# Comprehensive Study of Intermediate and Critical Quality Attributes for Process Control of High-Shear Wet Granulation Using Multivariate Analysis and the Quality by Design Approach

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Materials

^{®®}CL-F) were obtained from BASF AG (Ludwigshafen, Germany). Dicalcium phosphate and sodium hydroxide were obtained from Sigma-Aldrich Co. (St. Louis, MO, USA). Microcrystalline cellulose (MCC 101) was obtained from DFE Pharma (Dusseldorf, Germany). F-MELT

^{®®}(Type C) was obtained from Fuji Chemical Industries Co., Ltd. (Toyama, Japan). All other reagents were of analytical or HPLC grade and were used as received.

#### 2.2. Risk Assessment

#### 2.3. Experimental Design to Investigate High-Shear Wet Granulation in Lab Scale

^{®}software (version 10; Stat-Ease Inc., Minneapolis, MN, USA) was used. Response surface design was used to identify the optimal process parameters of high-shear wet granulation to prepare telmisartan granules. The three control factors were agitator speed (p

_{1}), massing time (p

_{2}), and binding suspension addition rate (p

_{3}). IQAs, such as granule hardness (q

_{1}), Carr’s index of granules (q

_{2}), granule size (q

_{3}), granule true density (q

_{4}), granule bulk density (q

_{5}), and granule FE (q

_{6}), and CQAs, such as tablet tensile strength (q

_{7}) and % dissolution (q

_{8}–q

_{11}) were evaluated as responses of experimental design, which were determined during the risk assessment. The best-fit empirical model was used to predict the relationship between control factors and response factors by comparing statistical parameters, such as the probability value (p value), the multiple correlation coefficient (R

^{2}), and adjusted multiple correlation coefficient (adjusted R

^{2}). Contour plots for control factors versus response factors were also presented using the software. To determine the optimized process parameters, the empirical models, which interpreted the relationship between control and response factors, were used. The target response values were the following: maximum tablet tensile strength within the lower (≥ 125 N/cm

^{2}) limit; minimum Carr’s index within the upper (≤ 15) limit; optimal intermediate ranges of dissolved drug at 5 min (72.29–83.29%), 10 min (78.68–88.58%), 15 min (85.00–92.10%), and 30 min (87.21–94.47%). In addition, 95% confidence intervals for the optimal condition were used for the control strategy.

^{®}, D-mannitol dicalcium phosphate, crospovidone, and MCC were filtered with a #30-mesh sieve and then blended for 3 min at 200 rpm; the powder mixture from each experiment was 475 g. A high-shear granulator (Mixer-granulator P 1, DIOSNA Dierks & Söhne GmbH, Germany) with a capacity of 1.23 L was used to prepare the granulation. The agitator speed, massing time, and binding suspension addition rate were set to the predetermined values of the experimental design, and the chopper speed was 2500 rpm. The end point of granulation was determined by monitoring agitator torque. The agitator torque profile provides wet granulation process stages, such as stages 1–4 [40]. Stage 1 represents the initiation of wetting and hence the agitator torque remains relatively constant. Stage 2 represents that agglomeration begins and agitator torque increases. Stage 3 involves the generation of useful granules and a plateau in agitator torque. Stage 4 corresponds to over-wetting with fluctuations in agitator torque. The granules were dried in an oven at 60 °C for 1 h. IQAs, such as granule hardness, Carr’s index, granule true density, granule bulk density, granule FE, and granule particle size were evaluated using the dried granules. The dried granules were introduced into a stainless steel double cone blender (Kopamtec, Anyang, Korea) with a capacity of 20,000 cm

^{3}and a fill-weight of approximately 30% of the mixer volume. Magnesium stearate was introduced into the blender and then blended at 50 rpm for 10 min. The resulting granules were compressed on a single-punch hydraulic laboratory press (Ichihashi Seiki Co., Ltd., Kyoto, Japan) at 15 MPa using a rectangle-shaped punch (15 × 8 mm). CQAs were evaluated using the tablets.

#### 2.4. Measurement of Quality Attributes

#### 2.4.1. Measurement of Granule Hardness and Tablet Tensile Strength

#### 2.4.2. Measurement of Granule Density and Carr’s Index

#### 2.4.3. Measurement of Particle Size

_{10}, d

_{50}, and d

_{90}) were determined. The measurements were repeated three times and the mean value of d

_{50}was used as particle size in this study.

#### 2.4.4. Powder Property Analysis Using a Rheometer

_{b}= 23.5 mm) moved downward into and through a powder bed contained within a cylindrical vessel with an inside diameter D

_{v}= 25 mm and volume of 25 mL. The blade speed was 100 mm/s with a −10° helix angle. As the blade moved through the sample, the FT4 measured both rotational and vertical resistances, in the form of torque and force, respectively. It was the composite of these two values that quantified the granule FE. It was calculated with the Equation (3):

#### 2.4.5. In Vitro Dissolution Test

#### 2.5. Multivariate Analysis between IQAs, CQAs, and CPPs

^{©}software (Sartorius Stedim Biotech., version 15, Umeå, Sweden) to evaluate the mutual relation between both IQAs of processes and drug product CQAs, and variable relationships in the experimental design of process parameters for high-shear wet granulation to prepare telmisartan granules. Moreover, the best fitted regression models between IQAs and CQAs were constructed with a prediction interval (95% PI), a confidence interval (95% CI), and a p value to be applicable at the pilot scale.

## 3. Results and Discussion

#### 3.1. Risk Assessment

#### 3.2. Effect of High-Shear Wet Granulation Process Parameters on IQAs and CQAs

#### 3.2.1. Significant Factors for Granules Hardness (q_{1})

_{1}) and massing time (p

_{2}) had positive effects, whereas binding suspension addition rate (p

_{3}) had a negative effect on granule hardness. This is presented in the contour plot in Figure 2a. The plot shows that higher agitator speeds and longer wet massing times increased granule hardness; granule hardness was also increased at low water addition rates. Higher agitator speeds and longer massing times could increase granule density and hardness because high shearing forces consistently exist between the particles [8,13,14,52]. Thereore, the bonding strength between particles strengthens the resistance to the separating forces.

#### 3.2.2. Significant Factors for Carr’s Index (q_{2})

_{1}) and massing time (p

_{2}) had significantly negative effects on granule flowability. The effect of agitator speed and massing time on Carr’s index of the granules is presented in the contour plot in Figure 2b. An increase in agitator speed and massing time could generate high shearing forces between the particles. Consequently, the growth and densification of the granules would progress. Therefore, Carr’s index decreases with increased agitator speed and massing time.

#### 3.2.3. Significant Factors for Granule Size (q_{3})

_{1}) had the largest effect on the surface mean diameter, followed by the massing time (p

_{2}). Both variables have a synergistic effect on the surface mean diameter. Long wet massing times and high agitator speeds may result in granule growth by coalescence, but large granules may undergo breakage until a steady state of granule size is reached. This implies that agitator speed and massing time have greater potential for controlling the granulation process [19,55,56].

#### 3.2.4. Significant Factors for Granule True Density (q_{4}) and Bulk Density (q_{5})

^{3}. Regression analysis of the response factor, with a significance value of p < 0.05, was used to generate the empirical model described by Equation (7).

_{1}), binding suspension addition rate (p

_{3}), massing time (p

_{2}), the mutual interactions between agitator speed and massing time, and mutual interactions between binding suspension, addition rate, and massing time. The contour plot presented in Figure 2d shows the effect of binding suspension addition rate and massing time on granule true density. It is evident that the granule true density increased with decreasing binding suspension addition rate and massing time (negative effect). Meanwhile, these two parameters exhibited a strong positive interaction (i.e., massing time showed a larger impact on the granule true density when using a larger binding suspension addition rate).

_{1}), binding suspension addition rate (p

_{3}), massing time (p

_{2}), the mutual interactions between them were the significant factors affecting granule bulk density. The effect of binding suspension addition rate and massing time on the granule bulk density is presented in Figure 2e as a contour plot. The granule bulk density increased with increasing binding suspension addition rate and massing time (positive effect). These two parameters also exhibited a strong positive interaction. Meanwhile, the mutual interaction between agitator speed and massing time had a negative effect on the granule bulk density.

#### 3.2.5. Significant Factors for Granule FE (q_{6})

_{1}), massing time (p

_{2}), binding suspension addition rate (p

_{3}), and the mutual interactions between binding suspension addition rate and massing time. The contour plot presented in Figure 2f shows the effect of blade speed and massing time on the granule FE. Based on the coefficients of the empirical model, the granule flow energy decreased with increasing blade speed and massing time (negative effect).

#### 3.2.6. Significant Factors for Tablet Tensile Strength (q_{7})

^{2}. Regression analysis of the response factor, with a significance of p < 0.05, was used to generate the empirical model described by Equation (10).

_{1}) had the highest effect on tablet tensile strength, followed by massing time (p

_{2}). The positive coefficients of agitator speed and massing time indicate that higher values for these parameters result in higher tensile strength. binding suspension addition rate (p

_{3}) had a negative effect on tensile strength. The mutual interactions between the agitator speed and binding suspension addition rate, and between the agitator speed and massing time had negative effects on the tensile strength of the tablet.

#### 3.2.7. Significant Factors for Dissolution (q_{8}–q_{11})

_{1}) and massing time (p

_{2}) could have significant, negative effects on dissolution, whereas the binding suspension addition rate (p

_{3}) may have no effect on dissolution. From 5–30 min, the larger p

_{1}value could indicate that dissolution over the initial 30 min is significantly controlled by agitator speed. The blade presents the mechanical energy to generate granules, whereas the mechanical energy generated by the chopper breaks up granules that are generated. Thus, the response of the granules, such as granule size and growth rate could be significantly influenced by the energy resulting from the agitator and chopper speed [63,64]. Generally, larger granules have a lower dissolution profile than smaller granules because smaller granules have a larger surface area in contact with the dissolution medium. Therefore, increased agitator speed could result in increased granule size, negatively influencing the dissolution profile. The massing time also had a negative effect on the dissolution profile. A high massing time could present the mechanical energy required for mixing the feed powder. This mechanical energy could increase the granule size; thus, the dissolution profile could be negatively influenced by the massing time.

#### 3.3. Optimal Process Parameters and Monte Carlo Simulations

^{2}) limit; minimum Carr’s index within the upper (≤ 15) limit; optimal intermediate ranges of dissolved drug at 5 min (72.29–83.29%), 10 min (78.68–88.58%), 15 min (85.00–92.10%), and 30 min (87.21–94.47%). In addition, a 95% confidence interval of the optimal condition was used for the control strategy. Based on these conditions, the responses were then combined to reveal the overall optimum region. This optimum region of the high-shear granulation process is shown in Figure 4a, in which the yellow area indicates the values of the control factors that achieve the desired responses simultaneously. To investigate the robustness of the optimum region, Monte Carlo simulations were performed with MODDE

^{®}software (Sartorius Stedim Biotech., version 12.0.1, Umeå, Sweden). To find the most robust process parameters in the operating space, the setting point should have a DPMO (Defects Per Million Opportunities) around or less than the specification. A DMPO result of 1000 means 0.1% risk of failure. The result is presented in Figure 4b with the probability of failure. The green area shows a quality certainty of 99.9%. Furthermore, as the probability of failure increased, the area was represented with red color. This suggested that it was not safe to obtain the target response at a higher agitator speed and higher massing time. However, agitator speeds ranging 700–900 rpm and massing times ranging 2.5–3.5 min in high-shear wet granulation had a low probability of failure less than 1%. To validate the optimal process parameters, these settings were performed at the lab scale. The spray rate was fixed with 5.20 mL/min. The agitator speed was selected 700, 800, and 900 rpm in the operating space. The massing time was also selected in the operating space as 2.5, 3.0, 3.5 min. Table 4 presents the experimental results with the target values of the response factors. Additionally, differences in biases and relative bias percentages between the response values generated by the optimal process parameters and the target values were small. The difference in biases relative bias percentages were lower than 2.20 and 5.08. This suggests that a risk reduction for the high-shear wet granulation at the lab scale can be obtained using QbD approach that controlled the development process from the initial stage with RA to robust operating space.

#### 3.4. Multivariate Analysis between CQAs of Drug Product and IQAs of Process

#### 3.5. Scale-Up to Validate the Mutual Effects between CPPs, IQAs, and CQAs

^{2}) and upper (≤ 145 N/cm

^{2}) limits; minimum Carr’s index within the lower (≥ 9) and upper (≤ 15) limits; optimal intermediate ranges of dissolved drug at 5 min (72.29–83.29%), 10 min (78.68–88.58%), 15 min (85.00–92.10%), and 30 min (84.47–94.47%); minimum friability less than 0.25%; optimal intermediate ranges of assay (95–105%); minimum content uniformity less than 5.0% RSD. This suggests that the integrated process parameters obtained from experimental design at the lab scale could be applied at the pilot scale with the scale-up strategy. Additionally, the similar relationship between IQAs, CQAs, and CPPs was shown regardless of batch size, it can be used to predict CQAs for control strategy.

## 4. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 1.**Pareto chart of the risk priority number (RPN). Individual values are represented in descending order by bars, and the cumulative total is represented by the line. The left vertical axis is the RPN. The right vertical axis is the cumulative percentage of the RPN.

**Figure 2.**Contour plots correlating response factors (IQAs) to optimize process parameters of high-shear wet granulation for the telmisartan test tablet. (

**a**) Granule hardness, (

**b**) Carr’s index, (

**c**) Granule size, (

**d**) True density, (

**e**) Bulk density, and (

**f**) Granule flow energy (FE).

**Figure 3.**Contour plots correlating response factors (CQAs) to optimize process parameters of high-shear wet granulation for the telmisartan test tablet. (

**a**) Tensile strength, (

**b**) % dissolution at 5 min, (

**c**) % dissolution at 10 min, (

**d**) % dissolution at 15 min, and (

**e**) % dissolution at 30 min.

**Figure 4.**(

**a**) Operating spaces (overlay plot) to optimize process parameters of high-shear wet granulation for the telmisartan test tablet. The yellow area is the operating space with 95% confidence intervals. (

**b**) Monte Carlo simulation results for the operating space. The green area shows a quality certainty of 99.9.

**Figure 5.**Cumulative R2 (R2X(cum)) and Q2 (Q2(cum)) of response factors (IQAs and CQAs) for two principal components.

**Figure 6.**(

**a**) Score scatter plot with two principal components (t[1] and t[2]) and (

**b**) loading plot (p[1] and p[2]) presenting variable relationships.

**Figure 7.**Score contribution plots showing variables contributing to the difference between run order 1 (

**a**) or run order 3 (

**b**) and the average of all run orders.

**Figure 8.**Predicted versus measured plots for tensile strengths (CQAs). (

**a**) % dissolution at 5 min, (

**b**) % dissolution at 10 min, (

**c**) % dissolution at 15 min, and (

**d**) tensile strength.

**Figure 10.**The fitted line plots of % dissolution ((

**a**) 5 min, (

**b**) 10 min, (

**c**) 15 min, and (

**d**) 30 min) versus granule hardness. The black dots, red line, blue dotted line, and black dotted line represent the experimental results, best fitted regression, 95% confidence intervals, and 95% prediction intervals, respectively.

**Figure 11.**The fitted line plots of % dissolution ((

**a**) 5 min, (

**b**) 10 min, (

**c**) 15 min, and (

**d**) 30 min) versus granule size. The black dots, red line, blue dotted line, and black dotted line represent the experimental results, best fitted regression, 95% confidence intervals, and 95% prediction intervals, respectively.

**Figure 12.**The fitted line plots of (

**a**) tensile strength versus granule FE and (

**b**) tensile strength versus Carr’s index. The black dots, red line, blue dotted line, and black dotted line represent the experimental results, best fitted regression, 95% confidence intervals, and 95% prediction intervals, respectively.

**Figure 13.**Agitator torque profiles at the lab scale (black line) and pilot scale (red line). The agitator powder consumption is plotted versus liquid/solid (L/S) ratio (%).

Purpose | Excipient | Amount (mg/tablet) |
---|---|---|

Active pharmaceutical ingredient | Telmisartan | 80 |

Solubilizing agent | NaOH | 6.7 |

Meglumine | 24 | |

Binding agent | PVP K25 | 12 |

Thicking agent | D-mannitol | 166 |

MCC 101 | 39 | |

Dicalcium phosphate | 9.3 | |

Disintegrant agent | F-melt typeC | 20 |

Crospovidone | 118 | |

Lubricant agent | St-Mg | 5 |

Total | 480 |

**Table 2.**Risk assessment based on prior knowledge and experience, and information about the control tablet from published literature or analyses. (RPN: risk priority number; IQAs: intermediate quality attributes; CQA: critical quality attribute; CU: content uniformity; FE: flow energy)

Unit Process | Failure Mode | S | P | D | RPN | Risk Degree | Related IQAs and CQAs |
---|---|---|---|---|---|---|---|

High shear granulation for test tablet | Agitator speed | 5 | 5 | 5 | 125 | High | dissolution, CU, tensile strength, Friability, granule size, granule hardness, Carr’s index, granule density, granule FE |

Chopper speed | 5 | 2 | 5 | 50 | Moderate | dissolution, CU, tensile strength, Friability, granule size, granule hardness, Carr’s index, granule density, granule FE | |

Solvent spray rate | 4 | 5 | 5 | 100 | High | dissolution, CU, tensile strength, Friability, granule size, granule hardness, Carr’s index, granule density, granule FE | |

Massing time | 5 | 5 | 5 | 125 | High | ||

Drying temp. | 3 | 1 | 3 | 9 | Low | tensile strength, Friability, granule hardness, Carr’s index, granule density | |

Drying time | 3 | 1 | 3 | 9 | Low | tensile strength, Friability, granule hardness, Carr’s index, granule density |

**Table 3.**Experimental design of high-shear wet granulation process parameters with three input control factors and values of the response factors for different process parameters

Run Order | Control Factors | Response Factors | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

CPP | Intermediate QAs | Drug Product CQAs | ||||||||||||

Agitator Speed (rpm) | Massing Time (min) | Spray Rate (mL/min) | Granule Hardness (N) | Carr’s Index | Granule Size (μm) | Granule True Density (g/cm^{3}) | Granule Bulk Density (g/mL) | Granule FE | Tensile Strength (N/cm^{2}) | % Dissolution | ||||

5 min | 10 min | 15 min | 30 min | |||||||||||

p_{1} | p_{2} | p_{3} | q_{1} | q_{2} | q_{3} | q_{4} | q_{5} | q_{6} | q_{9} | q_{10} | q_{11} | q_{12} | q_{13} | |

1 | 1200 | 5 | 5.2 | 4.96 | 9.00 | 393.59 | 1.438 | 0.469 | 121 | 145.19 | 83.25 | 89.77 | 91.25 | 93.54 |

2 | 800 | 3 | 5.2 | 2.06 | 13.40 | 256.00 | 1.457 | 0.471 | 180 | 138.00 | 80.21 | 84.56 | 87.12 | 89.94 |

3 | 400 | 1 | 5.2 | 0.90 | 16.39 | 186.94 | 1.478 | 0.468 | 220 | 125.88 | 82.56 | 88.12 | 89.22 | 91.45 |

4 | 800 | 1 | 3.7 | 1.46 | 15.27 | 232.85 | 1.492 | 0.471 | 205 | 134.15 | 82.25 | 87.58 | 88.67 | 91.21 |

5 | 800 | 3 | 5.2 | 2.14 | 13.29 | 265.95 | 1.455 | 0.472 | 179 | 138.25 | 78.11 | 83.55 | 87.45 | 89.79 |

6 | 800 | 5 | 6.7 | 2.53 | 12.33 | 299.63 | 1.465 | 0.479 | 166 | 140.12 | 79.87 | 84.97 | 89.97 | 91.22 |

7 | 1200 | 1 | 5.2 | 3.28 | 11.74 | 328.04 | 1.463 | 0.470 | 158 | 142.74 | 77.45 | 82.58 | 86.64 | 89.12 |

8 | 400 | 5 | 5.2 | 1.55 | 13.65 | 197.12 | 1.416 | 0.479 | 184 | 134.55 | 77.95 | 82.12 | 86.25 | 89.84 |

9 | 400 | 3 | 3.7 | 1.34 | 14.97 | 170.09 | 1.456 | 0.471 | 201 | 131.58 | 79.45 | 84.21 | 89.31 | 90.55 |

10 | 1200 | 3 | 6.7 | 3.56 | 11.56 | 359.09 | 1.454 | 0.473 | 155 | 141.00 | 77.75 | 82.98 | 87.12 | 90.05 |

11 | 400 | 3 | 6.7 | 1.07 | 15.54 | 172.72 | 1.443 | 0.475 | 209 | 133.42 | 78.58 | 84.97 | 89.54 | 91.21 |

12 | 1200 | 3 | 3.7 | 5.00 | 10.27 | 347.43 | 1.458 | 0.468 | 138 | 145.51 | 58.43 | 73.12 | 79.59 | 84.58 |

13 | 800 | 1 | 6.7 | 1.54 | 14.07 | 245.79 | 1.434 | 0.468 | 189 | 132.87 | 62.55 | 78.95 | 82.65 | 87.89 |

14 | 800 | 5 | 3.7 | 3.29 | 10.86 | 141.80 | 1.409 | 0.469 | 146 | 255.66 | 60.54 | 75.55 | 81.23 | 86.57 |

15 | 800 | 3 | 5.2 | 2.08 | 13.40 | 138.07 | 1.449 | 0.471 | 180 | 258.23 | 59.87 | 74.62 | 80.22 | 85.56 |

**Table 4.**Optimal process parameters, target values, and validated results at the lab scale for the response factors

Optimal Process Parameters | Response Factors | |||||||
---|---|---|---|---|---|---|---|---|

x_{1} | x_{2} | x_{3} | q_{1} | q_{2} | q_{7} | q_{8} | q_{9} | q_{10} |

Agitator Speed (rpm) | Massing Time (min) | Spray RRate (mL/min) | Carr’s index | Tensile Strength (N/cm^{2}) | Dissolution at 5 min (%) | Dissolution at 10 min (%) | Dissolution at 15 min (%) | Dissolution at 30 min (%) |

700 | 2.5 | 5.20 | 13.87 | 133.1 | 79.5 | 82.39 | 88.55 | 90.26 |

Target values | 13.97 | 135.1 | 81.05 | 84.47 | 89.27 | 91.45 | ||

Absolute viases | 0.10 | 2.00 | 1.55 | 2.08 | 0.72 | 1.19 | ||

Relative biases (%) | 4.50 | 1.47 | 2.07 | 2.54 | 0.82 | 1.33 | ||

700 | 3.5 | 5.20 | 13.12 | 134.72 | 79.25 | 82.23 | 87.13 | 87.9 |

Target values | 13.24 | 136.92 | 80.37 | 83.53 | 88.45 | 89.89 | ||

Absolute viases | 0.12 | 2.20 | 1.12 | 1.30 | 1.32 | 1.99 | ||

Relative biases (%) | 5.08 | 1.61 | 1.50 | 1.62 | 1.54 | 2.29 | ||

900 | 2.5 | 5.20 | 12.79 | 137.71 | 75.46 | 80.72 | 86.16 | 87.78 |

Target values | 12.85 | 138.35 | 75.62 | 81.48 | 87.24 | 89.10 | ||

Absolute viases | 0.06 | 0.64 | 0.16 | 0.76 | 1.08 | 1.32 | ||

Relative biases (%) | 2.33 | 0.46 | 0.22 | 0.95 | 1.26 | 1.51 | ||

900 | 3.5 | 5.20 | 12.08 | 139.66 | 74.18 | 79.61 | 85.93 | 87.64 |

Target values | 12.12 | 139.79 | 74.94 | 80.54 | 86.42 | 88.55 | ||

Absolute viases | 0.04 | 0.13 | 0.76 | 0.93 | 0.49 | 0.91 | ||

Relative biases (%) | 1.43 | 0.09 | 1.04 | 1.16 | 0.57 | 1.03 | ||

800 | 3 | 5.20 | 13 | 137.41 | 77.69 | 81.58 | 87.55 | 88.59 |

Target values | 13.04 | 137.54 | 78.45 | 82.51 | 88.04 | 89.50 | ||

Absolute viases | 0.04 | 0.13 | 0.76 | 0.93 | 0.49 | 0.91 | ||

Relative biases (%) | 1.43 | 0.09 | 1.04 | 1.16 | 0.57 | 1.03 |

Process Parameters | Scale | |
---|---|---|

Lab | Pilot | |

Used granulator | P1 | P6 |

Granulator capacity (L) | 1.23 | 6.05 |

Batch size (g) | 475 | 2375 |

Fill level (%) | 38.6 | 39.2 |

Agitator speed corresponding to agitator tip speed of 6.0 m/s | 800 rpm | 480 rpm |

Massing time (min) | 3 | 3 |

Spray rate (ml/min) | 5.2 | 5.2 |

Quality Attributes | Scale | Absolute Biases | Relative Biases (%) | ||

Lab | Pilot | ||||

IQAs | Granule hardness (N) | 2.17 | 2.25 | 0.08 | 3.69 |

Carr’s index | 13.12 | 13.45 | 0.33 | 2.52 | |

Granule size (μm) | 264.23 | 275.45 | 11.22 | 4.25 | |

Granule true density (g/cm^{3}) | 1.453 | 1.457 | 0.004 | 0.28 | |

Granule bulk density (g/mL) | 0.476 | 0.479 | 0.003 | 0.63 | |

Granule FE | 175 | 173 | 2 | 1.16 | |

CQAs | Tensile strength (N/cm^{2}) | 137.55 | 138.25 | 0.70 | 0.51 |

% dissolution at 5 min | 78.54 | 79.15 | 0.61 | 0.78 | |

% dissolution at 10 min | 82.55 | 82.96 | 0.41 | 0.50 | |

% dissolution at 15 min | 88.14 | 88.35 | 0.21 | 0.24 | |

% dissolution at 30 min | 89.52 | 90.08 | 0.56 | 0.63 | |

Friability (%) | 0.12 | 0.11 | 0.01 | 9.09 | |

Content uniformity (%) | 0.78 | 0.74 | 0.04 | 5.41 | |

Assay (%) | 101.43 | 100.78 | 0.65 | 0.64 |

© 2019 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 (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Han, J.K.; Shin, B.S.; Choi, D.H.
Comprehensive Study of Intermediate and Critical Quality Attributes for Process Control of High-Shear Wet Granulation Using Multivariate Analysis and the Quality by Design Approach. *Pharmaceutics* **2019**, *11*, 252.
https://doi.org/10.3390/pharmaceutics11060252

**AMA Style**

Han JK, Shin BS, Choi DH.
Comprehensive Study of Intermediate and Critical Quality Attributes for Process Control of High-Shear Wet Granulation Using Multivariate Analysis and the Quality by Design Approach. *Pharmaceutics*. 2019; 11(6):252.
https://doi.org/10.3390/pharmaceutics11060252

**Chicago/Turabian Style**

Han, Jong Kwon, Beom Soo Shin, and Du Hyung Choi.
2019. "Comprehensive Study of Intermediate and Critical Quality Attributes for Process Control of High-Shear Wet Granulation Using Multivariate Analysis and the Quality by Design Approach" *Pharmaceutics* 11, no. 6: 252.
https://doi.org/10.3390/pharmaceutics11060252