Discrete Element Modeling of a Pharmaceutical Blending Process
Peyman Aminpour Ph.D., Thomas Reynolds Ph.D., and Sanjay Konagurthu Ph.D.
peyman.aminpour@thermofisher.com | tom.reynolds@thermofisher.com | sanjay.konagurthu@thermofisher.com

Thermo Fisher Scientific

Simulated design of experiments were conducted to evaluate process scale-up for theV-blender from 1.2L to 2.5L and 10L as shown in the following figures.

PURPOSERESULTS

Scale-up of blending unit operations from the laboratory scale to commercial scale can bechallenging. Use of predictive tools for simulation of blending processes can be valuable in drug
product manufacturability. We provide a mechanistic model based on Discrete Element Method(DEM) for a pharmaceutical blending processes. This model can successfully predict theoperating ranges a priori to running the actual process and avoids a trial-and-error approach.
This predictive model allows for the calculation of critical process parameters and performanceearly in the blending process.

The experimental FT4 test results include Basic Flow Energy (BFE), Specific Energy (SE) andConditioned Bulk Density (CBP). Using ANN we trained a model on DEM FT4 simulation which had
high 𝑅2 values > 0.98 for the training and validation tests shown in Figure 1 (middle). The effects ofeach variable on the flow characteristics of the powder are shown in Figure 2.
The low p value indicates a significant effect. The
most significant interaction was Size × Fill (p=0.04)
indicating size dependence of the fill effect in mixing
performance.

OBJECTIVES
Our objectives in developing this mechanistic modeling of a blending process were two-fold:
1. Develop a systematic DEM model parameter calibration method by training an artificial neuralnetwork (ANN) on laboratory scale data for a spray-dried dispersion of a model compound, Z160,
2. Use the validated DEM model to simulate a V-blender process to identify the critical material
attributes, process parameters and to evaluate the change of operating space during scale-up.

METHODS
The simulation of a pharmaceutical blending unit operation is developed based on a viscoelastoplastic frictional adhesive DEM model. First, an FT4 powder rheometer (FreemanTechnologies) was used to characterize the mechanical properties of the powders in the Z160
formulation. The FT4 test was replicated using EDEM software (DEM Solutions Ltd.), and thecontact model parameters of each powder were optimized to reproduce the experimentalmeasurements using a space-filling design of experiments (DOE) as shown in Figure 1.
Leveraging machine learning techniques, a response model based on an artificial neural network(ANN) was trained on the results from 180 FT4 simulations. This response model was used todetermine the input parameters that provided the best fit to the experimental measurements.
This validated DEM model was then used to simulate a laboratory-scale V-blender containing theZ160 formulation. Changes in operating space that occur during the scale-up process were thenidentified by modeling a Pilot-scale V-blender.

Scaling-up from 1.2L to 10L
blender at constantrotation rate (RPM 15) hasa larger impact on theblending performance (20%increase) at a 75% fill level(right) compared to a 30%
fill level (left).

Figure 2. Prediction profiler showing the effects of independent variables, Particle radius (R), Shear modulus (G), Restitution
coefficient (e), Particle-particle static friction (μsp−p), Constant pull-off force (f0), Cohesive surface energy (Δγ), Particle solid
density (ρs), Particle-wall static friction (μsp−g) and Particle-blade static friction (μsp−b) on the FT4 responses.

Calibrated DEM models based on FT4 simulations wereused to simulate a 1.2L laboratory-scale V-blender for
the formulation of Z160 (Table. 1). These simulations
are shown in Figure 3. Images in the top row show thestate of the final blend after 1-minute blending time at
RPM 15 with fill levels of 30, 50 and 75%. Images at thebottom show the concentration of API by the means ofbin analysis, with red representing the highest Z160
concentration. An obvious deviation was identified
between fill level 30% to 75%. This indicates that filllevel is a critical process parameter that impacts blenduniformity. These same results can also be quantified
by the Coefficient of Blending (CBP) Performance
which is a computational micro-scale index.

Table 1. Z160 model compound prototype formulation
Unit
Z160 Formulation
Comp.
Component
wt%
40:60 Z160:PVP-VA64 SDD
49.0%
Lactose Monohydrate FlowLac 90
30.0%
Microcrystalline Cellulose (Avicel PH-105) 14.0%
Kollidon CL (Crospovidone)
5.0%
Colloidal Silicon Dioxide (Cab-O-Sil M-5P)
1.0%
PRUV Sodium Stearyl Fumarate (SSF)
1.0%
Total: 100.0%

This figure shows themixing performance of
1.2L and 10L blenders
when increasing the filllevel from 30-75%.
Increasing fill level has alarger impact in the 1.2L
blender when compared tothe 10L blender.

CONCLUSIONS➢ This work demonstrates the utility of DEM for modeling of pharmaceuticalblending unit operations. This approach enables process simulation and mapping
prior to experimentation and aids in optimization and scale-up.

Red=API
Yellow=Excipients

Figure 4. CBP vs. number of revolutions acrossdifferent fill levels in 1.2 L V-blenders with RPM=15.

Figure 1. DEM simulation of the FT4 powder rheometer (left). Prediction accuracy of the ANN model (middle).
Structure of the single-layer ANN model with 15 hidden nodes and TanH activation functions (right).

Figure 3. Snapshots of the final blends in 1.2 L V-Blenders after 1min
blending with RPM=15 and various fill levels (top). Concentration of API,
with red representing the highest concentration (bottom).

CBP provides information on thecontact number of similar anddissimilar particles. As illustrated inFigure 4, after 1 min of blending with
RPM 15, the CBP shows better mixing
quality for the 30% fill level.

➢ The DEM model was validated using a combination of experimental results from
FT4 measurements and machine learning methods. Critical material properties
were identified that play a significant role in blending performance.
➢ Critical process parameters were identified by statistical studies and showeddependence on blender size, fill level and the rotation rate.