Integration of Near-Infrared Spectroscopy and Mechanistic Modeling

Article pubs.acs.org/IECR

Integration of Near-Infrared Spectroscopy and Mechanistic Modeling for Predicting Film-Coating and Dissolution of Modified Release Tablets Huiquan Wu,*,†,‡ Robbe C. Lyon,† Mansoor A. Khan,† Randall J. Voytilla,§ and James K. Drennen, III§ †

Division of Product Quality Research, Office of Testing and Research, Office of Pharmaceutical Quality, CDER, FDA, HFD-940, White Oak Life Sciences Building 64, 10903 New Hampshire Avenue, Silver Spring, Maryland, 20993-0002, United States ‡ Process Assessment Branch II, Division of Process Assessment 1, Office of Process and Facilities, Office of Pharmaceutical Quality, CDER, FDA, Silver Spring, Maryland, 20993-0002, United States § Duquesne Center of Pharmaceutical Technology, Duquesne University, Pittsburgh, Pennsylvania 15282, United States S Supporting Information *

ABSTRACT: This study demonstrates the feasibility of predicting polymeric film coating and dissolution of theophylline (active pharmateutical ingredients, API) tablets based on integration of multivariate data analysis of near-infrared (NIR) spectra and first-principal modeling. Tablets of various API strengths were manufactured and were spray-coated in a fluid bed using a mixture of ethyl cellulose and hydroxypropyl methylcellulose. Tablets were subjected in NIR spectroscopy and in vitro USP dissolution testing. The characteristic peaks of coating materials were identified via Norris Gap second derivative preprocessing of the NIR spectra of coated tablets. Principal component analysis revealed a linear relationship between PC1 score and tablet coating level. Principal component regression and partial least squares calibration models were developed to correlate the NIR spectra with the dissolution data within the time window of 10−120 min. A linear relationship between tablet initial dissolution rate and tablet coating level was found with a slope of S. On the basis of a Fickian diffusion model, a mathematical equation was derived to relate S to the diffusion coefficient (D) of the drug across the polymeric film. The mechanistic modeling of the film-coated theophylline tablet dissolution profiles suggested that the film-coated tablet dissolution process might be governed by Fick diffusion control for the initial and early release, and governed by the Hixson and Crowell first order kinetics for the late release stage. Finally, some of the current challenges and future outlook on development of such a hybrid modeling approach for characterizing and understanding film-coated tablet manufacturing and dissolution via process analytical technology implementation was discussed from both a technical and a regulatory science perspective.

1. INTRODUCTION: REGULATORY SCIENCE RELEVANCE Near-infrared (NIR) spectroscopy, a rapid and nondestructive analysis technique has gained popularity in understanding sources of variability of pharmaceutical product and process for better pharmaceutical product quality and process control.1 NIR spectroscopy with tandem chemometric statistics offers methods to rapidly assess quality attributes in finished dosage forms in a noninvasive fashion. Monitoring the intact product directly eliminates the tedious and time-consuming tasks of sample preparation and maintains the integrity of the drug product. Many studies in literature support testing for identity, assay, content uniformity, tablet hardness, and moisture content by NIR spectroscopy.2 As an alternate method, the NIR/chemometric method holds promise for substitution of some product testing such as dissolution studies. There are several technical and regulatory science challenges to be addressed during implementation of the NIR/chemometric method for routine pharmaceutical applications. NIRS also constitutes one of the major techniques in process analytical technology (PAT)3 and may also be used as part of a real time release testing (RTRT) strategy. When used as such, NIRS is underpinned by the principles of quality-by-design (QbD).4 Many algorithms have been developed to achieve multicomponent determinations from the diffuse reflection spectra © 2015 American Chemical Society

of mixture samples. All of these algorithms yield the most accurate estimates of concentration when the intensity of each spectral feature is linearly proportional to the analyte concentration. However, when the NIR radiation interacts with the mixture system in a nonlinear manner, deviation from the estimates of concentration based on linearity assumption is expected. In addition, it has been challenging to separate the contributions from chemical and physical factors of a pharmaceutical dosage form5 solely based on NIR spectra. Several data preprocessing algorithms are available to reduce the impact of certain physical phenomena on the spectra for improving subsequent multivariate regression, classification model, or exploratory analysis;6,7 however, there is risk of an over reliance on preprocessing.8 For example, while removing the particle scattering effect, it was reported that standard multiplicative signal correction (MSC) preprocessing removes too much analyte (or chemical) information in the same time.9 This presents a challenge if a causal link or root cause for a pharmaceutical product quality issue is to be identified via a NIR/chemometric method. Received: Revised: Accepted: Published: 6012

November 28, 2014 April 9, 2015 May 19, 2015 May 19, 2015 DOI: 10.1021/ie504680m Ind. Eng. Chem. Res. 2015, 54, 6012−6023

Article

Industrial & Engineering Chemistry Research

and film thickness of the MR tablet are CQAs. Once administrated to the patients, those CQAs will largely impact drug release profile, pharmacokinetic and pharmacodynamic (PK and PD) profile, and pharmacological effect.17 Because of the critical therapeutic importance of the MR oral dosage form, the US Food and Drug Administration (FDA) has issued several regulatory guidance documents for the industry.18,19 Under certain circumstances, small changes in processing parameters may have the potential to greatly affect the properties of the final dosage form, a rapid and nondestructive analytical method which detects these differences and gives an indication of the final product characteristics, for example, dissolution rate, film thickness, etc., could prove profitable as a PAT or quality assurance tool. There are reports available discussing the monitoring of film coating processes via several techniques, such as NIR spectroscopy,20−23 optical coherence tomography,24 and other spectroscopy.25 In addition, several reports are available focusing on assessing or monitoring dissolution behavior of solid dosage form via techniques such as NIR,26−29 terahertz pulse imaging,30,31 etc. This work investigates the feasibility of NIR and chemometrics for monitoring and predicting of the polymeric film coating and dissolution process of theophylline tablets. A hybrid modeling approach of combining chemometric analysis and first-principle modeling methodology was developed to achieve mechanistic understanding of the model drug MR tablet dissolution process.

Therefore, it is important to develop a hybrid modeling approach, for example, integration of chemometrics and firstprinciple modeling methodology, to enable extraction of the critical information from spectroscopic data and product characterization data for better process and quality control. The quality attributes of a pharmaceutical product are a complex function of formulation variables and processing parameters.10 The multivariate nature of the pharmaceutical product and process has been well recognized in the definition of design space in ICHQ8(R2).4 From a regulatory science perspective, it is necessary to understand and characterize the variability of a pharmaceutical process and product such that both the regulator and the applicant know how much variability a proposed design space can handle. In addition, mechanistic understanding between material properties of input materials, process variables of unit operations, and critical quality attributes (CQAs) of the final dosage form can provide a higher level of confidence about the applicable range and associated regulatory flexibility of the proposed design space. When applying PAT tools3 to characterize the variability of a particular pharmaceutical manufacturing process and product, typically a large dimensional data set consisting of measurements of various formulation and process variables and/or quality attributes of final dosage form will be generated. Principal component analysis (PCA) is a proven technique to summarize the majority of the variability embedded with the data set with a few principal components. However, physical interpretation of a particular principal component (PC) can be challenging. Because PCs are a set of orthogonal vectors which are linear combinations of eigenvectors of the data set, each PC usually involves all original variables. One interpretation approach was to select a subset of the original variables and use this subset to approximate the PCs.11 However, it could be hard to make a balance in preserving the information in the PCA and in aiding interpretation of the main sources of variation. For situations only involving a limited number of variables, examining the weighted coefficients in the linear combinations of eigenvectors may provide a straightforward approach to interpret the PC physically, as demonstrated in the stock return rate analysis.12 In which case, the first PC is a roughly equally weighted sum, or “index” of the five stocks, and the second PC represents contrast between the chemical stocks and oil stocks. Therefore, the first PC might be termed as market component, while the second PC might be termed as industry component. However, for pharmaceutical PAT applications involving a large number of variables, it is often necessary to bring the process/ product domain knowledge into PAT data analysis and modeling practice. Examples include (i) discrimination between different dosage strengths of tablets in blister packs;13 (ii) combined experimental design methodology and PCA for identifying the main sources of variation in the spectra and estimation of their influence on the quantitative predictions;14 (iii) extraction process-related information from NIR microscopy data cubes;15 and (iv) comparison of analytical and data preprocessing methods for spectral fingerprint.16 Aqueous film coating is a process commonly employed in the pharmaceutical industry, especially for making modified release (MR) tablets. Tablets are often film coated with cellulose polymers in order to (i) control the dissolution of the drug from the dosage form, (ii) improve palatability or the aesthetic appeal of the drug, and (iii) improve the stability of photosensitive drugs. From a regulatory science perspective, the coating film properties such as film structure, film uniformity,

2. MATERIALS AND METHODS 2.1. Materials. The following pharmaceutical materials were used as received without any further processing or purification: anhydrous USP grade theophylline (BASF, Minden, Germany) (molecular formula: C7H8N4O2); monohydrate USP grade spray-dried lactose (Fast-Flo 316) (Foremost Farms, Baraboo, WI) (molecular formula: C12H22O11); NF magnesium stearate (Spectrum Chemical, New Brunswick, NJ) (molecular formula: C36H70MgO4); NF microcrystalline cellulose (Avicel PH-101) (FMC Biopolymer, Newark, DE) (molecular formula: (C6H10O5)n); Ethyl cellulose dispersion (Aquacoat ECD-30, FMC, Philadelphia, PA) (molecular formula: (C10H8O5)n); plasticizer-dibutyl sebacate (Sigma, St Louis, MO) (molecular formula: C18H34O4); hydroxypropyl methylcellulose (HPMC) (molecular formula: (C6H10O5)n); deionized water was used in the preparation of the starch paste in the wet granulation. Theophylline tablets of various formulations were manufactured via powder blending, followed by a direct compression then coating. Powder blending was performed in an 8 L bin blender (L.B. Bohle GmbH, Ennigerloh, Germany) at 25 rpm for 4 min. Magnesium stearate was added after the 4 min time point and blended for an additional 30 s. Tablets were compressed on an 18-station automated rotary tablet press (Elizabeth Hata, North Huntingdon, PA). Film-coated modified-release tablet core formulations are listed in Table 1. The theophylline Table 1. Film-Coated Modified-Release Tablet Core Formulations

6013

component

theophylline (mg)

Fast Flo lactose (mg)

Avicel PH 101 (mg)

MgS (mg)

total (mg)

80 mg 90 mg 100 mg 110 mg 120 mg

80 90 100 110 120

187 177 167 157 147

60 60 60 60 60

3 3 3 3 3

330 330 330 330 330

DOI: 10.1021/ie504680m Ind. Eng. Chem. Res. 2015, 54, 6012−6023

Article


where a, A are the principal component (PC) number and maximum number of PCs. The first PC summarizes the majority variability embedded in the data matrix; the second PC summarizes the majority variability left over from the first PC, etc. The principal components are orthogonal to each other. 2.2.3. Mechanistic Modeling of Film-Coated Tablet Dissolution Process. The quantitative analysis of the dissolution profiles is facilitated when mathematical formulas that express the dissolution results as a function of the dosage forms characteristics that are used. Theophylline is a highly soluble drug. Its water solubility was reported as 7360 mg/L (25 °C).34 Surelease is a platform of complete, extended release, aqueous coating systems utilizing ethylcellulose as the rate controlling polymer for drug release.35 The dispersions are a unique combination of film-forming polymer, plasticizer, and stabilizers. The primary means of drug release is by diffusion through the Surelease membrane and is directly controlled by film thickness.35 From the multiphase mass-transfer process engineering perspective,36 for a highly soluble drug that was tableted and then film-coated by Surelease, no mass-transfer resistance from the drug solubility process is expected under the perfect sink condition. Instead, the release rate of such highly soluble drug from film-coated tablet is determined by drug diffusion across the polymeric film.37 However, under many experimental conditions, the release mechanism of drug diffusion deviates from the Fickian equation and follows an anomalous (nonFickian) behavior.38 In reality, it is possible that more than one type of release phenomena are involved, which complicates the tablet dissolution process. For such complicated situations, it is worth determining to what extent the main control mechanism is applicable and what other mechanisms might be invoked during the entire course of the film-coated tablet dissolution process. To start with such exploration, the dissolution data for the polymeric film-coated theophylline tablet are analyzed by several mechanism-based models as discussed in the results and discussion section. 2.2.4. Theory on Drug Diffusion through Thin, Polymeric Films. For drug diffusion through thin, initially drug-free, planar films, the drug release kinetics can be described by the exact analytical solution to Fick’s second law of diffusion in a plane sheet,37

tablet core was 0.95 cm round, convex, no score on either side. The average tablet thickness was 0.576 cm. Coating material is a mixture of Surelease (aqueous ethyl cellulose dispersion) and Opadry Clear (HPMC) (80:20 w/w). Final solution was diluted to 15% by weight of solids before being sprayed inside a Freund Hi-coater HCT-48. Coating process parameters include 10 rpm pan speed, 60 °C inlet temperature, 40 °C exhaust temperature, 25 mL per minute spray rate. A retrofitted sample thief enabled sampling at predetermined time intervals. Before initiation of coating, the tablets (batch size of 400 g) were fluidized for a couple of minutes in order to equilibrate to the column temperature as well as to account for the weight lost due to fluidization. Samples of tablets were weighed before and after fluidization. The average weight after fluidization was taken as the base level for calculating the weight gain due to coating. For 80 mg tablets, coating levels are 1, 3, 5, 7, 9, 11, 13, 15, and 17% wt. gain; for 90, 100, 110, and 120 mg tablets, coating levels are 1, 3, 5, 7, and 9% wt. gain. Although tablets were coated up to 17% w/w, calibration models were developed using data on uncoated and tablets coated up to 9%. 2.2. Methods. 2.2.1. Near-Infrared Spectrometer and Tablet Dissolution Testing. Near-infrared analysis of the CR tablets collected at various time points during the coating process was performed on FOSS NIRSystems 5000 and 6500 (Silver Spring, MD) diffuse reflectance near-infrared (NIR) spectrometers. Each spectrum was composed of 32 coadded scans over a range of 1100−2500 nm. Data processing and analysis of the NIR spectra by PCA, PCR, and PLS was conducted using Unscrambler 9.2 software (Camo Technologies, Woodbridge, NJ). In vitro dissolution testing of the tablets was performed on a USP type II apparatus (Distek 2100B, New Brunswick, NJ) and 900 mL Peak vessels (Vankel, Cary, NC) at 50 rpm with an autosampler. Six tablets were tested for dissolution characteristics and release profiles at each prespecified time points during the coating process for each theophylline strength of core tablets. An Agilent 8453 UV−vis spectrophotometer at 272 nm was used to detect theophylline. The dissolution medium for all experiments was 900 mL of phosphate buffer pH 7.4 at 37 ± 0.5 °C. The averaged dissolution data and standard deviations of the six tested tablets with the same coating levels and same core theophylline strengths were reported in the results and discussion section. 2.2.2. Principal Component Analysis (PCA). PCA32 is a technique capable of linearly mapping multidimensional data onto lower dimensions with minimal loss of information. Its principle and early applications can be found in excellent review.33 Let X represents a data matrix with n rows for the objects and m columns for the features. The general form of the PCA model is

∞ ⎛ Dt ⎞1/2 ⎧ Mt nδ ⎫ ⎬ = 4⎜ 2 ⎟ ⎨π −1/2 + 2 ∑ ( −1)n i erfc ⎝δ ⎠ ⎩ M∞ 2 Dt ⎭ n=1

(2.2)

A

∑ tiapka a=1

+ eik(A)

t = 0,

c = c0 ,

− δ /2 ≤ x ≤ δ /2

t > 0,

x = ±δ /2,

c = c1

(2.5) (2.6)

where Mt is the cumulative amount of drug in the acceptor compartment at time t, M∞ the corresponding quantity after infinite time, δ the thickness of the polymeric film, c0 the initial concentration of the drug loading in the film, c1 the constant external concentration at the polymer/water interface, and D the diffusion coefficient of the drug. Certain simplifications39 to eq 2.4 can be made as shown below: (A). Fickian Diffusion Being the Predominant Mechanism of Drug Release. For early times of release, Mt/M∞ < 0.6, the

In scalar form: xik = xmean, k +

⎪

the following initial condition and boundary condition were used:

where T is scores matrix, P is X-loadings matrix, E is X-residual matrix. Usually the PCA model is centered, which gives X = 1·xmean + T(A)·P′(A) + E(A)

⎪

⎪

(2.4)

(2.1)

X = T·P′ + E

⎪

(2.3) 6014


Article

Industrial & Engineering Chemistry Research second term in the brackets of eq 2.4 vanishes quickly, a sufficient accurate expression can be obtained: ⎛ Dt ⎞1/2 Mt = 4⎜ 2 ⎟ = kt 1/2 ⎝ πδ ⎠ M∞ k=

4D1/2 δπ 1/2

(2.7)

(2.8)

Thus, under perfect sink conditions and assuming independence of D on drug concentration, the plot of (Mt/M∞) vs t1/2 should give a straight line. Differentiating the two sides of eq 2.7 with respect to δ gives rise to the slope (S) of the plot of initial drug release rate vs coating level at t = t1:

( )

∂ S=

Mt M∞

t = t1

∂δ

= −2

D 1 · πt1 δ 2

Figure 1. NIR spectra of the pure components of core tablets. (2.9)

The diffusion coefficient D can therefore be estimated as follows: D=

δ4 ·πt1·S2 4

(2.10)

(B). non-Fickian Behavior. For non-Fickian (anomalous) behavior, a general equation can be used: Mt = kt n M∞

(2.11)

The drug release rate equation can be expressed as dM t = nM∞kt n − 1 dt

(2.11a)

where n is the release exponent indicative of the mechanism of drug release. Figure 2. Mean centered NIR spectra of coated tablets of 80 mg API strength with various coating wt. gain levels.

3. DATA ANALYSIS RESULTS AND DISCUSSION In this work, the NIR spectra of coated tablets were grouped by their core tablet active pharmaceutical ingredients (API) strengths, 80 mg, 90 mg, 100 mg, 110 mg, and 120 mg, accordingly, and then subjected to PCA. It was found that the first two principal components (PC1 and PC2) are sufficient to characterize the majority of the total variance captured. Therefore, all of the subsequent work was focused on PC1 and PC2. In addition, multivariate calibration PCR and PLS models were developed to correlate the NIR spectra with the dissolution data of the coated tablets, respectively. The PCR and PLS models were then used to predict the dissolution data of the coated tablets. 3.1. NIR Spectral Analysis. To evaluate the ability of NIR spectroscopy to monitor the coating of tablets with the polymeric material and to monitor the dissolution of the coated tablets, as stated in the experimental section, off-line measurements of the tablets collected at various coating times from the coating process were conducted first. The raw NIR spectra of the pure components of core tablets are shown in Figure 1. The mean centered NIR spectra of coated tablets of 80 mg API strength with various coating weight gain levels are shown in Figure 2. To identify the characteristic peaks for each component in the core tablets and in the coating materials, all of the raw NIR spectra were subjected to the Norris Gap second derivative preprocessing (with Gap size of 1). The profound yet unique peaks which are corresponding to a certain component

on the second derivative preprocessed NIR spectra are identified as characteristic peaks of the component. On the basis of the chemical structures of components involved in the film-coated tablets and well-established NIR absorption bands of certain functional groups, the characteristic peaks were identified. The characteristic peaks along with their responsible functional groups and vibrations are shown in Table 2. As expected and shown in previous studies,22,40,41 absorbance values related to coating materials increased while absorbance values related to core tablet decreased because of coating. The Norris Gap second derivative preprocessed NIR spectra of coated tablets for successive collected samples are shown in Figures 3a−c. The correlation between mass of coating materials or coating thickness and spectral absorbance was clearly highlighted over the 1174−1210 nm (in Figure 3a), 1450−1490 nm (in Figure 3b), and 2210−2276 nm (in Figure 3c) NIR regions. The overlaid spectra of the 80 mg API tablets with various coating levels revealed that the three regions were linked to a main component of the coating materials, ethylcellulose. In addition, absorbance over the 1148−1210 nm region was attributed to the C−H second overtone. Absorbance over the 1370−1530 nm region was attributed to the C−H stretching, deformation vibrations, and O−H first overtone,32 respectively, and corresponding to functional groups of ethylcellulose. Furthermore, the Norris Gap second derivative 6015


Article

Industrial & Engineering Chemistry Research Table 2. Identification of Characteristic Peaks of Pure Components of the Core Tablets and Dried Coating Materials theophylline wavelength (nm) 1626

responsible functional groups and vibrations C−H 1st overtone

1662 1682 2134 2190 2294

lactose (Fast-Flo) wavelength (nm) 1936 2256

responsible functional groups and vibrations C−O + O−H combinations C−H + C−H combinations

N−H + C−H combinations

Mg stearate wavelength (nm) 1732

responsible functional groups and vibrations C−H 1st overtone

1764 2310 2350

coating materials (ethyl cellulose, HPMC) wavelength (nm) 1182

responsible functional groups and vibrations C−H second overtone

1196 C−H + C−H combinations

1422 1430 1452 1466 1478 2248 2266

C−H stretching, deformation vibrations, and O−H first overtone

C−H + C−H combinations

by the coated film and its underlying tablet to a large extent. However, after a prolonged exposure to the dissolution medium, the structure of coated film and its underneath tablet core may experience a destructive process (such as rapture, swelling, erosion, etc.); in such case a different solution chemistry process between the tablet core and the dissolution medium can play an increasingly important role. PCR and PLS models which solely rely on the intact tablet NIR information therefore become inaccurate in describing the dissolution data. As a representative example, the prediction results of the dissolution data at 60 min for the independent validation sample sets using the PCR and PLS models were summarized in Table 5. The prediction errors for 80 mg API 7% wt. gain, 90 mg API 7% wt. gain, 100 mg and 110 mg API 3% and 7% wt. gains, and 120 mg API 7% wt. gain are within 10% for PCR models. The prediction errors for 80 mg API 11% wt. gain, 90 mg API 3% wt. gain, and 120 mg API 3% wt. gain are 21−28% for PCR models. The PLS model generates similar prediction results compared to the PCR model. Some of the underlying assumptions of this multivariate PLS/PCR modeling approach include the following: (i) all of the tablets were assumed to be coated uniformly; (ii) there was no other quality issues for the coated tablets, such as no defects on the coating layer; (iii) minimal experimental errors from the dissolution data of the film-coated tablets. Situations significantly deviating from those assumptions could lead to profound prediction errors of the multivariate PLS/PCR models. The discrepancy of the multivariate model’s prediction errors between different samples might be possibly due to a number of facts, such as (1) limited amount of samples available for NIR scanning and modeling exercise, which could be the primary factor limiting the multivariate model’s performance; (2) coating uniformity and/or other quality issues (such as defects, content uniformity, etc.) of the film-coated tablets, which would degrade the NIR spectral data quality; and (3) potential experimental errors embedded with the dissolution data of the film-coated tablets. 3.4. Mechanistic Modeling of Film-Coated Tablet Dissolution Process. 3.4.1. Estimation of Diffusion Coefficients D of Theophylline in Polymer Film and Comparison with Literature Data. During the early stage of drug release, the initial dissolution rates at various coating levels for film-coated tablets with various core API strengths were calculated based on the dissolution profiles obtained, and plotted against the coating level in Figure 6. It shows that for all core API strengths, there are linear relationships between the initial dissolution rate and the coating level (or approximately coating

spectra of the coated tablets with 80 mg API strengths demonstrated a characteristics peak around 2266 nm: with the coating level increasing, the peak shape changed from concave (upward) to downward gradually. This peak position is unique to the coating layer because it was confirmed by the Norris Gap second derivative spectra of the coated tablets with all other API strengths (90 mg, 100 mg, 110 mg, and 120 mg). The increase in absorbance observed in the aforementioned three NIR regions, while the applied amount of polymer and the coating thickness increased, was likely linked to the presence of ethylcellulose. Thus, the results from off-line experiments demonstrated the ability of NIR spectroscopy to monitor the tablet coating process. 3.2. NIR Qualitative Monitoring. As discussed previously,17 coating thickness is a CQA for a modified release dosage form. NIR can provide qualitative monitoring of the coating process, because the PCA of NIR spectra of coated tablets sampled during the coating process has the capability to discriminate tablets coated with increasing mass of coating materials, as discussed below. Each and every group of the raw NIR spectra of the coated tablets with the same core API strength was subjected to PCA. For each core tablet with the same API strength, plotting the PC1 score vs film-coating wt. gain (%) generated a linear relationship with R2 value in the range of 0.95−0.99, as shown in Figure 4 and Table 3. PC1, which explains 98% of the total variance in the NIR spectral cluster associated with certain API strength of the tablet core, primarily correlates with the coating layer thickness. Apparently, the PC1 score is indicative of filmcoating weight gain (%) during the coating process. 3.3. Multivariate Modeling of the Film-Coated Tablet Dissolution Process. Representative dissolution profiles for film coated theophylline tablets for core tablet API strength of 80 mg API) are shown in Figure 5. The dissolution profiles for film coated theophylline tablets for core tablet API strengths of 90 mg, 100 mg, 110 mg, and 120 mg were shown in the Supporting Information as Figures S1−S4, respectively. PCR and PLS calibration models were developed to correlate the NIR spectra with the dissolution data of the coated tablets at various dissolution time points, respectively, as shown in Table 4. It can be seen that both PCR and PLS models can correlate the NIR spectra with the dissolution data well within the dissolution time window from 10 to 120 min. However, the models work better for the first 60 min (with R2 values of 0.95−0.99) than longer time (with R2 values of 0.85−0.89). This suggests that after the coated tablet is exposed to the dissolution medium, initially the dissolution can be described 6016


Article


(5.9−13) × 10−6 cm2/s and (1.5−3.3) × 10−6 cm2/s for 10 and 5 μm coating layer thicknesses, respectively, which are in close agreement with the literature data.42−44 The theophylline diffusion coefficient D in swollen sodium-alginate membranes was experimentally determined as (4.2−5.6) × 10−6 cm2/s.42 Theophylline D values in cross-linked PVA (10%) were determined as (9.0−20) × 10−6 cm2/s.43 The agreement between the estimated value and literature value for the diffusion coefficient D of theophylline in polymer film suggested that the initial dissolution process of the film-coated tablet might be governed by diffusion control. 3.4.2. Fitting the Film-Coated Tablet Dissolution Kinetics with Mechanistic and Semiempirical Models. To further identify the film-coated tablet dissolution kinetics after the initial dissolution period, several dissolution models including both mechanistic and semiempirical models such as zero order kinetics model, Fickan’s diffusion law (square root of time), Hixson and Crowell equation (first order kinetics), were used to fit the film-coated tablet dissolution profiles obtained experimentally and evaluated. Zero order kinetics was ruled out quickly because none of the film-coated tablet dissolution profiles was linear with time over the entire testing period, rather they all have appreciable curvatures. A. Fick Diffusion Law (Square Root of Time). Supposing the dissolution kinetics is governed by Fick diffusion law for early time of release (eq 2.7), then, M t /M∞ t 1/2

=

4D1/2 = k = constant δ

(3.1)

Computation of (Mt/M∞)/t1/2 would give a constant for each set of coated tablets with same core API strength. According to eq 3.1, the dissolution data for the film-coated theophylline tablets were analyzed and summarized in Table S1 in the Supporting Information. It shows that for each core API strength, the computation results of (Mt/M∞)/t1/2 gave an approximately constant value, the majority of the standard deviations was (10−30) % of the averaged k. This provided additional evidence that during the early time of release (Mt/M∞ < 0.6), the film-coated theophylline tablet dissolution process may be approximated by Fick diffusion law. In addition, the plots of (Mt/M∞)/t1/2 vs t for 80 mg API core tablets are shown in Figure 7. The plots of (Mt/M∞)/t1/2 vs t for core tablets of other API strengths (90 mg, 100 mg, 110 mg, and 120 mg) were shown in S5−S8 in the Supporting Information. It was shown that for thicker coating levels (for 80 mg API core tablet, coating level ≥13%; for 100 mg API core tablet, coating level ≥11%), an almost perfect horizontal line was obtained; for other cases, an initial pretty scattered yet dynamic period was observed where (Mt/M∞)/t1/2 changes with time, although eventually they converged when the time was prolonged. The deviation from the Fick’s diffusion model could be attributed to several factors: (i) oversimplification inherent from the Fick’s diffusion model; (ii) other possible phenomena such as polymer relaxation, swelling, and erosion that could occur but were not taken into account in this modeling study. B. Hixson and Crowell First Order Kinetics. Supposing the dissolution kinetics is governed by the Hixson and Crowell first order kinetics equation,45

Figure 3. Norris Gap 2nd derivative preprocessed NIR spectra of coated tablets with 80 mg core API strength. (a) 1174−1210 nm; (b) 1450−1490 nm; and (c) 2210−2276 nm. Zoomed in 1148 nm− 1210 nm to identify characteristic peaks at 1182 and 1196 nm corresponding to coating materials.

thickness, h) with an average slope in the range from −1.07 to −1.30. Under the process conditions of the film-coating unit operation in this study, it is reasonable to assume that 1% filmcoating weight gain is approximately equivalent to 5−10 μm film thickness. According to eq 2.10, the diffusion coefficients D of theophylline in polymer film can be estimated based on various initial coating thickness values and core API strengths, as shown in Table 3. The estimated D values are at the range of

⎛Q ⎞ ln⎜⎜ t ⎟⎟ = k1t ⎝Q0 ⎠ 6017

(3.2) DOI: 10.1021/ie504680m Ind. Eng. Chem. Res. 2015, 54, 6012−6023

Article


Figure 4. Plot of PC1 score vs coating level for different tablet core API strengths of film-coated tablets (no preprocessing).

Table 3. Linear Regression Results for the Relationships between PC1 Score (y) and Corresponding Film-Coating Weight Gain Level (x) for Various Tablet Core API Strengths estimated diffusion coefficient of theophylline in polymer coating film ×106 (cm2/s)

relationship between PC1 score (y) and film-coating wt. gain % (x) tablet core API strength (mg)

coating level range (wt. gain %)

80 90 100 110 120 (initial coating) 120 (after initial coating)

0−11 0−9 0−13 0−9 0−1 1−9

a

linear regression equation y y y y y y

= = = = = =

4.3921x − 37.901 5.0232x − 33.133 5.32x − 26.678 4.6229x − 33.357 49.595x − 42.098 4.5707x + 1.9609

R2

slope S calculated from Figure 6

δ = 10 μm

δ = 5 μm

0.9927 0.9645 0.9932 0.9758 not applicablea 0.9542

−1.07 −1.1 −1.143 −1.3 −1.226 N/A

9.0 9.5 10 13 5.9 N/A

2.2 2.4 2.6 3.3 1.5 N/A

Only two points available, no need to calculate R2

Table 4. Multivariate Calibration ResultsCorrelating the NIR Spectra of Theophylline Tablets to the Dissolution Data at Different Time Points Using PCR and PLS1 correlation coefficient dissolution time (min)

PCR

PLS1

10 30 60 90 120 150 180 210 240

0.9515 0.9667 0.9699 0.9301 0.8956 0.8766 0.8664 0.8617 0.8570

0.9815 0.9915 0.9693 0.9322 0.8978 0.8787 0.8682 0.8633 0.8582

ln(Q t /Q t ) 2

Figure 5. Dissolution profiles for film coated theophylline tablets for 80 mg API core tablets at various coating weight gain levels (average).

t 2 − t1

1

= k1 = constant

(3.3)

Therefore, either the plot of ln Qt vs t gives a horizontal line of constant k1, or computation of (ln(Qt2/Qt1))/(t2 − t1) gives a constant k1. The plots of ln Qt vs t for film-coated tablets with various API strengths are shown in Figure 8 for 80 mg API

where Qt is the amount of drug released at time t, Q0 is the initial amount of drug in the solution, and k1 is the first order kinetics constant. Then, 6018


Article


Table 5. Multivariate ValidationPrediction Results Based on Independent Validation Sample Sets Using the Multivariate Calibration Models Developed in This Work (Dissolution Time (diss): 60 min) Samples

PCR Prediction

PLS1 prediction

API (mg)

coating (wt. gain)

diss60pred

diss60expt

pred. error (%)

diss60pred

diss60expt

pred. error (%)

80 80 90 90 100 100 110 110 120 120

7% 11% 3% 7% 3% 7% 3% 7% 3% 7%

81.68 50.40 80.78 41.08 70.55 44.87 65.77 42.07 60.68 48.07

73.51 36.09 98.25 37.16 66.30 46.12 69.85 40.27 74.92 51.10

10.0 28.4 21.6 9.5 6.0 2.8 6.2 4.3 23.5 6.3

81.68 50.40 80.78 41.08 70.55 44.87 65.77 42.07 60.68 48.07

72.83 37.12 98.03 35.55 65.15 44.88 67.29 38.15 73.53 49.90

10.8 26.3 21.4 13.5 7.7 0.02 2.3 9.3 21.2 3.8

identified at all of the ln Qt vs t plots studied: (i) an initial period of highly variable and dynamic ln Qt for the initial dissolution stage (normally within the initial 50 min); (ii) followed by transition period where ln Qt quickly approaching a stabilized value in the next 25−30 min; and (iii) followed by a period of pretty stable and constant ln Qt for longer dissolution times. Apparently the third period demonstrated the characteristics that would be expected from the Hixson and Crowell first order kinetics. The implications of the three distinct regimes revealed by the Hixson and Crowell first order kinetics model can be further discussed here. First, it revealed the complicated characteristics of the dissolution behavior of the theophylline polymeric filmcoated tablets. Second, to develop process understanding of such complicated behavior, it is necessary to apply appropriate yet different models to different regimes, rather than applying one single mechanistic model to fit the data across the entire dissolution testing period. Third, an integrated modeling approach may aid in better process understanding. For example, when applying the same Hixson and Crowell first order kinetics to analysis dissolution data for the film-coated theophylline tablets over the entire dissolution testing period, as evidenced by the results in Table S5 in the Supporting Information, the

Figure 6. Initial dissolution rate vs coating level for film-coated theophylline tablets with various tablet core API strengths.

film-coated tablets and in Supporting Information, Figures S9−S12 for 90 mg, 100 mg, 110 mg, and 120 mg API film-coated tablets, respectively. Three distinct regimes were qualitatively

Figure 7. Plots of (Mt/M∞)/t1/2 vs t for film coated theophylline tablets of 80 mg API core tablet. 6019


Article


Figure 8. Plots of ln Qt vs t for film-coated tablets with 80 mg API in its core tablet.

standard deviation of the k1 over the entire dissolution testing period is about 1−2 folds of the averaged k1. Obviously this large estimated standard deviation of the k1 is artificial. As such, no reliable conclusion could be drawn solely based on this artificial k1 value. Simply relying on this oversimplified estimation can directly lead to the false rejection of the first order kinetics mechanism. In summary, the mechanistic modeling of the film-coated theophylline tablet dissolution profiles indicated that for the initial and early release, the film-coated tablet dissolution process might be governed by Fick diffusion control; while for the late stage release, it might be described by the Hixson and Crowell first order kinetics. 3.5. Current Challenges and Future Outlook on the Development of an Integrated Modeling Approach for Pharmaceutical Product and Process Understanding. The primary goals of this study include the following: (i) to obtain mechanistic understanding the tablet film coating process via NIR monitoring, film-coated tablet dissolution, multivariate data analysis, and modeling, and first-principal modeling; and (ii) to explore potential linkage between the formulation variables/coating process variables and the quality attributes of the film-coated tablets. It is important to recognize that in the traditional pharmaceutical chemistry, manufacturing, and control (CMC) regulation paradigm, dissolution has been used as a quality control (QC)/quality assurance (QA) tool for product and/or lot release testing. From a process understanding and process control perspective, empirical model fitting does not necessarily generate knowledge that can help to improve the predictability and controllability of the tablet filmcoating process and product performances. Empirical dissolution models have certain inherent drawbacks,45−47 such as (i) there is not any kinetic fundament and it could only describe, but does not adequately characterize, the dissolution kinetic properties of the drug; (ii) there is not any single parameter related with the intrinsic dissolution rate of the drug; (iii) it is of limited use for establishing in vivo/in vitro correlations, from a clinical point of view; and (iv) it is casespecific, thus of limited value in terms of general application

and process understanding and product understanding. Therefore, the main focus of this work has been to develop a hybrid approach of integrating multivariate data analysis and mechanistic modeling for enhanced process and product understanding. The interpretation of the NIR data in pharmaceutical development and manufacturing applications has become increasingly complex. In general, individual examination of the univariate process variables is relevant but can be significantly complemented by multivariate data analysis (MVDA). From application submission for product approval to the FDA point of view, there are several key aspects that should be addressed for MVDA modeling: (1) representativeness of the data; (2) robustness of the model developed; and (3) the extent that a model can be extrapolated with confidence. Currently, there are several ASTM standards48,49 available for MVDA in pharmaceutical development and manufacturing applications. The European Medicines Agency (EMEA) released its draft Guideline50 on the use of near infrared spectroscopy (NIRS) by the pharmaceutical industry and the data requirements for new submissions and variations. It is highlighted that “the development and implementation of a NIRS procedure, with its interdependent stages, is iterative and ongoing, and is amendable to the application of lifecycle concepts, which allow good change control practice.” At the time of this manuscript revision, the FDA released its draft guidance on development and submission of NIR analytical procedures,51 which provides recommendations to applicants of new drug applications (NDAs), abbreviated new drug applications (ANDAs), and drug master file (DMF) holders regarding the development and submission of near-infrared (NIR) analytical procedures used during the manufacture and analysis of pharmaceuticals (including raw materials, in-process materials and intermediates, and finished products). It also provides recommendations regarding the concepts described in the International Conference Harmonization (ICH) guideline for industry Q2(R1)52 and FDA’s PAT Guidance.3 Intuitively, a data-driven PCA model is valid only within the parameter space which is defined through designed experiments and is explored 6020


Article


Figure 9. Concept of developing a hybrid modeling approach via integration of NIR spectroscopy and mechanistic modeling for achieving in-depth process understanding and ensuring predictive manufacturing process.

through a variance−covariance structure as specified in the PCA iteration. Given its data-driven empirical nature and its dependence on different factors associated with various measurements, it would be helpful to have a reliable, repeatable, and well-defined mechanistic model for correlating various measurement results. To develop a mechanistic model for measurement and process, understanding the details of measurement principles and process phenomena is essential. Under the PAT umbrella and QbD framework, the mechanistic-based model is primarily used to investigate the effects of process and formulation parameters on the CQAs of final dosage form. For example, population balance models (PBMs) were developed for understanding the pharmaceutical granulation process53 and predicting drug product manufacturing.54 One recent paper55 provided a comprehensive review of the various models for predicting drug release from bulk-degrading polymers. In summary, when developing a hybrid modeling approach for achieving in-depth process understanding and ensuring predictive manufacturing processes, it is helpful to utilize the strengths and overcome the limitations of each modeling strategy and to ensure seamless integration across various processes and phenomena for better process control.56 Furthermore, as illustrated in Figure 9, visualizing variability interplay is not only helpful to understand the multivariate nature of the pharmaceutical PAT applications, but also helpful to design, implement, and execute process control to achieve predictive film-coating and dissolution. In this work, the process understanding and predictive film-coating and dissolution was achieved through several logical steps such as the following: (1) variability from both the formulation design and processing is built-in the tablets; (2) the variability can be captured by NIR scan either partially or completely, depending on the thickness of the tablet if transmission mode is used, or the penetration depth of NIR scan if reflection mode is used; (3) PCA lumps the variability together and partitions it mainly between PC1 and PC2; and (4) physical interpretation of multivariate statistical modeling can be linked to process

domain knowledge such as formulation design and process design, and further verified by mechanistic modeling. Through this iterative modeling process, critical process/ product knowledge can be extracted based on the integration of NIR spectroscopy, multivariate data analysis, and mechanistic modeling. In principal, the concept of an integrated modeling approach as outlined in Figure 9 can be adopted for other dosage form manufacturing processes.

4. CONCLUSIONS This study demonstrated the feasibility of integrating NIR spectroscopic data and first-principal modeling for predicting polymeric film coating and dissolution of theophylline tablets. The NIR spectra were initially evaluated by PCA. A linear relationship between PC1 and coating level was established for each tablet group with the same API strength. PLS and PCR models predicted the dissolution well for the first 60 min. For t > 60 min, PLS models could still predict the dissolution but prediction error increased significantly. A linear relationship between the tablet initial dissolution rate and tablet coating level was found with a slope of S. Based on a Fickian diffusion model, S was related to the diffusion coefficient (D) of the drug across the polymeric film. The D values estimated from S agreed well with the literature data. The mechanistic modeling of the film-coated theophylline tablet dissolution profiles indicated that for the initial and early release, the filmcoated tablet dissolution process might be governed by Fick diffusion control; while for the late stage release, it might be governed by the Hixson and Crowell first order kinetics. It was demonstrated that the integration of NIR/chemometrics and mechanistic modeling approach can provide an effective approach toward pharmaceutical product and process understanding, and process control to achieve desired product quality. Finally, some of the current challenges and future outlook on developing a hybrid modeling approach for process understanding and predictive manufacturing via PAT implementation was briefly discussed from both a technical and a regulatory science perspective. 6021


Article


■

(11) Al-Kandri, N. M.; Jolliffe, I. T. Variable selection and interpretation in correlation principal components. Environmetrics 2005, 16, 659. (12) Johnson, R. A.; Wichern, D. Applied Multivariate Statistical Analysis; Pearson Education: 2001. (13) Wu, W.; Walczak, B.; Massart, D. L.; Prebble, K. A.; Last, I. R. Spectral transformation and wavelength selection in near-infrared spectral classification. Anal. Chim. Acta 1995, 315, 243. (14) Rutan, S. C.; Noord, O. E.; de Andréa, R. R. Characterization of the sources of variation affecting near-infrared spectroscopy using chemometric methods. Anal. Chem. 1998, 70, 3198. (15) Clarke, F. Extracting process-related information from pharmaceutical dosage forms using near infrared microscopy. Vib. Spectrosc. 2004, 34, 25. (16) Luthria, D. L.; Mukhopadhyan, S.; Lin, L.-Z.; Harnly, J. M. A comparison of analytical and data preprocessing methods for spectral fingerprinting. Appl. Spectrosc. 2011, 65, 250. (17) Wu, H.; Khan, M. A. THz Spectroscopy: An emerging technology for pharmaceutical development and pharmaceutical process analytical technology (PAT) applications. J. Mol. Struct. 2012, 1020, 112. (18) FDA. Guidance for Industry. SUPAC-MR: Modified Release Solid Oral Dosage Forms. Scale-Up and Postapproval Changes: Chemistry, Manufacturing, and Controls; In Vitro Dissolution Testing and In Vivo Bioequivalence Documentation; FDA: Silver Spring, MD, 1997; http://www.fda.gov/downloads/Drugs/Guidances/ UCM070640.pdf (accessed 06/02/2014). (19) FDA. Guidance for Industry. Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlations; FDA: Silver Spring, MD, 1997; http://www.fda.gov/ downloads/Drugs/GuidanceComplianceRegulatoryInformation/ Guidances/ucm070239.pdf (accessed 06/02/2014). (20) Kirsch, J. D.; Drennen, J. K. Near-infrared spectroscopic monitoring of the film coating process. Pharm. Res. 1996, 13, 234. (21) Andersson, M.; Josefson, M.; Langkilde, F. W.; Wahlund, K.-G. Monitoring of a film coating process for tablets using near infrared reflectance spectrometry. J. Pharm. Biomed. Anal. 1999, 20, 27. (22) Gendre, C.; Genty, M.; Boiret, M.; Julien, M.; Meunier, L.; Baron, M.; Chaminade, P.; Péan, J. M. Development of a Process Analytical Technology (PAT) for in-line monitoring of film thickness and mass of coating materials during a pan coating operation. Eur. J. Pharm. Sci. 2011, 43, 244. (23) Möltgen, C.-V.; Puchert, T.; Menezes, J. C.; Lochmann, D.; Reich, G. A novel in-line NIR spectroscopy application for the monitoring of tablet film coating in an industrial scale process. Talanta 2012, 92, 26. (24) Markl, D.; Hannesschläger, G.; Sacher, S.; Leitner, M.; Khinast, J. G. Optical coherence tomography as novel tool for in-line monitoring of a pharmaceutical film-coating process. Eur. J. Pharm. Sci. 2014, 55, 58. (25) Knop, K.; Kleinebudde, P. PAT-tools for process control in pharmaceutical film coating applications. Int. J. Pharm. 2013, 457, 527. (26) Gendre, C.; Boiret, M.; Genty, M.; Chaminade, P.; Pean, J. M. Real-time predictions of drug release and end point detection of a coating operation by in-line near infrared measurements. Int. J. Pharm. 2011, 421, 237. (27) Neves, A. C. de O.; Soares, G. M.; Morais, S.C. de; Costa, F.S.L. da.; Porto, D. L.; Lima, K.M.G. de. Dissolution testing of isoniazid, rifampicin, pyrazinamide and ethambutol tablets using near-infrared spectroscopy (NIRS) and multivariate calibration. J. Pharm. Biomed. Anal. 2012, 57, 115. (28) Tatavarti, A. S.; Fahmy, R.; Wu, H.; Hussain, A. S. Marnane, W.; Bensley, D.; Hollenbeck, G.; Hoag, S. Assessment of NIR spectroscopy for nondestructive analysis of physical and chemical attributes of sulfamethazine bolus dosage forms. AAPS PharmSciTech 2005, 6, Article 15, E91. (29) Hattori, Y.; Otsuka, M. NIR spectroscopic study of the dissolution process in pharmaceutical tablets. Vib. Spectrosc. 2011, 57, 275.

ASSOCIATED CONTENT

S Supporting Information *

The dissolution profiles for film coated theophylline tablets for core tablet API strengths of 90 mg, 100 mg, 110 mg, and 120 mg were shown in Figures S1−S4, respectively. The plots of Mt/M∞/t1/2 vs. t for core tablets of other API strengths (90 mg, 100 mg, 110 mg, and 120 mg) were shown in Figures S5−S8, respectively. The plots of ln Qt vs. t for 90 mg, 100 mg, 110 mg, and 120 mg API film-coated tablets were shown in Figures S9−S12, respectively. According to Eq. (3.1), the dissolution data for the film-coated theophylline tablets were analyzed and summarized in Table S1. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/ie504680m.

■

AUTHOR INFORMATION

Corresponding Author

*Email: [email protected]. Notes

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

■

ACKNOWLEDGMENTS This work was partially supported by FDA CDER Regulatory Science and Review (RSR) Grant 04-16. Duquesne Center of Pharmaceutical Technology (Pittsburgh, PA) is graciously acknowledged for part of the work.

■

REFERENCES

(1) Ciurczak, E. W.; Drennen, J. K., III. Pharmaceutical and Medical Applications of Near-Infrared Spectroscopy; Marcel Dekker: New York, 2002. (2) Jamrógiewicz, M. Application of the near-infrared spectroscopy in the pharmaceutical technology. J. Pharm. Biomed. Anal. 2012, 66, 1. (3) FDA. Guidance for Industry: PATA Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance; FDA: Silver Spring, MD, 2004; http://www.fda.gov/downloads/ Drugs/Guidances/ucm070305.pdf (accessed 06/02/2014). (4) FDA/ICH, Q8(R2) Pharmaceutical Development Revision 2; FDA: Silver Spring, MD, 2009; http://www.fda.gov/downloads/ Drugs/Guidances/ucm073507.pdf (accessed 06/02/2014). (5) Candolfi, A.; Maesschalck, R. De; Jouan-Rimbaud, D.; Hailey, P. A.; Massart, D. L. The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra. J. Pharm. Biomed. Anal. 1999, 21, 115. (6) Rinnan, Å.; Berg, F. V. d.; Engelsen, S. B. Review of the most common pre-processing techniques for near-infrared spectra. Trends Anal. Chem. 2009, 28, 1201. (7) Martens, H.; Bruun, S. W.; Adt, I.; Sockalingum, G. D.; Kohler, A. Pre-processing in biochemometrics: Correction for path-length and temperature effects of water in FTIR bio-spectroscopy by EMSC. J. Chemom. 2006, 20, 402. (8) W. Windig, W.; Guilment, J. Interactive Self-Modeling Mixture Analysis. Anal. Chem. 1991, 63, 1425. (9) Blanco, M.; Coello, J.; Iturriaga, H.; Maspoch, S.; Pezuela, C. De La. Effect of data preprocessing methods in near-infrared diffuse reflectance spectroscopy for the determination of the active compound in a pharmaceutical preparation. Appl. Spectrosc. 1997, 51, 240. (10) Rekhi, G. S.; Nellore, R. V.; Hussain, A. S.; Tillman, L. G.; Malinowski, H. J.; Augsburger, L. L. Identification of critical formulation and processing variables for metoprolol tartrate extended-release (ER) matrix tablets. J. Controlled Release 1999, 59, 327. 6022


Article

Industrial & Engineering Chemistry Research (30) Ho, L.; Müller, R.; Gordon, K. C.; Kleinebudde, P.; Pepper, M.; Rades, T.; Shen, Y.; Taday, P. F.; Zeitler, J. A. Application of terahertz pulsed imaging to sustained-release tablet film coating quality assessment and dissolution performance. J. Controlled Release 2008, 127, 79. (31) Müller, J.; Brock, D.; Knop, K.; Zeitler, J. A.; Kleinebudde, P. Prediction of dissolution time and coating thickness of sustained release formulations using Raman spectroscopy and terahertz pulsed imaging. Eur. J. Pharm. Biopharm. 2012, 80, 690. (32) Osborne, B. G.; Fearn, T.; Hindle, P. H. Practical NIR Spectroscopy with Applications in Food and Beverage Analysis, 2nd ed.; Longman Group: 1993. (33) Wold, S.; Esbensen, K.; Geladi, P. Principal Component Analysis. Chemom. Intell. Lab. Syst. 1987, 2, 37. (34) US National Library of Medicine, ChemIDplus Lite. Physical Properties of Theophylline[USP]RN: 58-55-9; http://chem.sis.nlm. nih.gov/chemidplus/jsp/common/PhysicalProperties.jsp?calledFrom= lite (accessed 04/16/2014). (35) Colorcon, Product literature on Surelease Ethylcellulose Dispersion Type B NF; http://www.colorcon.com/productsformulation/all-products/film-coatings/sustained-release/surelease (accessed 04/16/2014). (36) Bird, R. B.; Stewart, W. E.; Lightfoot, E. N. Transport Phenomena; John Wiley & Sons: New York, 1960. (37) Clark. J. The Mathematics of Diffusion, 2nd ed.; Clarendon Press: Oxford, 1975; p 48. (38) Peppas, N. A. Analysis of Fickian and Non-Fickian drug release from polymers. Pharm. Acta Helv. 1985, 60, 110. (39) Siepmann, J.; Siepmann, F. Mathematical modeling of drug delivery. Int. J. Pharm. 2008, 364, 328. (40) Perez-Ramos, J.; Findlay, W.; Peck, G.; Morris, K. Quantitative analysis of film coating in a pan coater based on in-line sensor measurement. AAPS PharmSciTech 2005, 6, E127. (41) Tabasi, S. H.; Fahmy, R.; Bensley, D.; O’Brien, C.; Hoag, S. W. Quality by design: Part II. Application of NIR spectroscopy to monitor the coating process for a pharmaceutical sustained release product. J. Pharm. Sci. 2008, 97, 4052. (42) Grassi, M.; Colombo, I.; Lapasin, R. Experimental determination of the theophylline diffusion coefficient in swollen sodium-alginate membranes. J. Controlled Release 2001, 76, 93. (43) Varshosaz, J.; Koopaie, N. Cross-linked poly(vinyl alcohol) hydrogel: Study of swelling and drug release behavior. Iran. Polym. J. 2002, 11, 123. (44) Siepmann, F.; Hoffmann, A.; Leclercq, B.; Carlin, B.; Siepmann, J. How to adjust desired drug release patterns from ethylcellulosecoated dosage forms. J. Controlled Release 2007, 119, 182. (45) Costa, P.; Lobo, J. M. S. Modeling and comparison of dissolution profiles. Eur. J. Pharm. Sci. 2001, 13, 123−133. (46) Pedersen, P. V.; Myrick, J. W. Versatile kinetic approach to analysis of dissolution data. J. Pharm. Sci. 1978, 67, 1450−1455. (47) Christensen, F. N.; Hansen, F. Y.; Bechgaard, H. Physical interpretation of parameters in the Rosin-Rammler-Sperling-Weibull distribution for drug release from controlled release dosage forms. J. Pharm. Pharmacol. 1980, 32, 580−582. (48) ASTM2891-13. Standard Guide for Multivariate Data Analysis in Pharmaceutical Development and Manufacturing Applications; ASTM, 2013; DOI: 10.1520/E2891. (49) ASTM1655-05. Standard Practices for Infrared Multivariate Quantitative Analysis; ASTM, 2012; DOI: 10.1520/E1655-05R12. (50) European Medicines Agency. Guideline on the use of Near Infrared Spectroscopy (NIRS) by the pharmaceutical industry and the data requirements for new submissions and variations (Draft). EMEA/ CHMP/CVMP/QWP/17760/2009 Rev2, 2012; http://www.ema. europa.eu/docs/en_GB/document_library/Scientific_guideline/ 2012/02/WC500122769.pdf (accessed 06/02/2014). (51) FDA. Guidance for Industry: Development and Submission of Near Infrared Analytical Procedures (Draft), 2015; http://www.fda.gov/ downloads/Drugs/GuidanceComplianceRegulatoryInformation/ Guidances/UCM440247.pdf (accessed 04/04/2015).

(52) ICH Guideline Q2(R1): Validation of Analytical Procedures: Text and Methodology;http://www.ich.org/fileadmin/Public_Web_ Site/ICH_Products/Guidelines/Quality/Q2_R1/Step4/Q2_R1__ Guideline.pdf (accessed 04/04/2015). (53) Chaudhury, A.; Wu, H.; Khan, M. A.; Ramachandran, R. A mechanistic population balance model for granulation processes: Effect of process and formulation parameters. Chem. Eng. Sci. 2014, 107, 76. (54) Chaudhury, A.; Barrasso, D.; Pandey, P.; Wu, H.; Ramachandran, R. Population Balance Model Development, Validation, and Prediction of CQAs of a High-Shear Wet Granulation Process: Towards QbD in Drug product Pharmaceutical Manufacturing. J. Pharm. Innov. 2014. DOI 10.1007/s12247-014-9172-7. (55) Lao, L. L.; Peppas, N. A.; Boey, F. Y. C.; Venkatraman, S. S. Modeling of drug release from bulk-degrading polymers. Int. J. Pharm. 2011, 418, 28. (56) Wu, H.; Khan, M. A.; Hussain, A. S. Process control perspective for process analytical technology: Integration of chemical engineering practice into semiconductor and pharmaceutical industries. Chem. Eng. Commun. 2007, 194, 760.

6023


Integration of Near-Infrared Spectroscopy and Mechanistic Modeling

Recommend Documents