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Neha Jain
✶
, Triveni, Aarushi Kaith, Aditi Sinha, Tanya Mathur,
Shreya Kaul
✶
, Manisha Pandey, and Upendra Nagaich
8 QbD and artificial intelligence
in nanoparticulate drug delivery systems:
recent advances
Abstract: Nanoparticulate drug delivery systems (NDDSs) have emerged as promising
platforms for enhancing therapeutic efficacy and minimizing the side effects of phar-
maceutical compounds. The integration of quality by design (QbD), a systematic ap-
proach to drug development, with artificial intelligence (AI) technologies, has created
new opportunities in optimizing the formulation and manufacturing processes of
NDDS. This chapter examines the intricate role of QbD in nano drug delivery, empha-
sizing its elements such as quality target product profile, critical quality attributes,
critical material attributes, critical process parameters, risk assessment, factor screen-
ing, design of experiment (DoE), and the establishment of a control strategy for con-
tinuous production. These elements collectively contribute to a systematic and risk-
based approach that ensures the desired quality of NDDS. Additionally, the general
aspects of AI in nanomedicine, explaining how AI technologies enhance data analysis,
pattern recognition, and optimization processes in nano drug delivery have been dis-
cussed. Recent research advances in the integration of QbD and AI are presented,
showcasing successful applications and outcomes in the development of NDDS. The
chapter concludes by addressing the challenges faced in this integrative approach
and provides insights into the prospects of QbD and AI in the field. It can be con-
cluded that there is a promising future for the collaborative interaction of QbD and AI
in nano drug delivery, anticipating a significant transformation in the development of
pharmaceuticals. The insights provided offer a comprehensive understanding of re-
cent advances, laying the foundation for ongoing innovation and improvement in this
rapidly evolving field.
Keywords: Quality by design, artificial intelligence, design of experiment, nanotech-
nology, drug delivery
✶
Corresponding authors: Neha Jain, Shreya Kaul, Department of Pharmaceutics, Amity Institute of
Pharmacy, Amity University, Noida, Uttar Pradesh, India
Triveni, Aarushi Kaith, Aditi Sinha, Tanya Mathur, Department of Pharmaceutics, Amity Institute of
Pharmacy, Amity University, Noida, Uttar Pradesh, India; Department of Pharmaceutical Sciences,
Central University of Haryana, Mahendergarh, 123031, India
Manisha Pandey, Department of Pharmaceutical Sciences, Central University of Haryana,
Mahendergarh, 123031, India
Upendra Nagaich, Department of Pharmaceutical Sciences, Center for Global Health Research,
Saveetha Medical College, Saveetha Institute of Medical and Technical Science, Chennai, India
https://doi.org/10.1515/9783111208671-008
https://t.me/med1917
8.1 Introduction
Nanoparticles (NPs) are defined as solid particles or minute particulate dispersions
with sizes between 10 and 1,000 nm. Drugs that are dissolved, entrapped, encapsulated,
or affixed to the particle matrix can be transported by these NPs. This strategy allows
for controlled and targeted drug delivery within the body [1]. Nanoparticulate drug de-
livery systems (NDDSs) offer promising solutions to drug delivery challenges. These ver-
satile carriers enable targeted and controlled release of therapeutic compounds. They
hold the potential to address issues related to drug distribution, such as the precipita-
tion of hydrophobic drugs at higher concentrations and toxicity problems associated
with certain additives used to prevent drug aggregation. NDDSs have emerged as a po-
tential means to eliminate or mitigate these concerns, revolutionizing drug delivery
mechanisms [2]. The ongoing objective in NP development remains centered on enhanc-
ing delivery platforms to create a versatile and broadly applicable solution. The poten-
tial for precision medicine appears promising as lipid-based, polymeric, and inorganic
NPs are being meticulously crafted in increasingly tailored manners, optimizing their
use for individualized drug administration approaches [3]. The growing variety of drug
delivery methods using NPs is poised to transform the characteristics of newly devel-
oped medications and extend the half-life of established drugs [4].
The quality by design (QbD) approach was initially devised and presented by Dr.
Joseph M. Juran, a trailblazer in quality management. He advocated for embedding
quality within a product during its developmental stages. QbD is a systematic method-
ology aimed at ensuring specified product quality, thereby augmenting safety and ef-
ficacy for patients. It emphasizes comprehending both the product and process while
prioritizing control, all grounded in robust scientific principles and effective quality
risk management [5]. QbD represents a contemporary approach focused on the sys-
tematic creation of pharmaceuticals. It integrates statistical, analytical, and risk man-
agement methodologies throughout the entire process of designing, developing, and
manufacturing drug products [6]. QbD aims to provide detailed insights into critical
process parameters (CPPs) and material attributes that significantly impact critical
quality attributes (CQAs). By doing so, QbD facilitates the establishment of a design
space. When managed effectively, this design space can enhance product robustness
and overall quality [7]. QbD employs various tools such as risk assessment and Ishi-
kawa diagrams, which contribute to the development of a comprehensive quality tar-
get product profile (QTPP). To guarantee the safety, efficacy, and quality of products,
it is essential to have ample evidence and assurance that the processes align with the
principles of QbD [8]. There might be serious consequences from incomprehension of
the influence of distinct phases in a pharmaceutical process and the function of varia-
bles on both quality and performance. These repercussions could include increased
expenses for drug delivery systems involving NPs and a direct influence on the failure
rate of products. As a result, these problems may eventually have a negative impact
on the final product quality [9, 10].
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The man ufacturing process of NPs is confronted with considerable challenges
stemming from process variability linked to material characteristics, fabrication meth-
ods, and a limited understanding of critical material attributes (CMAs) and CPPs and
their influence on the final product. These challenges not only affect biopharmaceuti-
cal performance but also impact the safe disposal of drug-loaded NPs. The implementa-
tion of QbD offers solutions to these issues by enabling adjustments in particle size
(PS), surface charges, drug loading, entrapment efficiency (EE), polydispersity index
(PDI), and other factors. This integrated approach within QbD aids in addressing and
resolving these complexities associated with NP preparation [11]. The application of
QbD methodologies has significantly improved the pharmacodynamics, pharmacoki-
netics, and toxicity profiles in nanotechnology-based drug delivery systems. Employing
design of experiments (DoEs) has streamlined the testing process, utilizing diverse sta-
tistical formulas to decrease the necessary tests and enhance the consistency of the
final product. Additionally, various DoE design models aid in optimizing the formula-
tions of nanoscale systems [12]. Limited comprehension of production processes has
prompted researchers to adopt the QbD approach for optimizing nanosystems. This
strategy supports and expedites the de velopment of refined formulations based on
nanotechnology [13].
Artificial intelligence (AI) technology used in pharmaceutical research, drug de-
velopment, and healthcare is referred to as pharmaceutical AI. AI techniques and al-
gorithms are used in these areas. The pharmaceutical industry entails managing and
analyzing massive amounts of data, producing insights, and making predictions using
machine learning (ML), deep learning, natural language processing, and other AI tech-
niques [14]. Promising developments in AI and ML offer a game-changing prospect for
drug discovery, formulation, and dosage form testing. AI algorithms are essential for
thoroughly examining the complex interactions among pharmacological characteris-
tics, formulation elements, and physiological aspects [15]. This allows for accurate
drug behavior predictions at different scales. With the use of this cutting-edge tech-
nology, drug delivery mechanisms can be better understood, leading to the creation
of more effective drug delivery systems [16]. It is also capable of predicting drug sta-
bility, in vitro drug release profiles, and physicochemical characteristics. Additionally,
AI helps with drug distribution assessments, in vivo–in vitro correlation studies, and
the prediction of in vivo pharmacokinetic characteristics. Researchers can proactively
detect potential hazards and obstacles inherent in medication delivery systems at an
early stage of development by skillfully utilizing a suite of AI techniques. This multi-
modal strategy has a major positive impact on pharmaceutical research and develop-
ment by improving predicted accuracy while streamlining the optimization and
evaluation processes [17, 18]. Integrating automation and AI offers a promising oppor-
tunity to elevate the precision of targeted therapeutic nanocarriers tailored for dis-
tinct cell types and individual patients [19]. The effective utilization of diverse AI
techniques has notably reduced the duration of development, ensured the quality of
products, and fostered successful advancements in pharmaceutical research and de-
8 QbD and artificial intelligence in nanoparticulate drug delivery systems 165
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velopment [20, 21]. AI tools play a significant role in the analysis and interpretation of
genetic and biological data. These tools have proven instrumental in accelerating the
drug development process by integrating various methods. They aid in identifying di-
verse functions of small molecules and accurately predicting their behaviors [22]. The
future of AI in nanomedicine research involves several key areas. These include pre-
dicting new molecules for therapeutic use, improving AI algorithms to identify prom-
ising compounds, employing deep learning for structure– activity relationship (SAR)
forecasts, using generative modeling for molecular synthesis, integrating quantum
computing, advancement of multicriteria optimization in drug development, and cre-
ating virtual screening tools empowered by AI [23, 24]. Through leveraging AI’s com-
petencies in data analytics, pattern identification, and optimization, researchers in
nanomedicine can expedite the creation of innovative nano-sized interventions, aug-
ment diagnostic methods, optimize drug delivery, and propel advancements in per-
sonalized medicine. The integration of AI into nanodrug presents a transformative
prospect for transforming health service s, facilitating precision and targeted thera-
peutic medical interventions at the nanoscale [25]. Employing AI algorithms becomes
instrumental in the design and optimization of NPs by foreseeing their physicochemi-
cal properties, stability, and effectiveness. This predictive capability assists research-
ers in formulating NPs tailored to possess specific characteristics suited for distinct
applications, exemplifying the synergy between AI and nanomedicine in pushing the
boundaries of healthcare innovation [26].
The integration of QbD and AI technologies has embarked on a new era in the
field of NDDS. This synergistic approach combines the systematic framework of QbD
with the advanced analytics and predictive modeling capabilities of AI, offering novel
opportunities for refining drug formulations, optimizing processes, and transforming
the landscape of pharmaceutical development.
8.2 QbD elements and its role in drug delivery
The pharmaceutical sector uses a process-based approach called QbD to guarantee
the high standards of medical products. It includes several components intended to
design and develop procedures and products with predetermined goals to satisfy the
demands of patients. Improving the effectiveness, safety, and quality of the drugs that
are administered is a major function of QbD in drug delivery [27, 28]. Figure 8.1 illus-
trates the elements of QbD.
QTPP: The QTPP is a stipulated presented over view of pharmaceutical product fea-
tures required to ascertain optimal quality in terms of safety and efficacy, as well as
to identify the product’s CQAs. Finally, QTPP serves as a link between patient needs
and product quality. For the development of QTPP, it is imperative to initially estab-
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lish the correlation between the desired product profile based on biological perfor-
mance and the physicochemical characteristics of the product [27]. The primary bio-
logical performance aspects of a nano-formulation typically relate to nanocarrier
distribution to the target cell, ti ssue, or organ, the pharmacokinetics of the planned
formulation, the toxicological qualities of the medicinal product, and the safety and
efficacy profile of NPs. To determine the safety and efficacy of proposed nanoformula-
tions, various in vitro and in vivo disease models can be used. The QTPP outlining the
development of NPs encompasses various elements including, the method of delivery
(oral, injectable, topical); the form of dosage (lyophilized powder, dispersion, in-
jectable, gel, or ointment); quality parameters (purity and sterility); dosage potency
(considering enhanced bioavailability and controlled release of drug in NP formula-
tions), drug release patterns; pharmacokinetic attributes; and container specifications
with shelf life [30].
CQAs: CQAs are developed from the QTPP and are utilized to steer the development of
products and processes. Additionally, CQAs establish the parameters or thresholds de-
fining the acceptable quality limits for products, thereby ensuring the intended quality
of the nanoproducts. CQAs encompass physiochemical, biological, or microbiological
attributes of a therapeutic agent, which must fall within predefined ranges to assure
the desired quality of the product, according to ICH Q8. CQAs connect product quality
to therapeutic performance, as well as physical aspects such as color, PS, shape, and
visual characteristics; chemical elements such as entrapment, assay, drug release, im-
purity profile, stability, and consideration of residual solvents when organic solvents
Figure 8.1: Elements of QbD (adapted with permission under a Creative Commons [CC BY 4.0] license
from [29]).
8 QbD and artificial intelligence in nanoparticulate drug delivery systems 167
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are employed in nano-formulations; and biological/microbiological properties involv-
ing pharmacokinetic and pharmacodynamic attributes, as well as sterility [30].
CMAs: CMAs represent the primary set of elements capable of introducing variability
in CQA, and they are associated with the constituents of NPs formulation. To ensure
the intended quality of the completed prod uct, CMAs such as physicochemical, bio-
pharmaceutical, or microbiological properties of input materials should be within an
adequate standard limit. CMAs in NPs encompass factors such as the concentration of
surfactant, type of surfactant, volume of surfactant, concentration of organic solvent,
concentration of polymer/lipid, and the type and concentration of salts employed for
buffer preparation or conferring a charge on particles [27].
CPPs: CPPs are critical variables in the manufacturing of NDDS that have a large im-
pact on product quality, repeatability, and performance. These parameters include
things like stirring speed, homogenization methods, temperature, pH, and the rate of
solvent evaporation during formulation. CPP control is critical for achieving desirable
NP properties like as size, size distribution, drug encapsulation efficiency, and stabil-
ity. Variations in stirring speed or homogenization duration, for example, can have a
direct impact on PS and distribution. Controlling and optimizing CPPs ensures the re-
producibility of nanoparticulate formulations, impacting their efficacy, drug release
kinetics, and capacity to target specific tissues or cells. Understanding and regulating
these crucial characteristics is critical for maintaining consistent product quality as
well as meeting regulatory criteria for safe and effective NDDS in clinical applications
[27, 31].
Risk assessment and factor screening: The primary goal of risk assessment is to
establish the CPP of a drug product by measuring the influence of a particular vari-
able or essential features of raw materials, active pharmaceutical ingredients (APIs),
excipients, and packaging materials. Risk management strategies can examine and re-
solve factors such as material compatibility, stability concerns, toxicity, and inade-
quate medication release. The practice of carefully examini ng multiple formulation
components, process parameters, and their interactions to determine the most rele-
vant factors influencing NP characteristics is known as factor screening. Researchers
can easily examine and optimize several variables simultaneously by using screening
approaches such as DoE to find the essential parameters affecting NP properties such
as size, drug loading, and release kinetics. These approaches help streamline the de-
velopment process, improve product quality, and ensure the design of effective
NDDSs with minimal risks, hence accelerating their translation into clinical applica-
tions [32, 33].
DoE: DoE was first employed in agriculture by Fisher, and it has since become an insepa-
rable aspect of practically every sector, including medicines. As pharmaceutical manufac-
ture is an expensive endeavor, thorough and rational application of experimentation in
this business can save the industry a significant amount of money. DoE is used to conduct
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multivariate experiments to enhance comprehension of the impact of individual input
factors and their interactions with process parameters affecting the product, commonly
referred to as the design space. In contrast to the multivariate data analysis technique of
process optimization, which relies solely on historical data, DoE or experimental design is
asystematicmethodfordiscovering the interconnection of the elements impacting the
procedure and the attributes of the product. Essentially, this statistical technique has the
potential to significantly improve knowledge of the process. DoE is made up of mathemat-
ical models that employ computer-aided process design and process simulation. The most
used DoEs in pharmaceutical product development include full factorial, fractional facto-
rial, Plackett–Burman, Box–Behnken, central composite design (CCD), Taguchi, mixture
design, and surface design [27, 30].
Control strategy in order for continuous production: According to the ICH Q11, "con-
trol strategy is designed to ensure that a product of required quality will be produced
consistently." A control plan may comprise the following elements as specifications for
the finished products, control of input material qualities that affect processability or
product quality (e.g., raw materials, packaging materials, and in-process materials), in-
process or real-time release testing rather than end-product testing (e.g., temperature
measurement and control throughout processing) and controls for unit operations that
affect downstream processing or product quality (e.g., the effect of size reduction techni-
ques on medication release) [33, 34].
8.3 General aspects of AI in nanomedicine
AI and ML approaches define the fourth paradigm of scientific research. The integra-
tion and application of these approaches in the field of nanomedicine can result in a
potential acceleration of rationality in the design and development of efficient nanofor-
mulations [35]. AI supports transformation in combination therapy and precision or
personalized medicine along with scaling of nanotechnological approach [36]. AI can be
used in multiple aspects encompassing the development of nanomedicines such as pre-
diction of suitable nanomedicines, action and functioning of that na nomedicine to-
wards a particular approach, designing a suitable pathway for their development and
production scale-up techniques. During drug development, the employment of AI can
aid in the process of predicting molecular behaviors and make use of these prediction
mechanisms in designing the drug from concept to large scale. AI only requires data
regarding the target variables of nanomedicines to extrapolate the data to provide po-
tential options and results. Representative of its attribute of being a quicker and more
efficient alternative to humans in processing data with a higher degree of accuracy, AI
requires access to data regarding the variables needed to be covered. Owing to its dis-
position, the use of AI helps in the reduction of the time and cost towards the approach
of trial and error. It has the potential to interpret and analyze large amounts of data
8 QbD and artificial intelligence in nanoparticulate drug delivery systems 169
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such as complex databases and image-based high-content screening data, thus making
it well-suited for the analysis modeling of multivariate and large datasets in a shorter
period [37]. It is relatively easier for an AI to identify trends and properties of various
nanomaterials to produce an accurate and reliable result. The implementation of AI al-
gorithms can provide benefits in correlating the pharmacokinetic and pharmacody-
namic data generated in vitro w ith the in vivo results. This may result in better
understanding of the mechanism of absorption–distribution–metabolism–excretion of
nanomedicine. Hence, it helps in the selection of drug and nanocarrier combinations
and also promotes drug synergism, at the same time reducing toxicity resulting in im-
proved clinical outcomes. In combinatorial nanomedicine, the combination parame-
ters determined by AI can ease the achievement of optimal therapeutic outcomes that
are independent of the complex mechanism of the disease [38]. It even helps in the
selection of an ideal nanocarrier for the nanoformulation. Thus, AI algorithms aid in
the prediction and optimization of the steps involved in the production, design, and
development of nanomedicines. AI can help in the optimization of the nanomedicine for-
mulations by predicting the interactions that the NPs may withhold with the target drug,
cell membranes and biological media along with predictions regarding the encapsula-
tion efficiency and drug release kinetics [39]. Apart from the synthesis and design of
nanomaterials, AI can also search for any potential toxicity that the nanomedicine may
possess. An evolution in AI and ML algorithms has led to novel approaches towards tox-
icity testing of nanomedicines [40]. A collaborative approach between nanotechnology
and AI encourages the aim of precision medicine, that is, tailored nanomedicine [41]. AI
integration results in improved diagnostic and therapeutic efficacy by optimizing the im-
portant variables and nanomaterial properties through the application of classification
algorithms and pattern analysis [42]. AI has also found its application in the field of
nano informatics for the design of nanomaterials and implementation [43]. Various data-
sets can be employed such as OMICS (proteomics, genomics, and metabolomics) for
nanomaterial screening by identifying the targeted protein or site of NP interaction [44].
Physiologically based pharmacokinetic (PBPK) models can help in the prediction of the
behavior of nanomaterials while nano-quantitative SAR models can help in the toxicity
studies of those nanomaterials making them an important tool in nanotoxicology [45].
Through ML algorithms in PBPK modeling, the absorption, distribution, metabolism, and
excretion mechanisms of different nanomaterial classes can be predicted. Primary ML
algorithms such as regression [46], decision tree [47], support vector machines (SVMs)
[48], artificial neural networks (ANNs) [49], and partial least square can be employed for
nanotoxicity prediction [40]. AI algorithms have also been used for predictions regarding
drug efficacy in the case of individual patients and thus help in treatment optimization.
AI can model and predict the efficacy and biodistribution of the NP formulation by vari-
ous computational models. AI has been applied for biomedical image analysis including
microscopy images of the distribution of NPs in the tissues that can be analyzed by the
use of ML algorithms [37]. Especially in the case of cancer, the rapid development and
integration of AI technology with nanomedicines has contributed to the overall improve-
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ment in the effectiveness of cancer treatments and precision medicines targeting an an-
ticancer approach [50]. It has also found a way in the nanotheranostics [51]. Therefore,
AI not only serves as a key catalyst in the direction of increased drug approvals and im-
proved outcomes but also improves the accessibility towards affordable nanomedicines.
8.4 Recent advancements employing QbD and AI
in nanoparticulate-based drug delivery
The integration of AI algorithms with QbD approaches facilitates the optimization of
nanoparticulate compositions through the consideration of multiple parameters, in-
cluding stability, drug release kinetics, surface properties, and size of particles. Large
volumes of data can be analyzed by AI-driven models to locate significant variables
and how they interact, facilitating the development of more effective formulations.
In a study, Dawoud and colleagues combined AI with QbD to formulate a poten-
tially effective medication for liver cancer using the poorly water-soluble drug sily-
marin. The critical parameters of PS, distribution, and EE were studied using the QbD
approach. With an 18.33:1 lecithin: chitosan ratio and 38.35 mg silymarin, response sur-
face designs optimized lecithin/chitosan NPs, producing 161-nm particles with a PDI of
0.2, 97% entrapment, and a +38-mV zeta potential (ZP). Using a deep learning model, AI
predicted drug release with accuracy. The silymarin release from L/CH NPs was reliably
predicted by deep learning models at 2, 8, and 12 h (R2: 0.937–0.991), indicating that
these models are reliable for predicting pH 6.8 drug release in similar systems in the
future. The formulation increased the cytotoxicity of silymarin by reducing its IC50.
This strategy provides an in-depth understanding and shifting drug formulation toward
data-driven methodologies [52]. Lipid NPs and QbD were combined in a study by Ama-
sya and colleagues to treat advanced skin cancer. A 5-FU-loaded nanostructured lipid
carrier (NLC) with a PS of 205.8 nm, PDI of 0.279, ZP of −30.20 mV, and encapsulation
efficiency of 48.17% was optimized using ANNs. 53.17% of the NLC that had been formu-
lated into a hydrogel was released after 6 h, demonstrating controlled drug release. Its
potential was shown by increased cytotoxicity on epidermoid carcinoma cells (33.9%) as
opposed to keratinocytes (50.5%). Studies conducted in vitro revealed that skin tissues
enriched with NLC hydrogel had 20.11 μg/cm
2
5-FU, indicating enhanced skin penetra-
tion. AI and QbD facilitated the development of a high-quality, effective NDDS for skin
cancers [53]. In another study an innovative method that combined AI and QbD princi-
ples was used to formulate an optimized semisolid NLC formulation that was loaded
with caffeine and intended to target for the treatment of cellulite. To define the QTPP
for the final NLC formulation, QbD was utilized, which is a methodical approach that
aligns product quality attributes and manufacturing processes. For the first time in cos-
meceutical research, ANNs were used to predict and optimize CQAs. After 12 h, the opti-
mized formulation showed remarka ble properties, including an occlusion factor of
8 QbD and artificial intelligence in nanoparticulate drug delivery systems 171
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50.25% and a PS of 186.5 nm with a PDI value of 0.208. This exhibits a high degree of
accuracy as it closely matches the projected CQAs as defined by the ANN. The resultant
formulation that integrates caffeine and argan oil within semisolid NLCs holds promis-
ing potential for localized cellulite treatment, demonstrating measurable skin effects
[54]. A study by Dawoud and associates utilized QbD principles and ANNs to optimize
nanovesicular carriers for the hydrophobic drug Rosuvastatin calcium. CPPs like PS,
PdI, ZP, and EE were found using Ishikawa diagrams. Accuracy was assured in risk
analysis by using ANN and factorial design. An optimized NLC with 3.2% total lipid,
0.139% surfactant, and 0.1197 mg percent drug was produced using the defined design
space. When compared to the standard drug, this formulation demonstrated sustained
drug release over 72 h, markedly lowering triglycerides, low-density lipoprotein, and
total cholesterol concentrations while elevating high-density lipoprotein [55]. The study
of Kolettti and colleagues focused on QbD and AI-assisted development of gelatin nano-
particles (GNs) for systemic NSAID delivery. Diclofenac sodium GNs were subjected to a
comparison between nanoprecipitation and two-step desolvation techniques. Using
CCD, the standard conditions were identified. The PS range for nanoprecipitation was
183.3–340.3 nm, ζ-potential was in the range of −25.3 to −6.9 mV, and EE was 15.8–34.8%;
for two-step desolvation, the PS range, ζ-potential, and EE were 112.7–345.0 nm, −24.9 to
−12.4 mV, and 9.4–35.8%, respectively. In both cases, ANN performed better in predic-
tion (R2: 0.862–0.985 vs. 0.508–0.643) than MLR. Release in vitro for nanoprecipitation
demonstrated a rapid initial release that lasted up to 16 h, followed by a slower phase
that lasted up to 84 h and for two-step desolvation. A biphasic profile that was similar
had distinct release mechanisms, namely, diffusion-driven for nanoprecipitation and
solubilization-facilitated for two-step desolvation [56]. RNA-loaded lipid NPs (LNPs)
have diverse clinical applications, including infectious disease prevention, rare disease
treatment, and gene/cancer/protein therapies. To develop these nanosystems, research-
ers and manufacturers are implementing the principles of QbD. The development of
RNA-loaded LNPs requires optimization for safety and efficacy. DoE and QbD are two
essential tools in this complex process. Critical parameters (QTPP, CQAs, CMAs, and
CPPs) of LNPs have a major effect on their performance. Studies show that variables
like lipid chain length can affect both in vitro and in vivo efficacy. Safe RNA vaccine
development has spurred RNA technology’s advancement in medicine by showcasing
its flexible production methods. Interestingly, incorporating AI into statistical methods –
such as using neural networks and self-validated ensemble models (SVEM) comple-
ments them [57]. In research by Nguyen and colleagues, paclitaxel (PTX) loaded into
poly-lactic-co-glycolic acid (PLGA) NPs underwent optimization via chitosan (CS) coat-
ing. This study combined QbD principles with AI tools. Important input parameters (CS/
PLGA ratios, temperature, pH) were connected to output characteristics using Modde
8.0 for a D-optimal design. The PS of 161.53 nm, PDI of 0.270, ZP of 41.87 mV, and EE of
98.59% were observed in the AI-driven optimized CS-PLGA NPs. Controlled release from
CS-coated NPs was seen in drug release profiles. When compared to PLGA NPs, cytotox-
icity assays demonstrated increased anti-cancer efficacy in Hela and SK-LU-1 cells. Opti-
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