A review on key aspects of wet granulation process for continuous pharmaceutical manufacturing of solid dosage oral formulations

Ping Chen; Mohammad Javed Ansari; Dmitry Bokov; Wanich Suksatan; Md Lutfor Rahman; Mohd Sani Sarjadi

doi:10.1016/j.arabjc.2021.103598

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Review article

02 2021

:15;

103598

doi:

10.1016/j.arabjc.2021.103598

A review on key aspects of wet granulation process for continuous pharmaceutical manufacturing of solid dosage oral formulations

Ping Chen^{^a,⁎}, Mohammad Javed Ansari^{^b}, Dmitry Bokov^{^c,^d}, Wanich Suksatan^{^e}, Md Lutfor Rahman^{^f,⁎}, Mohd Sani Sarjadi^{^f}

a Pharmacy Department, Zibo Central Hospital, Zibo, Shandong 255020, China

b Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia

c Institute of Pharmacy, Sechenov First Moscow State Medical University, 8 Trubetskaya St., bldg. 2, Moscow 119991, Russian Federation

d Laboratory of Food Chemistry, Federal Research Center of Nutrition, Biotechnology and Food Safety, 2/14 Ustyinsky pr., Moscow 109240, Russian Federation

e Faculty of Nursing, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand

f Faculty of Science and Natural Resources, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia

⁎Corresponding authors. colourfulseason9@163.com (Ping Chen), lotfor@ums.edu.my (Md Lutfor Rahman) colourfulseason9@163.com (Md Lutfor Rahman)

Received: 2021-6-30, Accepted: 2021-11-25,

Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Peer review under responsibility of King Saud University.

Abstract

Wet granulation process is a major unit operation in production of pharmaceuticals as solid dosage oral formulation. Indeed, granulation is used to improve the formulation properties such as flowability, compressibility, and so on for pharmaceutical manufacturing. Different types of granulations can be used in pharmaceutical manufacturing in which the selection of proper process depends on the operational conditions as well as formulation properties. In current decades, twin-screw wet granulation has been of paramount interest owing to its superior properties. Pharmaceutical manufacturing industry are trying to move towards continuous mode by which the efficiency can be improved compared to the batch mode. Therefore, development of continuous granulation process is of great importance. In this review article, various processing units applicable for wet granulation of pharmaceutical formulations for solid dosage forms are reviewed and discussed. The advantages and disadvantages of the processes are discussed and listed along with modeling approaches for simulation of process. The governing models and numerical schemes applicable for design of wet granulation are also critically discussed. The main focus is on wet granulation as this method has attracted much attention in pharmaceutical processing.

Keywords

Granulation process

Modelling

Pharmaceutical processing

Particle technology

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1 Introduction

Preparation and manufacturing of solid dosage oral formulations constitutes main portion of pharmaceutical manufacturing due to the main application of solid dosage drugs which can be orally administered for different remediation and treatments (Raissy et al., 2022; Davoodnia, 2011; Halim, Mohamad, Pm, Zakaria, and Muhammad, 2019; Suliman et al., 2015; Fattepur et al., 2018 ). Manufacturing lines include various processing steps so that each unit plays its role in preparation of drug products. Compared to preparation of biopharmaceuticals, small molecules pharmaceutical production can be run in continuous mode. In the manufacturing line, granulation plays crucial role which deals with particle flow and solid-state processing (Ang et al., 2018; Itodo et al., 2020; Bibi et al., 2021; Emrani et al., 2011 ). Granulation is identified as the enlargement process of small particles into granules. This process is extensively applied in disparate industrial-based applications like pharmaceutics, food and chemical (Portier et al., 2020; Ghafarifarsani et al., 2021/12/01/ 2021.; Ghafarifarsani et al., 2022/02/15/ 2022.; Deng et al., 2021/02/01/ 2021.; Guo et al., 2020 ). Engineering control of granulation is of critical importance for manufacturing drugs with enhanced efficacy (Zulhabri, 2019; Abdul et al., 2021; Khan et al., 2020 ). Granulation is usually done to enhance the powders flowability and avoid the co-mixing of substances (Ennis, 2016). Important granule characteristics including granule size distribution (GSD) and porosity can be driven using the rate of disparate macroscopic mechanisms such as nucleation, aggregation and consolidation (Dhenge et al., 2012; Kumar et al., 2013; Emrani et al., 2011 ). Despite the emergence of different challenges, continuous processing has been recently of great interest for all important industries owing to its great potential of application with various advantages for the process. Table 1 aims to present the advantages and challenges towards the use of continuous processing for different types of industries.

Table 1 Positive points and challenges towards the use of continuous processing for different types of industries (Kumar et al., 2013; Rathore et al., 2021; Mollan and Lodaya, 2004; Davoodnia, 2011; Nguyen et al., 2020; Yang et al., 2021 ).

Advantages	Challenges
Improved level of throughput	Modulating flow and level control
Reduction of inventory and related storage	Real-time in-process quality
Enhanced process safety	Real-time quality control
Reduced amount of air, water and power application	The need for personnel training
Decrease in the clean-up time	Superfluous controls and instrumentation
Reduced operator involvement	Fast corrections in various operating conditions
Reduced process footprint	Advanced process control

One of the prominent positive points of continuous processes is the non-existence of scale-up between clinical production and commercial manufacturing. Hence, continuous processes may prevent time wasting and expensive bio-equivalence investigations (Teżyk et al., 2016; Vercruysse et al., 2015; Bibi et al., 2021; Babanezhad et al., 2020 ). Additionally, continuous manufacturing processes are appropriate for the implementation of process analytical technology (PAT) tools, which guarantee permanent product quality and therefore, permitting a real time release strategy (Portier et al., 2020; Byrn et al., 2015; Qahir et al., 2021; Pishnamazi et al., 2020 ). PAT is an important tool in development of continuous manufacturing and plays crucial role in the regard for pharmaceutical manufacturing.

Various granulation processes have been developed and employed for solid particles such as high shear granulation, roller compaction, fluidized bed, twin-screw granulator, etc. Application of granulation process depends on the formulation and material properties. Twin-screw granulation (TSG) is an outstanding continuous process, which possesses great potential of application and excellent efficacy in granulating powders. This technique has brilliant advantages such as negligible residence time, superior management of granule properties, and great combination of active pharmaceutical ingredients (APIs) (Seem et al., 2015.; Dhenge et al., 2010; Ismail et al., 2020; Cao et al., 2021; Shirazian et al., 2018 ). Due to the abovementioned characteristics, twin-screw granulation has been recently of paramount attention in continuous granulation of pharmaceutical powders for oral dosage formulations. With the aim of effective implementation of the twin-screw granulator, promising mechanistic modeling can be developed for the continuous granulation in twin-screw granulation process. Development of promising models are important to facilitate the continuous granulation of APIs and implementing the Quality-by-Design (QbD) paradigm. Figs. 1 and 2 illustrate the developed experimental setup and screw configuration applied for twin-screw wet granulation for pharmaceutical formulations. As seen the twin-screw granulator consists of different elements such as extruder, screws, liquid injection pump, and feeder.

Experimental set up for twin-screw wet granulation. Reprinted from (Shirazian et al., 2018) with permission from Elsevier.

Screw configuration applied for twin-screw wet granulation. Reprinted from (Ismail et al., 2020) with permission from Elsevier.

The prominent aim of this manuscript is to provide a comprehensive overview on the state-of-the-art of modeling approaches such as PBM, DEM and ANN towards granulation process. As the future outlook, a number of questions have been recognized and responded to fill the existed knowledge gap towards the development of the granulation process in pharmaceutical applications.

2 Current modeling approaches for different granulation techniques

It has been recently identified that the existence of promising mathematical models may considerably eventuate in the creation of functional knowledge-based control of process (Ismail et al., 2020; Cao et al., 2021; Pishnamazi et al., 2020; Ghafarifarsani et al., 2021 ). Unfortunately, the majority of applied parameters in high shear wet granulation models are hard to measure. The majority of analyses on the basis of disparate types of granulation models are usually perceived to be implemented under default parameter values. This is prominently because of significant difficulties in the achievement of experimental data or paucity of appropriate measurement equipment to validate the developed model. Hence, it would be of great importance to describe appropriate mathematical models for granulation process. This section aims to review the state-of-the-art advancements in modeling approaches for different granulation techniques, which results in the perception of functional/operational gaps and thus, finding possible ways to face with the challenges. It has been reported that there are three main methods for modeling granulation: 1) population balance model (PBM), 2) Discrete Element Method (DEM), and 3) Machine Learning (ML) methods (Kumar et al., 2013; Elveny et al., 2021; Barrera Jiménez et al., 2021 ).

2.1

2.1 Population balance modeling

The population balance modeling (PBM) is identified as an efficient model to statistically interpret a system of particles that undergoes size increment and/or reduction. Indeed, PBM delas with particulate processes to track the evolution of particles properties. Apart from granulation, PBM technique possess great potential of use in disparate engineering sciences such as crystallization, aerosols coagulation and polymerization (Ramkrishna, 2000). In the high-shear wet granulation process, the population balance equation for a well-mixed system can be derived as follows (Pinto et al., 2007):

(1)

\frac{\partial n}{\partial x} (x, t) + \frac{\partial}{\partial x} [n \frac{dx}{dt}] (x, t) = R_{birth} (x, t) - R_{death} (x, t)

In this equation, $\frac{\partial}{\partial x} [n \frac{dx}{dt}] (x, t)$ , $R_{birth} (x, t)$ and $R_{death} (x, t)$ are described as the continuous growth along the internal direction of the particle diameter, the net formation and depletion rates of particles, respectively. Contribution of the aggregation impacts and breakage eventuates in modifying the Eq.1 to the following equation (Cameron et al., 2005):

(2)

\frac{\partial n}{\partial x} (x, t) + \frac{\partial}{\partial x} [n \frac{dx}{dt}] (x, t) = \frac{1}{2} \int_{0}^{x} β (x - y, y) n (x - y, t) n (y, t) d y - n (x, t) \int_{0}^{\infty} β (x, y) n (y, t) d y + \int_{0}^{x} K_{break} (y) ξ_{break} (y, x - y) n (y, t) d y - K_{break} (x) n (x, t)

In the pharmaceutical industry, granulation is often implemented as batch processes and thus, the majority of modeling investigations regarding to pharmaceutical granulation have concentrated on batch processes. In batch process, there is no spatial coordinate involved in the model owing to the assumption of a well-mixed system, but the model of a continuous system consists of both internal and external coordinates, which is presented by the following equation (Drumm et al., 2009):

(3)

\frac{\partial n}{\partial x} (x, z, t) + \frac{\partial}{\partial x} [n \frac{dx}{dt}] (x, z, t) = \frac{1}{2} \int_{0}^{x} β (x - y, y) n (x - y, z, t) n (y, z, t) d y - n (x, z, t) \int_{0}^{\infty} β (x, y) n (y, z, t) n (y, z, t) d y + \int_{0}^{x} K_{break} (y) ξ_{break} (y, x - y) n (y, z, t) d y - K_{break} (x) n (x, z, t) - \frac{\partial}{\partial z} [\dot{Z} n (x, z, t)]

In the abovementioned equation, $\dot{Z} = \frac{dz}{dt}$ is explained as the spatial velocity in the external coordinate. Simultaneous application of computational fluid dynamics (CFD) and discrete element method (DEM) has shown brilliant potential for numerical definition of particle flow regime and their visit frequency through a special granulator zone (Garth et al., 2011; Babanezhad et al., 2020; Raissy et al., 2022 ). Moreover, the DEM may be combined with the PBM by considering a dense flow of particles (Freireich et al., 2011; Li et al., 2012; Gantt et al., 2006 ). For high-shear wet granulation, the combination of PBM and DEM approaches has shown appropriate performance. Fig. 3 represents the knowledge development framework (KDF) applying modeling and measurement equipment.

Schematic demonstration of the KDF applying modeling and measurement equipment. Reprinted from (Kumar et al., 2013) with permission from Elsevier.

2.2

2.2 Discrete element method

In recent years, discrete element method (DEM) is identified as a bridge to overcome the disadvantages of the PBM approach in micro- and meso-scales (Deng et al., 2021.; Nakamura et al., 2013; Ketterhagen and Ende, and Hancock, 2009; Marjani et al., 2021; Ghafarifarsani et al., 2021; Gantt and Gatzke, 2005 ). Two prominent classifications of DEM include hard-sphere (HS) and soft-sphere (SS) methods, which are of great application in granulation modeling. Table 2 aims to present comprehensive information about the advantages and disadvantages of different DEM methods. These approaches have been investigated by various researchers (Ang et al., 2018; Liu et al., 2013; Herrmann and Luding, 1998; Nakhjiri and Roudsari, 2016; Babanezhad et al., 2020; Kumar et al., 2021 ). The HS approach assumes the rigidity of particles. Therefore, the collision of particles is rapid and binary, which is not valid for extremely dense high-shear wet granulation system. In the SS approach, the instantaneity of contacts cannot be assumed and the simultaneous presence of more than one contact is possible. Fig. 4 schematically illustrates the normal and tangential contact forces applying a spring, dashpot and slider approach.

Table 2 Advantages of physical modeling approaches and their associated challenges.

Model	Advantages	Challenges	Ref.
PBM	Simulation of a great number of particles	Semi-mechanistic approach because of the paucity of process knowledge	(Ramachandran et al., 2009; Gantt and Gatzke, 2006)
DEM	Mechanistic attitude	Computational restrictions	(Ketterhagen and M. T. am Ende, and B. C. Hancock, , 2009)
Hybrid PBM-DEM	Ability of modeling complicated dynamic mechanisms by interconnection of micro-scale to meso-scale	Difficulty of implementation Owing to challenges like wet granule yield strength, asperity height and so on.	(Sen et al., 2012; Štěpánek et al., 2009)
PBM with volume of fluid VoF procedures	Mechanistic attitude spatial distribution of binder in wet granule	Difficulty of implementation Requirement of considerable simplification	(Rajniak et al., 2009)
Hybrid PBM-CFD	Mechanistic approach Ability of application for developing simplified model	Lack of suitability for dense particle system Neglection of particle–particle interaction	(Shirazian et al., 2017; Bouffard et al., 2012)
Hybrid PBM-CM-DEM	Enhanced precision of the PBM	Low number of particles	(Bouffard et al., 2012; Babanezhad et al., 2021)

Schematic demonstration of the normal / tangential contact forces applying a spring, dashpot and slider approach. Reprinted from (Kumar et al., 2021).

The following equations are derived to express linear / angular momentum equations for each granule in the granulator as follows (Kumar et al., 2013):

(4)

m_{i} \frac{d v_{i}}{dt} = m \vec{g} + \vec{F_{P}} + \vec{F_{W}}

(5)

I_{i} \frac{d ω_{i}}{dt} = \vec{M_{P}} + \vec{M_{W}}

DEM possesses some privileges in comparison with PBM in cases of good capability to express complicated particle–particle interaction laws and to permit distribution of properties. Unlike DEM, PBM doesn’t have the potential of application for theoretical-based process design owing to powder specifications and important hydrodynamic parameters regarding liquid–solid interaction. Additionally, DEM can be well applied to compute numerous particle-scale quantities of interest like local concentrations and particle phase stresses throughout the implementation of simulation (Ang et al., 2018; Ketterhagen and M. T. am Ende, and B. C. Hancock, 2009; Liu et al., 2013; Kazemi et al., 2016 ). Despite the abovementioned advantages, significant computational cost is the main drawback of DEM, which can be taken place owing to the small integration time-step applied in this technique.

2.3

2.3 Artificial neural network model

The artificial neural network (ANN) model is a novel and promising model approach which can be used for simulation of wet and dry granulation processes. This technique is classified as Machine Learning (ML) method in simulation of processes which needs measured data for model’s calibration. The technique has been successfully used for simulation of twin-screw granulation which include three input operational parameters such as liquid to solid ratio, screw speed and powder flow rate. In different circumstances, d-values of particle size distribution can be regarded as response parameters for the ML model. Fundamentally, the ANN model contains three layers including input, hidden and output layers (Yu et al., 2015; Khan et al., 2020; Babanezhad et al., 2020; Babanezhad et al., 2021; Gong et al., 2018 ). The hidden layers contain various nodes for prediction of the output. Validation procedure, activation function, hidden layers and nodes number in each layer of network are identified as the principal parameters in the advancement of ANN model for prediction of granulation process. The abovementioned parameters are of great importance to perceive the optimized ANN topology for estimating the process. Discovery of the optimum ANN topology is an important challenge towards the development of ANN model due to the non-existence of straightforward method for this. Fig. 5 illustrates the topology of the boosted ANN employed for a TSG process (Yu et al., 2015; Othman et al., 2019; Babanezhad et al., 2020; Shirazian et al., 2017 ).

Schematic demonstration of the boosted ANN topology for estimation of granules d-values. Reprinted from (Shirazian et al., 2017) with permission from Elsevier.

To solve the PBM, solution time is of great need to achieve great results in terms of stability and accuracy as well. In recent years, ANN has been developed to prognosticate the particles mean residence time (MRT), needed for the simulation of particle size distribution in a twin-screw wet granulation. Hence, the hybrid ANN-PBM model is of great interest to consider process parameters and materials properties as inputs (Ismail et al., 2020). Fig. 6 schematically demonstrates the optimized ANN topology achieved by trial and error for prediction of MRT.

Schematic demonstration of the boosted ANN topology for prediction of MRT. Reprinted from (Ismail et al., 2019) with permission from Elsevier.

2.4

2.4 Mass conserving finite volume scheme (FVS)

Apart from the modeling approaches applicable for granulation process, the numerical schemes also play crucial role, particularly for the solution of PBM. The numerical schemes have been currently of paramount interest to discretize the PBM such as finite volume scheme (FVS). This procedure is on the basis of total mass conservation in the system via the addition of two simple weights to the discrete formulation. In order to obtain the mathematical formulation of the FVS, the assumption of particle properties concentration on the mean of the cells must be taken into account. With the aim of developing the formulation of the FVS, the discrete computational domain is built from a continuous domain with upper limit. The mean of each cell of the domain can be derived as follows (Singh et al., 2015; Barrasso and Ramachandran, 2012):

(6)

u_{\frac{1}{2}} = 0, u_{i} = \frac{u_{i + \frac{1}{2}} - u_{i - \frac{1}{2}}}{2} f o r i = 1, 2, 3, \dots \dots \dots \dots I .

$Δ u_{i} = \frac{u_{i + \frac{1}{2}} - u_{i - \frac{1}{2}}}{2}$ is known as the step size of the ith cell. N_i is defined as the number of particles in ith cell and can be predicted using the following equation (Singh et al., 2015; Barrasso and Ramachandran, 2012):

(7)

N_{i} = \int_{u_{i - \frac{1}{2}}}^{u_{i + \frac{1}{2}}} n (t, u) d u

The FVS is on the basis of modifying the PBM equation into a series of ordinary differential equations via integrating the boundaries of the ith cell as follows (Singh et al., 2015; Barrasso and Ramachandran, 2012):

(8)

\frac{\partial N_{i}}{\partial x} = \frac{1}{2} \int_{u_{i - \frac{1}{2}}}^{u_{i + \frac{1}{2}}} d u \int_{0}^{ϑ} β (t, u - ε, ε) n (t, u - ε) n (t, ε) d ε - \int_{u_{i - \frac{1}{2}}}^{u_{i + \frac{1}{2}}} d u n (t, u) \int_{0}^{\infty} β (t, u, ε) n (t, ε) d ε + \int_{u_{i - \frac{1}{2}}}^{u_{i + \frac{1}{2}}} d u \int_{u}^{\infty} b (u, ε) S (ε) n (t, ε) d ε - \int_{u_{i - \frac{1}{2}}}^{u_{i + \frac{1}{2}}} d u S (u) n (t, u)

Owing to the assumption of concentrated particle properties on the cells, the number density function may be rendered by a set of delta functions as follows (Singh et al., 2015; Barrasso and Ramachandran, 2012):

(9)

n (t, u) = \sum_{i = 1}^{I} N_{i} δ (u - u_{i})

Combination of two abovementioned equations results in achieving the following equation (Singh et al., 2015; Barrasso and Ramachandran, 2012):

(10)

\frac{\partial N_{i}}{\partial x} = B_{i} - D_{i}

In this equation, B_i and D_i relate to simultaneous aggregation-breakage mechanisms in the ith cell. Table 2 aims to comprehensively review disparate physical modeling approaches in granulation investigations.

3 Conclusion and future perspectives

Granulation has been recently perceived as an indisputably important processes in the pharmaceutical industry for the efficient manufacturing of various types of drugs in commercial scale. The prominent purpose of this technique is to fabricate granules applied directly as drugs or perform as an intermediate product in tablets / capsules. This paper aims to provide a short overview on the state-of-the-art of modeling approaches such as PBM, DEM and ANN towards granulation process. Paradigm alteration from batch to continuous granulation process has been an important turning point for the pharmaceutical industry and manufacturing of solid dosage forms. First-principles modeling approaches have recently attracted paramount attentions due to playing an indisputable role in optimization and control of important quality parameters in pharmaceutical granulation. Despite their importance, they must possess an acceptable level of reliability to obtain the purpose of simulation for granulation. By increasing the model dimensions, the existed modeling approaches have demonstrated performance constraints. Therefore, this important limitation has encouraged the investigators to develop more computationally-efficient mathematical procedures to facilitate the numerical modeling of pharmaceutical processes such as granulation.

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Introduction

Current modeling approaches for different granulation techniques

Population balance modeling
Discrete element method
Artificial neural network model
Mass conserving finite volume scheme (FVS)

Conclusion and future perspectives

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A review on key aspects of wet granulation process for continuous pharmaceutical manufacturing of solid dosage oral formulations

Abstract

Keywords

Granulation process

Modelling

Pharmaceutical processing

Particle technology

1 Introduction

2 Current modeling approaches for different granulation techniques

2.1 Population balance modeling

2.2 Discrete element method

2.3 Artificial neural network model

2.4 Mass conserving finite volume scheme (FVS)

3 Conclusion and future perspectives

References

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