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9.6 Predictive nanotoxicological endpoints
Nanotoxicity produced by various nanomaterials can be detected by computational
methods with the help of well-defined endpoint. Endpoint refers to the measurement
of activity of a compound or any physicochemical properties, or biological activity in
relationship with the chemical structure of a molecule under specific conditions that
can be estimated and modeled [157]. As it is impossible to measure the nanotoxicity
produced by engineered NPs on the basis of case-by-case assessment, computational
methods provided a better alternative to predict in silico nanotoxicity [158]. QSPR is
one of the important methods to determine nanotoxicity with physicochemical de-
scriptors and toxicity endpoints, which involve cellular responses and cytotoxicity, bi-
odistribution and pharmacokinetics, and systemic and organ specific toxicity.
9.6.1 Cellular responses and cytotoxicity
The interaction of NPs with the biological environment occurs at the cellular level both
with the structural and functional cell systems like macromolecules, mitochondria, nu-
cleus, and other organelles [159]. The toxicity impact of NPs starts at the cellular level,
which involved the generation of reactive oxidative species (ROS)or free radicals by the
uptake of nanomaterial through biological cell membranes, which leads to oxidative
stress in tissues and may predict the cytotoxicity of NPs [160]. In mitochondria the ROS
like superoxide radicals, free hydroxyl ions, and hydrogen peroxide, are the by-products
generated by the reduction of water through various electron transport reactions in the
synthesis of ATP and these ROS play an important and beneficial role in the cell signal-
ing processes [161]. The metal ions like iron, copper, zinc, platinum, present in NPs when
incorporated in biological media, also participate in electron transport reactions, which
furtherleadsinROSgenerationandtheiroverproductionleadstothetissuedamageor
cell death [162]. The oxidative stress caused by the overproduction of ROS is the predom-
inant underlying mechanism of nanotoxicity, which interferes in the cell development
and cell functioning by causing modifications in proteins and nucleic acids, initiates
lipid peroxidation; abnormal gene expressions and inflammatory responses start via
modulation of signal transduction processes, and eventually DNA damage occurs, which
leads to cell apoptosis as also initiation and promotion of cancer[163]. The cytotoxicity
produced by the NPs depends on their size, shape, and chemical nature [164]. In the as-
sessment of carbon nanotube (CNT)-produced cytotoxicity the affected cell type needs to
be taken into consideration as different functional cells respond differently to these
CNTs. The most affected cells which encounter CNTs are macrophages, dendritic cells, B-
lymphocytes, and T-lymphocytes and they initiate the pro-inflammatory responses [165].
Computational studies showed that the immune system of the body recognizes the CNTs
as foreign bodies, and releases the inflammatory mediators such as interleukins and cy-
tokines or chemokines and can cause hypersensitivity and anaphylactic shock [166].
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These nanotoxicological endpoints can be assessed by computational models like QSPR
by considering the parameters like apoptosis induction, mitochondrial activity, mem-
brane damages, cytokine production, ROS generation, and DNA damages [167].
9.6.2 Biodistribution and pharmacokinetics
Nanotechnology is being widely used in the delivery of active therapeutic agents and
in diagnosis, but it is also associated with toxicity. For the assessment of nanotoxicity,
pharmacokinetics and biodistribution of nanomaterial in body should be taken into
consideration as it provides better perspective than other toxicity assays [168]. Al-
though NPs can cause tissue damage or cell apoptosis in vitro, there is also a need for
in vivo study of nanoparticles. The pharmacokinetic profile of API and after encapsu-
lated with nanomaterial are different, the monitoring of biodistribution of the nano-
medicine both long-term and subcellular distribution in the tissue can provide an
idea about the toxicity of NPs by analyzing the pathological changes they cause in the
localized tissue [169]. The pharmacokinetic parameters for the assessment of nanotox-
icity include volume of distribution, elimination of rate constant, rate of clearance,
and half-life of nanomedicines [170]. By the in vivo monitoring of ADME of nanodrugs
it becomes possible to know their distribution in systemic circulation, targeted tissue
localization or accumulation, and rate of elimination, and such studies significantly
predict the potential toxicity produced by NPs [171].
9.6.3 Organ-specific and systemic toxicity
The organs where nanodrugs are likely to end up depend upon the route of adminis-
tration. NPs after entering into the blood stream may enter into CNS, CVS, or the im-
mune system and can cause toxicity if not eliminated from the body and the “ order of
nanoparticle accumulation in major organ sites is as: liver > kidneys > spleen > lung >
brain > heart” [172]. The systemic evaluation of the impact of NPs on major organ sites
like liver, spleen, kidney, lungs, brain, heart, may provide a profound insight into the
field of nanotoxicology [173]. Liver, the first-pass metabolism site is more prone to
nanotoxicity. Gold nanoparticles accumulated in liver cause the activation of Kupffer
cells of liver, structural modifications in liver parenchyma. Hepatotoxicity due to gold
and silica NPs is thought to arise from inflammatory responses induced by NP accu-
mulation while si lver nanoparticles lead to oxidative stress changes[174]. Due to the
large surface area of lungs, accumulation of NPs within the pulmonary tissues makes
it vulnerable to nanotoxicity caused mostly by polymeric NPs and CNTs. NPs like tita-
nium dioxide or silica dioxide applied topically on the skin may have access to the
systemic circulation causing systemic toxicity if not eliminated from the body [175].
For the treatment of various neurological disorders like Parkinson’s disease, AD, and
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meningitis, targeted drug delivery is being preferred and NPs being very small in size
may have access to the brain by crossing the BBB and require toxicological analysis.
Gold nanopar ticles, silver nanoparticles, quantum dots, and super paramagnetic
nanoparticles can cause CNS nanotoxicity [176].
9.7 Applications in drug development
As the science of nanomedicine is gaining popularity because of the unique and dis-
tinct properties of NPs, it is also imperative to assess their toxicity [177]. Conventional
assays are now being replaced by computational model methods as they are cost ef-
fective, more precise, accurate, as also less tedious and time-consuming [178]. QSPR is
one of the computational methods used to predict nanotoxicity by relating a property
of interest at the quantum level with the structure of a molecule and is calculated
mathematically [179]. Co mputer-aided drug design and development is now widely
used as they display numerous advantages over conventional approaches.
9.7.1 Enhancing nanotherapeutic safety profiles
The aim of computational toxicology is to decipher or decode the parameters responsi-
ble for toxic or harmful interactions. In the assessment of nanotoxicity, computational
models are to develop a relationship between the pharmacokinetics and the biological
response of nanomedicines and what happens when it reaches to the target site [180]. It
may be feasible to include a range of nanomaterial properties that QSPR can foresee
into current nano-informatics workflows. It employs the mechanistic reasoning and sta-
tistical data to recognize the molecular or biological descriptors that are most likely pre-
dictive [181]. In silico models use two different approaches to predict nanotoxicity: (i)
the first strategy makes use of models that were developed by extracting and arranging
human knowledge and scientific publications and (ii) the second method employs sys-
temic analysis to examine the correlation between the chemical structure or molecular
descriptors and the nanotoxicological endpoints [182]. All the disadvantages that con-
ventional nanotoxicology assays display are bypassed by the computational models and
two main goals are achieved: the first step is to estimate or limit the late-stage attrition
during the design stage of novel chemical libraries and the second step is to focus exclu-
sively on the most promising compounds to optimize or streamline the screening and
testing [182, 183].
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9.7.2 Reducing animal testing and ethical considerations
One of the most important breakthroughs of using QSPR model is to limit the use of
animals for in vivo analysis of nanotoxicity and showing more accuracy and promis-
ing results than animal experimental predictions [184]. Animal experimentation
needs approvals from animal ethical committee, which may be time-consuming and
result in financial burden, but with computational models these processes may be by-
passed and it works as a virtual shortcut with more improved toxicity prediction and
safety profile [185].
9.7.3 Regulatory implications and accelerated approvals
Drug discovery and development has to undergo various regulatory aspects including
preclinical and clinical studies and various approvals, which take not less than
20 years for a drug to come into the market [186]. Due to the high cost, high attrition
rates, and slow rate of new drug discovery, drug repositioning is becoming a more
alluring idea to treat both rare and common diseases and this has been made possible
only through computational chemistry [187]. It accelerated drug discovery with fast
approvals and is economical. Through computational model methods, therapeutic re-
purposing of old drug candidates for new indications, which were not in the scope of
its original use, has become possible [188].
9.8 Challenges and limitations
9.8.1 Data gaps and biases in nanotoxicity datasets
Over an extended period of time, predictive models have been employed to quickly assess
the potential risks associated with chemicals, especially when there is insufficient or lack-
ing data regarding their potential hazards. This is particularly relevant when evaluating
new chemicals governed by the US Toxic Substances Control Act [189]. Developing predic-
tive models for nanomaterials requires a comprehensive research strategy with various
key components. First, more standardization is required for all stages of toxicity investi-
gations, from beginning materials to endpoints. While standardization may carry the risk
of limiting flexibility and innovative study designs, it is essential for generating uniform
data, which is crucial in the development of predictive models. Second, in a broad sense,
it is essential to conduct comprehensive testing of different aspects of NPs’ properties,
such as charge, size, and surface properties, in a systematic manner [190]. To understand
how exactly physicochemical qualities affect biological activity, this is essential. Due to
insufficient information, bias in publication, and the fact that many industry-sponsored
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studies stay unpublished, relying only on peer-reviewed publications for data is fre-
quently unfeasible. Furthermore, to facilitate effective collaboration and data sharing in
the diverse field of nanomaterials research, the establishment of specific formats and
standardsiscrucial.Thisobjectiveisactively being pursued by numerous organizations.
A framework called ISA-Tab Nano (Investigation – Study – Assay – Material Tab delim-
ited) was introduced to address this need. ISA-Tab Nano extends the functionality of ISA-
Tab, which is a versatile framework designed for the organized collection and effective
communication of experimental data. ISA-Tab Nano employs a series of technologies to
systematically gather and transmit complex nanomaterial data according to standardized
procedures. It organizes the data into four distinct files: the investigation file, study file,
assay file, and materials file, each with specific fields or columns. This data is manually
extracted from existing literature, following predefined templates, and is subject to vari-
ous business rules. The generated ISA-Tab Nano files can also be transformed into tab-
delimited text files using a Python program. These output files make database compatibil-
ity and computational analysis easier [191]. Challengeslinkedtothisframeworkinclude
the necessity to review and amend recorded data points including reaction rates and
chemical compositions, as well as prospective template design changes, which are diffi-
culties associated with this architecture. Another limitation is its suitability for various
experimental data types and in vivo toxicology studies, where it may not align seamlessly
[178]. In some circumstances, the collected data might not be up to the standards needed
for thorough analysis. Therefore, a review and improvement of the data collection proce-
dure are necessary to allow for its efficient application in predictive modeling. The need
of the hour is to facilitate increased and higher-quality endeavors in gathering pertinent
and reliable nano data, which was to some extent addressed by creating an information
extraction system, with an aim to obtain high-quality datasets. Ontology-based entity ex-
traction and rule-based attribute extraction are performed by this extraction method on
articles related to nanotoxicity. Taking into account the links between the data, the re-
trieved information is subsequently arranged in a structured table manner. It is vital to
note that this system may not be fully capable of identifying instances of completely new
entity types because it depends on ontology. To enhance this capability and expand the
system’s dictionary, the integration of ML methods can prove to be advantageous [192].
9.8.2 Extrapolation to real-world scenarios
It is essential to comprehend the complex interactions between nanomaterials, physi-
ological barriers, their interactions with cellular structures, and the consequent cellu-
lar responses. Such comprehension can significantly aid in the development of safer
nanomaterials, streamline the screening process of nanomaterials for regulatory com-
pliance, and help prioritize nanomaterials for more extensive toxicological testing.
Due to the lack of a logical, fact-based framework for determining the dangers con-
nected with these nanomaterials, evaluating the possible concerns associated with
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manufactured nanomaterials presents a considerable challenge. This becomes espe-
cially problemat ic as the number of nanomaterials in the market continues to rise.
Evaluating the potential haz ards associated with a specific class of nanomaterials,
such as carbon-based nanomaterials or metal oxide NPs, presents a considerable chal-
lenge due to the diverse range of sizes, shapes, chemical compositions, and surface
modifications that exist within this category. The behavior and toxicity of these com-
pounds can be considerably impacted by these changes. Given the constantly expand-
ing array of nanomaterials currently in use or anticipated in the future, it becomes
evident that it is impractical to assess the hazards by individually testing each nano-
material on a case-by-case basis, especially when taking into account the usage of
mammalian tests. However, due to the absence of a standardized system for assessing
hazards of nanomaterials, there is limited potential to compare findings across vari-
ous studies or pinpoint factors that could lead to heightened hazards and risks. The
challenges associated with evaluating nanomaterial risks are widely acknowledged in
the scientific community [193].
9.8.3 Incorporating nanoscale properties and interactions
The establishment of predictive models necessitates a wealth of information primarily
centered on interactions occurring at the molecular and cellular levels. Nanomaterials
exhibit unique characteristics, primarily stemming from their small size and the result-
ing high surface area-to-volume ratio. These traits may result in improved membrane
penetration, binding to biological molecules, easier molecular transport, possible bio-
cidal consequences, or even unexpected biological features not previously seen in nano-
materials. These characteristics may be helpful depending on the particular situation or
application, such as in the case of improved drug delivery. However, they can also pose
health and environmental risks, particularly when unintentional exposures or environ-
mental releases occur. Moreover, various physicochemical properties of nanomaterials
bring about several toxic effects. Extensive studies and experiments have revealed that
smaller nanomaterials tend to exhibit higher toxicity [70, 194], longer CNTs are often
more toxic compared to shorter CNTs, and depending on the unique structural traits of
the nanomaterial, toxicity may differ [65, 195, 196]. Furthermore, negatively charged
NPs tend to be more toxic than neutral or positively charged NPs [67], the deliberate
alteration of NP surface properties can significantly influence their toxic nature [197,
198], and also the collective behavior of nanomaterials can impact various critical prop-
erties, making it essential to consider this aspect when assessing toxicity [199]. There-
fore, a thorough understanding of these connections between physicochemical features
and toxicity is essential for risk analysis and comprehensive nanotoxicology research
[178]. There is a general consensus that in order to characterize the risks connected
with the wide range of nanomaterials being manufactured and used, high-throughput
and in vitro testing approaches are necessary [193]. As part of this endeavor, it is
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equally crucial to establish a foundation for deducing connections between in vitro and
in vivo data, with the aim of predicting potential human health hazards [200]. In con-
clusion with forward-looking note, given that the study of nanotoxicology is still in its
infancy, it presents an opportune moment to introduce fresh and inventive testing strat-
egies. The outcomes, whether successful or not, from these initiatives can serve as valu-
able insights to guide toxicity testing, along with associated risk assessment and risk
management policies, not only for other chemical categories but also for emerging tech-
nological domains that may not yet have garnered substantial research attention or
aligned with endeavors to advance alternative toxicity testing methods [201].
9.8.4 Future directions and emerging technologies
The unique and cutting-edge characteristics of nanomaterials demand an innovative
and sophisticated approach to their assessment. QSARs, nanoQSARs or similar predic-
tive models that are spe cifically designed for nanomaterials have been proposed or
stressed by numerous scientists [202, 203]. Nevertheless, there is a notable absence of
coordinated efforts to establish this approach, encompassing both the a llocation of
funding resources and the standardization of research methodologies, with a specific
focus on the development of predictive models. Other than this, the inadeq uacy of
proper and comprehensive experimental data and poor property characterization
procedures also contribute to the few other challenges being faced in this regard.
There is pressing requirement for standardized computational models that can aggre-
gate experimental data and transform it into consistent formats, facilitating further
processing for the development of prediction models. To fully harness the potential of
research data generated by bioscience groups, it is imperative to employ interopera-
ble and efficient techniques that promote an open data-sharing culture. In order to
systematically evaluate experimental data and predict the toxicity of nanomaterials
before their manufacturing and use, we support the implementation of data mining
techniques in the field of nanotoxicity, and adoption of a few more innovations by the
health scientists is immensely required.
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