Determining key parameters of continuous wet granulation for tablet quality and productivity: A case in ethenzamide

Journal Pre-proofs Determining key parameters of continuous wet granulation for tablet quality and productivity: A case in ethenzamide Kensaku Matsunami, Takuya Nagato, Koji Hasegawa, Hirokazu Sugiyama PII: DOI: Reference:

S0378-5173(20)30144-7 https://doi.org/10.1016/j.ijpharm.2020.119160 IJP 119160

To appear in:

International Journal of Pharmaceutics

Received Date: Revised Date: Accepted Date:

10 October 2019 29 January 2020 16 February 2020

Please cite this article as: K. Matsunami, T. Nagato, K. Hasegawa, H. Sugiyama, Determining key parameters of continuous wet granulation for tablet quality and productivity: A case in ethenzamide, International Journal of Pharmaceutics (2020), doi: https://doi.org/10.1016/j.ijpharm.2020.119160

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Determining key parameters of continuous wet granulation for tablet quality and productivity: a case in ethenzamide

Kensaku Matsunami1, Takuya Nagato2, Koji Hasegawa2, Hirokazu Sugiyama1,* 1Department

of Chemical System Engineering, The University of Tokyo,

7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan 2Technical

Division, Powrex Corporation,

5-5-5, Kitagawara, Itami, Hyogo, 664-0837, Japan

*Corresponding

author

[email protected] Tel & Fax: +81-3-5841-7227

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ABSTRACT This paper aims to determine key parameters that affect tablet quality and productivity in continuous tablet manufacturing. Experiments were performed based on design of experiments using a continuous high-shear granulator and ethenzamide as the active pharmaceutical ingredient. To guide a systematic and comprehensive parameter analysis, a parameter framework was defined that comprised five input parameters on raw material properties and process parameters, 11 intermediate parameters on granule properties, and 11 output parameters on tablet quality and productivity. The interrelationships were analyzed statistically and were described as matrix functions. The liquid/solid ratio was the key parameter that affected circularity, density, and flowability as the granule properties, and disintegration and dissolution as the tablet quality. The maximum acceptable manufacturing rate that governs productivity was also affected by the liquid/solid ratio. Circularity was found to affect disintegration and dissolution. This result was specific to the setup of the study, but suggested development opportunities for a new process analytical technology system/quality-by-design application based on circularity. In addition, practical findings were obtained as follows: (1) high-speed manufacturing favored a lower liquid/solid ratio, and (2) high circularity slowed down disintegration/dissolution. This obtained knowledge will enhance the applicability of continuous technology in an actual manufacturing environment.

Keywords: Continuous manufacturing High-shear granulation Design of experiments Liquid/solid ratio Circularity Process design and operation

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1. Introduction From 2015 to date, the US Food and Drug Administration (FDA) approved the application of continuous manufacturing for five solid drug products (Orkambi and Symdeko from Vertex, Prezista from Janssen, Verzenio from Eli Lily, and Daurismo from Pfizer) (Mullin, 2019). More solid dosage products are expected to be approved in the near future worldwide. Continuous manufacturing has become a realistic alternative of the process, but challenges remain in various aspects. Ierapetritou et al. (2016) highlighted the importance of characterizing critical process parameters (CPPs) or critical material attributes (CMAs) in continuous manufacturing. White papers published at the International Symposia on Continuous Manufacturing of Pharmaceuticals, which have been held biennially since 2014, describe control strategy, material traceability, and benefit assessment as the challenges in this field (Allison et al., 2015; Nasr et al., 2017). There have been numerous contributions on these issues in recent years. Regarding the monitoring and control systems associated with process analytical technology (PAT), Bhaskar et al. (2017) implemented an advanced model-predictive control system for the compression unit; Su et al. (2017) proposed a framework for process control design and risk analysis; Nicolaï et al. (2018) focused on the liquid/solid ratio as a CPP, and developed a control system for continuous twin-screw wet granulation using near-infrared spectroscopy. In several studies, the residence time distribution (RTD) is used to analyze the heterogeneity/back-mixing of the powder materials in the continuous process. Mangal and Kleinebudde (2017) and Escotet-Espinoza et al. (2019), respectively, experimentally analyzed the RTD of materials in dry granulation and blending. Martinetz et al. (2018) developed RTD models for the entire continuous line of dry granulation and tableting using transfer functions. Toson et al. (2018) used the discrete element method (DEM) to model the RTD of a powder mixing unit and identified the ideal mixing conditions. There are also studies that compare the continuous technology with the conventional batch technology from an economic perspective (Schaber et al., 2011; Matsunami et al., 2018), and from

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a quality perspective (Lee et al., 2013; Beer et al., 2014; Järvinen et al., 2015; Matsunami et al., 2019). In the actual design/operation of continuous manufacturing processes, the parameters of raw materials and processes need to be defined considering the potential consequences in various aspects. Quality by design (QbD) is an important approach in this regard, where the design space of CMAs and CPPs is determined based on critical quality attributes (CQAs) (FDA, 2009; Yu et al., 2014). Design of experiments (DoE) is the standard technique in QbD; a demonstration of the continuous technology for wet granulation can be found in Meng et al. (2016, 2017, 2019) and for dry granulation in Souihi et al. (2013). Physical modeling has also been applied; for conventional batch technology, more specifically for granulation and coating, a population balance model (Cameron et al., 2005), DEM (Boehling et al., 2016), and integrated multiscale models (Barrasso et al., 2014; Tamrakar and Ramachandran, 2019) have been applied. The continuous technology has been addressed by physical modeling, e.g., dry granulation (Hsu et al., 2010) and wet granulation (Van Hauwermeiren et al., 2019). Beyond investigating specific unit operations, holistic studies have been presented on the entire manufacturing process. Flowsheet modeling was used to analyze the effects of feeding rate variability on the active pharmaceutical ingredient (API) content in tablets (García-Muñoz et al., 2018) and the effects of five process parameters, i.e., flow rate, liquid/solid ratio, granulator screw speed, dryer air temperature, and drying time, on the tablet hardness (Metta et al., 2019). Statistical methods were applied to identify important physical properties for a specific API (Wang et al., 2017), and for various APIs and excipients (Van Snick et al., 2018). However, none of the previous studies investigated material- and process-related parameters holistically from multiple perspectives that are relevant for commercial manufacturing. Our study aims to determine key parameters of continuous wet granulation that affect tablet quality and productivity. We performed DoE-based experiments using a continuous high-shear granulator and ethenzamide as the API. To guide a systematic and comprehensive parameter analysis, a QbD-conscious parameter framework was defined. Five input parameters on raw material (API content and molecular

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weight of binder) and wet granulation (manufacturing rate, blade rotation speed, and liquid/solid ratio) were considered. The effects of varying these parameters were assessed for 11 intermediate parameters of granule properties, and 11 output parameters of tablet quality and productivity. The interrelationships were analyzed statistically and were described as matrix functions. The study identified key parameters, together with highlighting future development opportunities for enhancing continuous technology in the actual manufacturing environment.

2. Materials and methods 2.1 Materials Table 1 shows the formulation of the tablets used in this study. Ethenzamide (supplied by Iwaki Seiyaku Co. Ltd., Tokyo, Japan), which is an antipyretic, analgesic, and anti-inflammatory agent, was used as the API. The API content 𝑥API [–] was varied between high (raw material set A) and low (raw material set B). D-Mannitol (supplied by Mitsubishi Shoji Foodtech Co., Ltd., Tokyo, Japan) and microcrystalline cellulose (PH-101, Asahi Kasei Corporation, Tokyo, Japan) were applied as the excipients. Three types of hydroxypropyl cellulose with different molecular weights (M) (Nippon Soda Co., Ltd., Tokyo, Japan) were used as the binder: HPC-L (𝑀 [–] = 140,000), -SL (𝑀 = 100,000), and SSL (𝑀 = 40,000). Ion-exchanged water (Shimadzu Corporation, Kyoto, Japan) was used as the solvent in the granulation unit, and magnesium stearate (Taihei Chemical Industrial Co., Ltd., Tokyo, Japan) was used as the lubricant. The role of ethenzamide in experimental studies on continuous manufacturing is discussed elsewhere (Matsunami et al., 2019).

2.2 Manufacturing methods The investigated process consisted of mixing, wet granulation, drying, milling, blending, and compression units (Fig. 1). A batch mixer (VG-400, Powrex Corporation, Hyogo, Japan) mixed the API,

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excipients, and binder. The mixed powder material and water were filled into the material feeder (K-MLD5-KT35, Coperion K-Tron, Sewell, NJ, USA) and the liquid feeder (520S, Watson-Marlow, Falmouth, UK), respectively. The machine used in the experiments was CTS-MiGRA-CM (Powrex Corporation); it consists of a continuous high-shear granulator (MiGRA-CM-MG100) and a semicontinuous dryer (MiGRA-CM-FD01W). The granulator further consists of a twin-screw kneading part, where the liquid/solid ratio (i.e., the mass ratio of solvent to API, excipients, and binder) is defined as 𝛼 [–] (dimensionless), and a granulation part with a single center blade (with rotation speed 𝑟 [rpm]), and a scraper rotating at 50 rpm. Froude numbers of the granulator at r = 3000, 4000, and 5000 rpm were 15.3, 27.2, and 42.5, respectively. The dryer loads, dries, and discharges granules in a moving sequence across the four vessels (see section 2.3.3 for details). The milling unit was a Comil U-10 (Quadro Engineering, Ontario, Canada) with a sieve size of 1.575 mm. The blending unit comprised a continuous blender (MiGRA-CM-MG100) with a blade (1000 rpm, Froude number 1.70) and a scraper (20 rpm). The compression unit was a 102i (Fette Compacting, Schwarzenbek, Germany), for which the main and precompression forces were set to 6.0 and 3.0 kN, respectively. Also, the number of the punch set, the rotation speed, and the dwell time of the compression machine were set to 30, 30 min–1, and 21.2 ms, respectively. The precompression force was defined according to an expert recommendation (Kitamura, 2015). Two sections were interconnected in the experiments (Fig. 1). The first section included the units for granulation, drying, and milling, and the second included blending and compression. The granules were buffered after milling. The manufacturing rate 𝑣 [kg h−1], which was varied in the experiments, was the rate of feeding of the materials to the granulator, i.e., the flow rate in the first interconnected section. The rate 𝑣 was linked with the rotation speed of the twin screw in the kneading part and the loading rate in the dryer.

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2.3 Characterization methods 2.3.1 Granule properties The granules obtained from the milling unit (highlighted in Fig. 1, to be referred to as “granules” if not specified otherwise) were sampled and monitored to characterize each experimental run. To obtain the particle size distribution, sieves (Seishin Enterprise Co. Ltd., Tokyo, Japan) and Parsum IPP 70 (Parsum GmbH, Chemnitz, Germany) were applied as the offline and inline measurement tools, respectively. Parsum IPP 70 primarily generates the count-based distribution, and then automatically calculates the volume-based distribution that was used for the analysis of the study. For the volume-based granule size, the median 𝐷50 [m] and the geometric standard deviation 𝜎g [–] were obtained by assuming a lognormal distribution. The use of log-normal distributions for describing granule size distributions was reported previously (Mendez Torrecillas et al., 2017; Schæfer and Mathiesen, 1996). The geometric standard deviation for a log-normal distribution can be expressed as shown in Eq. (1):

𝜎g =

𝐷84.1

(1)

𝐷50

where 𝐷84.1 [m] represents the 84.1-percentile diameter (O’Shaughnessy and Raabe, 2003). Because the results differed for each measurement method, the arithmetical mean of the sieves and Parsum IPP 70 were used for analyzing the median and geometric standard deviation values. We also confirmed that the results hardly changed when the values obtained from each method were used. The loose bulk density 𝜌bulk [kg m−3] and tapped density 𝜌tap [kg m−3] (World Health Organization, 2012), as well as the repose angle 𝜃 [°] were measured by the powder tester PT-S (Hosokawa Micron Corporation, Osaka, Japan). The compressibility index CI [–] was calculated by using Eq. (2), according to Shah et al. (2008).

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𝐶𝐼 = 100 ∙

𝜌tap ― 𝜌bulk

(2)

𝜌tap

The circularity distribution of the granules, which is the property featured in this work, was measured by Morphologi 4 (Malvern Panalytical, Malvern, UK); 1,000–10,000 particles were measured for each run. The circularity 𝜓C [–] of each particle can be calculated from the area 𝐴powder [m2] and the perimeter 𝑙powder [m] of the particle, as shown in Eq. (3) (Wadell, 1934).

𝜓C =

4𝜋𝐴powder

(3)

𝑙2powder

As the indicators of circularity, the 10-percentile (circularity 0.1 𝜓0.1 [–]), the median (circularity 0.5 𝜓0.5 [–]), and the 90-percentile (circularity 0.9 𝜓0.9 [–]) of 𝜓C were used. The basic flowability energy (BFE), representing the powder’s flowability when forced to flow (Freeman Technology, 2014), was measured by Malvern Panalytical Japan using the Powder Rheometer FT4 (Freeman Technology, Tewkesbury, UK). The normalized BFE 𝑛𝐵𝐹𝐸 [–] was calculated as shown in Eq. (4):

𝑛𝐵𝐹𝐸 =

𝐵𝐹𝐸(𝑣air = 2) 𝐵𝐹𝐸(𝑣air = 0)

,

(4)

where 𝐵𝐹𝐸(𝑣air = 𝑥) [mJ] represents the BFE at 𝑥 mm s−1 air velocity; a lower 𝑛𝐵𝐹𝐸 indicates a higher dynamic flowability. While the values of the BFE at higher air velocity, 𝐵𝐹𝐸(𝑣air = 4, 6, 8, 10), were also measured in the experiments, the trend was similar to 𝐵𝐹𝐸(𝑣air = 2). Also, the water content 𝛼granule [wt%] of the granules was measured using a moisture analyzer (Sartorius MA150, Göttingen, Germany). In this study, it was reasonable to define granules as those present after the milling unit, because (a) the parameters in the drying and milling units were fixed in the experiment, (b) the purpose of the milling unit was to break up very large granules by applying a large sieve size (1.575 mm) compared with the granule size (the mean of which was less than 200 m), and (c) the milled granules are sampled for in8

process control in industrial practice. Friability of the granules was not measured in this study because of the complexity of its assessment.

2.3.2 Tablet quality The tablet weight was measured using an electronic balance (Shimadzu Corporation). The tablet thickness 𝑑 [mm] and the hardness 𝐻𝐷 [N] were measured by a tablet hardness tester (Erweka GmbH, Heusenstamm, Germany). The disintegration time 𝑡disintegrate [s] was defined here as the time needed to disintegrate after being placed in a liquid medium in a disintegration test. The test was conducted in 800 mL deionized water at 37 C by raising and lowering through a distance of 55 mm at 30 cycles per min using an NT-1HM instrument (Toyama Industry Company, Toyama, Japan). The friability 𝐹 [wt%], defined as the ratio of tablet weight after being rotated in the drum to the initial weight, was measured by a TFT-1200 apparatus (Toyama Industry Company). A dissolution test was conducted by sampling six tablets using an NTR-6200A apparatus (Toyama Industry Company) and the LC-10A series (Shimadzu Corporation) for 60 mins based on the Japanese Pharmacopeia (Ministry of Health, Labour and Welfare, 2016). The paddle method was used, in which the dissolution medium and the rotation speed were 900 mL deionized water at 37 C and 50 rpm, respectively. The Weibull model shown in Eq. (5) was applied to fit profiles, according to Pawar et al. (2016) and Vudathala and Rogers (1992):

𝑇𝑡 = 100 ∙ (1 ― 𝑒 where the parameters 𝑇𝑡 [%],

1 𝑘

{ ―𝑘(𝑡 ― 𝑡0)𝑏}

),

(5)

[–], 𝑡0 [min], and 𝑏 [–] represent percent of API dissolved at 𝑡 min,

and the scale parameter, time lag, and shape parameter, respectively. The least squares method was applied to estimate the values of these parameters from the experimental results. The validity of the Weibull model was confirmed by the R-squared values between the calculated and experimental results for all 22 profiles, where the mean, minimum, and maximum values were 0.9956, 0.9891, and 0.9996, respectively (see

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section 2.4.2 and Table 2 for detail). As indicators of dissolution, the percent of API dissolved at 3 min, 𝑇3 [%], and the parameters of 𝑘, 𝑡0, and 𝑏 describing the dissolution profiles were used. The API content was measured by sampling 10 tablets and using the LC-10A series, where the ratio of the mean API content per tablet to the target composition 𝑥tablet [%] and the content uniformity (i.e., standard deviation 𝜎API [%]) were calculated.

2.3.3 Productivity The drying time, 𝑡dry [h], was defined as the indicator of productivity because drying is known as rate determining in wet-granulation-based continuous processes. Fig. 2 shows the Gantt chart of the sequence of loading, drying, and discharging. Loading was performed at rate 𝑣, and proceeded to the next vessel when the weight of the wet granules in the vessel reached 1.0 kg. The drying was performed by air with an inlet temperature of 70 C at a flow rate of 1.4 m3 min−1, and was terminated when the product temperature reached 45 C. Subsequently, the dried granules were discharged to the milling unit by applying pneumatic power for 160 s. The time needed for loading the four vessels was defined as one cycle (Fig. 2). The drying time (the orange bar in Fig. 2) varies, but is also constrained by the cycle time, i.e., the discharging should be finished before the end of one cycle time. This mechanistic constraint is described as Eq. (6):

𝑡dry + 𝑡discharge ≤ 3 ∙ 𝑡load = 3 ∙

𝑚vessel 𝑣

,

(6)

where 𝑡discharge [h], 𝑡load [h], and 𝑚vessel [kg] represent the discharging time (160 s), the loading time, and the amount of granules (1 kg) per vessel, respectively. The drying times in the experiments were within one cycle time. Besides drying time, there are other indices that could represent productivity, e.g., the barrel-fill ratio in the granulator or the rotation speed of the screw. The drying time is subject to the parameters in the 10

drying unit, which were fixed in this study. However, in the experiment (presented in the next section), the required drying time exceeded the maximum allowed time for certain combinations of the input parameters, i.e., the constraint in Eq. (6) was not fulfilled. Thus, we considered the choice of drying parameter as a reasonable indicator of the productivity, because it would enable useful discussions on the process feasibility, such as investigation of the maximum acceptable manufacturing rate given the input parameters.

2.4 Assessment methods 2.4.1 Parameter framework Fig. 3 represents the framework of the parameters investigated in the study. The input parameters comprise material properties such as the API content and process parameters such as the liquid/solid ratio. Granule properties such as granule size distribution are defined as intermediate parameters. Tablet qualities such as the dissolution time and productivity represented by the drying time (see section 2.3.3) are defined as the output parameters. The overall framework was built upon the QbD concept. The input parameters could be considered as CMAs/CPPs, the granule properties in the intermediate parameters as CQAs/CMAs, and the tablet quality in the output parameters as CQAs. In this study, the focus was on the parameters associated with continuous granulation because this unit is a radical change from the conventional batch technology. The effects of changing the remaining units such as drying, milling, or compression were out of the scope of this study. The general relationships of the parameters in Fig. 3 can be formulated in Eqs. (7)–(9).

𝑇

𝒑𝑇granule = 𝑭1(𝒙material ⊕ 𝒙pre process) 𝑇

(7) 𝑇

pre post 𝒚𝑇tablet = 𝑭2(𝒑granule ⊕ 𝒙post process) = 𝑭3(𝒙material ⊕ 𝒙process ⊕ 𝒙process)

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(8)

𝑇

post 𝒚𝑇process = 𝑭4(𝒙material ⊕ 𝒙pre process ⊕ 𝒙process)

(9)

The raw vectors 𝒙material, 𝒙process, 𝒑granule, 𝒚tablet, and 𝒚process comprise the elements of material properties, process parameters, granule properties, tablet quality, and productivity, respectively. The superscripts “pre” and “post” for 𝒙process indicate the units of mixing, granulation, drying, and milling, and the units of blending and compression, respectively (“Granules” in Fig. 1 is the dividing point of “pre” and “post”). The symbol ⊕ and the superscript T indicate the direct sum of vectors and transpose of matrix, respectively. The matrix functions 𝑭1, 𝑭2, 𝑭3, and 𝑭4 express the relationships between input and intermediate parameters, intermediate parameters and tablet quality, input parameters and tablet quality, and input parameters and productivity, respectively. Using the parameters presented in Fig. 3, the vectors 𝒙material, 𝒙process, 𝒑granule, 𝒚tablet, and 𝒚process are specified as Eqs. (10)–(15):

𝒙material = (1 𝑥API

𝒑granule = (1 𝑥API

𝑀

𝑀)

(10)

𝒙pre process = (𝑣 𝑟 𝛼 𝑣𝛼)

(11)

𝒙post process = 𝟎

(12)

𝐷50 𝜎g

𝒚tablet = (𝐻𝐷

𝜓0.1 𝜓0.5 𝜓0.9 𝜌bulk

𝑑 𝑡disintegrate 𝐹 𝐶 𝑘 𝑡0 𝑏

𝜌tap 𝐶𝐼 𝜃 𝑥tablet

𝑛𝐵𝐹𝐸 𝛼granule) (13)

𝜎API)

𝒚process = (𝑡dry), where the unit value was needed for the first elements of 𝒙material and 𝒑granule to calculate the intercepts. Experiments were performed to determine the matrix functions 𝑭1–𝑭4.

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(14) (15)

2.4.2 DoE The five input parameters in Fig. 3, i.e., 𝑥API, 𝑀, 𝑣, 𝑟, and 𝛼, were selected as the factors to vary in DoE. Two levels were set for the API content (raw material sets A and B, Table 1), and three levels were determined for the other parameters. Table 2 presents the values of the factors in all 22 runs based on fractional factorial designs (25V ― 1 form) with additional repetitions of the center points (run nos. 5, 10, 11 for set A, and run nos. 16, 21, 22 for set B). In the experiment, two parameters were selected from the other material properties, e.g., material composition, excipient type, or material particle size. We narrowed down the parameters to 𝑥API and 𝑀 after considering their potential impact as well as the experimental feasibility. The parameter 𝑀 was varied as 4,000, 10,000, and 14,000. The center point in the experiment was not 9,000 because of the availability of the binder from the supplier.

2.4.3 Analysis of experimental results Five-way analysis of variance (ANOVA) was used for analyzing 𝑭1, 𝑭3, and 𝑭4. The null hypothesis was that “there is no difference in the mean of each property (i.e., granule properties, tablet quality, or productivity) when varying the five factors.” We set the significance level at 0.05 and for the sake of simplification, we focused only on the main effects in analyzing 𝑭1 and 𝑭3. The interactions between parameters were also analyzed but are not presented here because no notable interactions were found. The partial least squares (PLS) method was applied to characterize 𝑭2, i.e., the relationship between granule and tablet quality, because the intermediate parameters varied greatly in the 22 runs (Table 2). Before analyzing 𝑭2, the Pearson correlation coefficients between tablet qualities were analyzed (Fig. 3) to identify the relevant parameters and to obtain insights for the PLS. Two material factors, API content and molecular weight of the binder, were included in the PLS because they were also a part of granule

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properties (Fig. 3). Threefold cross-validation was performed 100 times for each tablet quality item. The number that minimized the average of the 100 mean squared prediction errors (MSPEs) for the response was specified as the number of principal components, 𝑛pc [–], for each tablet quality item. All the values for granule and tablet qualities were normalized with zero as the center and one as the standard deviation, and the PLS regression coefficients were calculated. Regarding 𝑭4, i.e., the relation between the input parameters and productivity, a model was developed to assess the feasibility of sustaining continuous manufacturing based on drying time. First, five-way ANOVA was applied to determine the relevant input parameters, and then an additional ANOVA identified relevant interactions. Using these parameters, a regression equation was formulated for the drying time. To describe 𝑡dry in Eq. (6), an energy balance-based approach (e.g., Ghijs, et al, 2019) could determine rigorously the impact of the liquid/solid ratio. Because our aim was to analyze the impact of multiple input parameters under one framework, we used a statistical approach here. The statistically defined 𝑡dry was integrated with Eq. (6), the mechanistic constraint on cycle time, to determine the maximum acceptable manufacturing rate as an investigation of process feasibility.

3. Results and discussion 3.1 Effects of input parameters on intermediate parameters Table 3 shows the results for the intermediate parameters and the p-values of the input parameters for each intermediate parameter, i.e., granule properties, indicating the statistical significance (p < 0.05). If the p-values of the input parameters (columns in the table) for the intermediate parameters (rows in the table) were < 0.05, the effects were judged to be significant. The main effect plots for the significant input parameters are presented in Fig. A.1 in the Appendix. The function 𝑭𝟏 is presented as Eq. (A.1) in the Appendix. The effect of the API content, a material property, was confirmed on the geometric standard deviation of granule size distribution and the loose and tapped density. When the API content was lower,

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the content of mannitol, which is hydrophilic, was higher; hence, the granules were thought to be strongly agglomerated. This caused an increase in the level of larger granules, which further resulted in a higher standard deviation, and increased density. By contrast, the molecular weight of the binder, another raw material property, showed no effect in any of the granule properties. Regarding process parameters, the change in manufacturing rate affected the 0.1 and 0.5 circularity values, and the loose bulk density. The blade rotation speed affected the median diameter together with the liquid/solid ratio, which is in line with the results shown in Meng et al. (2017). The higher blade rotation caused more breakage of the granules, which resulted in a smaller median diameter. Otherwise, the blade rotation speed did not affect any granule property. The liquid/solid ratio appeared to affect many properties related to size, circularity, density, and flowability probably because granulation proceeded faster with a high liquid content. The main effect plots in Fig. A.1 indicate that the increase in the liquid/solid ratio resulted in an increase in 0.1, 0.5, and 0.9 circularity values. The increase in manufacturing rate also contributed to the increase in 0.1 and 0.5 circularity values. This could be potentially attributed to the enhancement of the kneading/granulation processes due to the increase in the liquid/solid ratio, and the increase in manufacturing rate, which raised the rotation speed of the twin-screw in the kneading part. To conclude, from among the varied input parameters, the API content and the liquid/solid ratio were the most relevant to the intermediate parameters.

3.2 Effects of input parameters on tablet quality Table 4 shows the results for tablet quality and the p-values of the input parameters for each item of tablet quality, indicating the statistical significance (p < 0.05). The main effect plots for the significant input parameters are presented in Fig. A.2 in the Appendix. The function 𝑭3 is presented as Eq. (A.3) in the Appendix. The API content affected all the physical properties except for the standard deviation of the API content. The molecular weight of the binder affected the thickness, disintegration time, and

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dissolution-related parameters, although the molecular weight did not show any significance in the granule properties. Hydroxypropyl cellulose, which has a small molecular weight, is known to have higher wettability (e.g., Kodama, et al., 2015); we interpreted that the higher wettability of the binder increased the binder dispersion and tablet compressibility, and affected the disintegration/dissolution. Regarding the process parameters, the manufacturing rate and blade rotation speed did not show any effect on tablet quality, unlike the results for granule properties. The liquid/solid ratio, however, affected disintegration time and dissolution-related parameters because the liquid/solid ratio affected the granule properties more than other process parameters (Table 3). The main effect plots in Fig. A.2 indicate that the increase in the liquid/solid ratio resulted in a decrease of the dissolution rate (see plots for 𝑇3, 𝑘, 𝑡0, and 𝑏 for 𝛼), probably because of the enhancement of the granulation process. None of the input parameters affected the standard deviation of the API content, which suggests that the raw material powder was well mixed in the mixing unit. Overall, the raw material parameters, i.e., API content and molecular weight of the binder, tended to have more effect on tablet quality than on granule properties. This is probably because the investigated properties of the tablet were more materials related rather than shape related, which was the opposite to the investigation of the granules. Among the process parameters, the liquid/solid ratio was found to be the most influential. This finding is important because APIs and binders are usually fixed earlier than the liquid/solid ratio. From the perspective of granule properties, the liquid/solid ratio is the key parameter that could provide degrees of freedom for the design and operation of continuous manufacturing processes.

3.3 Effects of intermediate parameters on tablet quality The Pearson correlation coefficients between the tablet qualities are summarized in Fig. 4. Only the significant combination of the parameters is shown, with the values of the absolute coefficient in

16

descending order. The correlation between all dissolution-related parameters, i.e., 𝑇3, 𝑘, 𝑡0, and 𝑏, were relevant (see top three bars, for example). This result indicated that these parameters should be regarded as a whole to describe dissolution phenomena. A correlation between disintegration and dissolution was also found (see the bars in ranks 4 to 6, for example). When disintegration was fast, dissolution was also fast. Fig. 5 shows PLS regression coefficients of each granule property for each item of tablet quality. The horizontal and vertical axes represent the ID numbers of granule properties and the PLS regression coefficients for each item, respectively. The friability and standard deviation of API content were excluded because 𝑛pc = 0 gave the minimum MSPE; for the other items shown in Fig. 5, the presented 𝑛pc showed the minimum MSPE (see section 2.4.3). The function 𝑭2 is obtained as Eq. (A.2) in the Appendix. The hardness (Fig. 5(a)) was strongly correlated with API content (see property No. 1) and water content (No. 13). The thickness (Fig. 5(b)) was also affected by the API content (No. 1). The results for disintegration time (Fig. 5(c)) and dissolution-related parameters (Fig. 5(d)–(g)) showed a similar tendency, which can be explained by the intercorrelation found in the above analysis (see also Fig. 4). In the results of Figs. 5(c) to (g), the raw material parameters, i.e., API content and molecular weight of the binder, were commonly influential, as described in section 3.2. Beside these parameters, the circularity of 0.1 (No. 5) was the most relevant parameter in Figs. 5(c) to (g) except for Fig. 5(f). In particular, the largest and the second largest values were given to circularity 0.1 in the dissolution parameter 𝑏 (Fig. 5(g)) and disintegration time (Fig. 5(c)), respectively. When the circularity 0.1 was high, i.e., when the circularity was better, disintegration and dissolution were slow. Also, the standard deviation of the granule size distribution (No. 4) showed a certain relevance, as seen in the dissolution parameter 𝑡0 (Fig. 5(f)), for example. When the size distribution was broad, the dissolution was fast. This PLS analysis revealed circularity as a new key parameter for process monitoring. To date, granule size distribution has been typically monitored. In the results presented above, circularity 0.1 (No.

17

5) showed stronger relevance than the size distribution-related parameters, i.e., median (No. 3) and standard deviation (No. 4). A possible explanation for the correlation between circularity and dissolution (Fig. 5(d)–(g)) would be that high circularity is the result of the high level of the progress during granulation. This would have caused an increase of the true density of the granules, and a delay in dissolution. These results require more rigorous investigation, and were specific to the setup, but indicate an opportunity of exploring a new control strategy using circularity, e.g., through development of a new PAT system or a QbD application based on circularity. The authors HS, KM, and TN presented the correlation between circularity and dissolution, and the manufacturing method using this correlation in a patent application (Sugiyama, et al., 2019).

3.4 Effects of the input parameters on productivity For the drying time, the representative parameter of productivity, the five-way ANOVA determined that the manufacturing rate 𝑣 and the liquid/solid ratio 𝛼 were the relevant parameters. Then, a two-way ANOVA was performed, which confirmed the relevance of the interaction 𝑣𝛼. The p-values were obtained as: the main effect of the manufacturing rate (<0.0001), the main effect of the liquid/solid ratio (<0.0001), and interaction (0.0452). The regression model of drying time 𝑡dry [h] was defined as Eq. (16):

𝑡dry = 𝑐1 + 𝑐2𝑣 + 𝑐3𝛼 + 𝑐4𝑣𝛼

(16)

where 𝑐1 [h], 𝑐2 [h2 kg−1], 𝑐3 [h], and 𝑐4 [h2 kg−1] are the coefficients. The values of the coefficients were calculated by parameter fitting as: 𝑐1 = 7.104 × 10 ―2, 𝑐2 = ―8.449 × 10 ―3, 𝑐3 = ―6.186 × 10 ―1, and 𝑐4 = 9.089 × 10 ―2. The matrix representation of the function 𝑭4 is presented in Eq. (A.4) in the Appendix. Fig. 6 presents the regression model of 𝑡dry shown in Eq. (16) and its 95% prediction interval. The performance of the model was confirmed by the R-squared values (0.8766) and the root mean squared

18

error (0.0179). Three data points, which were randomly extracted from the initial data set of 22 points, were within the 95% prediction interval, which gave statistical validation to the model. Fig. 6 shows the trend that the required drying time became longer when the manufacturing rate and/or liquid/solid ratio became higher. By integrating Eq. (6), the mechanistically described constraint on cycle time, and Eq. (16), the statistically described 𝑡dry, an inequality equation can be obtained as Eq. (17).

3 ― + 𝑐1 + 𝑐2𝑣 + 𝑐3𝛼 + 𝑐4𝑣𝛼 + 𝑡discharge ≤ 0 𝑣

(17)

Eq. (17) yields the maximum acceptable manufacturing rate 𝑣max [kg h−1] as a function of 𝛼 as Eq. (18).

𝑣max =

0.6186𝛼 ― 0.1155 + 0.3827𝛼2 + 0.9478𝛼 ― 0.0880 0.1818𝛼 ― 0.0169

(18)

The relationship between 𝛼 and 𝑣max with a 95% prediction interval is shown in Fig. 7, where 𝑣max decreases when 𝛼 increases. Some experimental runs (e.g., run nos. 3, 4, 8, 9, 14, 15, 19, 20 with 𝑣 = 25 kg h−1), which violated the constraint of drying time in Eq. (6), were above the upper 95% prediction interval of 𝑣max. To enable long-time operation under these conditions, Fig. 7 suggests that the following are effective: (1) the upper shift of the interval of 𝑣max, which can be achieved by increasing the inlet air volume and temperature of drying; (2) decrease in 𝑣, by adjusting the feeding rate; and (3) decrease in 𝛼, which would be still possible during product development or even during commercial manufacturing. Fig. 7 indicates that vmax decreased from 20.9 kg h−1 to 18.1 kg h−1, which may appear small. However, according to the economic assessment presented by Matsunami et al. (2018), small changes in manufacturing rate can have a significant economic impact. The finding that high-speed continuous manufacturing favored a lower liquid/solid ratio would be useful in product development, e.g., selection and development of continuous processing-oriented excipients. In summary, the liquid/solid ratio is the key

19

parameter for productivity that could provide degrees of freedom in processes design/operation.

4. Conclusions and outlook In this work, we determined the liquid/solid ratio and circularity of the granules to be the key parameters of continuous wet granulation for tablet quality and productivity. DoE-based experiments were performed using a continuous high-shear granulator with a single center blade and a scraper, and using ethenzamide as the API. The investigated parameters were systematically defined in a QbDconscious framework, and were analyzed by ANOVA and PLS. The matrix functions that relate the input, intermediate, and output parameters could be quantitatively determined. The liquid/solid ratio was identified as the key parameter that affected circularity, density, and flowability as granule properties, and disintegration and dissolution as tablet qualities. The maximum acceptable manufacturing rate that governs the productivity was also affected by the liquid/solid ratio. Circularity was found to affect disintegration and dissolution. This result was specific to the setup of the study, but suggests opportunities for development of a new PAT system/QbD application based on circularity. In addition, practical findings were obtained as: (1) high-speed manufacturing favored a lower liquid/solid ratio, and (2) a high circularity slowed down disintegration/dissolution. This acquired knowledge will enhance the applicability of the continuous technology in an actual manufacturing environment. In future studies, more general analyses would be desirable, to cover a variety of raw materials (indicated by the significance of API and other raw material-related parameters), machine types (represented in blade/screw specifications), and process mechanisms (exemplified by wet/dry granulation). A multiobjective evaluation considering economy (e.g., start-up/shutdown) and safety (e.g., powder explosion or operator exposure) would also be needed to foster a more comprehensive understanding and assessment of the continuous manufacturing technology.

20

Acknowledgments The authors acknowledge Mr. Kazuyoshi Kotaka and Mr. Yosuke Tomita from Powrex Corporation for their support in the experiments. Useful discussions with Prof. Masahiko Hirao and Dr. Eri Amasawa at The University of Tokyo are sincerely appreciated. The study was funded by a research grant from Powrex Corporation. The financial support by the Grant-in-Aid for Young Scientists (A) from the Japan Society for the Promotion of Science (JSPS) [grant number 17H04964] is also gratefully acknowledged. K. M. is thankful for the financial support of Grant-in-Aid for JSPS Research Fellow [grant number 18J22793] as well as from the Leading Graduate Schools Program, “Global Leader Program for Social Design and Management,” by the Ministry of Education, Culture, Sports, Science and Technology.

21

Abbreviations ANOVA

Analysis of variance

API

Active pharmaceutical ingredient

BFE

Basic flowability energy

CMA

Critical material attribute

CPP

Critical process parameter

CQA

Critical quality attribute

DEM

Discrete element method

DoE

Design of experiments

FDA

Food and Drug Administration

MSPE

Mean squared prediction error

PAT

Process analytical technology

PLS

Partial least squares

QbD

Quality by design

RTD

Residence time distribution

Nomenclature Variable 𝐴powder

Area of a granule [m2]

𝑏

Shape parameter in the Weibull model [–]

𝐵𝐹𝐸

Basic flowability energy [mJ]

𝑐1

Coefficient associated with the drying time regression model [h]

𝑐2

Coefficient associated with the drying time regression model [h2 kg−1]

𝑐3

Coefficient associated with the drying time regression model [h]

22

𝑐4

Coefficient associated with the drying time regression model [h2 kg−1]

𝐶𝐼

Compressibility index [–]

𝑑

Tablet thickness [mm]

𝐷50

Median diameter of granules [m]

𝐷84.1

84.1-percentile diameter of granules [m]

𝐹

Friability [wt%]

𝐻𝐷

Tablet hardness [N]

𝑘

Reciprocal of a scale parameter in the Weibull model [–]

𝑙powder

Perimeter of a granule [m]

𝑀

Molecular weight of binder [–]

𝑚vessel

Quantity of granules per vessel in the drying unit [kg]

𝑛𝐵𝐹𝐸

Normalized basic flowability energy [–]

𝑛pc

Number of principal components used [–]

𝑟

Blade rotation speed [rpm]

𝑡discharge

Discharge time per vessel in the drying unit [h]

𝑡disintegrate

Disintegration time of tablets [s]

𝑡dry

Drying time per vessel in the drying unit [h]

𝑡load

Loading time per vessel in the drying unit [h]

𝑇𝑡

Percent of API dissolved at 𝑡 min [%]

𝑡0

Time lag in the Weibull model [min]

𝑇3

Percent of API dissolved at 3 min [%]

𝑣

Manufacturing rate [kg h−1]

𝑣air

Air velocity in the flowability test [mm s−1]

𝑣max

Maximum acceptable manufacturing rate [kg h−1]

23

𝑥API

API content [wt%]

𝑥tablet

Ratio of tablet mean API content to the target composition [%]

𝛼

Liquid/sold ratio [–]

𝛼granule

Water content of granules [wt%]

𝜃

Repose angle of granules [°]

𝜌bulk

Loose bulk density of granules [kg m−3]

𝜌tap

Tapped density of granules [kg m−3]

𝜎API

Standard deviation of API content of tablets [%]

𝜎g

Geometric standard deviation of granule size distribution [–]

𝜓C

Circularity of a granule [–]

𝜓0.1

10-percentile of granule circularity distribution [–]

𝜓0.5

Median of granule circularity distribution [–]

𝜓0.9

90-percentile of granule circularity distribution [–]

Vector 𝒑granule

Vector of granule properties

𝒙material

Vector of material properties

𝒙post process

Vector of process parameters in the units of blending and compression

𝒙pre process

Vector of process parameters in the units of mixing, granulation, drying, and milling

𝒚process

Vector of productivity

𝒚tablet

Vector of tablet quality

Matrices

24

𝑭1

Matrix expressing the relationship between input and intermediate parameters

𝑭2

Matrix expressing the relationship between intermediate parameters and tablet quality

𝑭3

Matrix expressing the relationship between input parameters and tablet quality

𝑭4

Matrix expressing the relationship between input parameters and productivity

25

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List of tables Table 1

Formulation of tablets used in the experiments.

Table 2

Values of factors in all 22 runs of the experiments based on fractional factorial designs with additional repetitions of the center points.

Table 3

Values of granule properties and p-values of input parameters for granule properties.

Table 4

Values of tablet quality items and p-values of input parameters for tablet quality.

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List of figures Fig. 1.

Investigated process of continuous tablet manufacturing using a high-sheer granulator.

Fig. 2.

Gantt chart of the drying unit using four vessels for loading, drying, and discharging continuously.

Fig. 3.

Parameter framework indicating the corresponding analytical methods and the obtained results.

Fig. 4.

Pearson correlation coefficients between tablet qualities X and Y, indicating only the significant parameter combination.

Fig. 5.

PLS regression coefficients of each granule property for each tablet quality item except for the friability and standard deviation of API content. The number of principal components is indicated as 𝑛pc.

Fig. 6.

Regression model and its 95% prediction interval of drying time with 19 plots used for modeling and three for the validation.

Fig. 7.

The relationship between liquid/solid ratio 𝛼 and maximum acceptable manufacturing rate 𝑣max with a 95% prediction interval.

Fig. A.1. Plot of the main effects of the significant input parameters on granule properties. Fig. A.2. Plot of the main effects of the significant parameters on tablet quality.

33

Table 1 Component

Substance

Composition [wt%] Raw material set A

Raw material set B

(higher API content)

(lower API content)

API

Ethenzamide

29.4

4.9

Excipients

Mannitol

58.8

79.4

Microcrystalline cellulose

9.8

13.7

Binder

Hydroxypropyl cellulose

1.5

1.5

Lubricant

Magnesium stearate

0.5

0.5

34

Table 2 Run no.

Raw

Molecular weight

Manufacturing

Blade

rotation

Liquid/solid

material set

of binder 𝑀 [–]

rate 𝑣 [kg h−1]

speed 𝑟 [rpm]

ratio 𝛼 [–]

[–] 1

A

40,000

15

3,000

0.20

2

A

40,000

15

5,000

0.16

3

A

40,000

25

3,000

0.16

4

A

40,000

25

5,000

0.20

5

A

100,000

20

4,000

0.18

6

A

140,000

15

3,000

0.16

7

A

140,000

15

5,000

0.20

8

A

140,000

25

5,000

0.16

9

A

140,000

25

3,000

0.20

10

A

100,000

20

4,000

0.18

11

A

100,000

20

4,000

0.18

12

B

40,000

15

3,000

0.16

13

B

40,000

15

5,000

0.20

14

B

40,000

25

5,000

0.16

15

B

40,000

25

3,000

0.20

16

B

100,000

20

4,000

0.18

17

B

140,000

15

5,000

0.16

18

B

140,000

15

3,000

0.20

19

B

140,000

25

3,000

0.16

20

B

140,000

25

5,000

0.20

35

21

B

100,000

20

4,000

0.18

22

B

100,000

20

4,000

0.18

36

Table 3 Mean

Standard

p-value

deviation API

Molecular

Manufacturing

Blade

Liquid/solid

content

weight of

rate 𝑣 [kg

rotation

ratio 𝛼 [–]

𝑥API [–]

binder 𝑀

h−1]

speed

[–]

𝑟 [rpm]

168 m

42.4 m

0.4284

0.0766

0.1199

0.0038*

0.0009*

2.13

0.180

0.0100*

0.9770

0.6477

0.9465

0.0093*

0.1

0.599

0.0216

0.1918

0.6881

0.0270*

0.3509

0.0003*

0.5

0.752

0.0105

0.7833

0.5444

0.0026*

0.2472

0.0003*

0.9

0.857

0.00598

0.1251

0.3419

0.0685

0.0872

0.0354*

529 kg

24.6 kg

<0.0001*

0.6701

0.0224*

0.1229

<0.0001*

density 𝜌bulk

m−3

m−3

Tapped density

645 kg

14.1 kg

0.0301*

0.3791

0.5240

0.4175

0.5705

𝜌tap

m−3

m−3

Compressibility

18.0

3.49

0.0091*

0.7840

0.1147

0.4547

<0.0001*

Median diameter 𝐷50 Geometric standard deviation 𝜎g Circularity 𝜓0.1 Circularity 𝜓0.5 Circularity 𝜓0.9 Loose

bulk

37

index 𝐶𝐼 Repose angle 𝜃

39.3°

3.15°

0.6516

0.2382

0.6323

0.7602

0.1758

Flowability

0.477

0.127

0.1384

0.4652

0.6692

0.8177

<0.0001*

0.465%

0.120%

0.0344*

0.1461

0.3369

0.3161

0.4553

𝑛𝐵𝐹𝐸 Water

content

𝛼granule *p-value<0.05

38

Table 4 Mean

Standard

p-value

deviation API

Molecular

Manufacturing

Blade

Liquid/solid

content

weight of

rate 𝑣 [kg

rotation

ratio 𝛼 [–]

𝑥API [–]

binder 𝑀

h−1]

speed

[–]

𝑟 [rpm]

Hardness 𝐻𝐷

42.7 N

8.38 N

<0.0001*

0.3230

0.6949

0.6949

0.7600

Thickness 𝑑

3.51

0.0229

<0.0001*

0.0077*

1.0000

0.4543

1.0000

mm

mm

Disintegration

34.1

11.3 sec

<0.0001*

0.0007*

0.4964

0.9317

0.0164*

time 𝑡disintegrate

sec

Friability 𝐹

0.349%

0.0537%

0.0322*

0.3103

0.1976

0.7474

0.4089

Percent of API

50.7%

20.9%

<0.0001*

0.0111*

0.8108

0.4398

0.0023*

0.518

0.285

<0.0001*

0.0153*

0.7425

0.9290

0.0013*

Dissolution

1.01

0.154

0.0015*

0.0161*

0.5823

0.1497

0.0142*

parameter 𝑡0

min

min

Dissolution

0.819

0.297

0.0001*

0.0444*

0.4960

0.4159

0.0019*

dissolved at 3 min 𝑇3 Dissolution parameter 𝑘

parameter 𝑏

39

Mean API

96.7%

3.30%

0.0022*

0.5158

0.2999

0.9374

0.9930

0.107

0.261

0.2158

0.9747

0.6313

0.5243

0.5541

content 𝑥tablet Standard deviation of API content 𝜎API *p-value<0.05

40

Fig. 1 Excipients

Binder

Solvent

API

Mixing

Granulation

Batch operation

Lubricant Drying

Granules

Milling

Blending

Interconnected

Compression

Interconnected Granulation

1st

Milling

41

2nd

3rd

4th vessel

Tablets

Fig. 2

Time 1st vessel

Loading

Drying Discharging

2nd vessel

…

3rd vessel 4th vessel Cycle time

Cycle time

42

Fig. 3 Intermediate parameters

Input parameters

Granule properties:

Material properties: • API content • Molecular weight of binder ANOVA Process parameters: • Manufacturing rate • Blade rotation speed • Liquid/solid ratio

Output parameters

Table 3

• • • • • • • •

Tablet quality:

(API content ) (Molecular weight of binder Granule size distribution Circularity distribution Bulk density , , Repose angle Flowability Water content

PLS )

Fig. 5

• • • • • •

Hardness Thickness Disintegration time Friability Dissolution API content

Table 4 ANOVA Fig. 6 ANOVA

43

Productivity: • Drying time

Fig. 4

Fig. 4

% of API dissolved at 3 min % of API dissolved at 3 min Dissolution parameter Disintegration time Disintegration time Disintegration time % of API dissolved at 3 min Disintegration time Hardness Dissolution parameter Thickness Thickness Thickness Dissolution parameter Hardness Thickness Hardness Hardness Thickness Thickness % of API dissolved at 3 min Hardness Hardness Dissolution parameter Hardness Disintegration time Hardness Dissolution parameter

Dissolution parameter Dissolution parameter Dissolution parameter % of API dissolved at 3 min Dissolution parameter Dissolution parameter Dissolution parameter Dissolution parameter Thickness Dissolution parameter % of API dissolved at 3 min Dissolution parameter Disintegration time Dissolution parameter Disintegration time Dissolution parameter Dissolution parameter % of API dissolved at 3 min Dissolution parameter Mean API content Mean API content Mean API content Dissolution parameter Mean API content Dissolution parameter Mean API content Friability Mean API content –1 –0.5 0 0.5 1 -1 -0.5 0 0.5 1 Correlation coefficient between and [–]

44

Fig. 5 ID number of granule properties 1: API content ; 2: molecular weight of binder ; 3: median diameter ; 4: geometric standard deviation of granule size 5: circularity 0.1 ; 6: circularity 0.5 ; 7: circularity 0.9 ; 8: loose bulk density ; 9: tapped density ; 10: compressibility index ; 11: repose angle ; 12: flowability ; 13: water content 0.6 0.6

Hardness

(

= 2)

(

Disintegration time

= 7)

0.5 0.5

0.3 0.3

00

00

00

-0.3 –0.3

-0.5 –0.5

-0.3 –0.3

-1 –1 1 2 3 4 5 6 7 8 9 10 11 12 13

(a)

(b)

% of API dissolved at 3 min

(

= 4)

0.3 0.3

0.4 0.4

1 2 3 4 5 6 7 8 9 10 11 12 13

(c)

Dissolution parameter

(

= 2)

Dissolution parameter

0.2 0.2

00

00

-0.3 –0.3

-0.2 –0.2

-0.1 –0.1 1 2 3 4 5 6 7 8 9 10 11 12 13

(d)

(e)

0.6 0.6

= 1)

-0.2 –0.2

-0.4 –0.4 1 2 3 4 5 6 7 8 9 10 11 12 13

Dissolution parameter

(

0.1 0.1

0.2 0.2

00

-0.6 –0.6

= 4)

-0.6 –0.6

1 2 3 4 5 6 7 8 9 10 11 12 13

0.6 0.6

(

0.6 0.6

0.3 0.3

-0.6 –0.6

PLS regression coefficients [–]

Thickness

11

;

(

= 4)

1 2 3 4 5 6 7 8 9 10 11 12 13

(f) 0.4 0.4

API mean

(

= 1)

0.2 0.2

0.3 0.3 00

00

-0.3 –0.3

-0.2 –0.2 -0.4 –0.4

-0.6 –0.6 1 2 3 4 5 6 7 8 9 10 11 12 13

1 2 3 4 5 6 7 8 9 10 11 12 13

(g)

(h) ID number of granule properties [–]

45

Fig. 6

0.25

[h]

0.2

95% prediction interval

Drying time

0.15 0.1

+ 19 points for modeling 3 points for validation

0.05 0 15 20 25

0.16

0.18

46

0.2

Maximum acceptable [kg h−1] manufacturing rate

Fig. 7

25 20 15 10

95% prediction interval

5 0 0.16

Standard value (Eq. 18) 0.17

0.18

Liquid/solid ratio

47

0.19

[–]

0.20

Fig. A.1 Geometric standard deviation

200

Standard deviation [–]

2.25

200

180

180

160

160

140

140

-1

0

Level of

[–]

1

-1

0

Level of

1

[–]

0.62

0.61

0.61

0.6

0.6

0.59

0.59 -1

0

Level of

2.2

2.15

2.15

2.1

2.1

2.05

2.05

2.0

-1

[–]

0.760

0.760

0.755

0.755

0.750

0.750

0.745

[–]

0

Level of

1

0

Level of

1

0.858

0.856

-1

[–]

0

Level of

540

530

530

530

520

520

520

510

510

510

0

Level of

1

[–]

-1

21

21

19

19

17

17

15

-1

Level of

1

[–]

15

Flowability [–]

Compressibility index

-1

0

Level of

1

0

Level of

[–]

Tapped density [kg m−3]

550

540

-1

-1

0

Level of

1

655 650 645 640 635

-1

Level of

Flowability

Water content

0.6

Water content [%]

Loose bulk density [kg m−3]

550

1

0.854

1

[–]

Tapped density

540

[–]

1

0.860

0.745 -1

[–]

550

-1

[–]

Circularity 0.9

Loose bulk density

Level of

0

Level of

Circularity 0.5

-1

1

-1

1

Level of

Circularity 0.5 [–]

0.62

Compressibility index [%]

Circularity 0.1 [–]

Circularity 0.1

2.25

2.2

Circularity 0.9 [–]

Median diameter [μm]

Median diameter

0.5

0.4

0.3

[–]

-1

0

Level of

48

1

[–]

1

[–]

0.52

0.48

0.44

0.40

-1

Level of

1

[–]

1

[–]

Fig. A.2 Hardness

-1

Level of

3.54

3.52

3.52

3.50

3.50

3.48

1

3.48

1

-1

[–]

Level of

0.666667

0

-1

[–]

Level of

42

34

34

34

26

% of API dissolved [%]

0.36

0.34

-1

1

Level of

1

-1

[–]

70

70

60

60

60

50

50

50

40

40

40

30

30

-1

[–]

1

Level of

-1

[–]

0

Level of

0.6

0.5

0.5

0.5

0.4

0.4

0.4 0.3

0.3

Level of

-1

[–]

Dissolution t0 [–]

0.7

0.6

0

Level of

-1

0.666667

[–]

0

Level of

1

[–]

1.1 1

0.9

0.9

0.9

0.8

0.8

0.8

0.7

0.7

0.7

0.6

[–]

1

[–]

1.0

1.0

-1

1

-1

0

Level of

0.666667

[–]

0.9

-1

[–]

0

Level of

0.666667

[–]

0.6

-1

0

Level of

49

1

[–]

100

98

96

94

-1

Level of

0.9

-1

0

Level of

Mean API content

1

1

0

Level of

1.0

Level of

1.1

-1

-1

1.1

0.9

1

1

0.6

30

0.666667

[–]

1.1

[–]

1.1

Level of

0

Level of

1.1

Dissolution parameter Dissolution b [–]

Dissolution k [–]

0.7

0.6

1

-1

0.666667

[–]

Dissolution parameter

0.7

-1

0

Level of

70

Dissolution parameter M

0.3

26

26 -1

Level of

Percent of API dissolved at 3 min

0.38

Friability [–]

42

[–]

Friability

0.32

42

Mean API content [%]

40

3.54

Disintegration time [s]

Thickness [mm]

Hardness [N]

45

35

Disintegration time

Thickness

50

1

[–]

1

[–]

Appendix

(

1 0 0 4.80 3.25 0.413 𝑭1 = 0.655 0.831 363 655 42.4 39.3 ―0.671 0.542

𝑭2

=

0 0 0 0 1 0 0 0 0 0 0 1.77 × 10 ―3 0 1.24 × 10 ―3 0 0 0 1.54 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 ―2.39 × 10 ―2 1.44 × 103 0 ―5.50 0 0.836 0 0.400 0 0.145 0 866 0 0 0 ―146 0 0 0 6.38 0 0

)

0 0 0 0 0 0 0 0 0 0 0 0 0 0

(A. 1)

(

2.24 × 102 3.85 358 0.349 ―142 ―4.34 ―5.51 × 10 ―2 10.8 61.0 0.107

)

0 1 0 0 ―7.56 × 10 ―3 0 0 0 ―1.21 ―0.575 0.112 0 0 ―4.49 × 10 ―3

0.228 1.72 × 10 ―3 0.335 0 ―0.858 ―7.99 × 10 ―3 2.42 × 10 ―3 8.46 × 10 ―3 ―7.58 × 10 ―2 0

(

―1.34 × 10 ―5 ―7.66 × 10 ―8 ―1.52 × 10 ―4 0 1.68 × 10 ―4 8.34 × 10 ―7 ―5.04 × 10 ―7 ―2.80 × 10 ―6 ―4.10 × 10 ―6 0

33.2 3.48 1.28 0.313 134 𝑭3 = 1.61 0.379 ―0.568 99.8 0.107

―2.39 × 10 ―3 ―1.03 × 10 ―4 2.80 × 10 ―2 0 ―6.87 × 10 ―2 ―5.15 × 10 ―4 2.06 × 10 ―4 8.04 × 10 ―4 ―7.59 × 10 ―3 0

―6.30 3.47 × 10 ―3 ―2.344 0 20.3 0.261 ―0.145 ―6.41 × 10 ―2 3.58 0

0.557 1.78 × 10 ―3 0.586 2.09 × 10 ―3 ―1.33 ―1.83 × 10 ―2 6.97 × 10 ―3 1.42 × 10 ―2 ―0.180 0

63.5 ―2.33 × 10 ―2 234 0 ―296 ―2.64 1.01 7.40 ―3.97 0

0 ―9.71 × 10 ―8 ―1.31 × 10 ―4 0 1.41 × 10 ―4 1.69 × 10 ―6 ―1.45 × 10 ―6 ―2.03 × 10 ―6 0 0

43.1 ―6.55 × 10 ―3 ―18.2 0 ―119 ―1.74 1.44 0.688 ―3.68 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

―198 ―0.491 ―490 0 378 4.40 0.924 ―13.9 18.7 0

0 0 194 0 ―409 ―5.20 3.59 7.39 0 0

―5.18 × 10 ―2 ―3.90 × 10 ―5 ―6.66 × 10 ―2 0 0.142 1.94 × 10 ―3 ―3.52 × 10 ―4 ―2.18 × 10 ―3 1.62 × 10 ―2 0

)

0 0 0 0 0 0 0 0 0 0

―7.14 × 10 ―2 1.19 × 10 ―4 ―5.49 × 10 ―3 0 7.89 × 10 ―2 3.74 × 10 ―3 ―1.43 × 10 ―3 ―3.63 × 10 ―3 2.28 × 10 ―2 0

(A. 2)

(A. 3)

(A. 𝑭4 = (7.10 × 10 ―2 0 0

―8.45 × 10 ―3 0

―0.619 9.09 × 10 ―2) 4)

50

0.245 6.88 × 10 ―4 0.502 0 ―0.924 ―7.01 × 10 ―3 ―3.28 × 10 ―4 8.88 × 10 ―3 ―7.64 × 10 ―2 0

0.152 ―4.73 × 10 ―4 5. ―0.234 0 0.639 7.59 × 10 ―3 ―2.84 × 10 ―3 ―6.17 × 10 ―3 ―3.64 × 10 ―2 0

51

Graphical abstract

52

Credit author statement

Conceptualization, K.M., T.N., K.H., and H.S.; Data curation, K.M.; Formal analysis, K.M., H.S.; Funding acquisition, K.M., and H.S.; Investigation, K.M., and T.N.; Methodology, K.M., T.N., H.S.; Project administration, K.H., H.S.; Resources, K.H., H.S.; Software, K.M.; Supervision, H.S.; Validation, T.N., H.S.; Visualization, K.M., H.S.; Roles/Writing - original draft, K.M.; Writing - review & editing, K.M., T.N, and H.S.

53

Declaration of interests

☐ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

A patent by the authors HS, KM, and TN (Sugiyama, et al., 2019) is in application. The study was funded by a research grant from Powrex Corporation.

54