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An Integrated Process Analytical Technology (PAT) Approach for Pharmaceutical Crystallization Process Understanding to Ensure Product Quality and Safety: FDA Scientist’s Perspective Huiquan Wu,*,† Zedong Dong,‡ Haitao Li,§ and Mansoor Khan† †

Division of Product Quality Research (DPQR), Office of Testing and Research, Office of Pharmaceutical Science, ‡Division of New Drug Quality Assessment I, Office of New Drug Quality Assessment, Office of Pharmaceutical Science, §Divsion of DMF Review, Office of Generic Drugs, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, Food and Drug Administration (FDA), Silver Spring, Maryland, United States S Supporting Information *

ABSTRACT: In this review, a brief overview of the current regulatory science framework pertinent to pharmaceutical crystallization and process characterization is made first. The FDA’s scientific research on pharmaceutical crystallization process understanding and product characterization is then illustrated via several aspects: (1) Combined real-time PAT monitoring and process chemometrics for mapping the state of a pharmaceutical crystallization process; (2) Combined real-time PAT process monitoring and first-principle modeling for elucidating the nucleation mechanisms of a dynamic pharmaceutical crystallization process; (3) Combined real-time PAT process monitoring, Design of Experiments (DOE), and General Linear Modeling (GLM) to establish a hybrid approach for process characterization and process design space development; and (4) Integrated PAT approach for nucleation induction time measurement. Finally, some of the current challenges and future outlook on pharmaceutical crystallization process and product characterization across the pipeline, from drug substance to drug product development, manufacturing, and process scale-up to ensure product quality and safety, and ultimately to protect and promote public health is discussed from both a regulatory science and process engineering point of view.

1. REGULATORY SCIENCE RELEVANCE ON PHARMACEUTICAL CRYSTALLIZATION PROCESS UNDERSTANDING AND CHARACTERIZATION The U.S. Food and Drug Administration (FDA) is responsible for protecting public health by applying the best possible science to its regulatory activities for medicinesfrom premarket review of efficacy and safety to postmarket product surveillance to monitoring product quality. In the past decade, the complexity of the FDA’s regulatory and public health portfolio has grown rapidly, in large part due to scientific challenges in evaluating a new generation of medicines based on rapidly evolving science and technology. New drugs, biologics, and medical devices have become increasingly complex in their development, manufacturing, and evaluation. The challenges in product development and globalization underscore the critical importance of modernizing and advancing regulatory science to match advances in basic and applied science and technology. In the August of 2011, the FDA issued a white paper on advancing regulatory science at FDA,1 which highlighted how FDA supports new approaches to improve product manufacturing and quality. As part of its Quality by Design effort, the FDA has been working in three areas: (1) continuous manufacturing where materials constantly flow in and out of a process; (2) the use of Process Analytical Technology (PAT)2 to monitor and control the process, as opposed to the current method of just end-product testing; (3) development of new statistical approaches to detect changes in process and product quality. In this review, the development of an integrated PAT approach to understand the crystallization process is discussed. The study is aimed to achieve the following This article not subject to U.S. Copyright. Published XXXX by the American Chemical Society

three goals: (1) the real-time monitoring and control of a pharmaceutical crystallization process; (2) a better understanding of pharmaceutical crystalline materials; (3) a capability to predict the results of pharmaceutical crystallization processes. Pharmaceutical crystallization is one of the most important unit operations for making (e.g., reactive crystallization), separating, and purifying crystalline drug substances and crystalline excipients. It has been well recognized that the crystallization process parameters can have great impact on the crystallization product quality attributes such as purity, morphology, size, polymorphic form, impurity profile, dissolution rate, etc. Each or all of these quality attributes can either independently or collectively impact drug dissolution, bioavailability, efficacy, and in some cases safety. Therefore, detailed description of the drug substance crystallization process and key quality attributes of crystalline materials are required to be included in the regulatory submissions in chemistry, manufacturing, and control (CMC) sections for evaluation. Several relevant regulatory guidance documents were published to address various quality and safety aspects of drug substances.3−7 Recent FDA process validation guidance8 and ICH Q11 Guideline9 provided regulatory directions on process validation, development, and manufacturing of drug substances which are in line with the PAT Initiative and quality by design (QbD) principles. During the past few years, an active FDA intramural research Special Issue: Process Analytical Technologies (PAT) 14 Received: February 15, 2014

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dx.doi.org/10.1021/op500056a | Org. Process Res. Dev. XXXX, XXX, XXX−XXX

Organic Process Research & Development

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Figure 1. A representative NIR spectrum of the quaternary system.

program10−16 on real-time online monitoring of pharmaceutical crystallization process in conjunction with online/off-line product characterization has been established to support the implementation of QbD and PAT. Using an integrated PAT approach which combines real-time process monitoring and appropriate modeling strategy, the crystallization of a model drug, naproxen, in the Naproxen−Eudragit L100−alcohol system via dynamic antisolvent (DI water) addition was studied. The goals of the study were: (1) to demonstrate the technical feasibility of real-time online process monitoring via various PAT techniques and understand their advantages and limitations; and (2) to create scientific case studies for illustrating several important concepts in the FDA’s PAT Guidance and ICH Q8(R2), such as process understanding and design space.

strated in various research reports in the areas of biotech, crystallization, and powder blending In our research,12−16 the crystallization of naproxen in the Naproxen−Eudragit L100−-alcohol system induced by the addition of antisolvent (DI water) was investigated. Naproxen USP (Lot No: NPX 368. Albemarle Corporation, Orangeburg, SC) was selected as a model drug as it is one of the most effective and tolerable commercial nonsteroidal anti-inflammatory drugs. It is soluble in alcohol and practically insoluble in water. Eudragit L100 (Lot No: 1221203048. Röhm America, Somerset, NJ) was selected as a model polymer. Both drug and polymer were dissolved in reagent alcohol (HPLC grade, Lot No: 053546. Fisher Scientific, Fair Lawn, NJ) first and then mixed together to form a multicomponent system. The supersaturation of the crystallization process was created by introducing antisolvent (DI water, obtained in-house from a Millipore Advantage A10 water purifier [18.2 MΩ resistivity] and kept refrigerated at 4 °C prior to use) to the solution of naproxen−Eudragit L100− alcohol in the reaction vessel in a controlled manner. This dynamic crystallization process was monitored in real-time via NIR spectroscopy. A representative NIR spectrum of the quaternary system is shown in Figure 1 for a batch run with drug/polymer ratio of 4.0 at 15 °C. The time series of nearinfrared (NIR) spectra acquired via real-time monitoring of the dynamic antisolvent process were subjected to principal component analysis (PCA). It was found the first and second PC captured the majority (>98%) of the process variance. The loading plots of both PC1 (Figure 2a) and PC2 (Figure 2b in Supporting Information [SI]) for a batch run with drug/polymer ratio of 4.0 at 15 °C demonstrated the characteristic peaks of key formulation components detected via NIR−PCA. The residual sample variance plots for PC1 (Figure 2c) and PC2 (Figures 2d in SI) revealed the characteristics of residual sample variance

2. FDA’S SCIENTIFIC RESEARCH ON PHARMACEUTICAL CRYSTALLIZATION PROCESS UNDERSTANDING AND PRODUCT CHARACTERIZATION 2.1. Combined Real-Time PAT Monitoring and Process Chemometrics for Mapping the State of a Pharmaceutical Crystallization Process. It has been recognized that for a time-varying signal in a dynamic system, the information content is not homogeneously distributed throughout the process trajectory. Some landmarks such as extreme values and shape changes in the data, termed as singular points (SPs), in the process trajectory contain more information about the dynamic behavior than others.17 Because SPs have physical meaning such as beginning or ending of a process event, they can be directly used for state identification, process monitoring, process supervision, and segmenting the process signal into regions with homogeneous properties,12,13,18,19 as previously demonB



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occurring at the NIR sample #7 was due to the initial water addition to the ternary system (naproxen−Eudragit L100− alcohol) which resulted in the liquid phase composition change; (2) SP2 occurred at the NIR sample #75 due to the second addition of DI water; (3) SP3 occurring at the NIR sample #145 was due to new phase formation of nuclei. These three SPs, when labeled on the PCA score-based process trajectory in Figure 3, clearly marked three distinct features: SP1, SP2, and SP3, associated with a minimal, moderate, and dramatic change of the gradient in its vicinity, respectively. In this case, the dramatic change of the gradient in the vicinity of SP3 represents a phase change or formation of new phase (the nuclei). Therefore, SP3 on the NIR−PCA score-based process trajectory mapped the nucleation onset and a process transition window. This case study illustrated that combined real-time PAT monitoring and process chemometrics not only can map the state of a pharmaceutical crystallization process but also can help to select appropriate process parameters for efficient process design space development. 2.2. Combined Real-Time PAT Process Monitoring and First-Principle Modeling for Elucidating the Nucleation Mechanisms of a Dynamic Pharmaceutical Crystallization Process. The nucleation mechanism is fundamental to the pharmaceutical crystallization process. To a large extent, it dictates certain critical quality attributes (CQAs) of crystal material, such as crystal size, crystal size distribution, polymorphic form, etc. Therefore, identifying the nucleation mechanism involved in pharmaceutical crystallization is of great importance for ensuring CQAs of crystalline drug substances to meet the release and stability specifications, thereby providing further assurance of the efficacy and the safety of the drug product. In theory, crystal nucleation is a first-order phase transition, and the nature of the transition depends on the studied material. However, the properties of microscopically small crystals in a fluid are difficult to study in atomic or molecular systems, due to the small size of these crystals (about ∼1 nm) and the short evolving time scale (less than 1 μs). Thus, it is challenging to observe nuclei before significant growth of the crystal has occurred.20 Nowadays, direct observation of nucleation of certain large species (for example, colloids and globular proteins, polymer blends) is possible via certain advanced techniques such as optical and in situ atomic force microscopy (AFM),21 modern laser scanning confocal microscopy,22 real-space imaging,23 and small-angle neutron scattering (SANS).24 However, direct experimental measurement and observation of small-molecule nuclei remains out of reach25 due to the small size and short time scales. On the other hand, indirect measurements of nucleation are possible,26−28 such as observation of crystal particles after growth to a large size and characterization of the final crystal structure, with attempts made to keep the nucleation rate low after initial nuclei formation. Examples include measuring the average nucleation rate, the nucleation induction time, and the effects of experimental conditions on crystal structures. The nucleation induction time29 (tind) can characterize the nucleation tendency for solution crystallization at isothermal conditions. It corresponds to the time elapsed between the start of creating supersaturation and the detection of a new phase in a system. However, it is not considered a fundamental property of a system because the calculated value depends on identifying and detecting when a new phase exists. A number of experimental techniques, such as differential scanning calorimetry,30 conductivity measurement,31 turbidity measurement,32 intensity of

Figure 2. Loading plots demonstrating characteristic peaks of key components detected via NIR-PCA. (a) PC1; (b) PC2 (in SI). The Y axis is loading of PC. (Adapted with permission from reference 15.) (Figure 2b in SI). Residual sample variance plots revealing characteristics of residual sample variance corresponding to water additions and singular points. (c) PC1; (d) PC2 (in SI). (Adapted with permission from ref 15.)

corresponding to water addition and signal singular points. The PCA score plot (PC2 score vs PC1 score as shown in Figure 3) was used to construct the process trajectory. It was evident that several singular points (SPs) such as points of discontinuities, trend changes, and extrema are present on the NIR-PCA score plot and residual sample variance plots. To identify the natures of each and every SP on the NIR-PCA-based process trajectory, the following complementary information regarding the dynamic antisolvent crystallization process have been examined in an integrated manner: (1) PCA loading plots (Figure 2a) and sample residual variance plots (Figure 2d in SI); (2) process and product domain knowledge (such as process recipes, dynamic process variable such as amount of antisolvent (water) addition being a function of time, observation of crystal nuclei grown to a detectable size, solution becoming cloudy, etc.); (3) prior knowledge. The PCA loading plots identified that the key contributors were the formulation components, including Naproxen, Eudragit L100, alcohol, and water. Comparing the residual sample variance plots with process recipes, water addition time and sequences, and observations from the dynamic experiment, it was concluded that: (1) the SP1 C



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Figure 3. Representative process trajectory (PC score plot) illustrating the inflection point (singular point) and phase change at the SP. (Adapted with permission from reference 15.)

Figure 4. Plots of ln(tind) vs (ln S)−2 based on nucleation detection by focused beam reflectance measurement and theoretical calculations. (Adapted with permission from reference 13; copyright (2011), Wiley.)

transmitted or scattered light method,33 IR transmission method,34 combined process NIR spectroscopy and turbidity measurement,12 and combined focused beam reflectance measurement (FBRM) technique and process NIR spectroscopy13 have been used for new-phase detection or nucleation induction time measurement. Several attempts were made

regarding the use of multiple real-time process analyzers to measure nucleation induction time simultaneously.15,35 To determine the nucleation mechanism from the tind measurement, one has to establish the relationship between tind and critical process parameter (thermodynamic parameter) supersaturation S. Several mechanism-based models have been proposed, including classical nucleation theory (CNT),36 the D



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Figure 5. Near real-time Particle Vision Microscopy (PVM) images of products obtained in the antisolvent crystallization process. (a) At the end of the process for batch J (low drug/polymer ratio, 0.01164); (b) at the end of the process for batch E (high drug/polymer ratio, 3.95); (c) after adding 150 mL water to the system for batch E; and (d) after adding 400 mL water to the system. (Adapted with permission from reference 13; copyright (2011), Wiley.)

two-step model,37 etc. With certain simplifications, the CNT predicts a linear relationship between ln(tind) and (lnS)−2 for two primary nucleation phenomena (see Figure 4): (i) For primary homogeneous nucleation (HON)

where Nc is crystal number, V is solution volume, T is absolute temperature of solution, k is Boltzmann constant, γ is the crystal−solution interfacial tension, ν is the molecular volume, A is the intercept, Bhom is a thermodynamic parameter, f(θ) is a function of the angle of contact (θ) between the crystalline deposit and substrate. Subscript hom stands for homogeneous, het stands for heterogeneous, J stands for nucleation rate. Obviously 0 ≤ f(θ) ≤ 1. When f(θ) = 1, the nucleation is homogeneous; when f(θ) < 1, the nucleation is heterogeneous. Since 0 ≤ f(θ) ≤ 1, the slope for heterogeneous nucleation should be smaller than that for homogeneous nucleation. The literature described that the plots of ln(tind) vs (lnS)−2 consisted of two linear segments29,38 for primary nucleation mechanisms. For high supersaturation, the nucleation is predominantly homogeneous; for low supersaturation, the heterogeneous nucleation prevails. However, identification of the nucleation mechanism based on the above procedure must be complemented with additional indications that corroborate the chosen mechanism.32 The slope and intercept of the linear relationships between ln(tind) vs (lnS)−2 are related to some physical parameters of the system, such as the crystal−solution interfacial surface tension and the diffusion coefficient, respectively. The calculation of these parameters by linear regression of the experimental data ln(tind) vs (lnS)−2, as well as their comparison with literature data should be carried out in order to support the nucleation mechanism assessment. For the model antisolvent crystallization system, our measurement and analysis showed that plots of ln(tind) vs (lnS)−2 are consistent with features expected from the CNT model.

⎛ N ⎞ B c ⎟⎟ + hom2 ln(t ind) = ln⎜⎜ ⎝ VAhom , J ⎠ (ln S) B hom , t ind =

16πγ 3v 2 3(kT )3

⎛ N ⎞ c ⎟⎟ Ahom , t ind = ln⎜⎜ ⎝ VAhom , J ⎠

(1)

(ii) For primary heterogeneous nucleation (HEN) ⎛ N ⎞ B c ⎟ + f (θ )· hom2 ln(t ind) = ln⎜⎜ ⎟ (ln S) ⎝ VAhet, J ⎠ B het, t ind =

16πγ 3v 2 · f (θ ) 3(kT )3

⎛ N ⎞ c ⎟ Ahet, t ind = ln⎜⎜ ⎟ ⎝ VAhet, J ⎠ f (θ ) =

(2 + cos θ )(1 − cos θ )2 4

(2) E



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Figure 6. Near-infrared chemical imaging microscopic data of the coprecipitate products from various batches (each image consists of 320 × 256 pixels. Each pixel is 39 μm × 39 μm). (a) Batch I (drug/polymer ratio 0.02165). (b) Batch D1 (drug/polymer ratio 0.0909). (c) Batch E1 (drug/polymer ratio 0.5). (d) Batch F1 (drug/polymer ratio 2.0). (Adapted with permission from reference 13; copyright (2011), Wiley.)

According to the literature,39 for primary nucleation followed by diffusion limited growth mechanism, the intercept from the linear relationship of ln(tind) vs (lnS)−2 is ⎛ v 2/3 ⎞ ⎟ A = ln⎜⎜ 1/5 ⎟ ⎝ 2Dixi ⎠

Therefore, the diffusion-limited mechanism is ruled out as a possible mechanism for crystal growth. In summary, this work described an enhanced process understanding and essential knowledge for determining a suitable operational space for crystallization process. For example, control of the crystallization raw material variables and process parameters can ensure that (1) the crystallization process proceeds within the same nucleation mechanism zone; and (2) the desired crystal size, crystal size distribution, crystal morphology, and/or polymorphic form meet the requirements of CQAs or drug product performance requirements. From pharmaceutical CMC regulatory science perspective, those critical material attributes are essential to achieve consistent product quality. In this quality by design (QbD) approach for crystallization process, when first-principle modeling is involved, it is necessary to validate the applicable conditions or assumptions being made for the first-principles, which oftentimes calls for in-depth process understanding. 2.3. Combined Real-Time PAT Process Monitoring, DOE, and General Linear Modeling (GLM) To Establish a Hybrid Approach for Process Characterization and Process Design Space Development. A key interest to a process engineer is how process parameters impact process outcomes for a given unit operation. A case study to address the technical issue could naturally fit into the process design space concept as defined in ICHQ8(R2).40 A drug/polymer ratio of 2 leading to formation of regularly shaped crystals was selected for the dynamic antisolvent crystallization study. In order to use our limited resources efficiently and effectively, a risk assessment tool was applied to

(3)

Where v, Di, and xi stand for molecular volume (m3), diffusion coefficient (m2/s), and solute molar fraction, respectively. The calculation results for the diffusion coefficient Di demonstrated that at low S level, Di is in the range of (1.39−2.79) × 10−21 m2/s; while at high S level, Di is in the range of (2.25−2.57) × 10−16 m2/s. Compared to the typical values for aqueous solutions (10−9−10−12 m2/s), these extremely low apparent diffusion coefficients suggest that molecular diffusion of naproxen in this model system probably does not play a significant role, i.e., the crystal growth after the nucleation is probably not diffusion limited. In addition, if the diffusion-limited growth occurs as the typical case of high supersaturation in the precipitation of sparingly soluble salts, typically small isometric crystalline particles (often agglomerated) should have been obtained. However, this is not the morphology of the final product observed, as shown in Figures 5a−d and 6a−d where either agglomerates with a few number of crystals (at low S level) or well-defined needle-like crystals with different cubic habits (at high S level) were evident. The two distinct morphological features of final products corresponding to the low and high supersaturation levels are consistent with the assessment of homogeneous and heterogeneous nucleation mechanisms. F



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Figure 7. Fishbone diagram for effects on antisolvent crystallization process rate. (Adapted with permission from reference 14; copyright (2011), Elsevier.)

Figure 8. Typical profile of chord-length-distribution (CLD) vs time for a model antisolvent crystallization process. (Adapted with permission from reference 14; copyright (2011), Elsevier.)

brainstorm and identify potential variables that could impact the desired quality attribute or the desired process outcome. A fishbone risk analysis was performed to select the high risk process variables for the design of experiments (DOE) study, as shown in Figure 7. The dynamic antisolvent crystallization process was monitored real-time in situ by Lasentec FBRM and particle vision microscopy (PVM) simultaneously. A typical profile of chordlength-distribution (CLD) vs time for the model antisolvent crystallization process is shown in Figure 8. The time points when sequential antisolvent addition was triggered were marked

on the profile. The counts/s for each chord-length range was increased remarkably right after each triggering of antisolvent addition. To visualize the process progress and process dynamics occurring during the course of dynamic antisolvent addition, a 3D map of counts/s−chord length−time was constructed based on the time series data of the FBRM data, as shown in Figure 9a. The 3D map revealed three distinguishable process stages: (i) incubation period when there was no significant particle counts detected, its time length depending on the combinations of G



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and final process outcomes, together with results of parameter estimation via GLM, were discussed using process engineering first-principles and the classic Nyvlt diffusion layer model.14 The real-time PVM images provided evidence of nucleation and crystal growth during the dynamic course of antisolvent addition, as shown in Figures 10a,b. As a representative example of pictorial design space for the model antisolvent crystallization process, a 3D contour plot of mode−temperature−stirring rate at the end of steady state is shown in Figure 11. In this case, no significant interaction was detected, which simplified the pictorial presentation. It is important to recognize that it is for illustrative purposes to demonstrate the establishment of a dynamic process design space. The mode of the final process slurry at the end of steady state is related to but not equal to the actual crystal size of final product. However, the mode range of the final process slurry together with the crystal dimensions measured via PVM images can provide a realistic starting point for setting up crystal size specifications for the final product. In addition, the pictorial process design space established can depict the reasonable ranges of critical process variables quickly for better process control and particle engineering to produce particles of a defined morphology, particle size distribution, and composition. A recent study41 reported that simple particle size distribution parameters like d10, d50, and d90, could be correlated with surface area and flow function coefficient measurements to act as a guideline to target these CQAs during process development. This case study illustrated the integration of three techniques namely: PAT real-time process monitoring, hybrid modeling strategy via first-principle and risk-based design of experiments (DOE), and a general linear model (GLM) in understanding pharmaceutical crystallization process. This integrated approach helped to develop a dynamic process design space for the model system at various process stages which can even provide information at any time point during the course of the dynamic process. As discussed previously13 and in section 2.2, as one of the advantages of combining PAT real-time process monitoring and first-principle modeling for process understanding, the integrated approach can help to identify the process mechanisms, to ensure the process progresses in the same process regime such that the same process mechanism is followed and product with desired CQAs can be produced in a consistent manner. A process design space model based on in-depth process understanding may predict the CQAs reliably. This approach can be valuable in process control product quality assurance, and regulatory oversight 2.4. Nucleation Induction Time Measurement. Nucleation tendency characterization has been an active research topic due to (1) its relevance to stability of crystalline drug substances and drug products and (2) its utility for inferring nucleation kinetics. Stability is one essential element for pharmaceutical quality regulation as it is tied to drug quality, efficacy, and safety. Nucleation induction time and nucleation onset based on measurement of different properties of the system are challenging, because nucleation induction is not a fixed property of the system and is open to various interpretations and measurement. In addition, under certain circumstances, nucleation could be a chaotic event which has a distribution. Given that each PAT tool has its own merit and sensitivity, from the perspectives of both validating emerging technology and process understanding of the nucleation phenomena; it is interesting to combine multiple PAT sensors for measuring tind simultaneously. An integrated PAT approach was developed to

Figure 9. (a) 3D counts−chord length−time map for an antisolvent crystallization process; (b) typical CLD at the end of steady-state period for an antisolvent crystallization process. (Adapted with permission from reference 14; copyright 2011, Elsevier.)

process conditions; (ii) transition period when significant nucleation and growth occurs thus significant counts were detected; (iii) steady state when the number of counts per second for small chord length ranges remains relatively constant throughout the remainder of the process, which indicates no significant nucleation occurs. Typical CLDs at the end of steady state are shown in Figure 9b. The characteristic parameters for this bell-shaped CLD, mode and peak frequency were taken as two response variables which represent the process outcome of the DOE. Additionally, a procedure was developed to derive the antisolvent crystallization process rates14 based on the profile of the FBRM counts/s vs time. The derived process rates at four chord length ranges were also taken as additional response variables which represent dynamic process kinetics. For this comprehensive DOE data set, rational data analysis and modeling were carried out to accomplish the following goals: (i) identifying critical process variables and interactions that impact antisolvent crystallization kinetics and final process outcome in a statistically significant fashion via analysis of variance (ANOVA); (ii) developing a statistical predictive model to link critical process variables with response variables via a general linear modeling (GLM) algorithm; (iii) establishing linkage between critical process variables and response variables via a neural network (NN) modeling algorithm; and (iv) establishing process design space for both the transition state and the steady state of the antisolvent crystallization process. The effects of critical process variables on the derived process kinetics H



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Figure 10. PVM images of (a) before; and (b) after the second addition of antisolvent (water) to the crystallization system. (Adapted with permission from reference 14; copyright 2011, Elsevier.) I



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chart-based monitoring of the mean gray intensity of the PC1 images sampled at 25 Hz.

3. CURRENT CHALLENGES AND FUTURE OUTLOOK From a regulatory science perspective, mitigation and minimization of risk associated with substandard quality of pharmaceuticals by applying the best available science and engineering practice is one of the highest priorities in the FDA. Collaborative effort among the industry, regulatory agencies, and academia to design, measure, analysis, and control pharmaceutical process is critical to gain process and product understanding, to achieve improved manufacturing process and product quality, and to better protect public health. In this review, we described an integrated PAT approach used in studying the antisolvent crystallization process of a model drug system in the FDA regulatory science research facility. To stimulate more innovative research and development activities in the future, some of current challenges and future outlook are discussed below. 3.1. Monitoring and Control of Polymorph Change during API Development and Manufacturing, Drug Product Manufacturing and Storage. Crystallization is one of the critical unit operations in the manufacturing of the Active Pharmaceutical Ingredient (API). Using PAT tools to monitor, predict, and control polymorphism changes during API development and manufacturing can be of great importance for achieving in-depth API crystallization process understanding and realizing the benefits of QbD. In addition, polymorph changes can occur during drug product manufacturing and storage due to process-induced issues or stability-related issues. Inconsistencies of the solid phase produced during manufacturing and storage of drug substances and drug products may have severe consequences.43 Reports available indicated that unanticipated polymorphism changes (as in the case of Ritonavir44,45) during the manufacturing or storage changed the drug’s dissolution characteristics and bioavailability. ritonavir is such an example of polymorphs influencing the solubility and dissolution profiles, which led to the product being withdrawn from the market because the manufacturing process was no longer able to reliably produce the desired polymorph. Eventually the product was reformulated with the most stable polymorph and relaunched. In the case of carbamazepine,46 a recall of 200-mg carbamazepine tablets (involving 53 lots, representing approximately 70 million tablets) was initiated based on several reports of clinical failures of the product, as well as observed changes in dissolution characteristics of the marketed products. Although the exact reasons attributed to the significant differences observed in both the dissolution and the bioavailability of different lots of the generic lots have never been identified, several studies suggested some factors which may, at least in part, be responsible. For example, an increase in the moisture content of the tablets during storage,47 changes in the sources of the carbamazepine raw material and differences in particle size of the material employed for different lots,48 and possibly rapid transformation from carbamazepine polymorph form III (the commercial form) to carbamazepine dehydrate in gastrointestinal (GI) fluids,49 etc. From a scientific point of view, applying PAT tools to the manufacturing, storage, and even dissolution characterization of carbamazepine may provide insights into what has been happening during those different processes. Given the critical importance of pharmaceutical solid polymorphism of an API to quality, efficacy, and safety of the drug product, the FDA issued a Guidance for Industry5 which provides: (i) FDA recommendations on assessing sameness

Figure 11. Pictorial design space for the model antisolvent crystallization process: 3D contour plots for mode at the end of steady state as a function of critical process variable temperature (X1) and stirring speed (X2) based on general linear model (GLM) results. (Adapted with permission from reference 14; copyright (2011), Elsevier.)

simultaneously measure tind of a model antisolvent multicomponent system using NIR spectroscopy, FBRM, and PVM.15 The correlations between the various measurement results are shown in parts a and b of Figure 12, respectively. It is important to recognize that each analytical technique has its own advantages and limitations. The shared advantages include that there is no need for sampling and the data acquired in real time can be used to better understand the process. A common limitation of the probe-based techniques is linked to the issue of representativeness: the data only represent the portion of the mass or volume being probed and not necessarily the process behavior across the entire crystallization vessel (especially for large-scale processes), unless those issues have been addressed by appropriate vessel design and hydrodynamic conditions. A few commonly recognized limitations for the FBRM technique include artifacts associated with stuck particles and difficulty in interpreting the chord length of irregular-shaped particles, especially at high particle concentrations. While PVM provides microscopic quality images of a particle’s morphology and size, as a direct method it is limited by its resolution of 2 μm. This resolution makes it difficult to detect the nucleation phenomena at its initial embryo stage, especially when the number density is low and its overall size is small. The NIR−PCA method is limited by the sensitivity of NIR to the new phase formation and the contribution of a new phase to the primary principal components. In other words, only when the inhomogeneity caused by the nucleation event is sufficient to be detectable via NIR will the nucleation onset be detected. Despite this limitation, as discussed above, our experimental results demonstrated that the NIR−PCA method is the most sensitive among the three methods used. In summary, each detection method has its own merit, and the measurement results are not identical due to differences in the measurement principles. Collectively, however, these tools can shed light on real-time information such as physical, chemical, and morphological of the crystal in the crystallization process. The information obtained can then be used to characterize the process transition window where nucleation takes place. Using bulk video imaging (BVI)-based multivariate imagine analysis, process control chart, and acoustic signal for assisting nucleation detection was reported42 a few years ago. It was found that the fastest methods for nucleation onset detection were the monitoring in the principal component score space and control J



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Figure 12. Correlations between (a) tind‑NIR‑PCA and tind‑FBRM; and (b) tind‑PVM and tind‑FBRM. (Adapted with permission from reference 15.)

when the drug substance exists in polymorphic forms; (ii) decision trees that provide recommendations on monitoring and controlling polymorphs in drug substances and/or drug products. It is recommended that drug substance manufacturers have robust drug substance and drug product manufacturing processes to reliably and consistently produce the intended product. In addition, identification and characterization of all possible polymorphs are critical, and relevant information on the polymorph landscape, drug stability, and solid-state properties of the drug substance is recommended to be included with the appropriate analytical methodology as part of the regulatory submission process. 3.2. Monitoring and Control of Phase Transformation during Drug Product Manufacturing and Storage. From

product development perspective, PAT may also be a powerful tool for formulation and process development, particularly for poorly water-soluble drugs in a supersatured system (such as a parenteral drug product or a drug product containing an amorphous drug substance), where phase transformation can potentially take place. In these scenarios, when properly qualified, PAT real-time monitoring may be an effective and efficient approach for formulation screening under stressed or accelerated conditions, establishment, and/or optimization of the manufacturing process parameters of the drug product developed with a supersaturated system. 3.3. PAT Monitoring and Control to Establish Linkage between Raw Materials and Process Parameters to the Drug Product’s Clinical Safety and Efficacy. As discussed in K



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Figure 13. Illustration of how the critical process parameters and critical material attributes are linked to the TPQP and Finally to TPP in the QbD paradigm. (With kind permission from Springer. [reference 50: Quality by Design: Concepts for ANDAs. AAPS J. 2008, 10(2), 268−276. Figure 2. copyright 2008 AAPS].)

models have demonstrated applicability to both macromolecules and small organic molecules.37 In some cases, statistical theory helps to interpret the chaotic nature or fluctuation of the nucleation process. In recent years, with the rapid development of crystal shape engineering and particulate engineering, nucleation and crystal growth can be controlled or tailored to achieve specific CSD, easy downstream processing ability, and stability. In addition, PAT real-time process monitoring provides an enabling tool to facilitate continuous pharmaceutical crystallization process development.

this review, PAT can be a valuable scientific tool for process understanding and development, as well as process monitoring and control. ICHQ8 (R2)40 highlighted that application of PAT may be part of a control strategy. From protecting and promoting the public health perspective, it is important to establish the linkage between raw material attributes, process conditions, and final product quality, clinical safety and efficacy. Figure 13 below provides an illustration50 of how the critical process parameters (CPPs) and critical material attributes (CMAs) are linked to the target product quality profile (TPQP) and finally to target product profile (TPP) under the QbD paradigm. This figure illustrates how certain CQAs associated with the pharmaceutical crystallization process (such as impurity, particle size, etc.) may impact final product performance, and may have the ultimate impact on clinical safety and efficacy in a general sense. 3.4. Real-Time Process Monitoring and Control for Seamless Crystallization Process Scale-Up. Scale-up of the crystallization process requires an expert team of experienced scientists and engineers working closely together to ensure reliable and seamless development, technical transfer, and process validation. PAT real-time and in situ process monitoring of several critical process parameters (CPP), such as crystal size distribution (CSD), crystal shape, polymorphic form conversion, and supersaturation, is very helpful to avoid issues in the first place and achieve the seamless development and process scale up. In fact, essential process thermodynamic (such as supersaturation) and process kinetic (such as nucleation rate or crystallization rate) information at the macroscopic level can be obtained by real-time process monitoring in conjunction with appropriate data analysis. However, innovative sensor technologies with better sensitivity and resolution are to be developed in order to assess small molecule nucleation at nanometer level directly. The CNT model, which has been applied to many crystallization systems successfully, still suffers from some limitations such as inadequacy of predicting the nucleation rate for the mixed nucleus due to oversimplifications. The two-step

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ASSOCIATED CONTENT

S Supporting Information *

Figure 2b: Loading plot of PC2 demonstrating characteristic peaks of key components detected via NIR−PCA. The Y axis is loading of PC2 (adapted with permission from reference 15 Ind. Eng. Chem. Res. 2014, 53, 1688−1701). Figure 2d: Residual sample variance plot of PC2 revealing characteristics of residual sample variance corresponding to water additions and singular points. (Adapted with permission from Reference 15: Ind. Eng. Chem. Res. 2014, 53, 1688−1701.) This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Telephone: 301-796-0022. Fax:301-796-9816. E-mail: [email protected]. Notes

The views and opinions expressed in this paper are only of those of the authors, and do not necessarily reflect the views or policies of the US FDA. The authors declare no competing financial interest. L



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ACKNOWLEDGMENTS We thank Dr. Lucinda Buhse (Acting Director, Office of Testing and Research), Dr. Robert Lionberger (Acting Deputy Director for Science, Office of Generic Drugs), and Dr. Xiaohui Jiang (Scientist, Office of Generic Drugs), Center for Drug Evaluation and Research, FDA, for proofing the manuscript and thoughtful inputs. H. Wu is grateful for the help and encouragement provided by Maury White (former ORISE fellow who worked for DPQR), Dr. Vince Vilker (retired), and Dr. Robbe Lyon (retired), Dr. Patrick Faustino (DPQR), and Dr. Ke Liu (CBER). H. Wu is grateful for funding supports from FDA CDER Regulatory Science and Review (RSR), Projects RSR04-16, RSR12-42, and RSR13-32, FDA CDER OTR Intramural Funding OTR-12 PAT, and FDA CDER OTR DPQR Intramural Funding from 2006 to 2014. The support from Mettler-Toledo AutoChem, Brimrose of America, Bruker Optics, Chemglass, Camo Technologies, Röhm America, Spectral Dimensions, FOSS NIRSystems, Dr. San Kiang at BMS, Dr. Richard Braatz at MIT, and Dr. Thomas Wheelock at Iowa State University are acknowledged.

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