Process Analytical Technology Tools for Monitoring

pharmaceutics

Review
Process Analytical Technology Tools for Monitoring Pharmaceutical Unit Operations: A Control Strategy for Continuous Process Verification
Eun Ji Kim 1,†, Ji Hyeon Kim 1,†, Min-Soo Kim 2, Seong Hoon Jeong 3 and Du Hyung Choi 1,*

1 Department of Pharmaceutical Engineering, Inje University, Gimhae-si, Gyeongnam 621-749, Korea; [email protected] (E.J.K.); [email protected] (J.H.K.)
2 College of Pharmacy, Pusan National University, Busandaehak-ro 63 heon-gil, Geumjeong-gu, Busan 46241, Korea; [email protected]
3 College of Pharmacy, Dongguk University-Seoul, Dongguk-ro-32, Ilsan-Donggu, Goyang 10326, Korea; [email protected]
* Correspondence: [email protected]; Tel.: +82-55-320-3395 † These authors contributed equally to this work.

Citation: Kim, E.J.; Kim, J.H.; Kim, M.-S.; Jeong, S.H.; Choi, D.H. Process Analytical Technology Tools for Monitoring Pharmaceutical Unit Operations: A Control Strategy for Continuous Process Verification. Pharmaceutics 2021, 13, 919. https://doi.org/10.3390/ pharmaceutics13060919
Academic Editors: Dimitrios G. Fatouros and Holger Grohganz
Received: 13 April 2021 Accepted: 16 June 2021 Published: 21 June 2021

Abstract: Various frameworks and methods, such as quality by design (QbD), real time release test (RTRT), and continuous process verification (CPV), have been introduced to improve drug product quality in the pharmaceutical industry. The methods recognize that an appropriate combination of process controls and predefined material attributes and intermediate quality attributes (IQAs) during processing may provide greater assurance of product quality than end-product testing. The efficient analysis method to monitor the relationship between process and quality should be used. Process analytical technology (PAT) was introduced to analyze IQAs during the process of establishing regulatory specifications and facilitating continuous manufacturing improvement. Although PAT was introduced in the pharmaceutical industry in the early 21st century, new PAT tools have been introduced during the last 20 years. In this review, we present the recent pharmaceutical PAT tools and their application in pharmaceutical unit operations. Based on unit operations, the significant IQAs monitored by PAT are presented to establish a control strategy for CPV and real time release testing (RTRT). In addition, the equipment type used in unit operation, PAT tools, multivariate statistical tools, and mathematical preprocessing are introduced, along with relevant literature. This review suggests that various PAT tools are rapidly advancing, and various IQAs are efficiently and precisely monitored in the pharmaceutical industry. Therefore, PAT could be a fundamental tool for the present QbD and CPV to improve drug product quality.
Keywords: process analytical technology; continuous process verification; quality by design; control strategy; quality attributes; critical process parameters

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

1. Introduction
Quality control in the pharmaceutical industry has traditionally depended on statistical process control (SPC) [1–4], which is used to understand the process and desired specification limits and to ensure a stable process by eliminating the allocable sources of variation. Statistical methods, including control charts and run charts, are used to inspect the quality of the post-manufacturing finished product and determine the performance suitability of unit operations in the pharmaceutical manufacturing process [1]. Moreover, most offline analyses and monitoring are conducted to evaluate the quality of the intermediate and finished products during the production batch process. For example, it is common to use control charts for monitoring general production processes, thereby ensuring that various aspects of the production process are controlled [5,6]. This traditional process verification is designed to perform process verification on finished batches under predesigned process conditions. Therefore, a disadvantage of this method is that the quality

Pharmaceutics 2021, 13, 919. https://doi.org/10.3390/pharmaceutics13060919

https://www.mdpi.com/journal/pharmaceutics

Pharmaceutics 2021, 13, 919

2 of 45
characteristics of intermediate products cannot be confirmed during the manufacturing process. Hence, identifying and solving problems that arise during the process requires a lot of time and results in relatively more high-quality defects. Moreover, there is no assurance that the entire lot conforms to the required specifications, and the method cannot be applied generally as a solution to all quality defects.
The International Council for Harmonisation (ICH) launched continuous process verification (CPV) to overcome SPC limitations, ensure process control, and improve the understanding of processes and product quality. Furthermore, ICH described CPV as an alternative approach to process validation, in which manufacturing process performance is continuously monitored and evaluated. In addition, CPV provides more information about variability and control, providing higher statistical confidence, improving the assessment of pharmaceutical manufacturing processes and higher assurance of continuous control status.
Another strategy introduced by the pharmaceutical industry to improve the understanding of the process and quality control is quality by design (QbD). QbD is defined in ICH Q8 guidelines as “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.” The development of a QbD-based pharmaceutical process involves a scientific risk-based systematic method to correlate critical process parameters (CPPs), input-materials attributes, and critical-quality attributes (CQAs) [3]. In general, QbD tools, including design of experiments (DoE), empirical modeling, and response surface analysis can develop a design space and reveal process variability during the pharmaceutical manufacturing process [7–9]. Unlike the existing quality by testing (QbT) system, in which the quality test of the finished product is mainly used, the QbD approach enables drug-quality management to enhance the quality of drugs based on science- and risk-based technology.
The US Food and Drug Administration (FDA)’s Center for Drug Evaluation and Research (CDER) discussed the need for FDA guidance to facilitate PAT implementation, and the FDA published the PAT guidance for innovative pharmaceutical manufacturing and quality in September 2004 [10]. It is recognized as an important paradigm shift in inspecting and approving processes for the continuous process verification of pharmaceutical production processes. This initiative is also implemented by the EMA, and the Ministry of Health, Labor, and Welfare (MHLW) in Japan adopted it immediately [11]. Interfacing manufacturing processes with analytical techniques is essential in PAT, as it facilitates process development according to QbD principles and enables real-time release testing (RTRT) [12]. PAT is applied to each unit operation in the manufacturing process; CPPs, which have a significant influence on CQAs, are controlled to present a high-quality product in the market [13–15].
PAT in CPV ensures product quality throughout the manufacturing process and enables the automation of transportation between product processes [16,17]. Furthermore, PAT is used as a control strategy for monitoring processes in real time, improving the understanding of the process, and RTRT [11,18,19]. The vast amount of information obtained by PAT enables rapid problem resolution, optimization, and defect detection. In addition, in the event of unexpected process changes, PAT can be applied to identify the root causes of undesired drug product-quality issues. Therefore, appropriate PAT enables the timely adjustment of process parameters, ensures good and stable product quality, and shortens the overall manufacturing time. These frameworks provide advantages that enable process control quickly and easily and are a trend that has been gradually adopted and introduced because it contributes significantly to establishing the control technology [18–21]. Furthermore, several studies have applied the QbD approach and PAT in pharmaceutical manufacturing processes [12,14,16–18].
This review focuses on applying PAT to QbD, RTRT, and CPV to improve drug quality in the pharmaceutical industry. It presents a significant relationship between the process and IQAs with the relevant literature, which could be monitored with PAT framework for

Pharmaceutics 2021, 13, 919

3 of 45
QbD, RTRT, and CPV. The recent PAT tools are presented with the relevant literature in pharmaceutical unit operations, including blending, granulation, tableting, and coating. Based on unit operations, IQAs measured by PAT, equipment type, PAT tools, multivariate statistical tools, and mathematical preprocessing are listed along with relevant literature.
2. Control Strategy for PAT Application
Appropriate control strategies should be applied during the manufacturing process to control variables affecting product quality. A control strategy comes from the understanding of products and processes and risk management. There are various approaches, such as in-process testing, RTRT, and finished product testing [11,14,15]. Traditional control strategies mostly rely on off-line analysis of finished-product testing. In addition, process verification has been performed on batches produced under predesigned process conditions. However, because it is difficult to predict the effect of process parameters during processing on finished-product quality, there is a limit to effectively controlling the process. It cannot be determined that all produced lots comply with the requirements. In addition, it is not easy to establish the feasibility of controlling the process variables of each unit process. Therefore, real-time process control is impossible and inefficient in terms of time and cost. The QbD approach has been introduced to overcome this and to improve understanding of product performance, identify critical process parameters (CPPs) during quality risk assessment of the product manufacturing process, and establish appropriate control strategies for each variable [13,22]. The QbD approach is applied for the accurate and reliable prediction of product-quality characteristics within the design space established, using each variable, manufacturing environment, and other conditions [12]. As this improves the understanding of products and processes, control strategies are applied to produce products of consistent quality that meet the desired quality attributes [23,24]. Introducing process control strategies to minimize the variability of the finished-product quality can justify an approach to quality assurance with an improved level of quality compared to finished-product testing using existing compendial standards.
2.1. The Effect of the Manufacturing Process on Intermediates during Processing
As described above, CPV was introduced in the pharmaceutical industry to produce high-quality drugs through quality control and quality assurance throughout the drug lifecycle. Therefore, in CPV, the quality control and process monitoring of intermediate products are recommended by using QbD to identify the quality of intermediate products that may affect the quality of finished products and by adjusting process parameters during the manufacturing process using the PAT framework. [12,13,22]. Table 1 presents the process parameters and quality of intermediate products that need to be adjusted in the manufacturing process, including blending, granulation, drying, coating, and tableting of solid dosage form based on the risk assessment using the QbD approach. Since the proposed process parameters and quality of the intermediate can greatly influence the quality attribute of the finished product, they should be adjusted by conducting appropriate process monitoring through a PAT framework during the manufacturing process [23,24].

Pharmaceutics 2021, 13, 919

4 of 45

Table 1. Effect of critical process parameters (CPPs) on intermediate quality attributes (IQAs) for the solid dosage form.

Process Blending

Critical Process Parameter
Blending time

Intermediate Quality Attributes
- Drug content - Blending
uniformity

Blending speed

- Blending uniformity

Order of input

- Drug content - Blending
uniformity

Environment -

Moisture content Drug content

Filling level - Drug content

Binder solvent amount

- Granule-size distribution
- Granule strength - Flowability

Justification

Ref

If the blending time is long, separation may occur depending on the particle characteristics, which may affect the content and content uniformity of the mixture.
When blending above the optimum blending speed, the particles adhere to the wall of the blender by centrifugal force, which may affect the uniformity of the content of the mixture.
The order of input of additives has little effect on content and content uniformity because of the blending process in the blender. However, the effect of the input of the lubricant may affect the content and content uniformity.
If temperature and humidity are not controlled, it may affect the moisture content of the mixture, and the content and content uniformity may be affected depending on the moisture and thermal stability of the drug.
Since the charging rate affects the movement of the particles, it can cause blending non-uniformity. This can affect the content and content uniformity of the mixture.
When the amount of liquid increases, the powder is completely wetted, which impedes the particle flow in the granulator, which can affect the particle-size distribution of the granules by increasing the residence time and torque value. When the amount of liquid is insufficient, weak granules are formed.

[25,26] [25–29]
[26] [26] [25–27] [30,31]

-
Binder solvent concentration -

High-shear Granulation granulation

Binder

-

solvent spray -

rate

-

Filling level -

Bulk/apparent/true density Granule-size distribution

The concentration of the binding liquid has a direct relationship with the binding force and can affect the density and particle-size distribution of the granules.

[32–34]

Drug content Granule size Granule strength
Drug content

The binder solvent spray rate is directly connected to the size of the granules. If it is too slow, the process time is lengthened, and it is difficult to form granules; if it is too fast, a mass may be formed. Therefore, it can affect the granule-size distribution and density.
The filling level affects the movement of particles in the granulator ball, so that fine granules may be generated due to an increase in the number of collisions between the granules and an increase in strength. This can affect the content and uniformity of the granules.

[35–38] [39,40]

Pharmaceutics 2021, 13, 919

5 of 45

Process

Critical Process Parameter
Impeller speed

Table 1. Cont.

Intermediate Quality Attributes

Justification

Ref

- Granule density

The speed of the impeller determines the state

- Flowability

of the granules. Accordingly, the porosity and

- Granule strength density of the granules may be affected, and

-

the particle-size distribution and flowability of [30,35,

Bulk/apparent/true the granules may be affected. In addition, as

41–45]

density

the impeller speed increases, it may affect the

- Granule-size

granule growth due to coalescence, so it may

distribution

affect the granule size.

-
Chopper speed -

Massing time -
-

-

Mill screen -

size

-

Nozzle type
-

Binder

amount

-

Fluidized-

bed

granulation

-

Binder concentration -

Granule-size distribution
Bulk/apparent/true density Flowability

Since the chopper speed plays a role in breaking the mass of granules, it can affect the density of the granules, the particle-size distribution, and the flowability of the granules.

[30,37, 41,46]

Granule-size distribution Granule strength Drug content uniformity
Bulk/apparent/true density Flowability

The massing time is a factor that determines the main physical properties of the granules. Depending on the massing time, the strength of the granules and the density of the granules can be affected, and thus, the flowability and particle-size distribution can also be affected. Excessive massing time can result in granule growth by coalescence, which can affect granule size. Accordingly, it may affect the content uniformity of the granules, which may affect formation of granules.

Granule-size distribution Flowability
Bulk/apparent/true density

The mill screen size can affect the physical properties of the granules, such as the density and flowability of the granules, due to a large correlation with the particle-size distribution of the granules.

[31,36, 41,47–
49]
[35]

Granule size Granule-size distribution Flowability
Granule-size distribution Flowability

The nozzle position affects the spray angle of the binder solvent, which can affect the agglomeration and growth of the granules, but the effect is negligible. In addition, the size of the nozzle hole affects the distribution of the binder solution. However, this has little effect when adjusted with other process variables.
When the amount of liquid increases, the powder is completely wetted, which impedes the particle flow in the granulator, which can affect the particle-size distribution of the granules by increasing the residence time and torque value. When the amount of liquid is insufficient, weak granules are formed.

[39,50] [51]

Bulk/apparent/true density Granule-size distribution

The concentration of the binding liquid has a direct relationship with the binding force and can affect the density and particle-size distribution of the granules.

[52–57]

Pharmaceutics 2021, 13, 919

6 of 45

Process

Critical Process Parameter
Binder spray rate
Air volume/ temperature/
humidity
Nozzle position

Table 1. Cont.

Intermediate Quality Attributes

Justification

-

The binder solvent spray rate is directly

Bulk/apparent/true connected to the size of the granules. If it is too

density

slow, the process time is lengthened and it is

- Granule size

difficult to form granules; if it is too fast, a

- Granule-size

mass may be formed. Therefore, it can affect

distribution

the granule-size distribution and density.

Higher temperature increases fineness due to

rapid drying, and lower temperature causes

granules to agglomerate, resulting in harder

and larger granules. This can affect the density,

-

flowability and particle-size distribution of the

Bulk/apparent/true granules. The flow of particles is determined

density

according to the air-supply flow rate, and if it

- Granule-size

is too high, the degree of blending due to

distribution

process loss may be lowered, which may affect

- Flowability

the density, flowability, and particle-size

distribution of the granules. The air-supply

humidity determines the size of the granules,

which can affect the particle-size distribution

of the granules.

- Granule size - Granule-size
distribution

The position of the nozzle affects the spray angle of the binder solvent, which can affect the agglomeration and growth of the granules, but the effect is negligible.

Ref [53–60]
[52,53, 59,61]
[54]

Nozzle type
-

Bulk/apparent/true density Granule-size distribution

The nozzle type affects the way the binder is sprayed into the fluidized-bed of the particles, which can affect the particle-size distribution or density of the granules.

[54,62]

Drying tempera- ture/time -
-

Environment -

Binder

-

viscosity

Twin-screw granulation
Liquid to solid ratio -

Granule-size distribution Flowability Granule density Moisture content
Moisture content Drug content
Granule-size distribution
Granule-size distribution Flowability

It can be determined according to the heat and moisture stability of the drug. If the drying time is short or the granules are not sufficiently dried due to the low drying temperature, the moisture content of the granules may be affected. If it is too high, fines may occur due to over-drying, which may affect the flowability and density of the particles.
If the temperature and humidity are not managed, it may affect the moisture content of the granules, and the moisture and thermal stability of the drug may affect the content and content uniformity of the granules.
When the binder solvent viscosity is high, there is a risk of granule mass, which may affect the size and particle-size distribution of the granules.
If the amount of liquid inside the granulator increases, the powder may become excessively moistened and impede the flow of the inside. This increases the residence time and can thus affect the size and particle-size distribution of the granules.

[59,61] [59,63]
[64] [65–69]

Pharmaceutics 2021, 13, 919

7 of 45

Process

Critical Process Parameter
Feeder rate
Screw speed

Table 1. Cont.

Intermediate Quality Attributes

Justification

-

The feed rate of the powder affects the

Bulk/apparent/true residence time, and due to the low feed rate,

density

the inside of the granulator is not completely

- Granule-size

filled, and the residence time may be

distribution

lengthened. This can affect granule properties,

- Flowability

such as the particle-size distribution, density

and flowability of the granules.

- Density

- Granule-size

The screw speed can affect the residence time

distribution

and, accordingly, the particle-size distribution

- Ribbon uniformity and density of the granules.

Ref
[65– 67,70]
[63,65– 67,71–
74]

Screw type -
Filling level -

Density Granule-size distribution

The type of screw is affected by the shape and angle of the screw to be engaged or the kneading pattern of the kneader part. This affects the amount of filling inside the granulator and can directly affect the compression and crushing of agglomerated particles and the distribution of the granules.

Granule-size distribution
Bulk/apparent/true density

The feeder amount is directly related to the residence time and can affect the particle-size distribution and density of the granules.

[65,66, 69,75]
[65,71]

- Granule size

Residence - Granule-size

time

distribution

- Ribbon density

Roller

- Granule-size

compactor

distribution

type

- Flowability

Roller compaction

Roller pressure

- Drug content - Granule-size
distribution - Flowability

Roller speed -
-

Ribbon density Drug content Granule-size distribution Flowability

The residence time of the powder can affect the size and particle-size distribution of the granules.

[66,72, 75]

Depending on the type of roller compactor, the principle of operation is different, which can affect the properties of the ribbon and the powdery properties of granules (roller width, roller diameter). The larger the diameter of the roller, the larger the compression area, so it may affect the characteristics of the ribbon, but, in general, the diameter of the roller is used as a fixed factor, so the effect on the intermediate product is insignificant.
Since the roller pressure determines the bonding force of the powder, it is judged to be directly related to the density of the ribbon. This may affect granule particle-size distribution, flowability and content uniformity after mill screening.
The roller speed is controlled by the screw speed, and it is judged that it has a direct relationship with the density of the ribbon as well as controlling the speed of the process. This affects the powder properties of the granules, which can affect the particle-size distribution and flowability of the granules.

[76]
[35,76– 80]
[35,78, 80–84]

Pharmaceutics 2021, 13, 919

8 of 45

Process
Drying process

Critical Process Parameter
Roller gap
Feeder rate
Feed screw speed
Residence time
Mill screen size
Mill speed
Drying time
Drying temperature
Inlet air temperature

Table 1. Cont.

Intermediate Quality Attributes
- Ribbon density - Granule density - Granule-size
distribution
- Ribbon Density - Granule-size
distribution - Flowability

Justification
The roller gap affects the bonding force of the powder fed into the feeder, and may affect the ribbon density. This affects the powder properties of the granules after mill screening, which may affect the particle-size distribution and flowability of the granules. As the width of the roller changes, it is directly related to the maximum pressure of the roller, which can affect the density of the ribbon and thus the density and particle-size distribution of the granules.
Input speed is directly related to roller pressure or roller spacing, which can affect the ribbon density, particle-size distribution and flowability of the granules.

Feed screw speed is a variable that is affected - Ribbon uniformity by roller pressure and roller spacing, and the
effect is negligible.

The residence time of the powder can affect the - Ribbon uniformity size and particle-size distribution of the
granules.

- Granule-size distribution
- Flowability

The size of the granulator can affect the physical properties of the granules, such as the density and flowability of the granules, due to a large correlation with the particle-size distribution of the granules.

- Granule-size distribution
- Flowability

The speed of the granulator can affect the powdery properties of the granules, but the effect is insignificant.

If the drying time is short, and the result is not

fully dried, the moisture content may be

- Particle size

affected. If the drying takes too long, fine

- Particle

powder may be generated due to over-drying,

distribution

which may affect the flowability and

- Drug polymorphic distribution of the particles.

form

If the drying temperature is low, and the result

- Moisture content is not fully dried, the moisture content may be

-

affected. If the drying temperature is too high,

Bulk/apparent/true fine powder may be generated due to

density

over-drying, which may affect the flowability

and particle distribution of the particles.

The thermal charge of the inlet drying gas reflects its capacity to dry the humid atomized - Moisture content droplets, and, therefore, higher inlet temperatures enable higher solvent evaporation rates.

Ref
[35,76, 78,79, 81,83]
[79,82] [80]
[85–87] [76,78,
88]
[89–91]
[92]

Pharmaceutics 2021, 13, 919

9 of 45

Process Coating process

Critical Process Parameter
Air flow rate

Table 1. Cont.

Intermediate Quality Attributes

Justification

- Particle

The flow of particles is determined according

distribution

to the air-supply flow-rate, and the air-supply

-

flow-rate determines the size of the granules.

Bulk/apparent/true This can affect the density and particle-size

density

distribution. In addition, an increase in the air

- Moisture content flow rate causes a higher evaporation rate.

Ref [89,93]

Rotation

-

speed

-

Nozzle

-

diameter -

Inlet air temperature -

Air flow rate -

Air volume -

-

Coating

-

solution

composition -

-

Spray rate

Atomizing air pressure

Coating uniformity
Coating thickness Weight gain Moisture content
Coating uniformity Moisture content
Coating efficacy
Coating efficacy
Coated drug appearance Coating uniformity Hardness Moisture content
Coating uniformity
Coating efficiency

As the speed increases, the tablets apparently tumble through the spray zone rather than sliding flat, so the end exposure is more frequent, and the coating becomes more uniform.
The size of the sprayed droplet varies depending on the nozzle diameter. Therefore, since the amount of the coating liquid to be sprayed varies, this affects the moisture content and residual solvent.
If the inlet air temperature is high, the tablets are excessively dried, and the surface becomes rough. If the inlet air temperature is low, the tablets stick together, and the moisture content of the tablets increases. Moisture content and coating uniformity are highly dependent on the incoming air temperature.
The air flow rate prevents the sprayed coating solution from reaching the tablet. The faster the air flow, the lower the velocity of the sprayed droplet and the smaller the droplet size. Therefore, it affects the coating efficiency.
An improper air layer due to worn or uneven drying may cause agglomeration between particles. An increase in air volume causes a decrease in spray density because the spray area increases as the droplet size decreases at the center of the spray.
In the case of functional coatings, the coating solution must contain an appropriate composition to deliver the desired effect of the drug, which affects the efficacy of the finished product. In addition, if the ratio of solids constituting the coating solution is high, efficient spraying becomes difficult, thus affecting the coating efficiency.
Too high a spray rate cause inadequate drying, twining, and sticking. Therefore, spray rate will have a significant impact on surface roughness and weight gain, thus affecting the coating uniformity.
Too high a spray pressure can lead to spray drying, and too low can cause agglomeration, which can have a significant impact on coating uniformity.

[94–96] [97,98]
[99]
[100]
[101]
[102, 103]
[96,99] [104– 106]

Pharmaceutics 2021, 13, 919

10 of 45

Process Tableting process

Table 1. Cont.

Critical Process Parameter

Intermediate Quality Attributes

Curing

-

temperature/ -

time

-

Coating efficiency Moisture content Hardness

-
Feeder speed -

Tablet porosity/density/solid fraction Drug content Weight variation

Rotary speed -
-

Drug content Hardness Weight variation

Precompression -

force

-

Main

compression force

-

Tablet appearance Thickness/ dimensions Tablet porosity/density/solid fraction Hardness

Dwell time - Weight variation

Ejection force - Tablet defects

Justification

Ref

The incorrect setting of the curing temperature and curing time will result in incomplete film formation. Thus, full film formation occurs when exposed to a certain curing temperature. The proper setting of curing time is necessary to achieve complete film adhesion.
Low feeder speeds can lead to improper die filling, which can lead to weight changes and changes in hardness and thickness. Fast feeder speeds can overfill the die cavity and lead to weight variations and hardness and thickness variations.
Rotary speed affects compressibility and even affects weight variation, which can affect drug content. A high rotary speed causes a much wider distribution of lubrication extent compared to the results from a low rotary speed. This may induce greater variability in hardness between tablets.

[107– 109]
[110]
[111]

Increasing compression force causes difficult particle rearrangement, deformation and fragmentation. Compression force affect tablet porosity, hardness, and density. In addition, depending on the tablet porosity, the degree to which moisture permeates into the tablet varies.
If the pressure holding time is too long, it deviates from the feeder speed, and inconsistent granules are filled into the die, which may cause weight fluctuations and affect the bonding force of the granules.
The optimal compression force must be determined to obtain the desired tablet hardness

[112– 116]
[110, 111,117–
119]
[120]

2.2. Workflow of PAT Framework for the Pharmaceutical Manufacturing Process
Before applying PAT to the process, first, it is necessary to understand the process and materials and consider the characteristics of the PAT tool. The most appropriate PAT tool is selected and applied to the PAT process. In this process, factors such as the location of the PAT tool and the measurement method should be considered. The measurement methods of PAT during the process are classified into at-line, on-line, and in-line. The at-line method is a measurement method that collects, separates, and analyzes a sample from a place very close to the process. On-line is a measurement method in which a sample is measured in the manufacturing process, suitability is determined, and the sample is returned to the process or discarded. In-line is a real time monitoring method using software without collecting a sample from the process flow [121]. After that, process monitoring is performed, and the collected data is analyzed and evaluated with statistical methods. The statistical methods could be divided into preprocessing technologies, chemometric modeling, and data evaluation. Standard normal variate (SNV), multiplicative scatter correction (MSC), and derivatives to reduce data interference and correct data are used in the preprocessing