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design and computational methods, molecular simulations can be used to optimize
the properties of 2D nanomaterials for specific drug delivery applications. While
there are still challenges to be addressed, such as the accuracy of force fields and the
computational cost of simulations, the potential applications of molecular simulations
in the field of nanomedicine are vast, and we can expect further advancements in this
field in the future.
References
[1] Chimene, D., Alge, D. L., & Gaharwar, A. K. Two‐dimensional nanomaterials for biomedical
applications: Emerging trends and future prospects. Advanced Materials, 2015 Dec, 27(45),
7261–7284.
[2] Casalini, T. Not only in silico drug discovery: Molecular modeling towards in silico drug delivery
formulations. Journal of Controlled Release, 2021 Apr 10, 332, 390–417.
[3] Wang, W., Ye, Z., Gao, H., & Ouyang, D. Computational pharmaceutics-A new paradigm of drug
delivery. Journal of Controlled Release, 2021 Oct 10, 338, 119–136.
[4] Goenka, S., Sant, V., & Sant, S. Graphene-based nanomaterials for drug delivery and tissue
engineering. Journal of Controlled Release, 2014 Jan 10, 173, 75–88.
[5] Murugan, C., Sharma, V., Murugan, R. K., Malaimegu, G., & Sundaramurthy, A. Two-dimensional
cancer theranostic nanomaterials: Synthesis, surface functionalization and applications in
photothermal therapy. Journal of Controlled Release, 2019 Apr 10, 299, 1–20.
[6] Goel, S., Chen, F., & Cai, W. Synthesis and biomedical applications of copper sulfide nanoparticles:
From sensors to theranostics. Small, 2014 Feb, 10(4), 631–645.
[7] Singh, A. P., Biswas, A., Shukla, A., & Maiti, P. Targeted therapy in chronic diseases using
nanomaterial-based drug delivery vehicles. Signal Transduction and Targeted Therapy, 2019 Aug 30,
4(1), 33.
[8] Jiang, Q., Zhao, S., Liu, J., Song, L., Wang, Z. G., & Ding, B. Rationally designed DNA-based
nanocarriers. Advanced Drug Delivery Reviews, 2019 Jul 1, 147, 2–1.
[9] Wang, W., Ye, Z., Gao, H., & Ouyang, D. Computational pharmaceutics-A new paradigm of drug
delivery. Journal of Controlled Release, 2021 Oct 10, 338, 119–136.
[10] Harish, V., Tewari, D., Gaur, M., Yadav, A. B., Swaroop, S., Bechelany, M., & Barhoum, A. Review on
nanoparticles and nanostructured materials: Bioimaging, biosensing, drug delivery, tissue
engineering, antimicrobial, and agro-food applications. Nanomaterials, 2022 Jan 28, 12(3), 457.
[11] Naqvi, S., Panghal, A., & Flora, S. J. Nanotechnology: A promising approach for delivery of
neuroprotective drugs. Frontiers in Neuroscience, 2020 Jun 9, 14, 494.
[12] Anfray, C., Mainini, F., & Andón, F. T. Nanoparticles for immunotherapy. Frontiers of Nanoscience,
2020 Jan 1, 16, 265–306.
[13] Zhang, L., & Sun, Y. Molecular simulation of adsorption and its implications to protein
chromatography: A review. Biochemical Engineering Journal, 2010 Feb 15, 48(3), 408–415.
[14] Karimi, M., Ghasemi, A., Zangabad, P. S., Rahighi, R., Basri, S. M., Mirshekari, H., Amiri, M., Pishabad,
Z. S., Aslani, A., Bozorgomid, M., & Ghosh, D. Smart micro/nanoparticles in stimulus-responsive
drug/gene delivery systems. Chemical Society Reviews, 2016, 45(5), 1457– 1501.
[15] Katiyar, R. S., & Jha, P. K. Molecular simulations in drug delivery: Opportunities and challenges.
Wiley Interdisciplinary Reviews: Computational Molecular Science, 2018 Jul, 8(4), e1358.
10 Molecular simulations strategies for designing 2D nanomaterials 243
https://t.me/med1917
[16] Yang, P. Y., Ju, S. P., Chuang, Y. C., & Chen, H. Y. Molecular dynamics simulations of PAMAM
dendrimer-encapsulated Au nanoparticles of different sizes under different pH conditions.
Computational Materials Science, 2017 Sep 1, 137, 144–152.
[17] Vlachakis, D., Bencurova, E., Papangelopoulos, N., & Kossida, S. Current state-of-the-art molecular
dynamics methods and applications. Advances in Protein Chemistry and Structural Biology, 2014
Jan 1, 94, 269–313.
[18] Schattke, W., & Muiño, R. D. Quantum Monte-Carlo Programming: For Atoms, Molecules, Clusters,
and Solids. 2013 Aug 30, John Wiley & Sons
[19] Bedrov, D., Piquemal, J. P., Borodin, O., MacKerell, A. D., Jr, Roux, B., & Schröder, C. Molecular
dynamics simulations of ionic liquids and electrolytes using polarizable force fields. Chemical
Reviews, 2019 May 29, 119(13), 7940–7995.
[20] Katiyar, R. S., & Jha, P. K. Molecular simulations in drug delivery: Opportunities and challenges.
Wiley Interdisciplinary Reviews: Computational Molecular Science, 2018 Jul, 8(4), e1358.
[21] Akkermans, R. L., Spenley, N. A., & Robertson, S. H. Monte Carlo methods in materials studio.
Molecular Simulation, 2013 Dec 1, 39(14–15), 1153–1164.
[22] Bernardi, R. C., Melo, M. C., & Schulten, K. Enhanced sampling techniques in molecular dynamics
simulations of biological systems. Biochimica Et Biophysica Acta (Bba)-general Subjects, 2015 May 1,
1850(5), 872–877.
[23] Verma, A., Parashar, A., Singh, S. K., Jain, N., Sanjay, S. M., & Siengchin, S. Modeling and simulation
in polymer coatings. In: Polymer Coatings. 2020 Nov 18, (pp.309–324). CRC Press.
[24] Hollingsworth, S. A., & Dror, R. O. Molecular dynamics simulation for all. Neuron, 2018 Sep 19, 99(6),
1129–1143.
[25] Andreo, P. Monte Carlo simulations in radiotherapy dosimetry. Radiation Oncology, 2018 Dec, 13(1),
1–5.
[26] Takada, S. Coarse-grained molecular simulations of large biomolecules. Current Opinion in
Structural Biology, 2012 Apr 1, 22(2), 130–137.
[27] Karelina, M., & Kulik, H. J. Systematic quantum mechanical region determination in QM/MM
simulation. Journal of Chemical Theory and Computation, 2017 Feb 14, 13(2), 563–576.
[28] Bagayoko, D. Understanding density functional theory (DFT) and completing it in practice. AIP
Advances, 2014 Dec 3, 4(12), 127104.
[29] Pagadala, N. S., Syed, K., & Tuszynski, J. Software for molecular docking: A review. Biophysical
Reviews, 2017 Apr, 9, 91–102.
[30] Musgaard, M., & Biggin, P. C. Steered molecular dynamics simulations predict conformational
stability of glutamate receptors. Journal of Chemical Information and Modeling, 2016 Sep 26, 56(9),
1787–1797.
[31] Hu, X., Zeng, Z., Zhang, J., Wu, D., Li, H., & Geng, F. Molecular dynamics simulation of the interaction
of food proteins with small molecules. Food Chemistry, 2022 Nov, 3, 134824.
[32] Salo-Ahen, O. M., Alanko, I., Bhadane, R., Bonvin, A. M., Honorato, R. V., Hossain, S., Juffer, A. H.,
Kabedev, A., Lahtela-Kakkonen, M., Larsen, A. S., & Lescrinier, E. Molecular dynamics simulations in
drug discovery and pharmaceutical development. Processes, 2020 Dec 30, 9(1), 71.
[33] Martinho, N., Florindo, H., Silva, L., Brocchini, S., Zloh, M., & Barata, T. Molecular modeling to study
dendrimers for biomedical applications. Molecules, 2014 Dec 8, 19(12), 20424–20467.
[34] Chen, S. H., Bell, D. R., & Luan, B. Understanding interactions between biomolecules and two-
dimensional nanomaterials using in silico microscopes. Advanced Drug Delivery Reviews,
2022 May 19, 186, 114336.
[35] Lee, H. Molecular modeling of protein corona formation and its interactions with nanoparticles and
cell membranes for nanomedicine applications. Pharmaceutics, 2021 Apr 29, 13(5), 637.
244 Shivang Dhoundiyal et al.
https://t.me/med1917
[36] Page, A. J., Ding, F., Irle, S., & Morokuma, K. Insights into carbon nanotube and graphene formation
mechanisms from molecular simulations: A review. Reports on Progress in Physics, 2015 Mar 9,
78(3), 036501.
[37] Barone, V., Hod, O., Peralta, J. E., & Scuseria, G. E. Accurate prediction of the electronic properties of
low-dimensional graphene derivatives using a screened hybrid density functional. Accounts of
Chemical Research, 2011 Apr 19, 44(4), 269–279.
[38] Chakraborty, S., & Kumar, H. Molecular dynamics simulations of two-dimensional materials. In:
Synthesis, Modeling, and Characterization of 2D Materials, and Their Heterostructures. 2020 Jan 1,
(pp.125–148). Elsevier.
[39] Calvaresi, M., & Zerbetto, F. Atomistic molecular dynamics simulations reveal insights into
adsorption, packing, and fluxes of molecules with carbon nanotubes. Journal of Materials Chemistry
A, 2014, 2(31), 12123–12135.
[40] Ray, U., Pang, Z., & Li, T. Mechanics of cellulose nanopaper using a scalable coarse-grained
modeling scheme. Cellulose, 2021 Apr, 28, 3359–3372.
[41] Hu, X., Liao, M., Gong, H., Zhang, L., Cox, H., Waigh, T. A., & Lu, J. R. Recent advances in short
peptide self-assembly: From rational design to novel applications. Current Opinion in Colloid &
Interface Science, 2020 Feb 1, 45, 1 – 3.
[42] Razavi, L., Raissi, H., Hashemzadeh, H., & Farzad, F. Molecular insights into the loading and
dynamics of anticancer drugs on silicene and folic acid-conjugated silicene nanosheets: DFT
calculation and MD simulation. Journal of Biomolecular Structure and Dynamics, 2021 Jul 24, 39(11),
3892–3899.
[43] Qu, J., Zhao, X., Ma, P. X., & Guo, B. Injectable antibacterial conductive hydrogels with dual response
to an electric field and pH for localized “smart” drug release. Acta Biomaterialia, 2018 May 1, 72,
55–69.
[44] Kmiecik, S., Gront, D., Kolinski, M., Wieteska, L., Dawid, A. E., & Kolinski, A. Coarse-grained protein
models and their applications. Chemical Reviews, 2016 Jul 27, 116(14), 7898–7936.
[45] Kashkooli, F. M., Soltani, M., & Souri, M. Controlled anti-cancer drug release through advanced
nano-drug delivery systems: Static and dynamic targeting strategies. Journal of Controlled Release,
2020 Nov 10, 327, 316–349.
[46] Cheng, Z., Li, M., Dey, R., & Chen, Y. Nanomaterials for cancer therapy: Current progress and
perspectives. Journal of Hematology & Oncology, 2021 Dec, 14(1), 1–27.
[47] Jyothi, V. G., Ghouse, S. M., Khatri, D. K., Nanduri, S., Singh, S. B., & Madan, J. Lipid nanoparticles in
topical dermal drug delivery: Does chemistry of lipid persuade skin penetration? Journal of Drug
Delivery Science and Technology, 2022 Feb 11, 69, 103176.
[48] Agarwal, V., & Chatterjee, K. Recent advances in the field of transition metal dichalcogenides for
biomedical applications. Nanoscale, 2018, 10(35), 16365–16397.
[49] Nobre, T. M., Pavinatto, F. J., Caseli, L., Barros-Timmons, A., Dynarowicz-Łątka, P., & Oliveira, O. N., Jr
Interactions of bioactive molecules & nanomaterials with Langmuir monolayers as cell membrane
models. Thin Solid Films, 2015 Oct 30, 593, 158–188.
[50] Wright, J. S. Colloidal characterisations for environmental exposure assessment in support of the
risk assessment of nano-biomaterials for biomedical applications.
[51] Comer, J., Chen, R., Poblete, H., Vergara-Jaque, A., & Riviere, J. E. Predicting adsorption affinities of
small molecules on carbon nanotubes using molecular dynamics simulation. ACS Nano, 2015
Dec 22, 9(12), 11761–11774.
[52] Modi, S., & Anderson, B. D. Determination of drug release kinetics from nanoparticles: Overcoming
pitfalls of the dynamic dialysis method. Molecular Pharmaceutics, 2013 Aug 5, 10(8), 3076–3089.
[53] Lin, S., Shih, C. J., Sresht, V., Rajan, A. G., Strano, M. S., & Blankschtein, D. Understanding the
colloidal dispersion stability of 1D and 2D materials: Perspectives from molecular simulations and
theoretical modeling. Advances in Colloid and Interface Science, 2017 Jun 1, 244, 36–53.
10 Molecular simulations strategies for designing 2D nanomaterials 245
https://t.me/med1917
[54] Duarte, Y., Márquez‐Miranda, V., Miossec, M. J., & González‐Nilo, F. Integration of target discovery,
drug discovery and drug delivery: A review on computational strategies. Wiley Interdisciplinary
Reviews: Nanomedicine and Nanobiotechnology, 2019 Jul, 11(4), e1554.
[55] Aminpour, M., Montemagno, C., & Tuszynski, J. A. An overview of molecular modeling for drug
discovery with specific illustrative examples of applications. Molecules, 2019 Apr 30, 24(9), 1693.
[56] Epp, J. X-ray diffraction (XRD) techniques for materials characterization. In: Materials
Characterization Using Nondestructive Evaluation (NDE) Methods. 2016 Jan 1, (pp.81–124).
Woodhead Publishing.
[57] Tang, C. Y., & Yang, Z. Transmission electron microscopy (TEM). In: Membrane Characterization.
2017 Jan 1, (pp.145–159). Elsevier.
[58] Ricci, A., Olejar, K. J., Parpinello, G. P., Kilmartin, P. A., & Versari, A. Application of Fourier transform
infrared (FTIR) spectroscopy in the characterization of tannins. Applied Spectroscopy Reviews,
2015 May 28, 50(5), 407–442.
[59] Hatzakis, E. Nuclear magnetic resonance (NMR) spectroscopy in food science: A comprehensive
review. Comprehensive Reviews in Food Science and Food Safety, 2019 Jan, 18(1), 189–220.
[60] Stetefeld, J., McKenna, S. A., & Patel, T. R. Dynamic light scattering: A practical guide and
applications in biomedical sciences. Biophysical Reviews, 2016 Dec, 8, 409–427.
[61] Jiang, T., & Zhu, Y. Measuring graphene adhesion using atomic force microscopy with a microsphere
tip. Nanoscale, 2015, 7(24), 10760–10766.
[62] Zhang, H., Chhowalla, M., & Liu, Z. 2D nanomaterials: Graphene and transition metal
dichalcogenides. Chemical Society Reviews, 2018, 47(9), 3015–3017.
[63] Lu, S., Mily, E. J., Irving, D. L., Maria, J. P., & Brenner, D. W. New method for extracting diffusion-
controlled kinetics from differential scanning calorimetry: Application to energetic nanostructures.
The Journal of Physical Chemistry C, 2015 Jun 25, 119(25), 14411–14418.
[64] Poltavsky, I., & Tkatchenko, A. Machine learning force fields: Recent advances and remaining
challenges. The Journal of Physical Chemistry Letters, 2021 Jul 9, 12(28), 6551–6564.
[65] Reina, G., González-Domínguez, J. M., Criado, A., Vázquez, E., Bianco, A., & Prato, M. Promises, facts
and challenges for graphene in biomedical applications. Chemical Society Reviews, 2017, 46(15),
4400–4416.
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Aditya Sharma, Sakshi Sagar, Md Aftab Alam
✶
, Manjeet Kaur,
Tarique Anwer, and Pramod Kumar Sharma
11 Applications and molecular simulation
strategies for excipient–excipient
compatibility
Abstract: Excipient–excipient compatibility is an important consideration in the devel-
opment of pharmaceutical products. Excipients are the inactive ingredients that are
added to a drug formulation to improve its physical properties, stability, and bioavail-
ability. However, the compatibility of different excipients can affect the stability and effi-
cacy of the final product. Molecular simulations can be a useful tool to predict the
compatibility of different excipients and guide formulation development. Molecular dy-
namic (MD) simulations can be used to study the behavior of excipients at the molecular
level. This approach can help predict the interactions between different excipients and
identify potential compatibility issues. MD simulations can also be used to study the ef-
fect of different processing conditions on excipient compatibility. Monte Carlo simula-
tions can be used to study the thermodynamic properties of excipient mixtures. This
approach can help predict the phase behavior of different excipients and identify poten-
tial compatibility issues. Molecular simulations can be a powerful tool to predict the
compatibility of different excipients and guide formulation development. By combining
molecular simulations with experimental techniques, researchers can gain a compre-
hensive understanding of the behavior of excipients in pharmaceutical formulations.
Keywords: Drug Delivery, Molecular dynamic, Bioavailability, Compatibility, Excipients
11.1 Introduction
Excipients are components, either natural or synthetic, that are mixed with the active
medication in formulations to serve as either functional or nonfunctional agents. Al-
most all human and animal dose formulations consist of excipients, which account for
about 90% of the total mass [1]. As per the International Pharmaceutical Excipients
✶
Corresponding author: Md Aftab Alam, School of Pharmacy Katihar Medical College Campus
Alkarim University Katihar, Bihar Pin-854106, e-mail: draftabalamresearch@gmail.com
Aditya Sharma, Sakshi Sagar, Manjeet Kaur, Pramod Kumar Sharma, Department of Pharmacy,
School of Medical and Allied Sciences, Galgotias University, Greater Noida, Uttar Pradesh, India
Tarique Anwer, Department of pharmacology and toxicology, College of Pharmacy, Jazan university,
Gizan, Saudi Arabia
https://doi.org/10.1515/9783111208671-011
https://t.me/med1917
Council, excipients are molecules other than the active pharmaceutical ingredient (API)
that are intentionally incorporated into drug delivery systems due to their efficacy. A
superior pharmaceutical journey begins with a careful identification of excipients. It is
important that the excipients and their concentrations in a formulation work well to-
gether and serve their purpose. Incompatibility occurs when a drug’s properties are al-
tered by its contact with one or more ingredients of a composition, the properties may
be physical, chemical, microbiological, or medicinal [2]. The primary goal of testing for
excipient compatibility is to anticipate any potential incompatibility between the active
ingredient and the finished medicinal product. Excipient selection and concentration
within a formulation must be supported by the results of these studies for submission
to regulatory authorities [3]. According to experts in the pharmaceutical industry, exci-
pients account for around 0.5% of the worldwide pharmaceutical market, or roughly
$4 billion. Excipients are employed in pharmaceutical formulations for a variety of rea-
sons, such as absorption enhancers, colorants, diluents, volume/weight extenders/fillers,
emulsifiers, flavors, preservatives, solvents, sustained release matrices, wetting agents,
and more. Making a reliable dosage form relies heavily on the excipients used. Drug
stability, bioavailability, and patient acceptability can all be protected, supported, and
enhanced with the help of these additives, as can product identification and other fac-
tors that contribute to the safe, effective, and timely administration of medications [4].
These fillers and binders might be the cause of poor results or even product failure.
API–excipient interactions, excipient–excipient interactions, API–package interactions,
and excipient–package interactions are all at the root of this issue. Reactive impurities
in the excipients (such as peroxides, aldehydes, reducing sugars, organic acids, nitrates,
and nitrites) as well as the API and packaging material are the primary causes of these
interactions. Poor product quality as well as patient safety might be jeopardized by
even trace levels of reactive contaminants. Regular and time-consuming compatibility
tests between excipients is a common solution. Excipient compatibility studies may
seem like a minor step in the drug development process, but the information gleaned
from them is crucial in making decisions about dosage form components, establishing
the stability profile of the drug, identifying degradation products, and understanding
reaction processes. Initiating steps to improve the drug’s stability is possible if this qual-
ity is deemed to be lacking [5]. For this reason, thorough, well-planned compatibility
studies may help reduce the expenses and development delays caused by stability is-
sues in the last stages of a product’s life cycle. Although many interactions between
APIs and excipients have been documented, the excipient–excipient interaction, which
can also lead to stability difficulties and, in the worst case, product failure, has received
less attention. Croscarmellose sodium’s relationship to basic excipients can delay the
tablet dissolution property of an acid labile drug; magnesium stearate can alter the
rate-control function of Eudragit RS and RL (thickening or coagulation); and hygro-
scopic excipients like sorbitol can slightly reduce the efficacy of disintegrants like cro-
scarmellose sodium in tablet formulations made via the wet granulation or direct
compression methods. Hydrolysis, oxidation, photodegradation, micellization, complex-
248 Aditya Sharma et al.
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ation, decarboxylation, and so on can all occur alongside excipient incompatibilities,
thus it is not surprising that they are blamed on reactive contaminants [6]. Because of the
potential for such interactions to negatively affect the final product, it is necessary to con-
duct compatibility testing between excipients to find the best possible combination of in-
gredients. To ensure the medicine remains physically and chemically stable in solid dosage
forms, this chapter examines the importance of excipient–excipient compatibility [7].
11.2 Molecular simulations strategies
The definition of “simulation” in the 2017 Oxford Dictionary is “the imitation of a situ-
ation or process,”“the action of pretending; deception,” or “the production of a com-
puter model of something, especially for the purpose of study.” To that end, computer
simulations have shown to be a useful tool to comprehend complex systems and put-
ting hypotheses to the test, especially in situations where conducting real-world ex-
periments would be too time-consuming, costly, or risky [8]. Beautifully animated
movies of the simulation results, on the other hand, might be deceptive because they
often disclose additional details than can be confirmed by testing. Molecular simula-
tions are extremely useful for modeling nanoscale phenomena because they provide
insights into the structure and dynamics that cannot be obtained using even the most
sophisticated imaging approaches. In spite of this, we have more faith in computer
models because many previous results are being confirmed by the present investiga-
tions [9]. To analyze the behavior of a molecule or a system of molecules, researchers
use a wide variety of theoretical and computational approaches, which are grouped
together under the umbrella term “molecular simulation” or “molecular modeling”
[14]. In a nutshell, a molecule’s activity is the same as that of its atoms, which is the
same as that of its atomic components [10]. Quantum mechanical (QM) methods that
successfully solve the Schrödinger equation are necessary for a precise description of
molecular activity. However, it has been found that QM techniques are computation-
ally expensive and often not needed for locating the properties of interest in intricate
molecular systems [11]. With the exception of their reactivity, atoms can be described
using a simplified, classical “atomistic” description in which their nuclei are taken to
be classical particles following Newtonian physics, and their motion is approximated
by the motion of their nuclei [12]. This is the theoretical basis of the MD method,
known as atomistic molecular dynamics, in which individual atoms make up the mol-
ecules and move in response to the interatomic forces (also known as the “force
field”), exerted by atoms of other molecules or those in the same molecule. Similar to
the MD technique, the atomistic Monte Carlo (MC) method bases its calculations on an
approximation and the idea of a force field [13]. The Boltzmann weights of possible
molecular arrangem ents are used to randomly select those arrangements in an MC
simulation. Trial steps (moves) are utilized to generate the sequential configurations,
11 Applications and molecular simulation strategies 249
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and they are made up of random atom displacements that are either accepted or re-
jected based on the global energy change that results from the trial move [14]. In con-
trast to MD, which integrates the equation of motion numerically with small time
steps, MC simulations have no concept of time, and the configurations selected in an
MC simulation do not reflect the true path of the atoms. However, when given enough
time to run, the MC and MD methods yield the same predictions for some equilibrium
features (such as pair distribution functions) [15].
Compatibility testing for excipients in pharmaceutical formulations is a growing area
of application for molecular simulations. In order to increase the drug’s stability, solubil-
ity, and bioavailability, excipients are added to medicinal formulations [16]. The quality,
safety, and efficacy of the therapeutic product may be compromised iftheexcipientsin-
teract with one another or with the API. When developing a stable pharmaceutical prod-
uct with the required quality qualities, knowledge of the excipients’ compatibility is
essential [17]. Excipient interactions can be studied at the molecular level via molecular
simulations, especially MD simulations. In MD simulations, atomic and molecular mo-
tions are modeled using classical mechanics. The structure, dynamics, and energies of
molecular systems can be predicted via MD simulations by solving Newton’sequationsof
motion [18]. In this chapter, we will examine the potential of molecular simulations for
studying excipient–excipient compatibility. The many different kinds of molecular simu-
lations used to investigate excipient compatibility will be the focus of this discussion [19].
These include force-field-based simulations, QM simulations, and hybrid methods that
combine force-field and QM approaches. We will also discuss the benefits and drawbacks
of using molecular simulations to investigate excipient–excipient compatibility and pro-
vide instances of its application. Force-field-based molecular simulations are the gold
standard for investigating excipient–excipient compatibility. Classical force fields are
used to characterize atomic and molecular interactions in these simulations. Mathemati-
cal functions called “force fields” are used to approximatively calculate the potential en-
ergy of a system of atoms and molecules, given their positions and orientations. The
shape, vibrational frequencies, and thermodynamic properties of small molecules are
just some of the experimental data utilized to parameterize force fields. Molecular sys-
tems, including interactions between excipients, can have their structure, thermodynam-
ics, and dynamics predicted by means of force-field simulations [20]. The stability and
solubility of drug formulations as well as any excipient incompatibilities can be better
understood with the help of computer simulations. The influence of external factors on
thestabilityofpharmaceuticalformulations,suchastemperature,pressure,andpH,can
be predicted using force-field simulations. Another type of molecular simulation used to
investigate excipient–excipient compatibility is the QM simulation. QM simulations use
the Schrödinger equation to describe the motion of electrons in a chemical system. When
it comes to chemical reactions and electronic properties, QM simulations are more accu-
rate than force-field-based models because they account for the QM behavior of elec-
trons. When investigating excipient interactions, including chemical reactions like acid-
base reactions, hydrogen bonding, and electron transfer, QM models excel. The electrical
250 Aditya Sharma et al.
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structure and energetics of molecular systems as well as the mechanics of chemical reac-
tions can be predicted using QM simulations [21]. QM simulations are limited to exploring
situations with very few molecules because of their high computational cost. The elec-
Table 11.1: List of molecular simulation methods commonly employed in drug delivery [23].
Simulation
method
Variants Applications
Monte
Carlo (MC)
Atomistic MC [], Meunier, et al.,
Coarse-grained MC (Pogodin, et al., ;
Yan & de Pablo, )
Lattice MC, kinetic MC (Martínez et al., ;
Vlugt-Wensink et al., ; Zeng, et al., )
Free energy, absorption/binding energy, and
docking
Self-assembly, swelling of gel carriers, and
membrane translocation
Drug release from excipient matrices and
crystallization
Molecular
dynamics
(MD)
Atomistic MD ([]; Zhao & Caflisch, )
Coarse-grained MD (Prates Ramalho, et al.,
; Thota, et al., ), Brownian
dynamics [], and dissipative particle
dynamics (Guo, et al., )
Solubility, hydrogen bonding, diffusivity,
membrane permeability, carrier–drug
miscibility, carrier–drug interaction, glass
transition, drug aggregation, and
crystallization
Self-assembly, drug release from excipient
matrices, and membrane translocation
Quantum-
mechanical
(QM)
Density functional theory, Hartree–Fock
theory, semiempirical and QM/molecular
mechanical methods
Potential energy, geometry optimization,
docking, and force-field parameterization
Figure 11.1: Basic steps of atomistic (Monte Carlo (MC)/molecular dynamics (MD)) simulations.
11 Applications and molecular simulation strategies 251
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tronic structure and spectroscopic properties of molecules as well as the interactions be-
tween them can be calculated using QM simulations. Hybrid simulations incorporate
both classical and QM approaches. Force fields are used to describe interactions between
the majority of molecules in these simulations, while QM techniques are used to evaluate
interactions between a small number of molecules or to determine attributes [22]. All
basic steps are shown in figure 11.1.
11.3 Molecular simulation software
The development of an efficient software for molecular simulations is a lengthy pro-
cess that calls for input from specialists in many fields. The good news is that experts
from all over the world have worked together to develop a wide variety of very effec-
tive and powerful commercial and freely available software (Table 11.2). Materials
Studio, Marvin, and many others are used to create molecules; SwissParam, ATB,
PRODRG,MKTOP,OBGMX,andothersareused to calculate force-field parameters;
VMD, PYMOL, RASMOL, CHIMERA, and many others are used for analysis and visuali-
zation [24]. The widespread use of molecular simulations due to a software’s simplic-
ity has prompted some to question the reliabil ity of the results due to worries that
they were generated and publ ished without a solid grounding in the complexity of
the algorithms used. This skepticism is warranted to some degree; before putting soft-
wareintoproduction,itisimportanttolearnasmuchaspossibleaboutitsinner
workings, capabilities, and limitations [25]. It is shown in figure 11.2.
Figure 11.2: Basic molecular dynamic simulation algorithm. Each particle moves according to
Newton’s second law or the equation of motion, F = ma (where F is the force exerted on the particle, m is
its mass, and a is its acceleration under a potential field), such that the particles in the system are
captured in the trajectory [1, 13]. r, position; v, velocity; t, time.
252 Aditya Sharma et al.
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