Article pubs.acs.org/IECR
Integrated Process Analytical Technology Approach for Nucleation Induction Time Measurement and Nucleation Mechanism Assessment for a Dynamic Multicomponent Pharmaceutical Antisolvent Crystallization System Huiquan Wu,* Maury White, Robert Berendt, Ryan D. Foringer, and Mansoor Khan Division of Product Quality Research (DPQR, HFD-940), Office of Testing and Research (OTR), Office of Pharmaceutical Sciences (OPS), Center for Drug Evaluation and Research (CDER), US Food and Drug Administration (FDA), Life Science Building 64, FDA White Oak Campus, 10903 New Hampshire Avenue, Silver Spring, Maryland 20993, United States S Supporting Information *
ABSTRACT: A comprehensive, real-time PAT process monitoring scheme of using near-infrared (NIR) spectroscopy, focused beam reflectance measurement (FBRM), and particle vision microscopy (PVM) was established for process characterization and process understanding of a model dynamic multicomponent pharmaceutical antisolvent crystallization system. The NIR spectra were subjected to principal component analysis (PCA) to construct the process trajectory; and the final products were characterized by X-ray powder diffraction (XRPD), raman spectrometry, and microscopy. Regardless of the PAT technique (i.e., the NIR−PCA method, the FBRM method, and the PVM method) used, this study shows that the nucleation induction time (tind) increases with temperature. In addition, correlations were observed with R2 of 0.70−0.98 between PVM method and FBRM method and of 0.58−0.84 between NIR−PCA method and FBRM method. Accounting for the dynamic nature of the experiments and changes in the liquid volume (V) as a function of time, a simplified classical nucleation theory model was derived to reveal the relationship between ln(tindV) and (ln S)−2 (S is the supersaturation ratio). Regions of very strong and very weak dependence on (ln S)−2 were identified. Final product characterization and in-process observations of particle morphology at t = tind collectively support that heterogeneous- and homogeneous-nucleation mechanisms are responsible for low S and high S regions, respectively. Therefore, the utility of an integrated-PAT approach for understanding a dynamic multicomponent antisolvent crystallization process and elucidating the nucleation mechanism was demonstrated.
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INTRODUCTION
Although direct measurements of small-molecule nucleation are limited by the small size and short time scales, indirect measurements of nucleation are possible.7−9 Indirect methods include the 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 of such indirect measurement of nucleation include measuring the average nucleation rate, the induction time, and the effects of experimental conditions on crystal structures. Among those indirect measurement methods, the nucleation induction time10 (tind) has been very important for characterizing 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. Different detection techniques often give rise to differences in the calculated nucleation onset, due to differences in measurement principles, detection sensitivities, etc.
Nucleation is the process of fluctuational appearance of nanosized molecular clusters of a new crystalline phase. Due to its essential role in impacting critical quality attributes of pharmaceutical crystallization product such as crystal size distribution, purity, morphology, and dissolution, the study of nucleation is an important area for pharmaceutical research and development. 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, as the size of these crystals is ∼1 nm and they evolve in a time scale shorter than 1 μs. Therefore, it is challenging to observe nuclei before significant growth of the crystal has occurred.1 Nowadays, direct observation of nucleation of certain large species is possible. For example, colloids and globular proteins are large enough and nucleate slowly enough for nucleation to be observed using techniques such as optical and in situ atomic force microscopy (AFM),2 modern laser scanning confocal microscopy,3 and real-space imaging.4 Smallangle neutron scattering (SANS) has also been used to observe nucleation of polymer blends.5 However, direct experimental measurement and observation of small-molecule nuclei remains out of reach.6 © 2014 American Chemical Society
Received: Revised: Accepted: Published: 1688
October 29, 2013 January 6, 2014 January 7, 2014 January 7, 2014 dx.doi.org/10.1021/ie4036466 | Ind. Eng. Chem. Res. 2014, 53, 1688−1701
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A number of experimental techniques have been used for new-phase detection or nucleation induction time measurement. These techniques include differential scanning calorimetry,11 conductivity measurement,12 turbidity measurement,13 intensity of transmitted or scattered light method,14 IR transmission method,15 and combined process near-infrared (NIR) spectroscopy and turbidity measurement.16 Previously, the focused beam reflectance measurement (FBRM) technique and Process NIR spectroscopy were combined to measure the nucleation induction time for a model dynamic pharmaceutical multicomponent (naproxen−Eudragit L100−alcohol−water) system at room temperature.17 However, few reports are available regarding the use of multiple real-time process analyzers to measure nucleation induction time simultaneously.18 The development of an integrated process analytical technology (PAT) approach for nucleation induction time measurement and nucleation mechanism assessment bears regulatory science implications. In the past decade, the US Food and Drug Administration (FDA) encouraged the use of innovative approaches, such as PAT,19 to improve pharmaceutical manufacturing and product quality.20 While validating emerging technologies could be challenging in the regulatory setting, a first-principle and multidisciplinary collaborative effort may provide a promising approach toward the adaption of emerging technologies for potential pharmaceutical PAT application.21 Remarkable efforts have been reported in applying various PAT tools to crystallization studies.22−25 Due to the complexity of nucleation phenomena, typically it is necessary to use multiple, complementary techniques to gather both the process and product information. For situations where a real-time PAT monitoring scheme is implemented, a modeling approach is often required to link the process information with the product quality attributes. To this end, one has to (1) select an appropriate theory or modify an existing model, (2) use the model to analyze the experimental data, and (3) assess whether the model is able to describe the process phenomena adequately. In the process engineering domain the knowledge of such process mechanisms and process models is essential for process, product, and equipment design. For example, such knowledge can guide the selection of appropriate process windows based on optimal combinations of certain critical process parameters (CPPs). Doing so can ensure a manufacturing process that is proceeded by a desired direction to produce product with the right quality attributes. For a pharmaceutical crystallization process, understanding of the nucleation mechanism is critical, because to a large extent, the nucleation mechanism dictates the crystallization pathway and critical quality attributes (CQAs) such as crystal morphology and the crystal size distribution. It is also linked to crystallization vessel design. In this work, an integrated yet comprehensive real-time online PAT monitoring scheme (including NIR-PCA/FBRM/ particle vision microscopy (PVM)), in conjunction with off-line characterizations of the final product via X-ray powder diffraction/raman spectroscopy/NIR chemical imaging microscopy, was developed for nucleation induction time measurement and elucidation of the nucleation mechanisms for a model dynamic antisolvent crystallization system. The classical nucleation theory (CNT) was modified to incorporate the dynamic nature of a model process. The CNT modeling result, online PVM images, and off-line characterization were analyzed together to corroborate the nucleation mechanism.
Article
EXPERIMENTS AND METHODS
2.1. Materials. Naproxen, a Biopharmaceutical Classification System (BCS) class II drug whose bioavailability is ratelimited by its dissolution, was selected as the model drug. It is a white powder that is practically insoluble in water but soluble in alcohol. Eudragit L100, an anionic copolymer based on methacrylic acid and methyl methacrylate, with an average molecular weight of approximately 125 000 g/mol, was selected as the model polymer. It is a white powder with a faint, characteristic odor, and it is soluble in alcohol and practically insoluble in water. Naproxen USP (lot No: NPX 368) was obtained from Albemarle Corporation (Orangeburg, SC). Eudragit L 100 (lot No: 1221203048) was obtained from Röhm America Inc. (Somerset, NJ). Solvent reagent alcohol (HPLC grade, lot No: 053546) was purchased from Fisher Scientific (Fair Lawn, NJ). DI water (used as the antisolvent) was obtained in-house from a Millipore Advantage A10 water purifier (18.2 MΩ resistivity) and was kept refrigerated at 4 °C prior to use. All chemicals, solvent, and antisolvent were used as is without any further processing or purification. 2.2. Experimental Setup for a Model Dynamic Antisolvent Crystallization System and Real-Time Process Monitoring Scheme. The preweighed naproxen and Eudragit L100 were dissolved separately in reagent alcohol and were then added to the 1-L reaction vessel of a Chemglass reaction kit (Chemglass,Vineland, NJ). The 200 rpm stir speed was set by the OptiChem digital overhead stirrer with an impeller diameter of 80 mm. The temperature (15, 25, or 35 °C) was set via the ThermoScientific Neslab RTE 740 thermoregulator. The supersaturation of the crystallization process was created by introducing antisolvent (DI water) to the solution of naproxen-Eudragit L100-alcohol in the reaction vessel at a flow rate of 1.8 mL/s via flexible Tygon R-3603 tubing (Fisher Scientific), a MasterFlex solid-state speed-control device (Chicago, IL) and a Cole-Parmer peristaltic pump (Chicago, IL). After the first antisolvent addition (50 mL), the system was allowed to equilibrate over a period of 10 min, after which the next addition of antisolvent (50 mL) was made to the vessel. Ten minutes later, another antisolvent (in 50-mL aliquots) was dispensed in this manner until precipitation or nucleation was detected. Real-time process monitoring, using Luminar 2000 NIR Spectroscopy (Brimrose Corporation of America, Baltimore, MD), a Lasentec FBRM D600L system (Mettler-Toledo, Columbia, MD), and a Lasentec PVM819 system (MettlerToledo, Columbia, MD), was started prior to any DI water addition. The acquisition parameters of Brimrose NIR spectroscopy included: coaddition of 50 scans; no background correction; normal scan type; a gain of 2. Every 6 s, one spectrum was acquired and saved. A 1.0-cm probe extension was attached to the 679B dark-field diffuse-reflectance/ transmission probe for the actual process monitoring. Both FBRM and PVM data were acquired every 2 s. Certain measures taken to ensure the experimental consistency and repeatability can be found in the Supporting Information. A previous computational fluid dynamics (CFD) simulation result26 suggested that this configuration would introduce the antisolvent in the hydrodynamically well-mixed location. Therefore, the mixing effect was minimized. When the process was completed, the slurry discharged from the Chemglass reaction vessel was collected for oven dry at 65 °C. 1689
dx.doi.org/10.1021/ie4036466 | Ind. Eng. Chem. Res. 2014, 53, 1688−1701
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2.3. Materials Characterization of Raw Materials and Final Crystallization Products using X-ray Powder Diffraction (XRPD) Patterns, Raman Spectrometry, and NIR Chemical Imaging Microscopy. Naproxen, Eudragit L100, and selected final products from several representative antisolvent crystallization experiments were characterized by Xray powder diffraction (XRPD), Raman spectroscopy, and NIR chemical imager microscopy. XRPD patterns were collected using a Bruker D8 Advance (Bruker AXS, Madison, WI), equipped with a LYNXEYE detector, using Cu Kα radiation (λ = 1.5405 Å) at a voltage of 40 kV and current of 40 mA. Each sample was prepared by backloading approximately 500 mg of powder into a stainless steel sample holder, backed by a zerodiffraction single-silicon-crystal plate (MTI Corporation, Richmond, CA). Diffraction patterns were collected over the range of 4−50° 2θ with a step size of 0.01° at 0.5 s per step (4472 total steps). Each sample was subjected to rotation at 15°/min. Each sample was run in duplicate. The XRPD operation, data collection, and data analysis were achieved through Diffract Suite (V2.2). Raman spectroscopy was performed using a Bruker MultiRam IRFS27 Spectrometer (Bruker OPTIK GmbH, Ettlington, Germany) with a laser source of 301 mW and 1064 nm. Here, 32 scans over the range of 0.7 to 3600 cm−1 (wavenumber resolution better than 0.8 cm−1) were coadded. The microscopic images of the final products were obtained via a Sapphire NIR Chemical Imaging System (Spectral Dimensions, Olney, Maryland). Each image captures 320 spatial points by 256 spectral points at a time, with approximate resolution of 39 μm per pixel. 2.4. Nucleation Induction Time in an Antisolvent Crystallization System. It is well-known that the degree of supersaturation and temperature have profound effects on crystallization kinetics, polymorphism, crystal habits, crystal sizes, and morphology.27−33 Various theoretical models have been proposed to explain the observed effects under experimental conditions, such as classical nucleation theory (CNT),27 Kashchiev’s model,34 Mersmann model,35 Söhnel and Mullin model,10 Garside model,36 etc. Some important first-principles for homogeneous (HON) and heterogeneous (HEN) nucleation phenomena, as well as nucleation-induction phenomena can be found in Supporting Information Table 1. A general expression of induction time (tind), valid for all nuclei that appear and grow in a supersaturated solution, was proposed by Kaschiev et al.37 t ind = tnucleation + tgrowth
where Nc is crystal number, J is primary nucleation rate (number m−3 s−1), V is the volume of the parent phase. Substituting J with expression from CNT27 into eq 2.4.2 yields the following simplified CNT model, which explicitly incorporates the dynamic nature of the process by accounting for the changes in liquid volume as a function of time: ⎛ N ⎞ 16πγ 3v 2 1 c ⎟ ln(t indV ) ≈ ln⎜⎜ ⎟+ A 3(kT )3 (ln S)2 ⎝ hom , J ⎠ for homogeneous mechanism
⎛ N ⎞ 16πγ 3v 2 1 ln(t indV ) ≈ ln⎜⎜ c ⎟⎟ + f (θ ) 3 A kT S)2 3( ) (ln ⎝ het, J ⎠ for heterogeneous mechanism
Nc JV
(2.4.4)
Where θ is contact angle between the crystalline deposit and substrate, f (θ ) =
(2 + cos θ )(1 − cos θ )2 4
(2.4.5)
Obviously, 0 ≤ f(θ) ≤ 1. When f(θ) = 1, the nucleation is homogeneous; when f(θ) < 1, the nucleation is heterogeneous. If the simplified CNT is applicable, plotting ln(tindV) vs (ln S)−2 should approximately give a straight line at a given temperature. 2.5. Principal Component Analysis (PCA) of the NIR Spectra and Singular Points on the NIR-PCA-Based Process Trajectory. It has been noticed that for a time varying signal in a dynamic system, the information content is not homogenously distributed throughout. 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.38 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.16,17,39,40 In this work, the time series of NIR spectra for each process run was subjected to PCA. It was found that the first and second principal components can capture the majority (>99%) of variance involved in the dynamic process. Therefore, the PCA score plot of PC2 vs PC1 constructs the process trajectory. It was evident that several singular points (SPs) such as points of discontinuities, trend changes, and extrema are present on the PCA plots. These SPs could indicate either a homogeneous solution chemistry change or the occurrence of a significant process phenomenon, for example, the formation of a new phase. Two critical data points, p0 and pSP, were determined in the NIR spectral time series. These points represent the initial addition of water to the ternary system and the onset of SP, respectively. Since the recording rate for NIR spectrum (vNIR) was set to 6 s/spectrum, the tSP can be calculated as follows:
(2.4.1)
where tnucleation stands for time for new phase formation, and tgrowth stands for time for new phase growth to a detectable size. Their relationship could be (1) tnucleation ≫ tgrowth, (2) tnucleation ≪ tgrowth, or (3) tnucleation and tgrowth are of comparable order of magnitude. For the majority of static crystallization experiments, the first scenario has been observed. In this work, the volume of the parent phase (V) is a function of time (t) due to the dynamic nature of water addition. Depending on the drug/ polymer ratio (Q) used in a particular experiment, the crystal growth rate G, and hence tgrowth, varies. PVM real-time monitoring shows that in this work, the orders of magnitude for tnucleation and tgrowth are approximately 2−3 and 0−1 (unit of time is seconds), thus tnucleation ≫ tgrowth holds. Therefore, eq 2.4.1 can be approximated as t ind ≈ tnucleation =
(2.4.3)
tSP = (pSP − p0 )vNIR = (pSP − p0 ) × 6
(2.5.1)
The calculated tSP value, together with NIR−PCA process trajectory and associated loading plots and residual sample variance plots, can be used jointly to identify the nature of a particular SP observed during one antisolvent crystallization process, i.e., what happened in the vicinity of the SP and what
(2.4.2) 1690
dx.doi.org/10.1021/ie4036466 | Ind. Eng. Chem. Res. 2014, 53, 1688−1701
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Table 1. Measured Nucleation Induction Time Based on Three PAT Techniques, Estimated Super-Saturation Level at Each Experimental Condition batch code
T (°C)
mnap (g)
mEud (g)
ratio Q
vAlco (mL)
vt (mL)
estimated S at time of nucleation
tind‑FBRM (s)
tind‑PVM (s)
tind‑NIR‑PCA (s)
A0 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 B0 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13
15 15 15 15 15 15 15 15 15 15 15 15 15 15 25 25 25 25 25 25 25 25 25 25 25 25 25 25 35 35 35 35 35 35 35 35 35 35 35 35 35 35
0 1 2 2.4705 3 3.6 4 4.2857 4.5 4.6667 4.8 4.9090 5 6 0 1 2 2.4705 3 3.6 4 4.2857 4.5 4.6667 4.8 4.9090 5 6 0 1 2 2.4705 3 3.6 4 4.2857 4.5 4.6667 4.8 4.9090 5 6
6 5 4 3.5295 3 2.4 2 1.7143 1.5 1.3333 1.2 1.0909 1 0 6 5 4 3.5295 3 2.4 2 1.7143 1.5 1.3333 1.2 1.0909 1 0 6 5 4 3.5295 3 2.4 2 1.7143 1.5 1.3333 1.2 1.0909 1 0
0 0.2 0.5 0.7 1 1.5 2 2.5 3 3.5 4 4.5 5 ∞ 0 0.2 0.5 0.7 1 1.5 2 2.5 3 3.5 4 4.5 5 ∞ 0 0.2 0.5 0.7 1 1.5 2 2.5 3 3.5 4 4.5 5 ∞
250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
350 300 250 200 200 200 200 150 150 150 150 150 150 100 400 350 350 250 250 250 200 200 200 200 200 200 200 150 450 450 450 350 350 300 300 300 300 250 250 250 250 200
0 n/a 1.0862 1.3418 1.6293 1.9552 2.1724 1.4620 1.5351 n/a n/a n/a n/a n/a n/a n/a 1.0579 1.3069 1.5869 1.9043 1.4333 1.5357 1.6125 1.6722 1.7199 1.7591 1.7916 1.3359 n/a n/a 1.1132 1.3752 1.6698 1.4572 1.6191 1.7348 1.8215 1.8889 1.3101 1.9871 1.3647 1.6376
1830 2626 2468 1920 1820 1236 1222 1216 1216 1270 1258 868 808 608 3028 3616 3460 2998 2450 2454 1872 1868 1846 1868 1858 1854 1842 1222 3794 3650 3624 3398 3010 2456 2438 2458 2402 2408 2290 2416 2379 1824
1706 2386 2370 1974 1858 1218 1204 1236 1154 1206 1142 946 866 528 2248 3610 3448 2968 2204 2238 1696 1678 1852 1692 1732 1662 1682 1072 2842 3620 3596 3610 3290 3062 2794 2372 2236 2232 2232 2238 2282 1662
1830 1818 1950 1668 1266 1436 828 858 834 984 846 822 616 n/a n/a 2070 2034 2052 1632 2050 1230 1254 1236 1254 1224 1248 1236 n/a n/a 2046 2040 2076 2040 1940 2034 1668 1680 1632 1632 1632 1638 n/a
caused the occurrence of the SP. From there, the tind based on the NIR−PCA method can be determined.
for tind (based on three real-time process-monitoring techniques (NIR−PCA, FBRM, and PVM)) and the corresponding process parameters are summarized in Table 1 and discussed below. 3.1.1. FBRM-Based tind. The methodology developed previously18 was applied to determine tind. The nucleation onset was determined by the first sharp increase of the fine particle (
