Article pubs.acs.org/IECR
Design Space Estimation of the Roller Compaction Process Nabil Souihi,† Mats Josefson,‡ Pirjo Tajarobi,‡ Bindhu Gururajan,‡ and Johan Trygg*,† †
Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, SE-90187 Umeå, Sweden Pharmaceutical Development, AstraZeneca R&D Mölndal, SE-431 83, Sweden
‡
S Supporting Information *
ABSTRACT: Roller compaction (RC) is a continuous process for solid dosage form manufacturing within the pharmaceutical industry achieving similar goals as wet granulation while avoiding liquid exposure. From a quality by design perspective, the aim of the present study was to demonstrate the applicability of statistical design of experiments (DoE) and multivariate modeling principles to identify the Design Space of a roller compaction process using a predictive risk-based approach. For this purpose, a reduced central composite face-centered (CCF) design was used to evaluate the influence of roll compaction process variables (roll force, roll speed, gap width, and screen size) on the different intermediate and final products (ribbons, granules, and tablets) obtained after roll compaction, milling, and tableting. After developing a regression model for each response, optimal settings were found which comply with the response criteria. Finally, a predictive risk based approach using Monte Carlo simulation of the factor variability and its influence on the responses was applied which fulfill the criteria for the responses in a space where there is a low risk for failure. Responses were as follows: granule throughput, ribbon porosity, granules particle size, and tablets tensile strength. The multivariate method orthogonal partial least-squares (OPLS) was used to model product dependencies between process steps e.g. granule properties with tablet properties. Those results confirmed that the tensile strength reduction, known to affect plastic materials when roll compacted, was not prominent when using brittle materials. While direct compression qualities are frequently used for roll compacted drug products because of their excellent flowability and good compaction properties, this study confirmed earlier findings that granules from these qualities were more poor flowing than the corresponding powder blend.
1. INTRODUCTION Roll compaction (RC) is a frequently used agglomeration technique in the pharmaceutical field and other industries.1 Although the roll compaction process has been in use for many years, it has recently grown considerably in pharmaceutical dosage forms.2 Roll compaction is intended to increase bulk density and uniformity of particulate formulations by preventing the segregation of the constituents of the powder. It offers advantages over wet granulation regarding moisture, solvent, or heat sensitivity of the drug substance since neither a liquid binder nor a drying stage is required.3 Moreover, roll compaction is an increasingly more attractive option for the manufacture of oral dosage forms for many reasons: facilitates continuous manufacturing, reduced floor space, high throughput at a minimum of operator presence, and scale up efforts are minimized.4 The use of multivariate approaches, such as design of experiments, response surface methodology, sensitivity analysis, and multivariate data analysis is needed to understand the multifactorial relationships between formulation, process, and quality attributes.5−8 Pharmaceutical quality by design (QbD) is a systematic, scientific, risk based, holistic, and proactive approach to pharmaceutical development that starts with predefined objectives and emphasizes product and process understanding. QbD identifies characteristics that are critical to quality from the perspective of patients translates them into the attributes that the drug product should possess and establish how the critical process parameters can be varied to consistently produce a drug product with the desired characteristics.9 The International Conference on Harmonization (ICH) Q8, Q9, and Q10 © 2013 American Chemical Society
guidelines describe principles and tools for the implementation and improvement of QbD.10−12 The goal of this study was to demonstrate how statistical design of experiments (DoE) and multivariate modeling principles can efficiently help in optimizing four critical process variables in roll compaction and established how the critical process variables can be varied to consistently to produce a product within the design space.
2. MATERIALS AND METHODS 2.1. Formulation. The formulation used was composed of 15% paracetamol (intermediate drug load), 55.3% mannitol (Parteck M200, Merck KGaA, Darmstadt, Germany), 23.7% microcrystalline cellulose (Avicel PH 102, FMC BioPolymer, Philadelphia, PA, USA), 4% croscarmellose (NaCMC; Ac-Di-Sol, FMC BioPolymer, Newark, DE, USA), and 2% sodium stearyl fumarate (NaSF, Pruv, JRS Pharma GmbH & Co KG, Rosenberg, Germany). A 20-kg scale batch was manufactured and processed as 23 sub batches for execution of the DoE to investigate the effects of the four roller compaction process parameters. See the Supporting Information for more information. 2.2. Design of Experiments. Experiments based on a reduced central composite face-centered (CCF) design was used Special Issue: John MacGregor Festschrift Received: Revised: Accepted: Published: 12408
December March 24, March 28, March 28,
23, 2012 2013 2013 2013
dx.doi.org/10.1021/ie303580y | Ind. Eng. Chem. Res. 2013, 52, 12408−12419
Industrial & Engineering Chemistry Research
Article
granules was measured with a flow tester (Erweka GmbH, Heusenstamm, Germany). See the Supporting Information for more information on ring shear tester and flow tester measurements parameters. The compressibility and permeability of the powder blend and granules were evaluated with a FT4 Powder rheometer (Freeman Technology, Welland, United Kingdom). Both the pressure drop across the powder bed (mbar) at the end normal stress of 15 kPa and the slope of the curve describing the relation between the pressure drop and normal stress (at 1, 2, 4, 6, 8, 10,12 and 15 kPa) were used for the statistical analysis of permeability. 2.5. Tabletting. The powder blend for direct compression (DC) and RC granules at different process settings (Table 1) were subsequently lubricated with 1% (w/w) NaSF and mixed for 2 min with the Turbula mixer, type T2C (Willi A. Bachofen AG mashinen fabrik, Basel, Switzerland). Then, all batches were compressed with the Korsch XL100 (Korsch GmbH, Berlin, Germany) at 5 different press forces (4, 8, 12, 16, and 20 kN) using a 8 mm round flat-faced punch aiming for 200 mg tablets. In addition, for each batch, 50 tablets were compressed at a press force resulting in tablets with a tensile strength approximating 2 MPa (TS2 tablets). The tablet weight variation (RSDm) in each batch was expressed as the relative standard deviation percentage of the weight of ten of the TS2 tablets. The content uniformity (CU) for each batch was estimated using transmission Raman spectroscopy.13 The tablet tensile strength (TS) was determined with the Multicheck Turbo III (Erweka GmbH, Heusenstamm, Germany). The disintegration time (Erweka ZT32) was measured as the average of six TS2 tablets. See the Supporting Information for additional details. The characterized properties and their abbreviations are listed in Table 2, while the actual results are shown in the Supporting Information, Table S-2. 2.6. Statistical Analysis. A reduced CCF design consisting of 23 experiments were used to model the influence of roll force
to model the influence of roll compaction process variables on the different intermediate and final products obtained after roll compaction, milling, and tableting. A reduced CCF design is a response surface methodology design which contains an imbedded reduced factorial design (12 instead of 16 runs in the case of four factors) with center points and is augmented with a group of axial points that allows estimation of curvature and support quadratic models. Twenty-three runs were performed for this study. See Table 1. Table 1. Target and Actual Values for Process Parameters of the Roller Compaction and Screen Size DOE Using a Reduced CCF Design on Roll Force (RF), Gap Width (GW), Roll Speed (RS), and Screen Size (SS) roll force [kN/cm]
gap width [mm]
batch
target
actual
target
actual
roll speed [mm/s]
screen size [mm]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
2 2 6 2 6 6 2 6 6 2 2 6 4 4 2 6 4 4 4 4 4 4 4
2 1.73 5.97 1.73 6 6.1 1.8 6 6.3 2.27 2.33 5.5 4 3.8 1.8 6.23 3.37 3.73 3.97 3.87 4 3.73 4
1 1 1 2 2 2 1 1 1 2 2 2 1.5 1.5 1.5 1.5 1 2.0 1.5 1.5 1.5 1.5 1.5
0.95 1 1 1.96 2 2 1 1 1 2 2 2 1.45 1.5 1.5 1.5 1 2.05 1.5 1.5 1.5 1.5 1.5
25 75 25 75 25 75 25 25 75 25 75 75 25 75 50 50 50 50 50 50 50 50 50
1 1 1 1 1 1 1.5 1.5 1.5 1.5 1.5 1.5 1.25 1.25 1.25 1.25 1.25 1.25 1 1.5 1.25 1.25 1.25
Table 2. Abbreviations and Units for Properties Measured of Ribbons, Granules, and Tablets parameter ribbon
2.3. Roll Compaction and Ribbon Characterization. The blend was roll compacted with Hosokawa Bepex roll compactor (Pharmapaktor C250, Hosokawa Bepex GmbH, Germany) according to the reduced CCF design varying four process parameters (Table 1): roll force, roll speed, gap width, and screen size. The controlled and constant parameters of the Pharmapaktor C250 in this study are shown in the Supporting Information (Table S1). Ribbon samples were analyzed for porosity and thickness. See the Supporting Information for more information on how these were measured and calculated. 2.4. Properties of the Powder Mixture and Granules. The distributions of the particles sizes of the powder blend and granules were determined by laser diffraction (Mastersizer 2000, Malvern Instruments Ltd., Malvern, United Kingdom). Bulk densities of powder mixture and granules were measured in duplicated according to the European Pharmacopoeia (2011). A ring shear tester (RST-XS, Dietmar Shultze) was used to measure the flow properties of the powder blend and granules. Moreover, the mass flow of the powder blend and the RC
granules
tablets
comments
RD Th P Prod D10 D50 D90 SD BD Perm PermS Comp ffc F CS CP TS TSn RSDm CU
12409
envelope density [g/cm3] thickness [mm] porosity (relative ribbon density) [%] milled granule throughput [g/s] particle size distribution d(0.1) [μm] particle size distribution d(0.5) [μm] particle size distribution d(0.9) [μm] size distribution = D90 − D10/D50 bulk density [g/mL] pressure drop across powder bed at 15 kPa [mbar] slope of the normal stress [kPa] vs pressure drop [mbar] curve end press compressibility [%] flow function mass flow [g/s] crushing strength [N] compaction pressure [MPa] tensile strength at a compaction force of 20 kN [MPa] tensile strength of TS2 tablets normalized with CP multiplied by 1000 tablet weight variation [%RSD] content uniformity estimated by Raman spectroscopy [%RSD]
dx.doi.org/10.1021/ie303580y | Ind. Eng. Chem. Res. 2013, 52, 12408−12419
Industrial & Engineering Chemistry Research
Article
⎡ yi − T ⎢ ∑ wi T − L D = log10⎢ M ⎢ ⎣
(RF), roll speed (RS), gap width (GW), and screen size (SS), see Table 1. The results were evaluated with the program MODDE version 9.1.0.0 (Umetrics AB, Umeå, Sweden). Furthermore, the orthogonal partial least-squares (OPLS) method was used to map the granule properties with tablet weight variation. The OPLS14 method has showed improved model interpretation compared to PLS and provides two sets of model components. One which is correlated to Y (predictive) and the other uncorrelated to Y (orthogonal). Their corresponding scores and loadings components of each set can be individually interpreted, and their relative size is calculated as R2X values that describe the fraction of the total variation in X. The relative variation of Y modeled by the OPLS model is given by the R2Y value and for prediction, its cross-validated analogue (Q2). PCA and OPLS analysis were performed with SIMCA software version 13.0.0.0 (Umetrics AB, Umeå, Sweden). Content uniformity calculations were performed in MATLAB version 7.11 (R2010b) (The MathWorks, Natick, Ma. USA) using the PLS_Toolbox version 6.2.1 (eigenvector Research Inc., Wenatchee, WA, USA). See the Supporting Information for additional details. 2.7. Establishment of the Design Space. Design Space (DS) concept is defined as ‘‘the multidimensional combination and interaction of input variables (e.g., materials attributes) and process parameters that have been demonstrated to provide assurance of quality’’ (ICH, Q9).12 The quality of the intermediate and final product obtained by roller compaction was determined by four important response variables (quality attributes): granule throughput, ribbon porosity, mean granules particle size, and tablet tensile strength. Those quality attributes are influenced by roller compaction process parameters such as roll force, gap width, roll speed, and screen size. Design of experiments is useful in translating how the combination of critical process parameters affects the product critical quality attributes. Finally, DoE helps in defining the combination of process parameters that will keep the product performance within the specifications with a quantified guarantee for the future use of the process. Predictive probability is essential since it allows quantifying the risks that specifications will (or will not) be met in the future runs of the process. Specifications express the minimal satisfying quality that the experimenters want to obtain.15 In this perspective, Monte Carlo simulations were utilized to estimate the risk of failure. Both, the uncertainty in the process factors as well as model uncertainty (residual standard variation (RSD), condition number of the design) were taken into consideration. The Monte Carlo simulations used a normal distribution to sample the random process factors settings based on an optimum value with set low and high limits. This is followed by 100 000 predictions of the responses. The results display a distribution of the predictions providing an estimated risk of being in or out of a specification. The results of the optimization were evaluated based on two parameters: DPMO and Log(D). Defects per million opportunities (DPMO) shows how many response predictions are outside the response specifications based on one million simulations. DMPO indicates the sensitivity of the responses to the external perturbations applied on the factor settings (in our case 5%). The ideal outcome of DPMO is zero. The “overall distance to target”, D, was another parameter used to evaluate the optimization results and was calculated according to eq 1
2
⎤
( ) ⎥⎥ ⎥ ⎦
(1)
where yi is the response, T is the desired target, L is the worst acceptable response value(s), wi is the weight for each response, and M is the number of responses. Log D value below zero means that we are between the target and the maximum for each response. The optimization of multiple response surfaces used the overlapping mean responses approach to find the so-called sweet spot. The sweet spot reflects a volume in subspace, situated in the total multidimensional experimental design, in which the combination and interactions of process inputs reliably deliver a product with the desired performance according to the profile of critical quality attributes defined for that product. The sweet spot plot represents one approach toward finding a suitable operating condition. However, there are a number of limitations with the sweet spot approach: 1) It can be interpreted too optimistically since it is only based on point estimates and uncertainty in Y-predicted is not taken into account. 2) It does not indicate how sensitive the sweet spot is to factor disturbances. 3) No risk estimation.16 These limitations represent a major drawback since ICH Q8 is clearly asking for a level of assurance guaranteeing the product specifications will be met.
3. RESULTS AND DISCUSSION All model results including constants, regression coefficients, p-values, Q2, and R2 are shown in Table 3. 3.1. Ribbon Properties. The actual ribbon thickness ranged from 2.09 to 3.46 mm for the RC runs (Table S2). Its regression model had a high R2 (0.996) and Q2 (0.994). As expected, an increase in gap width correlated to increase in ribbon thickness and a higher roll force produced thicker ribbons. The final regression model is summarized in Table 3. Ribbon porosity is used as an indicator of roller compacted product quality. Statistical analysis indicated that roll force had a significant negative effect on ribbon porosity (p-value < 0.0001, Figure S1). A quadratic relationship was noticed between the roll force and ribbon porosity (p-value = 0.0132). Within the gap width range recorded (1−2 mm), gap width did not have significant effect on ribbon porosity (p-value = 0.0955). Roll speed and its interaction with remaining factors were found to be not significant and not included in final model. Several researchers17−19 have shown that normal stress and ribbon porosity decreased near the ribbon edges due to side seal friction, resulting in variation of ribbon porosity across ribbon width. The variation of normal stress across ribbon width leads to variation of ribbon porosity. The presence of roll force quadratic term in our model could be accounting for this variation. The actual ribbon porosity values ranged from 25 to 44% for the RC runs (Table S2). The recommended range should be within (20−40%) for further tableting.20 The regression model of ribbon porosity had a high R2 (0.889) and Q2 (0.846). The final regression model is summarized in Table 3. 3.2. Granule Properties. In our experiments we obtained a granule throughput in the range of 1.46−14.68 g/s, representing an almost 10-fold increase in throughput. Granule throughput is a function of many factors such as diameter and width of the rolls, gap width, roll speed, and density. As expected, roll speed had a 12410
dx.doi.org/10.1021/ie303580y | Ind. Eng. Chem. Res. 2013, 52, 12408−12419
/ / 0.996 0.994 / / 0.889 0.846 −0.10 (0.01) / 0.912 0.856 / −0.04 (0.0025) 0.963 0.891 / −1.61 (0.0008) 0.948 0.885 / / 0.928 0.831 / / 0.742 0.564 / / 0.836 0.769
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2.8 33.2 0.82 2.27 1.11 0.54 0.09 3.74
0.099 (
