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drug delivery are compendial approaches for computational assessment [13]. Compu-
tational pharmaceutics is now a growing multidisciplinary field that has produced a
vast amount of data throughout the years ofitsdevelopment,thususingadata-
intensive approach and prepared multiscale formulations for clinical settings [14].
1.1.1 Conventional techniques in drug delivery
It is generally accepted that in the clinic, active medication molecules should be formed
into the right dosage forms, DDSs, or formulations. The number of NMEs introduced to
the market by the pharmaceutical companies per billion US dollars of R&D investment
has gradually decreased in recent years, according to an estimate of the NME R&D effi-
ciency [15]. According to research conducted in 2007 on 68 authorized drugs, it typically
takes 15 years and up to $2,558 million to bring a single NME to market. The US Food
and Drug Administration (FDA) struggles year after year to approve more than 20–50
NMEs [16]. Moreover, the bulk of currently licensed NMEs fall short of their potential in
the clinic due to weak solubility, stability, and targeting properties [17]. A low water-
soluble content and issues with bioavailability affect 40% or more of NMEs. Novel dos-
age forms are becoming more common in the pharmaceutical business as NME output
remains stagnant. Compared to the R&D of NMEs, the R&D of innovative formulations
is much more time- and cost-efficient [18]. DDS can be used to improve the pharmaco-
logical properties of NMEs, including their PK and PD [19].
Physical pharmacy is a new field of study that emerged during the first genera-
tion (the 1950s to the 1980s) after the integration of physical chemistry’s fundamentals
with pharmacy [20]. In this field, Prof. Takeru Higuchi was a well-known pioneer [21].
Many innovative dosage forms were successfully developed during this time, from
fundamental research to clinical applications, including the pressurized metered dose
inhaler and transdermal patch product (Scop®) [22].
Human insulin was approved by the FDA in 1982 as part of the advancement of
recombinant DNA technology. The clinic then received a large number of biopharma-
ceutical items. In order to distribute peptides and proteins for months, new biomacro-
molecular formulations were created, such as the biodegradable poly(lactic-co-glycolic
acid) microsphere Decapeptyl® (1986), solid implant Zoladex® Depot (1989), and poly-
ethylene glycol (PEG)-ylated protein Adagen® (1990) [23]. A lot of research was done on
DDS based on nanotechnology during this time, and thousands of articles were pub-
lished per year. Unfortunately, the so-called nanomedicines have very little potential to
be transformed into clinical treatments, with the exception of the conventional lipo-
some and nanocrystal formulations [24]. This raised significant dispute. The intricacy of
the human body, which prevents existing nanomedicine from properly treating the tar-
get site, may be the cause [25]. Drug distribution research has recently begun to use
computers to produce smart and targeted delivery methods, such as 3D printing and
digital medications [26]. Although pharmaceutics has advanced significantly over the
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years, formulation R&D continues to rely on time-consuming, expensive, and unreliable
classical trial-and-error studies. Preformulation, formulation screening, process scale-
up, and in vivo testing are required for the traditional formulation design. A new test of
the entire procedure must be conducted if the outcome is unacceptable [27].
Computational methods, such as molecular dynamics, Monte Carlo simulations,
and finite element analysis, enable the detailed study of drug-carrier interactions, re-
lease kinetics, and transport phenomena within biological systems. These tools facili-
tate the prediction of drug behavior under various conditions, significantly reducing
the need for exhaustive experimental trials and expediting the development of more
efficient and targeted drug delivery systems. The integration of artificial intelligence
and machine learning further enhances the capabilities of computational tools, allow-
ing for the analysis of large datasets and the optimization of drug delivery strategies
with unprecedented precision. Together, conventional techniques and computational
tools complement each other, driving innovations in drug delivery and improving
therapeutic outcomes.
1.1.2 Computational drug delivery: a new era of research
In the past 10 years, a brand-new field call ed “computational pharmaceutics” has
emerged that combines pharmaceutics with multi-scale modelling and artificial intelli-
gence (AI), with the potential to fundamentally alter how formulations are developed
today [31]. Pharmaceutical scientists can benefit from computational pharmaceutics’
multi-scale lenses, which show physical, chemical, mathematical, and data-driven infor-
mation on topics including chemical stability, polymorphism, formulation screening,
and precision medicine. Quantum mechanics (QM), molecular dynamics (MD) simula-
tion, mathematical modeling, physiologically based pharmacokinetic (PBPK) modeling,
process simulation, AI) and machine learning algorithms are just a few of the computa-
tional methods that are crucial to all facets of pharmaceutics [32]. By using the Schro-
dinger equation, QM accurately describes the spatial positions of electrons as well as
those of other atomic- and molecular-scale particles. It can forecast the physicochemical
and structural characteristics of molecules [33].
The physical motion of atoms and molecules according to Newton’s principles of
physics is mimicked by MD simulation. Based on molecular mechanics and the empir-
ical force field, molecular modeling can explore the structural, dynamic, and ener-
getic characteristics of drugs and excipients as well as the molecular mechanism of
formulations [34]. The numerical simulation of a physical process, such a production
line, is called process modelling. The PK/PD behavior of a formulation in humans can
be predicted using PBPK modeling. In order to create a quantitative formulation pre-
diction model, machine learning and AI algorithms must be able to make data-driven
predictions. A well-designed AI system may maintain goods constant, amass and pre-
serve the specialized knowledge and expertise of formulation experts, and consider-
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ably speed up development, formulation optimization, cost savings, and product de-
velopment [35].
Over the past three decades, these computational techniques have become more
and more connected to pharmaceutical research. In the Web of Sciences, 92,343 publi-
cations have been found using the search strategy [TOPIC: (“mathematical model*”
OR “computer model*” OR “computer simulation” OR “process simulation” OR “molec-
ular model*)”] and [TOPIC: (“drug delivery” OR “85% of the 4,277 publications are re-
search articles, 9% are rev iew papers, and 6% are of other sorts)] [36]. Ac cordingly,
the number of publications per year in the field of computational pharmaceutics has
been rising significantly since 2000 and more than 1,000 papers are expected to be
published in 2022 (Figure 1.1). The top 10 nations or regions in the area are listed in
Table 1.1. The USA tops the list, with China coming in second [37]. A number of special
issues have been published in the computational pharmaceutics field, including
“Computational drug delivery” (2006), “Modeling the human skin barrier – Towards a
better understanding of dermal absorption” (2013), “Mathematical modelling of sys-
tems pharmacogenomics towards personalised drug delivery” (2013) in Advanced
Drug Delivery Reviews, “Mathematical modelling in drug delivery system” (2011) in In-
ternational Journal of Pharmaceutics, and “Fifty-Eight Years and Count” [38].
Several significant research funding/grants about computational pharmaceutics were
launched globally in line with this trend. A 4-year, £20.4 million project called Ad-
vanced Digital Design of Pharmaceutical Therapies (ADDoPT) aims to revoluti onize
the UK pharmaceutical business by making it possible to design cutting-edge pharma-
ceutical manufacturing processes digitally in the future [39]. A framework for the logi-
cal use of predictive biopharmaceutics tools for oral drug delivery is what the project
Oral biopharmaceutics tools (OrBiTo), which was launched in 2012 and includes 29
partners from academic and industry domains, wants to offer. In order to use datasets
Figure 1.1: Publication statistics of drug delivery through computational pharmaceutics (2000–2023).
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effectively in the future, this research highlights PBPK and MD modeling as prediction
tools and recommends using AI technology. The FDA and other regulating bodies be-
lieve that t he use of computational methods in pharmaceutics should improve the
quality of the final product since it emphasizes the importance of process knowledge
in product design and is in line with the quality by design (QbD) strategy [40].
The Knowledge-aided Assessment & Structured Application (KASA) system, which
the FDA just unveiled, will employ rules and algorithms to evaluate drug goods based
on information about the product, manufacture, and facilities. Also, the recently pro-
posed methods, model-informed drug development (MIDD) or model-informed drug
discovery and development (MID3), demonstrate the FDA’s and the European Medi-
cines Agency’s favorable outlook (EMA). The Japanese Pharmaceuticals and Medical
Devices Agency also released a report of a similar nature in 2017. The Center for Drug
Evaluation in China has been compiling proposals for MIDD guidelines [41]. The use
of PK modeling in drug development is primarily highlighted in these documents. Re-
viewing the current uses of various in silico technologies in the pharmaceutical indus-
try is necessary to understand where we are and where we need to go because
computational methods are altering the drug R&D paradigm and th e way we think.
This review primarily attempts to compile findings from pharmaceutical studies in-
volving process simulation, PBPK modeling, molecular modeling, and AI technologies.
The best way to tackle difficulties in the future is to integrate a variety of tools, as
each method has benefits and drawbacks [42].
1.1.3 Computational simulations (CSs): cutting-edge techniques
in drug delivery
At the middle of the twentieth century, the CS approaches developed in tandem with the
early computer revolution. Early applications of these techniques, however, were re-
stricted to toy models like the “hard sphere” model, which simulates collisions with per-
fect elasticity, or spheres interacting with a Lennard-Jones potential, which simulates the
behavior of liquid argon. Application of these techniques to more complicated atomic
models of complex molecules (like proteins) is now possible thanks to an exponential
expansion in processing capacity and the creation of efficient computational approaches
[43]. Nowadays, with moderate computational resources, simulation times of 100 ns (for
MD) and system sizes of 15 nm are routinely achievable. Consider a system of N atoms
with pair interactions between each pair of atoms to have some understanding of the
computational complexity of these simulations. Calculating the forces or energy involved
in N2 pair interactions would be one step of the simulation. These computations must be
done after each simulation step and the simulation must continue until the parameters
of interest converge to an average value (thermodynamic equilibrium), as pair interac-
tions would vary with interparticle distances. The fact that short-ranged interactions,
such van der Waals interactions, do not require the computation of every pair interac-
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tion leads to significant speedups because the interaction energies are substantially
lower than the thermal energy (kBT) at long distances. In actuality, only couples that are
close to a 1-nm cutoff distance are often taken into consideration. Even for long-ranged
interactions (like Coulomb interactions), approximations like Ewald summation are used
to speed up pair interaction calculation at the cost of minor errors in the estimate of
interaction energy [44].
For CSs, the time step must be small enough to allow for the exploration of all po-
tential states of the N-particle system or the minimization of errors in the numerical
integration of the equation of motion. One million of these steps are needed to analyze
the behavior of the system for one ns in MD simulations of atomistic systems, which
typically require time steps of the order of one fs. Simulations must be run over a pe-
riod of time that is much longer than the time scales of the relevant processes in order
to determine a specific thermodynamic feature [45]. For instance, while nucleation and
crystal formation occur at much longer time scales (>1 s) [48], hydrogen bonds form
and break at time scales of 1 ps (Luzar & Chandler, 1993), often exceeding the current
computing capabilities. The choice of N is also not completely random because the gen-
erated thermodynamic behavior must be indicative of the real system, even though the
number of simulated molecules is nearly always significantly lower than the number of
molecules in a real system. This is a problem in particular for macromolecule modeling,
especially for proteins and polymers, which are frequently seen in drug delivery and
whose individual molecule size is significantly bigger than the computationally possible
simulation box size. Prior to 2000, it was the bottleneck for the drug delivery commun-
ity’s widespread adoption of molecular simulations. The use of molecular simulations
in drug delivery has been reported in an increasing number of researches recently,
which has caused a change in the situation [46].
The most utilized simulation technique adopted uses MD, which utilizes “coarse-
grained” descriptions, where several molecules’ atoms are clustered together and
then interact via an “effective force field” produced after averaging out the lost de-
grees of freedom; significant improvements in practicable time and length scales can
be made. In general, N/N
c
atoms can be combined into bigger coarse-grained entities
to represent a system with N atoms using N
c
coarse-grained entities (beads). Larger
values of the coarse-graining parameter N/N
c
produce bigger computational speedups
(N2=N2)/sc times the atomistic simulations in the example given in the last para-
graph) at the expense of loss of knowledge regarding phenomena happening at reso-
lutions lower than N/N
c
atoms [47]. Another promising strategy is to remove the
solvent molecules altogether (“implicit solvent”), resulting in dramatic computing
speedups in simulations of liquid-phase systems frequently seen in drug delivery [48].
Although extremely promising and extensively used, the coarse-grained descriptions
must be taken with a pinch of salt, since the importance of the lost degrees of freedom
is often underestimated. This also results in systematic mistakes in the estimated
forces and energy. There is a variety of simulations software and libraries, which op-
erate in input tethered option to simulate various DDSs [49].
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1.1.4 Simulation plan for computer simulations
The series of actions which is commonly involved in an atomistic CS simulation is de-
picted in Figure 1.2. Certain applications, such grand canonical ensemble simulations
and free energy calculations, demand a more intricate procedure, which is covered
separately later. The initial geometry of the drug and any other molecules being stud-
ied (such as the carrier, membrane, and solvent) is first created in a molecule builder
program (such as Materials Studio, Marvin, and GaussView) or retrieved from a data-
base (such as Protein Data Bank [RC SB PDB, 2017] and ZINC ZINC15, 2017) [50 ]. The
unique bound and unbonded atomic pairs in the system are then listed using these
geometries, and force field parameters are given for each pair. The next step involves
creating a simulation box and adding molecules of various species to it in accordance
with their concentration. The standard procedure is to randomly add molecules one
at a time. Nevertheless, such a method frequently leads to a small number of atom
pairs being excessively close or overlapping one another. Such pairs of atoms would
have extremely high interatomic repulsions if a CS simulation was begun from this
arrangement, leading to numerical instability [51]. Therefore, it is advised to do a
brief CS simulation or an energy minimization with a signif icantly lower step size
(time step) than what is generally used in the actual simulation. Small atom relaxa-
tions are used in these simulations, which are carried out until all pair-inter action
forces are below a predetermined threshold [52].
The configuration acquired following energy minimization is used as the starting
point for MC/MD simulations, which also include production and equilibration pro-
cesses. The goal of the equilibration simulations, as implied by their name, is to reach
“thermodynamic equilibrium.” As there are far less molecules in this system than in a
macroscopic system, it is crucial to realize that the term “thermodynamic equilibrium”
is used here with a very broad definition [53]. Particularly, it is common practice to ex-
aggerate thermodynamic property fluctuations that diminish as the inverse square root
of system size. Also, it is common practice to track an important thermodynamic pa-
rameter during the course of the simulation and declare “equilibration” accomplished
when the variable appears to converge, that is, when it fluctuates around an average
value. This technique succeeds in capturing faster relaxation modes of the system, but
fails to capture those that occur at time scales slower than the simulated time, such as
those of smaller molecules (e.g., of macromolecules) [54]. Our skills to completely repli-
cate the relaxing behavior of macromolecules are far from ideal because the total
amount of simulation time that is feasible is constrained by computer capabilities and
nearly invariably far shorter than experimental time scales. Similar limitations apply to
the study of uncommon occurrences like crystal nucleation, despite the fact that small
molecules can now replicate such processes [54].
The initial setup of the production simulation is one that is balanced. In order to
sample a statistically significant number of equilibrium configurations, they are car-
ried out [55]. These configurations are then utilized to compute the desired attributes.
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In order to calculate structural parameters like molecule size and pair distribution
function, MC and MD simulations both monitor the change in atom locations as a
function of time. Yet MD simulations also keep track of the atoms’ momenta, which
can be utilized to calculate dynamical values like the diffusion coefficient. The sam-
pled configurations must be uncorrelated in order to obtain accurate measurements
of thermodynamic averages and fluctuations, which means that the time interval be-
tween two samples must be sufficiently large than the correlation time [56]. The first-
order decay time constant of the autocorrelation function of the relevant attribute is
used here to determine the correlation time. We also consider the system to be ergo-
dic, meaning that the time-averaged characteristics discovered through simulations
for a sample subsystem would be the same as the spatial-averaged characteristics for
the entire system. It is questionable whether this assumption holds true for a simu-
lated system (Cho & Joannopoulos, 1992), but the ability of MC/MD methods to accu-
rately predict equilibrium characteristics for a wide range of different systems lends
support to the ergodic hypothesis [57].
1.1.5 Simulation software/packages utilized in drug delivery
It takes a lot of time and knowledge from many different areas to create effective
computer systems that can do molecular simulations. Fortunately, a worldwide effort
by scientists has produced a variety of very effective and powerful open source and
commercial software (Table 4). Also, numerous programs and websites are used indi-
vidually for creating molecules (such as Materials Studio and Marvin), calculating
force-field parameters (such as SwissParam, ATB, PRODRG, MKTOP, and OBGMX), and
analyzin g and visualizing findings (such as VMD, PYMOL, RASMOL, and CHIMERA)
Figure 1.2: Basic steps of atomistic Computational Score Derived simulations.
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[58]. This software’s user-friendliness has encouraged widespread usage of molecular
simulations, but it has also raised concerns that findings may be generated and re-
ported without a basic understanding of algorithmic subtleties and may therefore be
inaccurate. This cynicism is somewh at warranted, and before employing a software
program in a practical application, it is important to thoroughly understand how it
operates, as well as its features and l imitations [59]. For QbD, the most frequently
used software is Design Expert software for preparing experimental design and risk
assessments. Moreover, process capability and significant interactions among various
excipients can also be used by this software utilizing the QbD approach. For PK/PD
analysis, different aided simulation experiments were performed to determine the
compartmental and non-compartmental kinetics in absorption profile of drugs follow-
ing a different route, which has wide scope in biopharmaceutics. Moreover, some
emerging techniques of dynamic simulations that involve principal component analy-
sis (PCA) have also been carried by the visualizer suite and QSPR software [60].
Table 1.1: Software and packages used for computer-aided simulation systems.
S. no. Simulation type Variables/input Software/package applied
. Quality by design Risk assessment/screening and
optimization
Design Expert software
. Mesodyme simulations Coarse graining NAMD visualizer
. Monte Carlo simulation Pymol variables and energy
minimization
NAMD, MARTINI, Gromacs
. Molecular dynamic
simulations (visual field)
Pymol variables and energy
minimization
Gromacsand visual field,
molecular discovery studio
. Molecular dynamic
simulations (docking
algorithm)
OPLS-AA and OPLS-UA, energy field
generation, simulation experiment
inputs
AMBER studio, Gromacs,
field visual viewer,
nanooptics
. Principal component
analysis
Clusters of data variants PCA compounder viewer
. Integrated genomic
networking (drug
repurposing)
Data clustering, data fitting, and
genomic variant finding
GENO
®
Viewer, Compass,
GAFF-R
. ADMET viewer (for
pharmacokinetic
simulations)
Input variables for csv file for drug
properties, chemical structure of drug
ADMET predictor
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1.2 Applications of computer simulations
in drug delivery
1.2.1 Role of CS in drug delivery
In the last few years, tens of thousands of research have been published that apply
molecular simulations to medication delivery. The majority of these research focus on
drug-receptor docking or binding, which is not the subject of this study. The capacity
of a drug to pass across the lipid bilayer membrane has a significant impact on its
bioavailability [61]. The traditional Meyer-Overton rule states that hydrophobic drugs
are lipophilic and easily migrate through the lipid barrier because of this. According
to Lipinski, Lombardo, Dominy, and Feeney (2012), the hydrophobicity/lipophilicity of
a molecule can be roughly determined by counting the number of hydrogen-bond do-
nors and acceptors within it. This information can be obtained by looking at the
chemical structure of the molecule. The orientation of the drug molecule (specified in
relation to the bilayer normal) and the energetic interactions of the drug with the bi-
layer constituents are two examples of the comprehensive information that may be
obtained from molecular simulations [62]. These studies have been successfully ap-
plied to a wide variety of drug and membrane chemistries. Moreover, molecular sim-
ulations can reveal the function that transporter m olecules serve as well as other
potential sources of deviations from the Meyer-Overton rule. Finally, molecular simu-
lations can provide in-depth understanding of the dynamics and structure of bilayers
as well as the mechanism of channel development [63].
1.2.1.1 Application of CS-based QbD in drug delivery
A QbD risk assessment method for developing a cost-effective formulation must be
started at the drug discovery stage in order to get the greatest benefits from it. The
formulator will begin to become familiar with the solution behavior, stability profile,
pCQAs, and any potential difficulties that the candidate proteins might bring (such as
excessive viscosity and chemical hotspots) [64]. The formulator can offer helpful guid-
ance for the first handling of the protein and the choice of storage buffers by being
included in the discovery research team. This kind of interven tion will increase the
likelihood of finding strong candidates by preventing the use of low purity or deterio-
rated material in biological screening investigatio ns [65]. Finally, and perhaps most
crucially, if multiple compounds are found to have equivalent efficacy for a given tar-
get, this technique will offer a new set of criteria by which to screen candidate mole-
cules. Purified protein is often more expensive during the discovery research stage.
This makes it difficult for the formulator to implement QbD principles in various
ways. The formulation team needs to have extensive training in handling and charac-
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terizing sub-milligram quantities of protein for an iterative development interface to
be successful. The formulator must also be ready to deal with the dynamic nature of
the finished product because cell culture and purification methods have not yet been
established. The data collected at this point will not be absolute, but rather serve as
relative indications of stability tendencies, and it is crucial to understand this. As
more of a higher purity of material is accessible in larger amounts, it will probably be
necessary to do optimized studies. The absence of product-specific assays is the final
factor to take i nto account for the formulation/drug discovery interface [66]. O nce
more, the formulator must rely on experimental methods that reduce the amount of
protein eaten while also making use of universal indicators of physical, chemical, and
functional stability. Last but not least, strategies must be used to connect potential
chemical and physical instability to some degree of biological impact. Due to their
poor throughput, in vivo research and cell-based in vitro investigations cannot feasi-
bly serve this goal [67]. As a high-throughput substitute for these biological experi-
ments, binding analysis based on surface plasmon resonance or enzyme-linked
immunosorbent assay techniques is available. Nevertheless, cell-based assays are fre-
quently required for formulation development at the late stage.
1.2.2 Role of PCA in drug delivery
An API interacts with other formulation ingredients (excipients) in a dosage shape to
facilitate the administration and release of an active substance and protect it from the
environment. Excipients can interact with drugs in dosage forms, even when they are
pharmacologically inactive. These interactions might impact the physical stability of
the drug product through sensory qualities, slower dissolving rates, or chemical fea-
tures that lead to drug degradation. The detection of API compatibility with excipients
or other active ingredients is therefore regarded as one of the essential elements and
is at the cutting edge of pharmaceutical science and technology development. Com-
monly used analytical methods for determining compatibility include thermal techni-
ques like differential scanning calorimetry and thermogravimetric analysis. Because
the peaks of APIs and excipients occasionally overlap, it might be challenging to interpret
results collected using the aforementioned analytical approaches [68]. Factor analysis is a
powerful tool that can be used in this case to process and resolve data in a better and
more accurate way. PCA is one of the FA techniques. Finding the subspace in the variable
space with the highest variation in the data is the goal of PCA [69]. Principal components
are created by linearly transforming the primary variables, which are typically corre-
lated, into a smaller set of uncorrelated variables. PCA follows these rules: X=T+A+P+
E, where X is an N-by-M data matrix, TA is an N-by-A scores matrix with projection of
the objects into the A PCs subspace, PA is an M-by-A loadings matrix with a linear combi-
nation of the variables indicated in each PC, and EA is an N-by-M residuals matrix. PCA
can be used to identify correlations between variables with significant variance. With
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