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Apporva Chawla, Prince Ahad Mir, Md Sadique Hussain,
Sameena Ramzan, Tooba Dedmari, Roohi Mohi‑ud‑din,
Pooja A. Chawla
✶
, and Reyaz Hassan Mir
✶
9 Nanotoxicity prediction
in nanotechnology-driven drugs using
QSPR modeling
Abstract: The rapid expansion of nanotechnology in drug development has ushered in
innovative therapeutic approaches while simultaneously raising concerns about potential
nanotoxicity. This comprehensive review provides a thorough exploration of the evolving
field of quantitative structure–property relationship (QSPR) modeling as a potent tool for
predicting and mitigating nanotoxicity associated with nanotherapeutic agents. The re-
view commences by underlining the transformative impact of nanotechnology on drug
design, emphasizing the critical necessity of ensuring the safety and efficacy of nanomedi-
cines. QSPR modeling emerges as an advanced computational approach harnessing physi-
cochemical properties, structural descriptors, and toxicity endpoints to anticipate and
comprehend nanotoxicity. The core of this review delves into the principles and method-
ologies of QSPR modeling, intricately describing the process of descriptor selection, data-
set compilation, and model development. It scrutinizes the versatility of QSPR models in
predicting a wide array of nanotoxicological endpoints, encompassing cellular responses,
biodistribution patterns, and organ-specific toxicity profiles. Furthermore, the article
underscores the significant clinical implications of QSPR modeling, discussing its potential
for expediting nanotoxicity assessment during drug development, reducing reliance on
animal testing, and facilitating regulatory approvals. It accentuates the pivotal role of
QSPR modeling in optimizing nanotherapeutic formulations and minimizing adverse ef-
fects. Concluding on a forward-looking note, the review underscores the growing impor-
tance of QSPR modeling as an indispensable tool within the field of nanomedicine. It calls
✶
Corresponding author: Pooja A. Chawla, University Institute of Pharmaceutical Sciences and
Research, Baba Farid University of Health Sciences, Faridkot 151203, Punjab, India,
e-mail: pvchawla@gmail.com
✶
Corresponding author: Reyaz Hassan Mir, Pharmaceutical Chemistry Division, Department of
Pharmaceutical Sciences, University of Kashmir, Hazratbal, Srinagar 190006, Kashmir, India,
e-mail: reyazhassan249@gmail.com
Apporva Chawla, Khalsa College of Pharmacy, G.T. Road, Amritsar 143001, Punjab, India
Prince Ahad Mir, Md Sadique Hussain, School of Pharmaceutical Sciences, Jaipur National University,
Jaipur 302017, Rajasthan, India
Sameena Ramzan, Tooba Dedmari, Pharmaceutical Chemistry Division, Department of Pharmaceutical
Sciences, University of Kashmir, Hazratbal, Srinagar 190006, Kashmir, India
Roohi Mohi‑ud‑din, Department of General Medicine, Sher‑I‑Kashmir Institute of Medical Sciences
(SKIMS), Srinagar 190001, Jammu and Kashmir, India
https://doi.org/10.1515/9783111208671-009
https://t.me/med1917
for sustained collaborative efforts to expand predictive models, address data gaps, and
enhance our comprehension of nanotoxicity mechanisms. In summary, this review navi-
gates the landscape of QSPR modeling for nanotoxicity prediction in the realm of nano-
technology-driven drugs, offering profound insights into its implications for advancing
safe and efficacious nanotherapeutics.
Keywords: Nanotoxicity prediction, nanotechnology-drive n drugs, QSPR modeling,
drug development, safety assessment
9.1 Introduction
The utilization of nanotechnology in drug development has garnered significant atten-
tion, owing to its distinctive characteristi cs concerning size, conformation, and tar-
geted delivery. Nanomedicine, with its ability to address numerous limitations of
conventional medicine, has proven particularly promising. Moreover, nanotechnolog-
ical advancements offer innovative solutions, including in resource-limited settings
[1]. Nanotechnology finds applications in diverse medical domains, including cancer
therapy, such as cancer immunotherapy. Notably, nanodiamonds have emerged as a
subject of heightened interest, attributed to their unique chemical-mechanical proper-
ties, particularly on their faceted surfaces [2, 3]. Additionally, nanotechnology plays a
crucial role in antiviral drug development and preparedness for pandemics [4, 5]. In
recent years, considerable attention has been directed toward nanosystems tailored
for targeted delivery of cargos to mitochondria, especially for cancer treatment [6].
Furthermore, nanotechnology contributes to the development of nanopa rticle-based
pharmaceuticals that adhere to the principles of quality by design (QbD), ensuring en-
hanced drug quality [7]. In the realm of gene editing and immunotherapy, nanotech-
nology facilitates the delivery of CRISPR/Cas9 for cancer-related applications [8].
Furthermore, the potential of nanotechnology in liver fibrosis treatment is highly
promising, reflecting its substantial impact on the field of drug delivery [9].
Nanotoxicity pertains to the potential adverse effects of nanomaterials on biological
organisms. The escalating integration of nanotechnology across diverse domains, nota-
bly drug development, has prompted concerns regarding the safety profile of nanoma-
terials. Nanomaterials can infiltrate the organism through various routes, including
inhalation, ingestion, or dermal exposure. Their diminutive dimensions and distinctive
properties accentuate their potential for toxicity [10, 11]. The toxic attributes of nanoma-
terials can arise through diverse mechanisms, encompassing the generation of reactive
oxygen species (ROS). These ROS can incite oxidative stress, thereby inducing cellular
harm, DNA damage, aberrant cell signaling, alterations in cellular mobility, cytotoxicity,
apoptosis, and even the initiation of cancer. It is imperative to meticulously assess the
environmental implications of nanomaterials and their associated risks to human
health to ensure the prudent and secure integration of nanotechnology [12, 13].
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Quantitative structure–property relationship (QSPR) modeling presents a valu-
able approach for forecasting nanotoxicity by establishing a connection between the
chemical composition of nanoparticles and their toxicological characteristics. In the
context of nanotoxicity prediction, QSPR modeling formalizes the chemical structure
of nanoparticles into a comprehensive set of molecular descriptors. Subsequently, it
endeavors to discern a mathematical correlation that links these descriptors to a spe-
cific toxicity property of interest. The utility of QSPR modeling transcends nanotoxic-
ity and has proven efficacious in various d omains of chemistry, encompassing the
evaluation of toxicity [14]. Nevertheless, a comprehensive assessment of nanomateri-
als necessita tes a multifaceted appr oach, which includes a thorough exploration of
their biocompatibility and environmental compatibility aspects. To achieve this, a
spectrum of in vitro and in vivo scientific models is employed [15]. Therefore, while
QSPR modeling serves as a valuable complementary tool to anticipate the toxicity of
nanoparticles through computational means, it should be seamlessly integrated with
experimental methodologies to ensure a holistic evaluation of nanomaterial safety.
9.2 Principles of QSPR modeling
9.2.1 Definition and scope
QSPR is a contemporary discipline within chemistry that focuses on predicting complex
properties of chemicals, encompassing physical, chemical, biological, and technological
attributes, based on simplified descriptors, preferably derived solely from molecular
structure information [16]. QSPR methodologies employ mathematical models to estab-
lish connections between molecular structure and specific properties of interest, includ-
ing but not limited to boiling points, melting points, flash points, cetane numbers, and
lipophilicity [17–19]. These models can exhibit linearity or nonlinearity and employ var-
ious machine learning algorithms, such as genetic function approximation, partial least
squares, feed-forward artificial neural networks, general regression neural networks,
support vector machines, and graph machines. The development of QSPR models begins
with the selection of pertinent molecular descriptors, which are numerical representa-
tions capturing diverse aspects of molecular structure, such as connectivity, topology,
and functional groups [20, 21]. Techniques like harmony search and genetic algorithms
can be employed for variable selection to identify the most pertinent descriptors for a
specific property of interest [16, 22, 23].
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9.2.2 Importance of molecular descriptors
and physicochemical properties
In the realm of QSPR, both molecular descriptors and physicochemical properties hold
significant importance. Molecular descriptors are numerical representations that en-
capsulate diverse facets of molecular structure, encompassing attributes like connectiv-
ity, topology, and functional groups. The judicious selection of pertinent molecular
descriptors serves as the foundation for establishing associations between molecular
structure and properties of interest, including but not limited to boiling points, melting
points, flash points, cetane numbers, and lipophilicity. Techniques like harmony search
and genetic algorithms are valuable tools for pinpointing the most pertinent descriptors
tailored to a specific property [24, 25]. Physicochemical properties, such as water solu-
bility, octanol-water partition coefficient, and vapor pressure, also play a pivotal role
within QSPR. QSPR models can be systematically developed to prognosticate these prop-
erties, facilitating assessments of the environmental fate and toxicological impacts of
compounds, such as pesticides. Furthermore, QSPR models extend their applicability to
foretelling the properties of nanomaterials, including zeta potential, and can be instru-
mental in the innovation of novel therapeutic agents aimed at combatting ailments like
cancer [26–30].
9.2.3 Application of QSPR in drug development
and safety assessment
QSPR has carved a niche for itself with a multitude of applications, particularly in the
realms of drug development and safety evaluation. Among these applications, one cru-
cial facet is the prediction of blood–brain barrier (BBB) permeation, a pivotal consider-
ation for drugs intended to act within the central nervous system. This holds immense
significance in the context of addressing various human diseases such as epilepsy, de-
pression, Alzheimer’s disease (AD), Parkinson’s disease, and schizophrenia [31].
QSPR techniques extend their utility further to the domain of drug innovation, par-
ticularly in the development of novel antituberculosis medications aimed at combating
Mycobacterium tuberculosis [32]. Additionally, QSPR models come into play when pre-
dicting the physicochemical properties of fibrates. These properties are determined by
degrees and distances calculated from topological indices, and they exert influence on
lipid metabolism and overall lipid profile improvement [33]. The applicability of QSPR
does not stop there; it also extends to the realm of pesticide risk assessment, aligning
with prevailing European legislation [34]. Lastly, these models find relevance in foresee-
ing critical parameters like the DNA-binding constant and growth-inhibiting concentra-
tion of anthracycline drugs, contributing significantly to the field of drug research and
development [35].
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9.3 Data collection and compilation
9.3.1 Sources of nanotoxicity data
The wellspring of nanotoxicity information stems from a variety of sources, encom-
passing diverse studies and databases that furnish insights into the deleterious effects
of nanomaterials. These valuable sources encompass published research findings,
protocols for biogenic synthesis, physicochemical and toxicological data meticulously
compiled from the annals of scientific literature, and comprehensive databases dedi-
cated to in vitro toxicity assessments of nanomaterials [12, 36]. In the quest for unrav-
eling the intricacies of nanotoxicity, cutting-edge tools such as ar tificial intelligence
and molecular simulations have come to the fore. They serve as instrumental means
to sift through voluminous data streams, discerning pivotal patterns and building ro-
bust quantitative relationships between nanostructures and their toxicity [36–38]. Ex-
ploring the domain of nanotoxici ty, researchers frequently turn to fish models as
experimental systems to gain insights into the intricac ies of toxicity manifestations
[39]. In dissecting the landscape of nanotoxicity, priority factors emerge as key deter-
minants. These encompass the specific nanoparticle type under scrutiny, the pathway
of exposure, the c hoice of toxicity indicators, and external environmental elements,
including exposure to sunlight irradiation, the presence of natural organic matter,
and the influence of mineral particles. Interestingly, despite the remarkable strides
made in understanding the behavior of nanomaterials in environmental contexts and
their associated toxicological effects over the past decade, these insights are yet to be
seamlessly integrated into life cycle impact assessment methodologies. To bridge this
knowledge gap effectively, it becomes imperative to systematically gather physico-
chemical and toxicological data directly from scientific literature and establish coher-
ent frameworks capable of modeling the fate of engineered nanomaterials in the
environment, all the while considering their potential detrimental repercussions [12,
37–43].
9.3.2 Curation and preparation of datasets
Nanotoxicity datasets are meticulously assembled and readied through diverse chan-
nels, drawing from a rich array of sources. These invaluable reservoirs encompass pub-
lished research findings, biogenic synthesis blueprints, and meticulous compilation of
physicochemical and toxicological data harvested from the annals of scientific litera-
ture. Furthermore, databases specifically dedicated to in vitro toxicity assessments of
nanomaterials are a crucial resource in this endeavor. In the realm of harnessing tech-
nology to unravel the complexities of nanotoxicity, both artificial intelligence and mo-
lecular simulations play pivotal roles. They serve as sophisticated instruments for
navigating and distilling crucial insights from the voluminous data streams, ultimately
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paving the way to construct quantitative relationships linking nanostructures to their
inherent toxicity. When probing the domain of nanotoxicity, scientists often turn to fish
models as a critical experimental framework. These models provide an invaluable
glimpse into the multifaceted manifestations of toxicity stemming from nanomaterial
exposure. Within the intricate landscape of nanotoxicit y, several key factors rise to
prominence as prime determinants. These encompass the specific nature of the nano-
particles under scrutiny, the pathways through which exposure occurs, the judicious
selection of toxicity indicators, and the pervasive influence of environmental factors.
These environmental considerations span from exposure to sunlight irradiation, the
presence of natural organic matter, to the presence of mineral particles in the vicinity.
Collectively, these diverse sources and multifaceted factors contribute to a comprehen-
sive understanding of nanotoxicity, underpinning the ongoing efforts to decipher and
mitigate the potential hazards posed by nanomaterials [41, 42, 44–48].
9.3.3 Challenges in data quality and availability
The landscape of nanotoxicity research is marked by several formidable challenges
that impact data quality and accessibility [49–51].
9.3.3.1 Lack of standardization
The absence of standardized methodologies for evaluating nanotoxicity poses a signif-
icant hurdle. This lack of uniformity makes it arduous to compare findings across dif-
ferent studies, potentially leading to data inconsistencies and hindering the ability to
formulate conclusive assessments regarding the safety of nanomaterials.
9.3.3.2 Limited data availability
The scarcity of comprehensive data pertaining to nanomat erial toxicity, particularly
in vivo data, is a persistent challenge. Factors contributing to this scarcity include the
inherent complexities of conducting toxicity investigations on nanomaterials, includ-
ing their minute size and propensity for aggregation.
9.3.3.3 Dataset preparation and curation
Preparing and curating datasets for nanotoxicity analysis can be a formidable task.
This arises from the diverse sources of data, necessitating efforts to standardize data
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formats and ensure data quality and accuracy. The reliability of the data is para-
mount in this context.
9.3.3.4 Reproducibility concerns
Reproducibility stands out as a significant concern in nanotoxicity research. Varia-
tions in the physicochemical properties of nanomaterials, stemming from differences
in preparation methods and other variables, can result in data disparities. These in-
consistencies challenge the ability to derive definitive conclusions regarding nanoma-
terial safety.
9.3.3.5 Demand for comprehensive studies
Addressing the complexities of nanotoxicity necessitates more comprehensive investi-
gations. These studies should encompass a wide spectrum of exposure scenarios and
endpoints. Achieving this will require interdisciplinary collaboration and the estab-
lishment of standardized assessment methodologies. In navigating these challenges,
the field of nanotoxicity research can progress toward a more robust understanding
of the potential risks associated with nanomaterials, ultimately contributing to their
safer and more responsible utilization.
9.4 Descriptor selection
9.4.1 Essential molecular and physicochemical descriptors
It is crucial to comprehend the impact of chemical makeup on how biological pro-
cesses operate. Due to this, quantitative structure–activity relationship (QSAR) is re-
garded as a key technique in the development of new drugs. QSAR modeling aims to
link the physiological function of a chemical substance to a collection of factors re-
lated to structure, often known as molecular descriptors. The creation, assurance, and
assessment of the model are all steps in the QSAR modeling process, as well as choos-
ing the molecular descriptors and toxicological target descriptors [52, 53]. Several mo-
leculardescriptorsareabletobeassessed physically or computationally and are
often related to the steric and electrostatic characteristics of the chemical molecule in
standard QSAR modeling. Traditional QSAR investigations typically start with the de-
scriptor computation and molecular modeling stages [54, 55]. Conventional molecular
descriptors can be classified into a variety of categories. For example, theoretical mo-
lecular descriptors comprise zero-dimensional, one-dimensional, two-dimensional,
9 Nanotoxicity prediction in nanotechnology-driven drugs using QSPR modeling 189
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three-dimensional and four-dimensional descriptors [56]. The molecular descriptor of
a nanotechnology, neverth eless, differs from conventional QSAR molecular descrip-
tors, since nanoparticles are not any more straightforward organic substances; one
would refer to it using the term “structural descriptor” in QSAR modeling. Two dis-
tinct groups ought to be used to categorize the metal oxide nanoparticles used in the
QSAR study: (a) coated metallic oxide nanoparticles. In a QSAR investigation, the
structural descriptor of an organically surface alteration is consistently employed for
coated metallic oxide nanoparticles, and (b) raw metallic oxide nanoparticles’ QSAR
research: Since metallic oxide has an inorganic makeup and nanostructure, choosing
structural descriptors for raw metallic oxide nanoparticles is difficult [57, 58].
The type of descriptors used to describe the fragment’s property will have an im-
mediate impact on the chemical knowledge that may be gleaned from the QSAR. The
actual type of the protein-ligand relationships at that specific substructural domain
ought to, in theory, have the ability to be determined using the fragment-mediat ed
technique in conjunction with physicochemical descriptors. As a result, it might be-
come easier to comprehend the physical and chemical contributions of e very frag-
ment and to change or create unique lead compound.
By using descriptors generated for the desired molecular target, it provides a
method for understanding the connections amongst their chemical makeup and biolog-
ical functions and allows for the change of the principal structure to build products
with increased potency. The outcomes will also be useful in simulating the physiologi-
cal effects of newly created compounds. An indicator that impacts the absorption, dis-
tribution, metabolism, and excretion features of the compounds is the logarithmic
partition coefficient parameter (logP) [59], which is found to possess an immediate as-
sociation with how well the ligand bonds. The amount of H-bond acceptors as well as
donors was additionally taken into accou nt as descriptors since H-bonding relation-
ships are a significant component of ligand-receptor associations. Bond lipole (BL) was
an additional descriptor that was taken into account while generating QSAR. BL is a
measurement of the substituent’s lipophilic dispersion that is derived from the total of
its atomic logP measurements. The molecular mass and molecular volume are addi-
tional descriptors that have been taken into consideration while determining QSAR.
The molecular steric properties are described by the molar refractivity. A group of
multivariate steric substitution variables known as sterimol characteristics was sug-
gested by Verloop [60]. Whenever there is no structure data accessible for the object
being studied, as the scenario in this instance, determining QSAR gets more important.
For CDK4, no 3D structural knowledge is currently available, despite exploratory struc-
tural details being provided for CDK2 and CDK6. An effective technique in this situa-
tion is ligand-associated medicament design. Only a handful of descriptors may be
related to the physical property of molecules, despite the fact that there are several
among them that they differ in intricacy, the amount of data that encode, and the du-
ration needed to calculate these. To further comprehend the resulting QSARs and,
even more crucially, their limits, thorough examination of the outliers is required [61].
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The fact that these outliers interact with the receptor in a distinct affinity fashion and
as a result fall short of the pattern tracked by the other compounds incorporated into
the research is a frequently used justification for the result of outliers in QSAR [62].
Their structural dist inctiveness, a significantly different physicochemical attri-
bute, and erroneously determined experimental relevance have all been suggested as
further explanations for their prevalence in QSAR research. Although specific descrip-
tors or characteristics are absent in the research conducted by Mascarenhas and Gho-
shal, the QSAR for the substances in the overall information pool might still contain
outliers. The modeling method that was inadequately employed to create the model
could have been the cause of outliers. Few outliers were found throughout the model
generation procedure in the present investigation [63].
The different characteristics of nanomaterials cause harmful consequences. Recog-
nizing the characteristics of nanoparticles that hasten their hazardous potential is the
primary and crucial step in developing the toxicity forecasting model. The OECD’s nano-
materials team had supplied a comprehensive list of important physicochemical charac-
teristics suitable for toxicological investigations [64]. To avoid erroneous interpretation,
QSAR also requires quantifier details in addition to the fundamental features Table 9.1.
Table 9.1: Fundamental characteristics of nanoparticles and associated physicochemical descriptors that
have previously been studied for toxicity assessment.
S. no. Physicochemical
descriptors
Properties Example Inference References
. Size Particulate size and
its distribution
Titanium dioxide Lesser the dimensions
are, higher will be the
toxic level.
[]
. Shape Round,
rectangular,
lengthy, brief, and
rod-shaped
C-nanotubes,
Ag-nanoparticles
Compared to shorter
C-nanotubes, longer
ones indicate greater
toxicity.
[, ]
. Crystal framework Ortho-rhombic,
tetragonal, and
monoclinic
Titanium dioxide
and nano silica
Toxicity may alter,
contingent upon
structure.
[, ]
. Surface charge Positive charge,
negative charge,
and neutral charge
Silicon
nanoparticles
Inversely charged
nanoparticles tend to be
more harmful than
neutrally as well as
positively charged
particles.
[, ]
9 Nanotoxicity prediction in nanotechnology-driven drugs using QSPR modeling 191
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To determine the toxic potential of TiO
2
and nanosilver, the size of the particle as a
basic measure and quantitative factors such as particle size as well as dispersion were
used. According to the investigation, smaller-sized nanoparticles tend to be more harm-
ful than larger-sized nanoparticles. In this case, the size distribution information produ-
ces better predictions than the average size measurement. Consequently, before using
the QSAR technique, all potential quantitative factors must be examined. In addition to
this, there are a handful of other physicochemical characteristics that affect product
toxicity, including purity, chemical makeup, dissolution, hydrophobicity, permeability,
dustiness, manufacturing technique, and a number of others [70, 71].
The research team employed the HYBOT physicochemical descriptors to construct
correlation networks explaining the process of sublimation enthalpy equations [69]. It
must be emphasized that all HYBOT characteristics were used to create one-parametric
models in order to explore relevant associations amongst the set of training variables
involving the two thermodynamic measures. Then, descriptors with the highest paired
correlation coefficient levels were chosen. Researchers attempted to investigate poten-
tial triple-parametric model changes on the framework of the descriptors group. Struc-
tural polarizability (mostly the bulk impact descriptor), R, the total of all H-bond
receiver components in a molecule that exists, ΣCa, and the total number of H-bond
donor elements, ΣCd, were the factors that were determined to be the best suited (with
the highest correlation values) after the method [72].
The specified descriptions exhibit the primary molecular crystallic relationship
patterns and are strictly physical in nature. Since this technique makes it possible to
quickly figure out the sublimation entropy element and, as a result, anticipate all ther-
modynamic variables related to the procedure of sublimation, they used identical de-
scriptors for the two types of sublimation enthalpies and the Gibbs energies. The
subsequent criteria led to similar descriptors being chosen for both of the activities.
Table 9.1 (continued)
S. no. Physicochemical
descriptors
Properties Example Inference References
. Surface
functionality
Coating Silver
nanoparticles
It is possible to
purposefully integrate
the surface of
nanoparticles, therefore
will change how
poisonous it is.
. Aggregate phase Solid and fluid
suspension
Nanoparticles
have an urge to
make interaction
and generate
fragments.
Collective form must be
taken into account since
it has an impact on
many crucial features.
[]
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