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Industrial Scale Experiments towards the Development of Process Evaluation Models for Continuous Pharmaceutical Tablet Manufacturing
Anton Friedl, Jiří J. Klemeš, Stefan Radl, Petar S. Varbanov, Thomas Wallek (Eds.) Proceedings of the 28th European Symposium on Computer Aided Process Engineering June 10th to 13th, 2018, Graz, Austria. © 2018 Elsevier B.V. All rights reserved. https://doi.org/10.1016/B978-0-444-64235-6.50288-6
Industrial Scale Experiments towards the Development of Process Evaluation Models for Continuous Pharmaceutical Tablet Manufacturing Kensaku Matsunami,a Takuya Nagato,b Koji Hasegawa,b Masahiko Hirao,a Hirokazu Sugiyamaa,* a
Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan b
Research & Development Department, Powrex Corporation, 8-121-1 Kitaitami, Itamishi, Hyogo, 664-0831, Japan [email protected]
Abstract Large-scale experimental results are presented, which compared batch and continuous pharmaceutical tablet manufacturing towards the development of process evaluation models. In the experiment, fluidized bed, high shear, and continuous granulators were used with the same raw materials, and the similarities and differences in the process and the products were analyzed. The maximum scale was 100 kg/lot. The experimental results were compared regarding quality of granules as well as tablets, and also regarding economic performance of the process. The analysis revealed significant differences in the product such as size/shape of granules; yield of the process was found to be an improvement opportunity for the continuous technology. Based on these findings, a strategy was created to develop models that can support process design of tablet manufacturing with considering product quality and process performance. Keywords: Continuous manufacturing, granulation, product quality, economy, process design
1. Introduction Continuous manufacturing is attracting attention in the pharmaceutical industry as an alternative to conventional batch manufacturing (Lee et al., 2015). Different authors contributed to the computer-aided design/operation of continuous manufacturing processes, such as Jolliffe and Gerogiorgis (2016) for active pharmaceutical ingredients (APIs), or Singh et al (2015) and Kruisz et al (2017) for tablets. Currently, the industry is highly interested in the capability of this novel manufacturing technology, especially regarding product quality and process economy. With a focus on tablet manufacturing, Järvinen et al (2015) compared product quality, and Sugiyama et al (2017) compared the economic performance of batch and continuous technologies. For process designers, models would be of high relevance that could estimate the resulting performance depending on the technology choice given the product components. Meng et al (2017) presented the effects of the process- and designparameters on granule size distribution in the continuous technology, with comparing physical properties of granules. However, the previous experiments remained on a small
K. Matsunami et al.
1656
scale, which leaves the opportunity for a larger scale investigation to develop realistic models. In this work, we
API
Excipients
Binder
Blending
Granulation
Lubricant Drying
Fluidized bed granulator High shear granulator
Blending
Tableting
Tablets
Continuous granulator
Figure 1 Manufacturing processes and granulators used in the experiments
performed large-scale experiments at maximum 100 kg/lot, for comparing batch and continuous tablet manufacturing technologies, towards the development of designsupport models for the industry.
2. Materials and methods Tablets were produced in the experiment, which contained 30 % ethenzamide and 70 % consisting of mannitol and crystalline cellulose as excipients, hydroxypropyl cellulose as a binder, and magnesium stearate as a lubricant. Figure 1 shows the manufacturing process consisting of the units of mixing, granulation, drying, blending, and compression. Wet granulation method was applied in the experiments; Figure 1 also shows the granulators used. The initial experiment was conducted at the scales from 5 to 10 kg/lot, and the full-scale experiment was done at the scale of 100 kg/lot. As shown in Figure 1, two types of batch technology and one type of continuous technology were adopted in the experiment. The speed of the continuous granulation was set as 25 kg/h. Although the mechanics of granulator were different between batch and continuous technologies, same materials were used with the same condition through the experiments. During and after the experiments, quality of granules and tablets, and the characteristics of the process were assessed for analyzing the differences between batch and continuous technologies. Particle shape, particle size, and bulk density were measured for the granules, and hardness, API content, and dissolution were measured for the tablets. For assessing the tablets, the following was set as the target: (i) tablet hardness is higher than 40 N, (ii) API content is between 95 % and 105 % of an absolute target, and (iii) dissolution rate is higher than 80 % within 30 minutes. The processes were characterized by the yield of the entire process, the causes of losses, and the amount of each loss cause. Here, yield was defined as the mass ratio of the total amount of the final tablet to that of the input raw materials. For the sake of space, this paper reports the results of the full-scale experiment mainly, with focusing on fluidized bed granulation and continuous granulation.
Industrial scale experiments
1657
Table 1 Characterization results of granules and tablets
Batch technology (fluidized bed)
Continuous technology
Granules SEM pictures
100 μm
100 μm -4
2.031×10 m
3
348 kg/m
510 kg/m3
Tablet hardness
65 N
65 N
API content
97.5 %
99.3 %
Mean diameter Loose bulk density
-4
1.627×10 m
Tablets
3. Results 3.1. Characterization results of granules The upper three rows of Table 1 shows the characterization results of granules: scanning electron microscopy (SEM) pictures, mean diameter, and loose bulk density. The SEM pictures suggest that the granules produced by fluidized bed granulation were porous, and those by continuous granulation were spherical. From the results of mean diameter and loose bulk density, the granules produced by the fluidized bed were smaller and had a lower density than those by continuous granulation. 3.2. Characterization results of tablets The lower two rows of Table 1 shows the characterization results of tablets: tablet hardness and API content, which satisfied the target values specified. Figure 2 (a) shows the comparative results of dissolution test between batch and continuous technologies, where all tablets achieved the target value, i.e., higher than 80 % within 30 minutes. In the pharmaceutical industry, so-called similarity factor 𝑓𝑓2 is widely used to evaluate the similarity of one dissolution profile with the other. The 𝑛𝑛
−0.5
1 𝑓𝑓2 = 50 ∙ log ��1 + �(𝑅𝑅𝑡𝑡 − 𝑇𝑇𝑡𝑡 )2 � 𝑛𝑛 𝑡𝑡=1
factor 𝑓𝑓2 is defined as Eq. (1):
× 100�
(1)
where the parameters 𝑛𝑛 and 𝑅𝑅𝑡 /𝑇𝑇𝑡 represent the number of time points, and the dissolution values at time 𝑡𝑡 . If value of 𝑓𝑓2 is higher than 50, dissolution of produced tablets is regarded as equivalent to the reference in the industry. The calculated value of 𝑓𝑓2 between
K. Matsunami et al.
100
Dissolution rate [%]
Dissolution rate [%]
1658
80 60 40
Batch Continuous
20 0
0
10 20 Dissolution time [min]
30
(a)
100 80 60 40
Large scale Small scale
20 0
0
10 20 Dissolution time [min]
30
(b)
Figure 2 Results of dissolution test: (a) comparison between batch and continuous and (b) comparison between large and small scale in continuous technology
batch (fluidized bed) and continuous was 76.0, which conformed the capability of the continuous technology at an industrial scale. Further new findings were obtained by comparing the results of dissolution tests between small- and large-scale experiments. Figure 2 (b) shows the comparative results of dissolution test between the initial (5 kg/lot) and the full-scale (100 kg/lot) experiments in continuous technology. Although same equipment was used in both two experiments, the value of 𝑓𝑓2 was calculated as 38.2, i.e.,
the profiles were judged as different. When the full-scale experiment was conducted for the first time, all the conditions were specified the same as in the initial experiment, except for the running time (changed from 12 minutes to 4 hours). However, the completion of the four-hour-operation needed adjustment of machine equipment and also the inlet water content, which caused the difference of the dissolution profile. This experiment was an actual case where the scale-up, which is believed to be easy in continuous technology, was not straightforward. 3.3. Characterization results of process
Regarding the performance of the process, yield was analyzed by measuring and calculating the amount of the final products and losses, which is summarized in Figure 3. In batch technology, yield was more than 93 % where the major causes of losses were sticking to the granulator and condition setting of compression. However in continuous technology, yield was 89 %, and a major cause of losses was stabilization at the beginning of the process. Regarding this improvement opportunity of the continuous technology, a further investigation was performed on the dynamic variation of mean granule diameter monitored by a process analytical technology (PAT) tool. This PAT tool was only for monitoring, and not for controlling the process. In the experiment, nine minutes were needed until the mean diameter of granules became stable, until which the products were discarded. The total rejected amount was 3.75 kg, i.e., 3.53 % lowering of yield, indicated as “stabilization of the process” Figure 3 (b). However, after this nine minutes, mean granule diameter and also dissolution profiles of the sampled tablets were not changing significantly, i.e., the process was stable for the remaining running time. These findings indicate that stabilizing the start-up operation is a great improvement opportunity for continuous technology to be economically competitive against batch technology.
Industrial scale experiments
1659
Condition setting Sticking to the granulator Condition setting Stabilization of the process of compression of compression
Yield: 93 %
Yield: 89 %
(a)
(b)
Figure 3 Results of yield and losses: (a) batch technology (high shear granulation), and (b) continuous technology
4. Strategy for model development This chapter presents the way to utilize the results of large-scale experiments towards the development of process evaluation models. Figure 4 shows an overview of the models to be developed. The input parameters are defined, which could affect the quality of granules and tablets, such as physical properties of raw materials, machine types, and process conditions. The final objectives are the set of information that the process designer would require for decision making, such as dissolution profile of tablets. Also, the intermediate objectives are defined that are affected by the input parameters and affect the final objectives, such as bulk density of granules. The strategy is to describe each relationship in the entire system (indicated by arrows in Figure 4) as a mathematical model (indicated by the equation in Figure 4). The experimental results shown in the previous chapter serve as the initial step of such model development. For example, a relationship was observed between the machine types, i.e., fluidized bed or continuous granulator, and the physical properties of granules/tablets, e.g., size/shape, or tablet hardness. Process conditions such as the water content amount during granulation process were also found to influence on the dissolution profile of the tablets in the continuous manufacturing. Quantifying such tendency on continuous technology is already novel by itself, because the knowledge of the process/technology is still to mature, as compared to long-established batch technology. Our strategy for the model development is to conduct further experiments with varying the values of the input parameters and to investigate the impact on the outcome. Design of Experiments (DoE) based approach is currently undertaken to find the critical input parameters, considering both physical properties of raw materials and process conditions.
5. Conclusions and outlook We presented 100 kg-scale experimental results on batch and continuous manufacturing technologies towards the development of design-oriented process evaluation models. To our knowledge, this is the first time report of the industrial scale experiments of continuous technology, being compared with the conventional batch technology. In the experiment, differences were observed in the granule properties between two technologies, however, looking into the obtained tablets, the dissolution profile was
K. Matsunami et al.
1660 Input parameters
Intermediate objectives •
Physical property of granules (e.g., bulk density) etc.
・・ ・
・・ ・
・・・
• Physical property of raw materials (e.g., formulation) • Machine type (e.g., batch/cont.) • Process condition (e.g., water content) etc.
Final objectives • Physical property of tablets (e.g., dissolution) • Economy (e.g., yield) • Process stabilization etc.
Figure 4 Overview of process evaluation models similar, and the quality was all above the target values. Regarding continuous technology, stabilization of the start-up operation was identified as the opportunity to reduce the product loss and to improve economic performance. As to the future work, further DoE-based experiments will provide the information for developing process evaluation models that can be comprehensively used for process design of pharmaceutical tablet manufacturing.
Acknowledgement Financial supports by Grant-in-Aid for Young Scientists (A) No. 17H04964 from Japan Society for the Promotion of Science are acknowledged.
References M. A. Järvinen, M. Paavola, S. Poutiainen, P. Itkonen, V. Pasanen, K. Uljas, K. Leiviskä, M. Juuti, J. Ketolainen, K. Järvinen, 2015. Comparison of a continuous ring layer wet granulation process with batch high shear and fluidized bed granulation process, Powder Technol., 275, 113–120 H. G. Jolliffe, D. I. Gerogiorgis, 2016. Plantwide design and economic evaluation of two Continuous Pharmaceutical Manufacturing (CPM) cases: Ibuprofen and artemisinin, Comput. Chem. Eng., 91, 269–288 J. Kruisz, J. Rehrl, S. Sacher, I. Aigner, M. Horn, J. G. Khinast, 2017. RTD modeling of a continuous dry granulation process for process control and materials diversion, Int. J. Pharm., 528, 334–344 S. L. Lee, T. F. O’Connor, X. Yang, C. N. Cruz, S. Chatterjee, R. D. Madurawe, C. M. V. Moore, L. X. Yu, J. Woodcock, 2015. Modernizing pharmaceutical manufacturing: from batch to continuous production, J. Pharm. Innov., 10, 191–199 W. Meng, S. Oka, X. Liu, T. Omer, R. Ramachandran, F. J. Muzzio, 2017. Effects of process and design parameters on granule size distribution in a continuous high shear granulation process, J. Pharm. Innov., 12, 283–295 R. Singh, A. D. Román-Ospino, R. J. Romañach, M. Ierapetritou, R. Ramachandran, 2015. Real time monitoring of powder blend bulk density for coupled feed-forward/feed-back control of a continuous direct compaction tablet manufacturing process, Int. J. Pharm., 495, 612–625 H. Sugiyama, K. Matsunami, K. Yabuta, 2017. Process systems engineering approaches for drug product manufacturing from tablets to injectables, Comput. Aided Chem. Eng., 40, 2785–2790
Industrial Scale Experiments towards the Development of Process Evaluation Models for Continuous Pharmaceutical Tablet Manufacturing Kensaku Matsunami,a Takuya Nagato,b Koji Hasegawa,b Masahiko Hirao,a Hirokazu Sugiyamaa,* a
Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan b
Research & Development Department, Powrex Corporation, 8-121-1 Kitaitami, Itamishi, Hyogo, 664-0831, Japan [email protected]
Abstract Large-scale experimental results are presented, which compared batch and continuous pharmaceutical tablet manufacturing towards the development of process evaluation models. In the experiment, fluidized bed, high shear, and continuous granulators were used with the same raw materials, and the similarities and differences in the process and the products were analyzed. The maximum scale was 100 kg/lot. The experimental results were compared regarding quality of granules as well as tablets, and also regarding economic performance of the process. The analysis revealed significant differences in the product such as size/shape of granules; yield of the process was found to be an improvement opportunity for the continuous technology. Based on these findings, a strategy was created to develop models that can support process design of tablet manufacturing with considering product quality and process performance. Keywords: Continuous manufacturing, granulation, product quality, economy, process design
1. Introduction Continuous manufacturing is attracting attention in the pharmaceutical industry as an alternative to conventional batch manufacturing (Lee et al., 2015). Different authors contributed to the computer-aided design/operation of continuous manufacturing processes, such as Jolliffe and Gerogiorgis (2016) for active pharmaceutical ingredients (APIs), or Singh et al (2015) and Kruisz et al (2017) for tablets. Currently, the industry is highly interested in the capability of this novel manufacturing technology, especially regarding product quality and process economy. With a focus on tablet manufacturing, Järvinen et al (2015) compared product quality, and Sugiyama et al (2017) compared the economic performance of batch and continuous technologies. For process designers, models would be of high relevance that could estimate the resulting performance depending on the technology choice given the product components. Meng et al (2017) presented the effects of the process- and designparameters on granule size distribution in the continuous technology, with comparing physical properties of granules. However, the previous experiments remained on a small
K. Matsunami et al.
1656
scale, which leaves the opportunity for a larger scale investigation to develop realistic models. In this work, we
API
Excipients
Binder
Blending
Granulation
Lubricant Drying
Fluidized bed granulator High shear granulator
Blending
Tableting
Tablets
Continuous granulator
Figure 1 Manufacturing processes and granulators used in the experiments
performed large-scale experiments at maximum 100 kg/lot, for comparing batch and continuous tablet manufacturing technologies, towards the development of designsupport models for the industry.
2. Materials and methods Tablets were produced in the experiment, which contained 30 % ethenzamide and 70 % consisting of mannitol and crystalline cellulose as excipients, hydroxypropyl cellulose as a binder, and magnesium stearate as a lubricant. Figure 1 shows the manufacturing process consisting of the units of mixing, granulation, drying, blending, and compression. Wet granulation method was applied in the experiments; Figure 1 also shows the granulators used. The initial experiment was conducted at the scales from 5 to 10 kg/lot, and the full-scale experiment was done at the scale of 100 kg/lot. As shown in Figure 1, two types of batch technology and one type of continuous technology were adopted in the experiment. The speed of the continuous granulation was set as 25 kg/h. Although the mechanics of granulator were different between batch and continuous technologies, same materials were used with the same condition through the experiments. During and after the experiments, quality of granules and tablets, and the characteristics of the process were assessed for analyzing the differences between batch and continuous technologies. Particle shape, particle size, and bulk density were measured for the granules, and hardness, API content, and dissolution were measured for the tablets. For assessing the tablets, the following was set as the target: (i) tablet hardness is higher than 40 N, (ii) API content is between 95 % and 105 % of an absolute target, and (iii) dissolution rate is higher than 80 % within 30 minutes. The processes were characterized by the yield of the entire process, the causes of losses, and the amount of each loss cause. Here, yield was defined as the mass ratio of the total amount of the final tablet to that of the input raw materials. For the sake of space, this paper reports the results of the full-scale experiment mainly, with focusing on fluidized bed granulation and continuous granulation.
Industrial scale experiments
1657
Table 1 Characterization results of granules and tablets
Batch technology (fluidized bed)
Continuous technology
Granules SEM pictures
100 μm
100 μm -4
2.031×10 m
3
348 kg/m
510 kg/m3
Tablet hardness
65 N
65 N
API content
97.5 %
99.3 %
Mean diameter Loose bulk density
-4
1.627×10 m
Tablets
3. Results 3.1. Characterization results of granules The upper three rows of Table 1 shows the characterization results of granules: scanning electron microscopy (SEM) pictures, mean diameter, and loose bulk density. The SEM pictures suggest that the granules produced by fluidized bed granulation were porous, and those by continuous granulation were spherical. From the results of mean diameter and loose bulk density, the granules produced by the fluidized bed were smaller and had a lower density than those by continuous granulation. 3.2. Characterization results of tablets The lower two rows of Table 1 shows the characterization results of tablets: tablet hardness and API content, which satisfied the target values specified. Figure 2 (a) shows the comparative results of dissolution test between batch and continuous technologies, where all tablets achieved the target value, i.e., higher than 80 % within 30 minutes. In the pharmaceutical industry, so-called similarity factor 𝑓𝑓2 is widely used to evaluate the similarity of one dissolution profile with the other. The 𝑛𝑛
−0.5
1 𝑓𝑓2 = 50 ∙ log ��1 + �(𝑅𝑅𝑡𝑡 − 𝑇𝑇𝑡𝑡 )2 � 𝑛𝑛 𝑡𝑡=1
factor 𝑓𝑓2 is defined as Eq. (1):
× 100�
(1)
where the parameters 𝑛𝑛 and 𝑅𝑅𝑡 /𝑇𝑇𝑡 represent the number of time points, and the dissolution values at time 𝑡𝑡 . If value of 𝑓𝑓2 is higher than 50, dissolution of produced tablets is regarded as equivalent to the reference in the industry. The calculated value of 𝑓𝑓2 between
K. Matsunami et al.
100
Dissolution rate [%]
Dissolution rate [%]
1658
80 60 40
Batch Continuous
20 0
0
10 20 Dissolution time [min]
30
(a)
100 80 60 40
Large scale Small scale
20 0
0
10 20 Dissolution time [min]
30
(b)
Figure 2 Results of dissolution test: (a) comparison between batch and continuous and (b) comparison between large and small scale in continuous technology
batch (fluidized bed) and continuous was 76.0, which conformed the capability of the continuous technology at an industrial scale. Further new findings were obtained by comparing the results of dissolution tests between small- and large-scale experiments. Figure 2 (b) shows the comparative results of dissolution test between the initial (5 kg/lot) and the full-scale (100 kg/lot) experiments in continuous technology. Although same equipment was used in both two experiments, the value of 𝑓𝑓2 was calculated as 38.2, i.e.,
the profiles were judged as different. When the full-scale experiment was conducted for the first time, all the conditions were specified the same as in the initial experiment, except for the running time (changed from 12 minutes to 4 hours). However, the completion of the four-hour-operation needed adjustment of machine equipment and also the inlet water content, which caused the difference of the dissolution profile. This experiment was an actual case where the scale-up, which is believed to be easy in continuous technology, was not straightforward. 3.3. Characterization results of process
Regarding the performance of the process, yield was analyzed by measuring and calculating the amount of the final products and losses, which is summarized in Figure 3. In batch technology, yield was more than 93 % where the major causes of losses were sticking to the granulator and condition setting of compression. However in continuous technology, yield was 89 %, and a major cause of losses was stabilization at the beginning of the process. Regarding this improvement opportunity of the continuous technology, a further investigation was performed on the dynamic variation of mean granule diameter monitored by a process analytical technology (PAT) tool. This PAT tool was only for monitoring, and not for controlling the process. In the experiment, nine minutes were needed until the mean diameter of granules became stable, until which the products were discarded. The total rejected amount was 3.75 kg, i.e., 3.53 % lowering of yield, indicated as “stabilization of the process” Figure 3 (b). However, after this nine minutes, mean granule diameter and also dissolution profiles of the sampled tablets were not changing significantly, i.e., the process was stable for the remaining running time. These findings indicate that stabilizing the start-up operation is a great improvement opportunity for continuous technology to be economically competitive against batch technology.
Industrial scale experiments
1659
Condition setting Sticking to the granulator Condition setting Stabilization of the process of compression of compression
Yield: 93 %
Yield: 89 %
(a)
(b)
Figure 3 Results of yield and losses: (a) batch technology (high shear granulation), and (b) continuous technology
4. Strategy for model development This chapter presents the way to utilize the results of large-scale experiments towards the development of process evaluation models. Figure 4 shows an overview of the models to be developed. The input parameters are defined, which could affect the quality of granules and tablets, such as physical properties of raw materials, machine types, and process conditions. The final objectives are the set of information that the process designer would require for decision making, such as dissolution profile of tablets. Also, the intermediate objectives are defined that are affected by the input parameters and affect the final objectives, such as bulk density of granules. The strategy is to describe each relationship in the entire system (indicated by arrows in Figure 4) as a mathematical model (indicated by the equation in Figure 4). The experimental results shown in the previous chapter serve as the initial step of such model development. For example, a relationship was observed between the machine types, i.e., fluidized bed or continuous granulator, and the physical properties of granules/tablets, e.g., size/shape, or tablet hardness. Process conditions such as the water content amount during granulation process were also found to influence on the dissolution profile of the tablets in the continuous manufacturing. Quantifying such tendency on continuous technology is already novel by itself, because the knowledge of the process/technology is still to mature, as compared to long-established batch technology. Our strategy for the model development is to conduct further experiments with varying the values of the input parameters and to investigate the impact on the outcome. Design of Experiments (DoE) based approach is currently undertaken to find the critical input parameters, considering both physical properties of raw materials and process conditions.
5. Conclusions and outlook We presented 100 kg-scale experimental results on batch and continuous manufacturing technologies towards the development of design-oriented process evaluation models. To our knowledge, this is the first time report of the industrial scale experiments of continuous technology, being compared with the conventional batch technology. In the experiment, differences were observed in the granule properties between two technologies, however, looking into the obtained tablets, the dissolution profile was
K. Matsunami et al.
1660 Input parameters
Intermediate objectives •
Physical property of granules (e.g., bulk density) etc.
・・ ・
・・ ・
・・・
• Physical property of raw materials (e.g., formulation) • Machine type (e.g., batch/cont.) • Process condition (e.g., water content) etc.
Final objectives • Physical property of tablets (e.g., dissolution) • Economy (e.g., yield) • Process stabilization etc.
Figure 4 Overview of process evaluation models similar, and the quality was all above the target values. Regarding continuous technology, stabilization of the start-up operation was identified as the opportunity to reduce the product loss and to improve economic performance. As to the future work, further DoE-based experiments will provide the information for developing process evaluation models that can be comprehensively used for process design of pharmaceutical tablet manufacturing.
Acknowledgement Financial supports by Grant-in-Aid for Young Scientists (A) No. 17H04964 from Japan Society for the Promotion of Science are acknowledged.
References M. A. Järvinen, M. Paavola, S. Poutiainen, P. Itkonen, V. Pasanen, K. Uljas, K. Leiviskä, M. Juuti, J. Ketolainen, K. Järvinen, 2015. Comparison of a continuous ring layer wet granulation process with batch high shear and fluidized bed granulation process, Powder Technol., 275, 113–120 H. G. Jolliffe, D. I. Gerogiorgis, 2016. Plantwide design and economic evaluation of two Continuous Pharmaceutical Manufacturing (CPM) cases: Ibuprofen and artemisinin, Comput. Chem. Eng., 91, 269–288 J. Kruisz, J. Rehrl, S. Sacher, I. Aigner, M. Horn, J. G. Khinast, 2017. RTD modeling of a continuous dry granulation process for process control and materials diversion, Int. J. Pharm., 528, 334–344 S. L. Lee, T. F. O’Connor, X. Yang, C. N. Cruz, S. Chatterjee, R. D. Madurawe, C. M. V. Moore, L. X. Yu, J. Woodcock, 2015. Modernizing pharmaceutical manufacturing: from batch to continuous production, J. Pharm. Innov., 10, 191–199 W. Meng, S. Oka, X. Liu, T. Omer, R. Ramachandran, F. J. Muzzio, 2017. Effects of process and design parameters on granule size distribution in a continuous high shear granulation process, J. Pharm. Innov., 12, 283–295 R. Singh, A. D. Román-Ospino, R. J. Romañach, M. Ierapetritou, R. Ramachandran, 2015. Real time monitoring of powder blend bulk density for coupled feed-forward/feed-back control of a continuous direct compaction tablet manufacturing process, Int. J. Pharm., 495, 612–625 H. Sugiyama, K. Matsunami, K. Yabuta, 2017. Process systems engineering approaches for drug product manufacturing from tablets to injectables, Comput. Aided Chem. Eng., 40, 2785–2790