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to the creation of a modular open-source system using multidimensional distributed
product structures to span various process units. Moreover, at minimal computational
cost, reduced order models enable the incorporation of initially highly detailed mod-
els with FSS techniques [14].
Agent-based (AB) models, as opposed to conventional dynamic systems modeling
techniques, enable the representation of process chain behaviors, determination of
heterogeneous product attributes, consideration of various degrees of detail, and in-
terdependence with processes. To mimic intermediate product modifications, as well
as their consequences on the procedure and vice versa, AB models can be utilized
[15]. Furthermore, cellular automata can be represented using AB models. This makes
it possible to simulate the interactions between nearby materials and processes,
which is a hallmark of discrete element simulation. Consequently, AB simulations
serve as a practical alternative to the current modeling methodologies and aid in im-
proving knowledge of process chains, sub-processes, as well as product attributes.
Traceability and the transfer of product attributes for entities are benefits of AB meth-
ods, particularly when considering not only individual procedures but the entire pro-
cess chain of pharmaceutical manufacture [16].
16.2 Tableting and its simulation
Particulate dosage forms come in a wide range of forms, from powder particles to dis-
persions, and they can be produced using a variety of techniques and for a wide range
of therapeutic purposes. The tablet is likely among the best-known particulate dosage
form, owing to its huge market share. The manufacture of tablets can be done using a
variety of processes. The recommended tableting technique is direct compression,
which only involves excipient blending and punching a tablet. This requires a mini-
mum of handling and processing stages, and the powder components are subjected to
low temperatures and moisture impacts, and quick processing periods. Figure 16.2 high-
lights and illustrates the process chain of the direct compression technique. Some of the
abovementioned issues are addressed by this process chain, such as the blending of
batch and continuous processes to create a finite finished product or the modification
of intermediate product structures, leading to critical quality attributes (CQAs) of the
finished product.
Moreover, the significant constraints on the dosage form for superior flowability
and reduced separation propensities restrict the use of direct compression. The funda-
mental direct compression process chain must obviously be expanded by other poten-
tial process steps, based on the characteristics and size of API particles, which include,
for instance, various granulation techniques or later tablet coating.
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16.2.1 Tableting process
CQAs are commonly employed to define tablet quality. The weight of the tablet, its
porosity, tensile strength, and the mass fraction of the appropriate excipient are ex-
amples of CQAs. The porosity, which is influenced by the excipient characteristics and
compression stress, affects how quickly a tablet dissolves. Moreover, the tablets must
have enough tensile strength to allow for transport and additional processing.
Several computational methods, including artificial intelligence, stochastic, and
various simulation methods – of which, the current FSS and DEM techniques are de-
tailed in more depth below – have already been used to model the tableting process
in the literature.
16.2.2 Strategies for existing simulation and agent-based
simulation
In the current scenario, few methods consider the entire direct compression range.
There are numerous DEM and FEM approaches that take the compression process
into account, compared to the shortage of FSS approaches.
16.2.2.1 Dynamic flowsheet simulation modeling
Direct compression and dry granulation are two case scenarios in tablet formulation
where Boukuwala et al. addressed the difficulties in developing flowsheet models for
the simulation of solid formulation-based pharmaceutical operations and showed the
Figure 16.2: A typical (intermediate) product structure for the direct compression manufacturing
(adopted and modified with permission from Martin et al. [12]).
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benefits of doing so. A hybrid, population-balance and data-based model is used in
the designed FSS. Results demonstrate that the content of the tablet and the mixing
homogeneity of the mixture both exhibit fluctuations because of feeder refill fluctua-
tions,. Recycling, though, has the potential to lessen this impact. The generated flow-
sheet was subjected to a dyn amic sensitivity analysis, which identified the material
characteristics like mean particle size and powder’s bulk density as the most substan-
tial causes of variation [8].
Furthermore, the same cohort created a dynamic flowsheet model for the continu-
ous wet granulation process employed in the manufacturing of tablets. Based on the
process information for each individual component, the unit operation models that are
merged to build the process line constitute a hybrid configuration that combines mech-
anistic models, population balance models, and empirical correlations. The major goal
of this study was to offer direction regarding the steps that must be taken to progress
from the level of a unit operation to the simulation of an integrated continuous process
plant. The relationship between the manufacturing history of powders that undergo
wet granulation is incorporated in every manufactured tablet and the rate of release of
the pharmaceutical constituent is made possible by the incorporation of the dynamic
flowsheet with a finished product of tablet dissolution. The created flowsheet is em-
ployed to simulate various operational situations and operational disturbances that are
commonly observed to determine how they will affect important material qualities,
product features, and the performance of downstream operations. According to the re-
sults of the simulation, granulation, and milling, which regulate the particle size distri-
bution of the treated powder composition, have a significant impact on the hardness
and solubility of the tablets that are manufactured [17].
16.2.2.2 Discrete and finite element modeling
Conversely, suboperations like compression or die-filling are described using discrete
element modeling techniques. In a traditional DEM methodology, the particles are rig-
idly modeled and primarily used for conceptualizations of physical behavior instead
of for engineering purposes, which is somewhat analogous to the method of FEM,
which cause stress concerns in the machine design.
To examine things like the density distributions and stress maps in tablets or the
tooling shape, Baroutaji et al. [18] explain the construction of an FEM that simulates
the compression of tablets.
Using a combination of the finite-discrete element approach and contact dynam-
ics for asymmetrical particulates, the pharmaceutical powder and process of tableting
are simulated. The suggested method’s overall computation efficiency for particle in-
teraction features is improved by the development of a particle-scale composition and
a two-stage contact detection algorithm. A pseudo-particle arrangement with a scaled-
up geometry that is based on the variances of genuine powder particles is used to sim-
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ulate uneven particle morphologies and variable sizes. These simulations demon-
strate that the performance of compaction and deformation is significantly influenced
by particle size, morphology, and mechanical characteristics [19].
16.2.2.3 Agent-based modeling
A standard AB model has three main components: agents, their interactions with
other agents, and their surroundings. Each agent has a behavior that could change
according to the regulations adjusting the behavior, and the characteristics of an ob-
ject can be either dynamic or static. Such laws may, for instance, be physical equa-
tions like the Heckel equation or they may be conditional on the condition of the
individual. Prior to reaching the following state, transitions between the states adhere
to a set of rules. Such transitions change their behavior in accordance with time, at
ratios, or in response to a trigger, based on the rationale of the condition. These
events may result from an agent’s interactions with some other agents or its sur-
roundings. Figure 16.3 illustrates the common AB model and its iterations.
The goal of the AB simulation approach was to model the dynamic behavior of complex
systems made up of groups of independently active, interacting elements [20].
As per the literature review, neither tableting nor pharmaceutical procedures, in
general, have used AB simulation. In contrast to the DEM and FSS, AB simulation of-
fers the ability to analyze the evolution of certain product attributes throughout the
whole production chain [21].
Figure 16.3: Standard agent framework and its interactions (adopted and modified with permission from
Martin et al. [12]).
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16.3 Agent-based simulation model for
the tableting process
A full comprehension of the suboperations is necessary for the creation of an AB
model of the tableting method. Process models are required to explain the connection
between process variables and product structures.
According to Macal and North, modeling agents often engage in the following three
interconnected steps, namely, the recognition of agents, agent groupings, and their char-
acteristics, as well as the description of the agents’ actions and interactions. The AB
model was established after these three phases. Although the resulting AB model can sim-
ulate any rotary press, the pilot-scale rotary press XL 100 (KORSCH AG, Berlin, Germany)
was taken into consideration for the model’s derivation. Step I: Agent’s recognition: There
can be two distinct kinds of agents, namely, material and process. Figure 16.4 highlights
the process and material agents for the tableting procedure. Step II: Establishing the
agent’s behavior specifications. Step III: Describing the agent’sinteractions:InABmodels,
interactions can take place amid agents and their surroundings.
16.4 Recent advances
Pharmaceutical manufacturers can lead from the front by lowering costs and enhanc-
ing effectiveness in the development of new treatments. These improvements may ad-
ditionally accelerate the launching of life-saving drugs. In the current circumstances,
more emphasis has been laid upon researching the diverse simulation approaches for
the tableting process.
Ru et al. have focused on the development of tiny tablets for placement into the
subconjunctival region of the eye that will prevent scarring, following glaucoma filtra-
tion surgery (GFS). Researchers used MD simulations to examine how tablets contain-
ing 5-fluorouracil (5-FU) and, ilomastat dissolve after surgery. These two drugs are
responsible for preventing fibrosis after GFS. Simple point charge water molecules
were used to simulate the dissolution, and the liquid turnover of the aqueous humor
in the subconjunctival area was replicated by periodically removing the drug mole-
cules that had dissolved and replacing them with freshwater molecules. Owing to the
swelling of the tablet and dissolution of solute into its solution form, the 5-FU tablet’s
total molecular solvent accessible surface area was enhanced sixty times as compared
to ilomastat. In the researcher’s MD simulations, the pattern of tablet dissolution is
consistent with the release profiles observed in experiments. This research shows
that it is possible to forecast how a drug’s molecular characteristics would affect its
dissolving profile, using a sequence of MD simulations [22].
Furthermore, another cohort predicted the drug-release rate of a single-layer os-
motic-controlled-release tablet as a function of several additives and drugs, the geom-
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Figure 16.4: Illustration of the current model components and the corresponding variables (adopted and modified with permission from Martin et al. [12]).
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etry of the tablet, and coating thickness properties. The model considers all the major
events that take place throughout the drug release from a semipermeable membrane-
coated tablet, including the solvent influx, caused by the osmotic pressure gradient
across the coating, the diffusion of core excipients (drug, polymer, and osmogen), tab-
let swelling (owing to solvent absorption), building up of osmotic pressure within the
tablet, and tensile stress exerting on the coating. Based on the viscosity of the moist-
ened phase and other system constraints, the researchers determined the prerequisite
for successful drug particle entrapment. By contrasting the predicti ons with drug-
release statistics for two compounds with different solubilities, the model was proven
to be accurate. The model’s applicability for modeling the drug-release mechanism in
the osmotic-controlled-release tablet was proven by the concordance between the pro-
jected release and the results [23].
Chaturvedi et al. worked to better understand the relationship between thermal
analysis and molecular simulations, the molecular cause of adhesion that results in
sticking was examined. According to the theory, the initial stage in the process of adhe-
sion is an intermolecular interaction between a therapeutic molecule and a punch face,
and the order of importance of adhesion during compression of the tablet should
match the sequence of these interaction energies. The sticking tendency was examined
in the current study using the model drugs ibuprofen, flurbiprofen, and ketoprofen. An
experimental method was proposed to calculate the adhesion work between these
three drugs in differential scanning calorimetry (DSC) aluminum pan at the intermolec-
ular stage. A linear connection between the enthalpy of vaporization and sample mass
has been established to show the precision of the tools utilized. In comparison to ibu-
profen and flurbiprofen, ketoprofen had a twofold greater threshold weight, which sug-
gests that it will attach with the greatest tendency. Applying Materials Studio, it was
calculated that the order of importance of the work of adhesion between three drugs
with the metal surface was 75.91, 44.75, and 96.91 kcal/mol, respectively. Ketoprofen, fol-
lowed by ibuprofen, and then flurbiprofen, has the lowest position in the drug’s associa-
tion with the iron superlattice. The outcomes show that the thermal model can be
effectively used to evaluate a medication’s molecular sticking tendency. The interaction
energy of the medicinal molecule with iron was also successfully established using a
novel molecular simulation script [24].
Yu et al. investigated abuse-deterrent formulations (ADFs), which frequently con-
tain poly(ethylene oxide) (PEO), to make tablets harder. This study tries to compre-
hend how PEO sources and grades affect the model ADFs’ compression properties. A
Styl’One compaction simulator was used to investigate PEOs from Dow Chemical and
Sumitomo Chemical with various molecular weights at slow, medium, and fast tablet-
ing velocities. Particle-size dis tribution, thermodynamic behavior, tabletability, com-
pressibility using the Heckel model, compactibility, and recovery from elastic stress
were assessed for the neat PEOs and model ADFs, and the results were compared. To
determine the impact of compression factors, PEO grades, and sources, a multivariate
linear regression approach was used. According to findings, neat high-molecular-
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weight (HMW) PEOs have a high tabletability. The variations in tabletability, out-die
compressibility, compactibility, and recovery from elastic stress are influenced by the
source of neat PEOs. HMW PEO tablets have excellent crushing force in this model
ADFs, but Dow Chemical PEO tablets have an inferior elastic recovery [25].
16.5 Conclusion and future insights
The development of new active compounds has been aided by molecular modeling,
which has als o been effective in the logical design of materials and equipment for
drug delivery. Using mathematical mechanics and the laws of Newton, can we de-
velop drug delivery systems for the years to come? Given the quick development of
the molecular simulation area, this apparently impossible dream might someday be-
come a reality. The difficulties, however, come in two parts: first, figuring out the
chemical process behind the drug effect in the human body, which requires to be sim-
ulated, and second, developing computers and computer programs that can perform
such simulations quickly. There remains quite a long way to go until such a dream
becomes real, despite the speed of expansion in both domains over the past few deca-
des being quite encouraging. In comparison to in vitro and in vivo experiments, in
silico technologies based on the idea of molecular simulations have consequently
served a supporting role. They offer ways to get molecular insight into certain systems
and a way to rank the effectiveness of potential medication compositions. Most of the
time, the simulation predictions offer helpful correlations b etween the anticipated
amounts and empirical observations rather than allowing for a direct comparison
with an observable from the experiment. Therefore, it is necessary to accept and test
the idea that simulation anticipates can come before or even guide systematic investi-
gations. Additionally, collaborations between the industry and computational experts
in this field can speed up work in fields where molecular simulations have the poten-
tial to have a significant influence.
References
[1] Chawla, R., Rani, V., Mishra, M., & Kumar, K. Computer simulation and modeling in
pharmacokinetics and pharmacodynamics. In: Computer Aided Pharmaceutics and Drug
Delivery. 2022.
[2] Bunker, A., & Róg, T. Mechanistic understanding from molecular dynamics simulation in
pharmaceutical research 1: Drug delivery. Front Mol Biosci. 2020;7:604770. 2020, 7.
[3] Mouvet, F., Villard, J., Bolnykh, V., & Rothlisberger, U. Recent advances in first-principles based
molecular dynamics. Chem. Res. 2022, 55, 3, 221–230, 2022, 55.
[4] Arbe, A., Alvarez, F., & Colmenero, J. Insight into the structure and dynamics of polymers by neutron
scattering combined with atomistic molecular dynamics simulations. Polymers 2020, 12, 3067. 2020, 12.
380 Neha Jain et al.
https://t.me/med1917
[5] Katiyar, R. S., & Jha, P. K. Molecular simulations in drug delivery: Opportunities and challenges.
Wiley interdisciplinary reviews: Computational Molecular Science. 8(1):e1358, 2018, 8.
[6] Peraman, R., Bhadraya, K., & Padmanabha Reddy, Y. Analytical quality by design: A tool for
regulatory flexibility and robust analytics. International Journal of Analytical Chemistry 2015; Article
ID 868727, 2015, 2015.
[7] Lee, S. L., O’Connor, T. F., Yang, X., Cruz, C. N., Chatterjee, S., Madurawe, R. D., et al. Modernizing
pharmaceutical manufacturing: From batch to continuous production. Journal of Pharmaceutical
Innovation 2015; 10(3): 191. 2015, 10.
[8] Boukouvala, F., Niotis, V., Ramachandran, R., Muzzio, F. J., & Ierapetritou, M. G. An integrated
approach for dynamic flowsheet modeling and sensitivity analysis of a continuous tablet
manufacturing process. Computers and Chemical Engineering 2012; 42: 30– 47, 2012, 42.
[9] Thiede, S. Environmental sustainability of cyber physical production systems. Procedia CIRP, 2018, 69.
[10] Borshchev, A., & Filippov, A. From system dynamics and discrete event to practical agent based
modeling: Reasons, techniques, tools. The 22nd International Conference of the System Dynamics
Society., 2004.
[11] Ketterhagen, W. R., Am Ende, M. T., & Hancock, B. C. Process modeling in the pharmaceutical
industry using the discrete element method. J Pharm Sci. 2009;98(2):442–70. 2009, 98.
[12] Martin, N. L., Schomberg, A. K., Finke, J. H., Abraham, T. G. M., Kwade, A., & Herrmann, C. Process
modeling and simulation of tableting – An agent-based simulation methodology for direct
compression. Pharmaceutics. 2021;13(7):996. 2021, 13.
[13] Heinrich, S. Dynamic Flowsheet Simulation of Solids Processes. ISBN : 978-3-030-45167-7 2020.
[14] Dosta, M., Litster, J. D., & Heinrich, S. Flowsheet simulation of solids processes: Current status and
future trends. Advanced Powder Technology 2020; 31(3):947–953. 2020, 31.
[15] MacAl, C. M., & North, M. J. Tutorial on agent-based modelling and simulation. 2010, 4, 151–162.
[16] Macal C. and North M., “Introductory tutorial: Agent-based modeling and simulation,” Proceedings
of the Winter Simulation Conference 2014, Savannah, GA, USA, 2014: 6–20.
[17] Boukouvala F., Chaudhury, A., Sen, M. et al. Computer-Aided Flowsheet Simulation of a Pharmaceutical
Tablet Manufacturing Process Incorporating Wet Granulation. J Pharm Innov 2013; 8, 11–27.
[18] Baroutaji, A., Bryan, K., Sajjia, M., & Lenihan, S. Mechanics and computational modeling of pharmaceutical
tabletting process. In book: Reference Module in Materials Science and Materials Engineering. 2017.
[19] Lewis, R. W., Gethin, D. T., Yang, X. S., & Rowe, R. C. A combined finite-discrete element method for
simulating pharmaceutical powder tableting. 2005, 62. Int. J. Numer. Meth. Engng. 2005, 62: 853–869.
[20] Macal, C. M. Everything you need to know about agent-based modelling and simulation. J
Simulation 2016; 10, 144–156, 2016, 10.
[21] Schönemann, M., Bockholt, H., Thiede, S., Kwade, A., & Herrmann, C. Multiscale simulation approach
for production systems. Int J Adv Manuf Technol 2019; 102, 1373–1390. 2019, 102.
[22] Ru, Q., Fadda, H. M., Li, C., Paul, D., Khaw, P. T., Brocchini, S., et al. Molecular dynamic simulations of
ocular tablet dissolution. J Chem Inf Model. 2013;53(11):3000–8. 2013, 53.
[23] Tomar, B. S., Tirumkudulu, M. S., Yu, W., Berchielli, A., Manthena, S., & Doshi, P. A model to predict
drug release from a single-layer osmotic controlled-release tablet. J Drug Del Sci Tech 2023; 80:
104138, 2023, 80.
[24] Chaturvedi, K., Shah, H. S., Morris, K. R., & Dave, R. H. Modeling of adhesion in tablet compression
at the molecular level using thermal analysis and molecular simulations. Molecular Pharmaceutics,
2022, 19, 26–34.
[25] Yu, D., Seelam, R. R., Zhang, F., Byrn, S. R., & Hoag, S. W. Evaluation of tableting performance of Poly
(ethylene oxide) in abuse-deterrent formulations using compaction simulation studies. J Pharm Sci.,
2021, 110, 2789–2799.
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