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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5443_Библиотеки_им_академика_М_И_Перельмана
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Malti Arya, Sarita K. Yadav, Madhuri Verma, Pranay Wal,
Pooja A. Chawla, and Viney Chawla
✶
6 Computational methods in the pragmatic
development of nanoemulsions, polymeric
micelles, and dendrimers for drug delivery
Abstract: The chapter delves into the realm of drug delivery advancements, focusing on
nanoemulsions, polymeric micelles (PMs), and dendrimers as promising carriers for drug
delivery. It highlights the pivotal role of computational methods in designing, optimizing,
and characterizing these nanocarriers. The chapter emphasizes the importance of ratio-
nal design in enhancing therapeutic outcomes through considerations like droplet size,
surface charge, and stability. Computational approaches, including molecular dynamics
simulations and quantum mechanics computations, are explored for their role in formu-
lating nanoemulsions with precision and efficiency. The chapter also discusses the signifi-
cance of molecular dynamics simulations in decoding nanoemulsion dynamics, micelle
formation, and dendrimer design, providing valuable insights into stability, drug encap-
sulation, and molecular interactions within these drug delivery systems.
Keywords: Drug delivery, Nanoemulsions, Polymeric micelles, Dendrimers, Molecular
dynamics simulations, Rational design, Therapeutic outcomes, Formulation optimiza-
tion Drug encapsulation
6.1 Introduction
The field of drug delivery has witnessed significant advancements in recent years,
driven by the continuous quest for innovative strategies to enhance therapeutic out-
comes while minimizing side effects. Nanoemulsions, polymeric micelles (PMs), and
dendrimers have emerged as promising carriers for drug delivery due to their unique
properties, such as increased stability, improved bioavailability, and targeted delivery.
Computational methods play a crucial role in the design, optimization, and characteriza-
✶
Corresponding author: Viney Chawla, University Institute of Pharmaceutical Sciences and Research,
Baba Farid University of Health Sciences, Faridkot 151203, Punjab, India.
e-mail: drvineychawla@gmail.com
Malti Arya, Madhuri Verma, Department of Pharmaceutics, Chandra Shekhar Singh College of
Pharmacy, Kaushambi, Uttar Pradesh, India
Sarita K. Yadav, Department of Pharmacy, MLN Medical College, Prayagraj, Uttar Pradesh, India
Pranay Wal, Pranveer Singh Institute of Technology (Pharmacy), Kanpur, Uttar Pradesh, India
Pooja A. Chawla, University Institute of Pharmaceutical Sciences and Research, Baba Farid University
of Health Sciences, Faridkot, Punjab, India
https://doi.org/10.1515/9783111208671-006
https://t.me/med1917
tion of these nanocarriers. In this chapter, we explore the computational approaches em-
ployed in the pragmatic development of nanoemulsions, PMs, and dendrimers for drug
delivery (Figure 6.1) [1].
6.2 Nanoemulsions
Nanoemulsions provide benefits for medication delivery applications due to their tiny
droplet size and great stability. Computational modeling allows for a thorough knowledge
of the physicochemical factors that influence nanoemulsion production and stability. Mo-
lecular dynamic (MD) simulations have been widely used to explore the molecular behav-
ior of nanoemulsion components such as surfactants and lipids. These simulations give
insights into the interactions that determine nanoemulsion stability and can help guide
formulation parameter choices. A research used MD simulations to investigate the effect
of various surfactant types on the stability of nanoemulsions. The findings emphasized
the relevance of surfactant chain length and concentration in nanoemulsion stabilization,
directing the rational design of formulations with improved stability [2–4].
6.2.1 Importance of rational design for enhanced
therapeutic outcomes
The logical design of these colloidal carriers is critical to the success of nanoemulsion-
based medication delivery. To optimize drug encapsulation, bioavailability, and targeted
distribution, rational design requires rigorous consideration of different aspects such as
droplet size, surface charge, and stability. Achieving this level of accuracy is critical for
Figure 6.1: Structural design of nanoemulsions, polymeric micelles, and dendrimers as promising drug
delivery system.
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improving treatment results because it allows for individualized solutions that meet the
unique obstacles offered by various medications and medical conditions [5].
6.2.2 Role of computational approaches
Computational techniques have emerged as crucial tools in the quest for rational de-
sign, providing a window into the intricate molecular symphonies within nanoemul-
sions. These approaches, which include MD simulations and quantum mechanics
computations, offer researchers a virtual laboratory in which to investigate and com-
prehend the dynamic behavior of nanoemulsions at the molecular level. Computational
insights assist formulation optimization, ensuring that the developed nanoemulsions
fulfill stringent stability, biocompatibility, and controlled release criteria [36].
6.2.3 Significance in achieving precision and efficiency
The use of computational methodologies not only speeds up the design process, but
also allows for remarkable precision and efficiency. Researchers may fine-tune for-
mulation parameters, overcome drug solubility difficulties, and eventually construct
nanoemulsions with enhanced drug delivery effectiveness by grasping the molecular
subtleties drivi ng nanoemulsion dynami cs. This accuracy leads to higher treatment
efficacy, fewer side effects, and better patient compliance [4].
This introduction sets the foundation for unraveling the complexity of this trans-
formational topic as we start on a deep investigation of the computational methodolo-
gies driving the rational design of nanoemulsions in drug delivery. The combination
of nanoemulsions with computational methods has the potential to revolutionize
medication delivery by providing personalized solutions that push the frontiers of
therapeutic possibilities [6, 36].
6.2.4 Decoding nanoemulsion dynamics through molecular
dynamics simulations
As finely distributed colloidal systems, nanoemulsions display dynamic behavior at
the molecular level, which has a profound i mpact on their stability, behavior, and
functioning. MD simulations are a powerful computational tool for deciphering these
complex dynamics, providing researchers with a virtual microscope to investigate
and interpret the molecular symphony within nanoemulsions [4].
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6.2.4.1 Molecular dynamics simulations
In MD simulations, numerical integration of Newton’s equations of motion is used to
simulate the time-dependent behavior of a system of interacting molecules. These
simulations allow for the tracking of individual molecules in nanoemulsions through-
out time, providing insigh ts into their trajectories, intermolecular interactions, and
structural changes. By imitating the real-world conditions, researchers can track the
dynamic development of nanoemulsions with atomic-level precision. MD simulations
use the finite difference approach, which is most typically used with Verlet, leapfrog,
Beeman, and corrective prediction algorithms, as detailed by Wang et al. [5, 7–9].
6.2.4.2 Processes involved in molecular dynamics simulation
6.2.4.2.1 Deployment of the system
The initial stage in MD simulations is to develop an appropriate simulation system,
with build quality directly impacting the accuracy of the findings obtained. In an illog-
ical design, reaching a stable equilibrium state is difficult, and forecast accuracy can-
not be guaranteed. High-energy conformations should be avoided as much as possible
to guarantee simulation stability. Many simulation programs, such as Materials Studio
and GROMACS, may be used to create the initial model. Furthermore, suitable force
field parameters and ensemble/boundary conditions should be chosen for initial
model creation. For various simulation systems, different force field parameters need
be addressed, and force field constraints frequently directly impact forecast accuracy.
As the buildup of molecules may generate overlap and cross difficulties during model-
ing, the energy minimization approach should be used for conformation optimization
prior to calculations on the built initial system to limit the development of inappropri-
ate conformations [10–12].
6.2.4.2.2 Calculation of simulation
The initial state with a given condition, that is, the initial location, initial velocity, and
force of all particles, is set first during simulation computations. Then, by solving the
motion equation, the spatial location, velocity, and acceleration of particles at the
next instant are acquired, and the motion trajectories of all particles in the whole sim-
ulation system are tallied until the system characteristics become stable. Analyzing
the system structure and thermodynamic parameters enables one to assess if the sim-
ulation has reached equilibrium. This state is thought to be attained when the struc-
ture, energy, temperature, pressure, and other thermodynamic characteristics of the
entire system no longer vary with time or change by less than 5% [11, 13].
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6.2.4.2.3 Data interpretation
The study and discussion of data acquired after equilibrium establishment is the final
phase in MD simulation. Notably, MD simulation provides the locations and velocities of
all particles in the system at each time point, thatis,themotiontrajectory.Thetrajectory
field may be used to extract a huge quantity of thermodynamic and statistical information,
as well as information about the study item for processing, discussion, and analysis [13].
6.2.4.2.4 Insights gained from molecular dynamics simulations
MD simulations provide valuable insights into several key aspects of nanoemulsion
dynamics:
– Stability: MD simulations illustrate the interactions between surfactant mole-
cules and the oil–water interface, assisting in the identification of ideal stability
conditions.
– Behavior under stress: Nanoemulsions can be subjected to a variety of pressures
during formulation, storage, and administration. Researchers may use MD simu-
lations to explore how nanoemulsions respond to external pressures such as
shear or temperature changes, revealing insights into their resilience.
– Optimized drug encapsulation: The simulations can investigate drug molecule
encapsulation inside nanoemulsion droplets. This data may be used to optimize
formulation parameters for improved drugs loading and controlled release [2, 6].
6.2.5 Optimizing nanoemulsion formulations
for controlled release
This section delves into the formulation parameters influencing nanoemulsion perfor-
mance and explores how computational insights contribute to the optimization process.
6.2.5.1 Formulation parameters and challenges
Nanoemulsion formulations are influenced by a myriad of parameters, each playing a
critical role in determining their performance:
– Droplet size: The size of nanoemulsion droplets influences drug encapsulation
efficiency and s tability significantly. Smaller droplets frequently have a larger
surface area, allowing for quicker medication release.
– Surfactant composition: The selection and concentration of surfactants affect
the stability of nanoemulsions. Surfactants help to maintain medication release
by reducing interfacial tension and preventing droplet coalescence.
– Oil phase composition: The oil used effects the solubility of hydrophobic medi-
cines and can have an impact on the overall stability of the nanoemulsion.
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– pH and ionic strength: Environmental parameters such as pH and ionic strength
can alter the stability and behavior of nanoemulsions, hence influencing their
in vivo performance [2, 6].
Obtaining the most appropriate formulations, however, is a difficult undertaking since
these characteristics are interrelated and their effects might be synergistic or antagonis-
tic. Computational techniques provide a systematic way to manage this ambiguity.
6.2.5.2 Computational insights into optimization
Computational techniques, notably MD simulations and quantum mechanics compu-
tations, offer vital insights into nanoemulsion formulation optimization (Figure 6.2):
– Predicting stability: Researcher s can anticipate the stability of nanoemulsions
using MD simulations by modeling their behavior under various environmental
circumstances. This aids in the identification of formulations that are resistant to
influences such as temperature fluctuations or dilution.
– Optimizing surfactant arrangements: Computational studies on the arrange-
ment of surfactant molecules at the oil–water interface assist in the creation of
formulations with improved stability and controlled release.
– In silico screening: Quantum mechan ics simulations enable in silico testing of
various oil and surfactant combinations to anticipate their influence on medica-
tion solubility and release kinetics.
Figure 6.2: Physicochemical properties of nanoformulations for maximizing their outcomes.
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– Tailoring droplet size: Computatio nal techniques can estimate the influence of
formulation modifications on droplet size distribution, which is important for op-
timizing drug encapsulation and release patterns [2, 6].
As MD simulation provides microstructure characterization, it has become a key tool in
investigating interactions between phases. Future research in the field of MD simulation
of oil–water emulsification/demulsification systems should concentrate on overcoming the
limitations of model simplification and single-factor simulation, integrating emulsion inter-
nal and external phase characteristics, and accounting for external environmental factors.
6.3 Polymeric micelles in drug delivery
PMs are colloidal structures made of amphiphilic block copolymers in which hydrophobic
blocks cluster in the center, generating an aqueous protective shell of hydrophilic blocks.
This unique structure endows the micelles with amphiphilic capabilities, allowing them to
encapsulate hydrophobic medicines in their core while presenting a hydrophilic outside
surface. PMs are good carriers for enhancing the solubility and bioavailability of poorly
water-soluble medicines due to their nanoscale size and propensity to self-assemble [14, 15].
The key attributes of PM include:
– Self-assembly: Spontaneous formation driven by the amphiphilic nature of block
copolymers.
– Drug encapsulation: Efficient encapsulation of hydrophobic drugs within the hy-
drophobic core.
– Biocompatibility: Hydrophilic shell enhancing biocompatibility and stability in
biological fluids.
– Targeted delivery: Opportunities for active targeting through surface modifications.
6.3.1 Computational modeling approaches
6.3.1.1 Role of computational approaches
A thorough understanding of PM generation, dynamics, and interactions at the molec-
ular level is required for rational design. Computational techniques are critical in un-
derstanding the complexities of PM, providing insights that influence their design and
optimization. Among these approaches are:
– Molecular docking: Predicting the binding affinity of polymer chains, elucidating
the arrangement of amphiphilic blocks during micelle formation.
– MD simulations: Tracking the movement of individual polymer chains over time,
providing insights into micelle dynamics, stability, and drug release kinetics.
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6.3.1.2 Micelle formation: a molecular perspective
Micelle production is a dynamic molecular process with substantial implications for
drug administration. It is a vital stage in the self-assembly of amphiphilic block co-
polymers. The amphiphilic character of block copolymers causes the creation of dif-
ferent areas within the polymer chains, resulting in the spontaneous organization of
these chains into micellar structures at the molecular level. This section presents a
thorough molecular view of PM self-assembly, emphasizing the importance of under-
standing the intermolecular interactions that regulate their creation:
– Micelle formation dynamics: Hydrophobic and hydrophilic segments make up
the amphiphilic block copolymers. In an aquatic environment, these polymers
self-assemble as a result of hydrophobic interactions. Hydrophobic segments
form a core that protects them from the surrounding aqueous environment,
while hydrophilic segments form a stabilizing shell around this core. As a result,
tiny PMs with a hydrophobic core and a hydrophilic corona develop.
– Role of hydrophobic interactions: The requirement to minimize unfavorable in-
teractions between hydrophobic segments and water is the motivating reason un-
derlying micelle production. This causes the water molecules surrounding the
hydrophobic core to accumulate entropy, stabilizing the micellar shape. Under-
standing the unique interactions between various polymer chains is critical for
predicting micelle stability and characteristics [16, 17].
6.3.1.3 Molecular docking
Molecular docking is a useful computational method for analyzing and forecasting
polymer chain interactions during micelle formation. This approach simulates the
binding of individual polymer segments, revealing their spatial arrangement inside
the micellar structure.
6.3.1.4 Principles of molecular docking
– Binding affinity prediction: The binding affinity between distinct polymer seg-
ments is calculated via molecular docking. This knowledge is critical for forecasting
whether individual polymer chains will form the hydrophobic core or contribute
to stabilizing the corona.
– Arrangement prediction: The spatial arrangement of polymer chains inside the
micelle is predicted using molecular docking simulations, offering insight into the
orientation and organization of hydrophobic and hydrophilic segments.
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6.3.1.5 Applications in micelle design
– Optimizing hydrophobic core: Molecular docking aids in the selection of poly-
mer chains with favorable interactions, hence optimizing the composition of the
hydrophobic core for increased stability.
– Surface modification planning: Understanding the arrangement of hydrophilic
segments assists in surface modification planning for specific capabilities such as
increased biocompatibility or active targeting.
Researchers obtain important insights into the molecular subtleties of micelle forma-
tion by using molecular docking, allowing for the rational creation of PMs with custom-
ized structures and optimized characteristics for drug administration. This molecular
knowledge lays the groundwork for future computational techniques that investigate
the dynamics and behavior of PMs [18–20].
6.3.2 Molecular dynamics
Computational techniques, such as MD, have been widely used to examine molecular
interactions on PM and to g et microscopic insight into molecular interactions in
modeling. MD is theoretically based on Newton’s second law and records the sequen-
tial molecular movements or conformational changes of the components in solution
based on the modeling’s computation of the interactions between molecules. In the
instance of MD simulation on PM formulations, the interactions between hydrophobic
drug and BCPs are computed to create the next subsequent conformations in a given
modeling, indicating the self-assembly process involving drug loading in the core. To
elucidate the MD of drug delivery systems such as PMs, fully atomistic simulations are
often inappropriate due to the time scale of the simulation (typically in the order of
microseconds or longer for molecular interaction within PM), so coarse-grained simu-
lations are more frequently used, in which the number of degrees of fr eedom is re-
duced to expedite molecular simulations [21, 22].
Several MD modeling studies on PM formulations have been carried out in order to
evaluate drug loading and molecular interactions within PM formulations. Patel et al.
[37] successfully conducted a series of experiments on MD modeling for PM formulation
to explain the critical aspects on hydrophobic solubilization, using poly(ethylene gly-
col)-b-poly(caprolactone) (PEG-b-PCL). In their initial work, they discovered that MD
modeling could accurately predict the solubility of model medicines (finofibrate and ni-
modipine) in PEG-b-PCL with good experimental data agreement [23, 24, 37].
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6.3.3 QSPR (quantitative structure–property relationship)
MD techniques are frequently inadequate for big dataset prediction because MD sim-
ulations of a large number of PM formulations need significant time and expense for
such scale of simulation. Statistical techniques, on the other hand, may be used to pre-
dict polymer-drug compatibility, and numerous promising studies have recently been
published that show the efficacy of a statistic-based computational strategy. QSPR
(quantitative structure–property relationship) modeling is based on statistical data
analysis, which was previously used in medicinal chemistry and chemical toxicology
to predict the efficacy/toxicity of small compounds. Several studies have recently used
QSPR modeling to predict solubilization and have found encouraging results [22, 25].
Wu et al. developed a QSPR model of doxorubicin-loaded PM on four-/six-arm
star polymer structures using the genetic function approximation technique. Six poly-
mers of four-miktoarm star polymers (PCL)2(PDEA-b-PPEGMA)2, four polymers of six-
miktoarm star polymers (PCL)3(PDEA-b-PPEGMA)3, and three polymers of four homo-
arm star polymers (PCLb-PDEA-b-PPEGMA)3 were used. The link between drug load-
ing and polymer structure showed quantitative assessment of drug loading in the
polymers discussed above using the QSPR technique [26]. To maximize the capability
of prediction by QSPR, a bigger dataset is necessary for a statistical analysis by QSPR,
which may potentially increase predictability by QSPR. Recently, our group reported
cheminformatics-driven discovery of PM formulation for water-insoluble pharma-
ceuticals in order to demonstrate the reasonable design of PM formulation using
QSPR technique. Totally, 41 hydrophobic drugs were tested in PM formulation using
poly(2-oxazoline)-based block copolymer at different concentrations either individu-
ally or in combination with other drugs, yielding 408 data points as micelle formula-
tion that provides both loading efficiency and loading capacity in PMs [27].
The integration of molecular docking, MD simulations, and DFT (Density functional
theory) provides a comprehensive framework for optimizing micelle characteristics,
demonstrating the capability to solve particular issues in medication solubility, bioavail-
ability, and targeted administration. The future horizons include new trends in compu-
tational micelle design, paving the path for continuing drug delivery system innovation.
6.4 Dendrimers
Dendrimers, derived from the Greek word “dendron” meaning tree, are three-
dimensional, tree-like macromolecules wi th a central core and branches radiating
outward. They are highly defined nanoparticles with sizes varying from 1 to 15 nm.
The iterative branching architecture results in a globular structure, resembling the
branches of a tree. This precisely controlled architecture distinguishes dendrimers
from conventional polymers, offering a unique platform for drug delivery.
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