material quantities are available in early development, and in most cases, surrogate materials capable of fully capturing the powder properties of the API are nonexistent. Unlike liquid systems, solids are discontinuous, and there is not a universal theory for their behavior. As an example, there is no set of material properties that can be measured and used to determine the response of a compound to mechanical stress.2 There has also been limited work to characterize the processing environments that the compounds are exposed to during drying at scale, and novel approaches are needed to project performance and behavior observed at smaller to larger scales. Typically, connections between small and large scale have been made primarily through empirical testing with results having limited applicability across compounds and dryer types.2,3 Other challenges include the utilization of several different dryer types when moving from laboratory to pilot to commercial scales and between commercial facilities.4 Often, the knowledge gained from processing in one dryer type is not applicable to another, and each dryer type has unique features that can impact the final API CQAs in different ways.5 Dryer options that are commonly used in the pharmaceutical industry include variations of vacuum contact dryers where the outer 421

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Figure 2. Possible crystal form transition pathways during drying of a hypothetical compound.

temperature is typically generated that guides the identification of the crystallization, isolation, and drying design spaces.1,8 The complexity of the form phase map helps determine the control efforts needed in the drying process.9 In the simple case consisting of only one known form and assuming that the risk of amorphous formation is low, the crystal form remains stable over a wide range of drying process parameters, thus simplifying the strategy to deliver the specified form. In other cases, the form phase map may be more complicated (Figure 2). For example, compounds isolated from an aqueous/organic solvent mixture may exhibit multiple hydrate, solvate, and/or anhydrate forms in which dehydration, desolvation, and changes in crystallinity can be the most common changes in the crystal form during drying.10 For the study of physical form stability, commonly applied PAT includes hygrometers, near-infrared (NIR) spectrometry, mass and Raman spectrometry, as well as gas chromatography (GC).11 In the case of a hydrate, it is often sufficient to monitor the humidity of the dryer effluent using a cost-effective dew point hygrometer.1 Although NIR has also been applied to analyze the dryer effluent, it may be more powerful when used in direct contact with the cake as it has been shown to be capable of quantitatively differentiating between free and bound water in the solids and is therefore a suitable methodology for monitoring and controlling a compound’s form.12 However, online mass spectrometry is perhaps the most versatile and commonly used methodology for monitoring the dryer effluent stream, giving an indirect indication of form change by desolvation or dehydration, as it does not require the development of complex chemometric models to obtain quantitative data, greatly facilitating process optimization. Although these instruments have limitations, they have been successfully used to ensure that form control is maintained throughout the drying process. Upon scale-up, the area for specific heat transfer relative to product volume is typically reduced. As a result, mass transfer rates also decrease, often leading to an increase in drying time and a decrease in the rate of solid conversions of crystal forms. Residual solvent and internal temperature profiles observed at the lab scale may differ at large scale and between dryer types, and the spatial temperature profiles in the cake can be difficult to measure or compute. Also, existing computational dryer models generally require experimentally determined process parameters that can be difficult to generate, and the models are generally insufficient to accurately calculate the temperature

profiles. Therefore, an empirical approach is generally employed for scale-up to ensure chemical and physical stability requirements are met. During drying scale-up, a jacket temperature or pressure ramp is frequently employed to help avoid overloading the condenser/vacuum system at the start of drying and minimize agglomeration that can result from liquid condensation on the dryer surfaces.13 A high initial temperature can also result in partial dissolution of compound at the start of drying. The durations of these ramps are often extended during scale-up due to the typical increase in drying time with scale, but the same final jacket temperature and pressure is maintained across scales to ensure that chemical degradation and form are controlled. For form control, drying can be closely monitored during scale-up by means of simple sensors and PAT (e.g., internal thermocouple, hygrometer, mass spectrometry, and Raman) as well as by offline sample analysis including GC and X-ray powder diffraction (XRPD). The change in crystallinity of the API should also be monitored due to the increased normal and shear forces experienced at increased scales.11c,14 If the target form is a hydrate, a common scale-up approach is to apply staged humidified drying.12,15 Initially, either a dry nitrogen sweep may be applied to remove most of the water not incorporated into the crystal structure or a fully humidified sweep may be used to accelerate the removal of organic solvent and/or convert the solvate to the hydrate. As the water (or organic solvent) level of the solid approaches the level corresponding to the desired hydrate, a switch is made to a nitrogen sweep with controlled humidity. This staged humidified nitrogen approach has the advantage of improving the efficiency of the drying process with careful dryer humidity control. If multiple hydrate forms exist, then it may be necessary to precisely control the humidity within a certain range defined by simple lab scale dynamic vapor sorption experiments. When a solvate (e.g., ethanolate) is the target form, a drying approach similar to that used to stabilize hydrates with humidified nitrogen may not be possible. In these cases, the drying temperature and pressure profiles are maintained in a range to prevent desolvation. This same approach can also be used when the target form is a hydrate in the event that humidified nitrogen is unavailable. For drying crystals containing two or more solvents, it is important to understand the impact of the relative removal rates of the different solvents. In some cases, preferential removal of an individual solvent can 422

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drying mechanisms from diffusion limited to the breaking of a solvate or hydrate.16 At a minimum, the initial and final amount of residual liquid on the solids is needed to determine an average drying rate. However, additional data should be collected throughout the drying process to capture more details about the overall mechanism, and ideally the process should be monitored continuously to allow for real-time optimization and end point determination. There are many approaches for generating kinetic drying data spanning from the use of commercial equipment to custom chambers, depending on the material quantities available. A few approaches used within the pharmaceutical industry are described below. When material quantities for development are limited (milligram to gram scale), drying kinetic data can be generated by static methods assuming uniform drying across the sample using, for example, vacuum dynamic vapor sorption (DVS) techniques.1,5,17 These studies can be useful for understanding the impact of temperature, pressure, and solvent partial pressure on the drying kinetics and to elucidate the drying mechanism. In many drying studies, custom chambers, such as those shown in Figure 4, have also been used to gather information and data beyond what DVS can provide, such as information on the solid state. In the design shown in Figure 4A,18 solids are loaded onto a drying pan mounted on a high precision analytical balance or load cell within a drying chamber. The system temperature, pressure, and composition of the gas sweep through the chamber can be controlled, and the loss of weight is measured over time as the solids are exposed to specified drying conditions. Mass spectrometry can be employed to determine the vapor composition of the effluent gas stream and, in some cases, form conversion can be monitored using analytical technologies such as noncontact Raman spectrometry. The flow cell (or drying chamber) shown in Figure 4B consists of a jacketed glass vessel connected to a drying manifold equipped with instrumentation for monitoring the inlet nitrogen flow rate, temperature, pressure, humidity of the inlet and outlet streams, and concentrations of individual solvents of the outlet gas stream. If desired, a NIR, Raman, or temperature probe can also be installed through the top of the flow cell to monitor the solid state. When quantities are not limiting, drying experiments can be performed at larger scales in agitated dryers from which the impact of agitation on the overall drying kinetics can be determined. For instance, experiments of this type have been performed in an ∼8 L agitated dryer that required 0.2−1.0 kg of solids for mixing to be reasonably representative of performance at larger scale19 and allow for multiple samples to be collected over time to generate suitable drying curves. PAT can be used to improve the accuracy of the profile and greatly aid the ability to predict performance and complete scale-up successfully. Processing at the multikilogram scale in a pilot plant offers further opportunities to confirm results obtained at smaller scale, assess the impact of specific dryer configurations, and verify drying time predictions. Once drying kinetics data have been collected, scale-up can be performed using various commercially available or custom models to predict drying cycle times and process performance.20 Drying times can be estimated relatively accurately from laboratory scale data using heat and mass balance equations along with the known batch size at each scale. In the simplest models, the loss of liquid per time per heated surface area is

cause crystal structural change that slows or prevents removal of the other solvent from the interior of the crystal. In this case, an improper temperature/drying profile may result in slow drying or failure of the residual solvent specification. For poorly crystalline compounds, the drying profile can impact the degree of crystallinity of the dried compound, which can lead to an XRPD analytical result that does not meet the product identification specification. As a result, the rate of desolvation must be controlled within an acceptable range to achieve a consistent degree of crystallinity. For cases in which the amorphous state is the target form, conditions need to be selected to prevent the compound from crystallizing and/or chemically degrading. Although isolation of amorphous solids is beginning to become more prevalent, it has not been typically accomplished in the pharmaceutical industry. Therefore, it is currently premature to generalize industry approaches for development and scale-up. Drying Kinetics. Once thermodynamic conditions for maintaining chemical stability and form have been identified, the process can be further optimized within these defined boundaries (i.e., temperature, humidity, moisture or solvent content, etc.) to ensure all CQAs of the drug substance are met while improving process robustness, increasing drying rate, and reducing process cycle time. To achieve this, a detailed understanding of the system drying kinetics is required.3f Processing parameters such as temperature, pressure, inert gas flow through the solids, as well as the applied agitation protocol, agitator design, and effective dryer heated surface area can greatly influence the drying kinetics. During drying there will typically be at least two distinct drying regimes or periods, which include the so-called “constant rate period” associated with removal of the unbound free liquid and the “falling rate period” associated with removal of the more tightly bound liquid from within the solids (Figure 3).

Figure 3. Typical drying curve.

During the “constant rate” period, the solvent or moisture content in the solids decreases linearly and is limited by the rate at which heat is added to the solids. The slope of this curve and hence the drying rate can be affected by the conditions in the dryer including agitation, operating pressure, and the presence of an inert gas sweep. In the falling rate period, there is a more gradual decrease in solvent content as solvent concentration decreases, and the path for diffusion increases resulting in a slower drying rate. Additional drying regimes can be present including the rising rate period associated with the initial heating of the solids as well as multiple falling rate periods associated with, for example but not limited to, a change in the 423

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Figure 4. Examples of types of custom drying chambers developed by (A) Bristol-Myers Squibb Co. (New Brunswick, NJ, USA) and (B) Merck & Co., Inc. (Kenilworth, NJ, USA).

would be prohibitively long, and inhomogeneities in the material would be present. In general, agglomeration becomes dominant in the early stages of drying in which the onset of particle−particle cohesion can occur. Therefore, agitation during this drying stage should be minimized.23 Furthermore, as the scale and/or bed height changes, the powder flow dynamics can vary. Attrition can dominate in the areas of the dryer where particles tend to have the greatest flow velocity, namely, at a lower bed height and in areas near the impeller, whereas agglomeration tends to prevail in the other areas.13 Once agglomerates are formed, because of their spherical nature, they tend to experience lower shear from the agitator blade as they tend to roll over the blade, especially for batches with low fill volume where the hydrostatic pressure from the bulk is low. To better understand the challenges with studying agglomeration and attrition, the current methodologies used to investigate each one as well as the common strategies to address each one upon scale-up are discussed. Agglomeration Control and Scale-Up in Agitated Dryers. Although agglomeration can be observed during filtration as a result of agitation and/or compression of the wet-cake, it is most prevalent during drying. Agglomerates are defined as clusters of particles that are fused together, typically through the formation of solid bridges, and are differentiated from aggregates that are held together by weak interparticular forces and readily dissociate. To identify and study the key process parameters resulting in agglomeration, it is critical to measure both the extent, such as relative quantity of agglomerated material in the batch, and severity of agglomeration as determined by agglomerate hardness. Sieving, particle size, and texture analysis have all been used for the quantification and characterization of agglomeration.3a,c,d,g,13 In sieving, the percentage of a compound retained on the sieve after a defined period of shaking at a defined amplitude is used as a comparative measure of the extent of agglomeration. This methodology has recently been extended to determine agglomerate hardness by measuring the rate of decrease of the percentage of powder retained on this sieve through successive cycles.3d Three main empirical tools are used within the pharmaceutical industry to study the agglomeration potential in agitated dryers including a mixer torque rheometer (MTR), an acoustic granulator, and a small-scale agitated filter dryer (Figure 5).3a,b,d,g The MTR is used to determine the onset of particle−particle cohesion, which is the region of temperature and moisture/solvent combinations where aggregates are held together by forces resulting from the presence of mobile liquid

determined in each distinct drying regime. These rates can then be applied for scale-up using the heated surface area of the larger scale dryer, the batch size, and the known starting and transitional compositions for the compound. This assumes that the highest resistance to heat transfer is defined by the properties of the solids, which is independent of scale. Accuracy of these predictions requires the application of consistent drying conditions (T, P, sweep rate, initial solvent content) and comparable mixing efficiencies within similar dryer types between scales. More sophisticated models have been developed and are available within commercial software packages. For instance, models and tools exist within DynoChem (Scale-Up Systems) that use a mass and energy balance by assuming a moving drying front based upon the heat penetration theory described by Schlunder.20a,b,d−f The front moves from the jacketed wall, where solids are dry, to the bulk cake bed, where solids are wet in a mixed bed of solids. Process Systems Enterprise includes a population balance in addition to the mass and energy balance and has the capability of modeling agglomerate formation and breakage as well as accommodating custom modifications. Several options are also available for calculating the mass transfer coefficient from the dimensionless Sherwood number using external diffusion limitations. Typically, kinetic data from smaller scale experiments are used to fit model parameters to estimate performance at scale.19,21 Complications can arise when the compound is a hydrate/solvate that can dehydrate/ desolvate or the required data for the compound and/or the processing equipment are not available. Powder Bulk Property Control. A significant risk in using agitated dryers to isolate powders is the potential that they may cause agglomeration and/or attrition, which could adversely impact the target powder bulk properties of the API. This is of particular concern for APIs being developed as oral formulations because both the downstream DP manufacturing process and the API bioavailability can be severely impacted. The lack of fundamental understanding of agglomeration and attrition during drying can lead to issues with process transfer between different dryer types, identification of key process parameters, and scale-up. During drying there is a complex interplay between agitation protocol, agglomeration, and attrition.22 Agitation is necessary to improve the heat and mass transfer rates by redistributing particles inside the dryer because the wet-cake in the dryer is heterogeneous with material near the dryer edge drying faster and containing less solvent than the material near the dryer center. Therefore, in the absence of agitation, drying times 424

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solvents.3a,g,22,24 Furthermore, greater solubility of the compound in the final wash solvent results in a greater number of crystalline bridges forming during drying and hence more severe agglomeration. Therefore, a final wash solvent with poor product solubility should be selected, and when a solvent mixture is used, both solvents should be evaluated independently as one solvent may become enriched during drying. Solvent viscosity will also impact the wet-cake’s deliquoring efficiency and ability to navigate through the onset of particle− particle cohesion with minimal agitation. Typically, lower viscosity solvents deliquor much more efficiently. For scale-up, agitation is typically initiated below the critical moisture content at which the onset of agglomeration can occur,3a,g which is often challenging to accomplish in practice due to the heterogeneity of the wet-cake prior to agitation. The initial solvent composition of the wet-cake is typically determined by the deliquoring efficiency during the filtration. Although some compounds deliquor very efficiently (with a solvent content of 25% or less), those of higher agglomeration potential (e.g., those with a broad and/or small PSD) tend to retain significant solvent. This can be mitigated to some extent by solvent and/or equipment selection (e.g., use of a lower viscosity solvent and/or a centrifuge for the filtration). To reduce the risk of agglomeration, a nitrogen blow-through is an option that, as long as the wet-cake does not crack or pull away from the filter walls, can reduce the residual solvent levels below the onset of particle−particle cohesion before agitation is initiated. In case of cracking, smoothing of the wet-cake should take place, which will improve the deliquoring efficiency. Equivalent blow-through conditions between the laboratory and the plant can be maintained by using the Carman−Kozeny equation.3c In cases where a nitrogen blow-through is not possible and an agitated filter dryer is used, the solvent content of the wet-cake can be reduced by pressing the wet-cake with the agitator.3a This is achieved by smoothing the wet-cake (i.e., operating the agitator in a counterclockwise fashion) while progressively lowering the agitator. However, care is needed to avoid particle breakage as particles are exposed to higher mechanical stress when agitation is operated in this manner. In many cases, scale-up in the plant relies upon visual observation, especially during the initial stages of the process and upon performing a number of full-scale engineering runs.3a Processing parameters such as agitator speed, height (i.e., vertical depth of penetration into the cake), frequency, and direction of agitation are manually adjusted to control and break down agglomerates, which can introduce operator-tooperator and plant-to-plant variability. However, this strategy is not always successful as in some cases small agglomerates are formed that are not easily visible to the operator. Therefore, there can often be some trial and error in identifying the optimal drying protocol to prevent agglomeration. Predictive tools for determining the agglomeration potential of a compound as well as suitable scale-up conditions that will minimize and/or control agglomeration are not yet robust, and until there is further improvement, empirical approaches will continue to be the standard practice for studying and understanding agglomeration during development. Attrition Control and Scale-Up in Agitated Dryers. It has been shown that laboratory scale dryers designed to use small quantities of material (10−100 g) are not capable of reproducing the shear stresses and hence the attrition observed at scale.2,3e This is primarily a result of the hydrostatic pressure being lower in the lab scale dryer than that in the plant. To

Figure 5. Empirical tools used for the study of agglomeration during drying: (A) mixer torque rheometer (MTR), (B) acoustic granulator, and (C) small-scale agitated filter dryer (AFD).

between the particles that upon drying give rise to solid bridges.23 In this region, the force required to rotate a blade through the wet-cake increases markedly relative to that in a wetter or dryer state, resulting in an increase in the torque registered by the MTR. The acoustic granulator can also be used to independently identify the onset of particle−particle cohesion3d or as a small-scale method to verify the MTR findings.3g Lastly, the small-scale agitated filter dryer is used to aid in the determination of the key process parameters. Using these tools, it has been shown that particle size, particle size distribution (PSD), and morphology can all have a significant impact on a compound’s agglomeration potential.3a,b,d,g,13,24 In general, small acicular crystals exhibit a high risk of agglomeration as a result of their high specific surface area, which enables effective contact between each particle.25 Broad PSDs and irregularly shaped crystals can allow for efficient packing of the particles by filling available voids and therefore result in the formation of strong agglomerates. Solvent choice has also been shown to be critical as crystals in wet-cakes are held together by liquid bridges, the strength of which are dependent on the surface tension of the 425

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Figure 6. Laboratory setups for the study of attrition designed and used by (A) Merck & Co., Inc. (Kenilworth, NJ, USA) and (B) Bristol-Myers Squibb Co. (New Brunswick, NJ, USA).

A more rigorous approach has been recently reported in the literature that used the bulk friction coefficient and the total amount of work done by the impeller to predict the degree of attrition expected during scale-up.3e Laboratory-scale experiments were conducted during which the impeller torque was measured and used to calculate the average shear stress during agitation. The bulk friction coefficient was then determined by measuring the shear stress under different hydrostatic pressures. Finally, the total work done by the impeller was determined from knowledge of the measured torque, impeller speed, and agitation time and plotted against the corresponding particle size. The anticipated work done during scale-up was then calculated to estimate the resulting product particle size. The risk of attrition during scale-up is most commonly mitigated empirically by using “standardized” agitation protocols that have shown minimal attrition as experienced in batches for a wide range of compounds.19a Although these protocols can assist in decreasing the amount of attrition during agitated drying, they rely on operator experience and judgment to control a key attribute of the API and do not result in an efficient and robust process. Current Technology and Knowledge Gaps. It is clear from the preceding sections that although many tools and practices have been developed to aid the study and scale-up of drying, there are still many knowledge and technology gaps that inhibit the creation of standardized work-flows and allow for the seamless scale-up and process equipment transfer. In an effort to advance the pharmaceutical industry’s drying development capabilities and unit operation process understanding, the ETC Drying WG identified the following as the most important current knowledge and technology gaps relating to drying: 1. Limited real-time drying process understanding due to limitations of PAT tools and/or material sampling. 2. Lack of effective and efficient universal mathematical models able to describe and scale-up the unit operation accounting for chemical and physical stability, drying kinetics, and physical property control.

overcome this limitation, attrition has been studied in small cylindrical vessels mechanically agitated by an impeller in which suitable normal loads have been applied by placing a weight on top of the particle bed (Figure 6).2,3e These scale-down setups have been used to study the propensity of attrition during the different stages of drying. Using such laboratory-scale equipment, it has been shown that in general particles with large aspect ratios (e.g., rod and needle particles) are highly susceptible to attrition with the particles breaking mostly along the major axis.3e On the other hand, “platelike” particles tended to be much less susceptible to attrition in which the particles experience attrition through chipping rather than fracturing.2,26 Particles with secondary structures, such as agglomerates, can sometimes lose these structures when agitated, as the solid bridges holding the primary particles together are broken. In these instances, the strength of these solid bridges is significantly lower than the strength of the primary particles, such that little, if any, breakage of the primary particles is observed. The liquid content of the particle bed during agitation has also been shown to affect the degree of attrition.2 For some particles, higher levels of attrition were observed when agitated dry than when wet, suggesting that the solvent acted as a lubricant.3e,27 In other cases, as the solvent content of the particle bed was reduced, so was the extent of attrition.2 This highlights the complexity and the lack of understanding of the mechanism of particle attrition that is not only dependent on the process and a particle’s physical properties but also on its material properties. Finally, the equipment configuration, such as impeller blade design and angle of the walls, can also have a significant impact on the extent of attrition, and simple geometrical arguments have been used to attempt to predict the extent of attrition expected for a particular compound in different dryers.28 However, these arguments are often an oversimplification of the complex mechanism occurring during drying and fail to provide accurate predictions. 426

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of scales and equipment configurations. Ultimately, a universal model should be able to address the issues for each drying element including drying kinetics, chemical and physical stability, and powder properties. Although a general purpose model would be ideal, it is understood that first principle models are lacking in key areas such as desolvation or dehydration kinetics as well as agglomeration and attrition. It is expected that further advances in these areas are required that will further support the development of a more robust drying model. Modeling desolvation and dehydration kinetics are currently challenging because there are complex resistances to heat and mass transfer that are not fully captured in existing models. Some models include the impact of mixing the powder bed but only by approximating spatial redistribution of solvent concentration and thermal gradients. The models generally do not include the thermodynamics of desorption and the mass transfer resistances needed to simulate breaking of a hydrate or solvate.3f This lack of understanding can greatly impact the final drying time end point to meet form or solvent content specifications. It was demonstrated earlier that agglomeration and attrition can have profound impacts on powder property behavior. Although recent studies are beginning to elucidate the key mechanisms that induce agglomeration and attrition during agitated drying, many gaps still exist, and robust models are absent. The role of particle morphology on the tendency for agglomeration and/or attrition to occur at the different stages of drying is still not fully understood. Recent studies performed with particles of high aspect ratio have provided some insights into how agglomeration and attrition can be impacted by the aligning and stacking of rodlike particles during drying.3e However, it remains unclear whether the trends observed with high aspect ratios apply to particles with different morphologies. The effect of liquid content during agitation on particle agglomeration and/or attrition is also not well understood. Additionally, a multivariate interaction between particle morphology, degree of wetness, and the intrinsic mechanical properties of the materials may exist, but no theory is currently available that can explain this relationship. Lastly, it is not uncommon for a product during its development lifecycle to be transferred to a new manufacturing facility and concurrently to a different size and/or a different type of agitated dryer (e.g., from agitated filter drying to conical dryer). Therefore, it would be of extreme value if powder property outcomes could be simulated across different equipment and in silico predictions conducted to more effectively match operating conditions between dryers. Although complex models can be helpful, the predictive models also need to be computationally efficient. For instance, although discrete element method (DEM) simulations have furthered our understanding of stress generation and attrition during agitation, this technique cannot simulate realistic industrial dryers in whole or in compartmental regions consisting of millions or billions of particle−particle contacts. Mechanistic models that can take into account the effect of key material properties and process parameters can provide a theoretical framework for the design and optimization of these unit operations. The accuracy of the developed models relies directly on the type and quality of data used to train or calibrate the models. In the process development space, these data sets will ideally come from both scale-down experiments and pilot or production scale runs.

3. Lack of standardized, commercially available scale-down equipment. The following subsections expound on these gaps with the intent to encourage further research and development in these areas for the common benefit of the pharmaceutical industry and any industry with interests in drying. Drying Evaluation by PAT and Material Sampling. As discussed in the previous sections, many PAT tools are available and utilized in the pharmaceutical industry for both drying process development and batch monitoring. Unfortunately, current PAT technologies such as NIR or mass spectrometry are limited to single point assessment or determination of average compositions, respectively. Probe applications are hindered by the need to avoid contact with mechanical agitators and often suffer from loss of contact with the solids, blinding, or limited penetration depth, which provides only a limited view of dryer contents.29 Furthermore, because of the heterogeneous nature of the wet-cake prior to agitation, the data from the current probes are not representative of the whole batch. These limitations make it unfeasible to effectively incorporate data for model verification or pursue the design of scale-down equipment. The ideal PAT solution for drying would allow for real-time monitoring of lab to commercial scale vessels throughout the entire volume of the cake during the drying process and would not require significant retrofitting of the existing large scale equipment. This capability would be beneficial to more fully verify models or design scale-down equipment that help identify and properly simulate dominant forces experienced by materials during drying. PAT that could provide a threedimensional profile of the dryer to determine spatial heterogeneity of solvent content for drying kinetics, powder properties, and form visualization are desired to simultaneously assess impacts on all three of the key drying elements. Electrical impedance, capacitance, inductance tomography, and other technologies look promising but may currently be too difficult or costly to fit to existing equipment. Furthermore, an evaluation would need to be made on the effectiveness of these techniques to monitor solvent composition, particle size, and form. If further developed, the advantages of such capabilities would allow for the design of suitable process controls with feedback and/or forward capabilities to improve product quality, consistency and optimize cycle times. To complement the PAT approach, techniques for representative sampling of materials within the vessel during drying would further improve drying process understanding. Currently, retrieving representative quantities of material from the dryer or sampling while drying is in progress is difficult or impossible in many large scale dryers. Having these capabilities would allow for better characterization of materials during drying using standard characterization techniques to assess solvent content (e.g., gas chromatography), powder properties (e.g., particle size by laser light scattering, specific surface area, bulk density, scanning electron microscope, etc.), and form (e.g., XRPD). This approach would still offer an opportunity to give a coarse representation of material changes that occur throughout the drying process and validate real time analysis. Modeling. Models have a key role in drying process understanding, optimization, and scale-up and are central to the development of appropriate process control strategies. General purpose models are needed that can reliably predict both drying times for cycle time optimization and the impact of various process parameters to the API CQAs over a wide range 427

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equipment. These data will provide immediate insight on relating drying process parameters to API CQAs and will be essential in designing and validating improved models that are robust and practical. Ideally, PAT and model development should occur concurrently to be able to satisfy modeling data requirements and ensure that desired data can be effectively collected. These coordinated efforts would greatly aid the design of more effective lab scale-down equipment. It is the aim of the ETC Drying WG to initiate research efforts through collaborating with relevant technology companies, academics, and funding independent research to address the identified knowledge and technology gaps to advance the current state of the art and ultimately develop a robust work flow for the study and scale-up of the drying unit operation. Updates on progress made toward this end as well as opportunities for collaboration will be communicated in subsequent publications and on the ETC Web site (www. etconsortium.org). Any comments or suggestions to the ETC, and the drying WG in particular, are welcome and any inquiries can be made directly to [email protected].

In some cases, models may require input data that are currently not gathered in the pharmaceutical industry. As an example, DEM simulations have shown that the surface roughness of particles will affect stress generation and attrition during agitation.30 These simulations have also shown that the aspect ratio and the mechanical strength of the crystal will significantly influence the degree of attrition observed.30f These microscopic material properties cannot be determined from the traditional bulk property tests performed. The ability to measure these microscopic properties can facilitate the construction of mechanistic models that support more efficient and reliable design of agitated drying processes to ensure PSD targets are met. For any model that is developed within the pharmaceutical industry, attention will need to be given to the capabilities of generating certain data inputs that are critical to the model. If the model requires data that are not currently feasible to attain, the model will likely have limited benefit to the industry. Scale-Down Equipment. Standardized laboratory equipment for scale-down experimentation that accurately mimics the scale-up conditions is critical to enabling the study and successful scale-up of the drying unit operation. Most of the scale-down equipment used to date is custom-made and ranges from small agitated filter dryers to vapor sorption chambers. Current setups that mimic heat and mass transfer characteristics as well as the normal and shear forces encountered on the manufacturing scale require the use of hundreds of grams of material per experiment that is not often available during the early stages of process development. They may also not be able to incorporate the PAT needed for process understanding and modeling. Having standardized, commercially available equipment that improves upon custom approaches will reduce the barrier to performing some of these studies, allowing data on more compounds to be collected, ultimately advancing the current state of the art. It will also avoid continuous reinvention of the wheel, allowing researchers to focus on studying the unit operation rather than designing drying equipment. Ideally, this equipment will require only limited amounts of material, account for important scale-dependent processes including resistances to heat transfer, mass transfer, and mixing, and would allow for adequate in-process monitoring of the headspace, powder, and system as a whole. Scale-down equipment that can measure in real time key properties such as solvent content, particle size, and impeller torque will aid process design in a material-sparing way. This will then enable better process design earlier in development when only small quantities of API are available for lab studies. Looking Forward. The future state of drying development would include robust scale-down equipment with PAT that can enable modeling for predictive process scale-up and/or equipment transfer. Unfortunately, this has been difficult to achieve in practice because the necessary components for effective development (scale-down equipment, PAT, modeling) are interrelated. For example, modeling improvements may require improved PAT and scale-down equipment. Although simultaneous independent efforts to address specific gaps may be a necessity as a result of resource constraints, it is expected that advances in one area will have an influence on others. It is our opinion that greater effort should be invested in improving the current state of the art in monitoring the real time behavior of powders during drying and developing suitable PAT that can be easily integrated with existing large-scale processing

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. *E-mail: [email protected]. ORCID

Joshua D. Engstrom: 0000-0002-4496-1191 David J. Lamberto: 0000-0001-9529-9559 Charles D. Papageorgiou: 0000-0001-7959-6289 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The ETC Drying WG would like to thank the ETC Secretariat and ETC Board for providing the forum and guidance for this work to be accomplished.

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REFERENCES

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