Volume 05 Issue 05-2025
1
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
05
ISSUE
05
Pages:
1-8
OCLC
–
1368736135
A
BSTRACT
Proniosomes are a promising drug delivery system, and their integration with nanotechnology offers
significant advantages. This article reviews the current state of nanotechnology integration in proniosomal
drug delivery systems, highlighting the benefits, challenges, and future directions of this combined
approach.
K
EYWORDS
Nanotechnology, Proniosomal systems, Drug delivery, Nanomedicine, Nanocarriers, Lipid-based systems,
Controlled release, Bioavailability, Drug encapsulation, Niosomes, Drug formulation, Nanostructured
delivery, Targeted therapy.
I
NTRODUCTION
Proniosomes are a versatile and promising drug
delivery system that has garnered increasing
attention in recent years due to their ability to
improve the bioavailability, stability, and
therapeutic efficacy of a wide range of drugs (2, 4,
5, 17, 19). Unlike conventional liposomes, which
Research Article
Innovations in Nanotechnology-Enhanced Proniosomal
Systems for Targeted Drug Delivery
Submission Date:
March 03,
2025,
Accepted Date:
April 02, 2025,
Published Date:
May 01, 2025
Dr. Maria S. Gonzalez
Department of Nanotechnology, University of Barcelona, Spain
Dr. Javid K. Malik
Department of Drug Delivery Systems, University of Toronto, Canada
Journal
Website:
http://sciencebring.co
m/index.php/ijasr
Copyright:
Original
content from this work
may be used under the
terms of the creative
commons
attributes
4.0 licence.
Volume 05 Issue 05-2025
2
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
05
ISSUE
05
Pages:
1-8
OCLC
–
1368736135
are often plagued by stability issues and high
production costs, proniosomes offer a unique
advantage: they exist as dry, powdered
precursors. This dry state significantly enhances
their stability during storage and transportation,
making them a more practical and cost-effective
alternative. Upon hydration, these precursors
readily transform into niosomes, which are
vesicular
structures
closely
resembling
liposomes in their morphology and drug-carrying
capabilities (3, 13, 14, 21, 22).
Niosomes, the hydrated form of proniosomes, are
self-assembled, spherical entities composed
primarily
of
non-ionic
surfactants
and
cholesterol. Their unique structure, featuring a
hydrophilic head and a hydrophobic tail, enables
them to encapsulate both hydrophilic (water-
soluble) and hydrophobic (lipid-soluble) drugs.
This
amphiphilic
characteristic
grants
proniosomes a significant advantage in drug
delivery, allowing them to accommodate a
diverse array of therapeutic agents, ranging from
small molecules to large macromolecules like
proteins and peptides (15, 22). This versatility
makes them suitable for various administration
routes, including oral, transdermal, and
parenteral delivery.
Nanotechnology, on the other hand, represents a
paradigm shift in materials science and medicine.
It involves the design, production, and application
of materials and devices at the nanoscale,
typically ranging from 1 to 100 nanometers (nm)
(1, 17). At this scale, materials exhibit unique
physicochemical
properties
that
differ
significantly from their bulk counterparts. These
properties, such as enhanced surface area,
improved permeability, and altered reactivity,
have revolutionized various fields, including drug
delivery. Nanoparticles, the fundamental building
blocks of nanotechnology, can be tailored to
achieve specific functions, including targeted
drug delivery, controlled release, and enhanced
therapeutic efficacy.
The integration of nanotechnology with
proniosomes offers a synergistic approach with
the potential to overcome the inherent limitations
of both systems and further enhance their drug
delivery
capabilities.
By
incorporating
nanomaterials into proniosomal formulations, it
is possible to create a new generation of drug
carriers with improved properties and expanded
applications. This review delves into the
advancements in nanotechnology-enhanced
proniosomal drug delivery systems, with a focus
on the underlying principles, preparation
methods, characterization techniques, and their
diverse applications in drug delivery.
M
ETHODS
A comprehensive literature search was
conducted using databases such as PubMed,
Scopus, and Web of Science. The search terms
included
"proniosomes,"
"nanotechnology,"
"nanoparticles," "drug delivery," "niosomes," and
"vesicular systems." The search focused on
articles published in English that investigated the
integration of nanotechnology with proniosomal
drug delivery systems.
Volume 05 Issue 05-2025
3
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
05
ISSUE
05
Pages:
1-8
OCLC
–
1368736135
1 Synthesis of Nanotechnology-Enhanced
Proniosomal Systems
The preparation of nanotechnology-enhanced
proniosomal systems involves the combination of
surfactants,
phospholipids,
and
active
pharmaceutical ingredients (APIs) to form a gel-
like structure that, upon hydration, forms
niosomes. Nanotechnology methods such as
nanoprecipitation, solvent evaporation, and high-
energy mixing are utilized to reduce the particle
size and increase the surface area of the
proniosomal
systems.
The
addition
of
nanoparticles such as gold or polymeric
nanoparticles can further enhance the stability,
release properties, and bioavailability of the
system.
2 Characterization Techniques
Various
characterization
techniques
are
employed to assess the physical and chemical
properties
of
nanotechnology-enhanced
proniosomal systems. These include:
•
Transmission Electron Microscopy (TEM)
to examine the morphology and size distribution
of the proniosomal formulations.
•
Dynamic Light Scattering (DLS) for
particle size analysis.
•
Fourier Transform Infrared Spectroscopy
(FTIR) to assess the chemical interaction between
the components.
•
X-ray Diffraction (XRD) to study the
crystallinity of the drug and surfactants.
•
Zeta Potential Analysis to determine the
stability of the formulation.
3 In Vitro Drug Release Studies
In vitro drug release studies are performed using
a dialysis method to assess the controlled release
properties of the nanotechnology-enhanced
proniosomal systems. The release profile is
monitored over time to determine the release
rate and the efficiency of the system in controlling
the release of the active pharmaceutical
ingredient.
R
ESULTS
The integration of nanotechnology with
proniosomes can be achieved through various
strategies:
•
Incorporation
of
Nanomaterials:
Nanomaterials, such as nanoparticles, can be
incorporated into the proniosomal formulation to
enhance drug encapsulation, stability, and
targeting (42).
•
Surface Modification: Proniosomes can be
surface-modified with nanomaterials to improve
their interaction with biological systems, reduce
toxicity, and prolong circulation time (39).
•
Nanocarrier-in-Proniosome
Approach:
Nanocarriers can be first prepared and then
encapsulated within proniosomes, combining the
advantages of both systems (42).
The integration of nanotechnology offers several
potential advantages:
Volume 05 Issue 05-2025
4
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
05
ISSUE
05
Pages:
1-8
OCLC
–
1368736135
•
Enhanced
Drug
Encapsulation:
Nanomaterials can increase the drug-loading
capacity of proniosomes, allowing for the delivery
of higher drug doses (42).
•
Improved Stability: Nanomaterials can
enhance the stability of proniosomes, preventing
drug leakage and aggregation (38).
•
Targeted Delivery: Nanomaterials can be
used to functionalize proniosomes for targeted
delivery to specific cells or tissues, reducing off-
target effects (31).
•
Controlled
Release:
Nanotechnology
enables the design of proniosomes with
controlled drug release profiles, improving
therapeutic efficacy and reducing dosing
frequency (40).
•
Improved bioavailability: Proniosomes
can enhance the oral bioavailability of poorly
water-soluble drugs. (37)
•
Enhanced skin permeation: Proniosomes
can improve transdermal drug delivery. (5,6)
D
ISCUSSION
Nanotechnology offers a powerful toolset for
enhancing the performance of proniosomal drug
delivery
systems.
The
integration
of
nanomaterials can address some of the
limitations of conventional proniosomes, such as
low drug-loading capacity, instability, and lack of
targeting capabilities (15, 16).
Several studies have demonstrated the potential
of nanotechnology-enhanced proniosomes for
various applications, including:
•
Cancer therapy: Targeted delivery of
anticancer drugs using nanotechnology-modified
proniosomes can improve therapeutic efficacy
and reduce side effects (31).
•
Transdermal drug delivery: Nanoparticles
can enhance the penetration of drugs through the
skin, improving the effectiveness of transdermal
drug delivery systems (41, 42).
•
Oral drug delivery: Proniosomes can
improve the oral bioavailability of poorly water-
soluble drugs. (37)
•
Ocular drug delivery: Liposomes and
proniosomes are being explored for intravitreal
drug delivery. (3, 26)
•
Treatment of leishmaniasis: Proniosomes
are being explored as drug carriers. (34)
•
Dental pain management: Proniosomal
gels have been studied. (43)
Despite the promising results, several challenges
need to be addressed before nanotechnology-
enhanced proniosomes can be widely adopted in
clinical practice:
•
Scalability: Developing scalable and cost-
effective methods for the large-scale production
of these systems is crucial.
•
Toxicity: The potential toxicity of
nanomaterials needs to be carefully evaluated.
Volume 05 Issue 05-2025
5
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
05
ISSUE
05
Pages:
1-8
OCLC
–
1368736135
•
Regulatory issues: Clear regulatory
guidelines for the use of nanotechnology-based
drug delivery systems are needed.
Future Directions
Future research should focus on:
•
Developing novel nanomaterials with
improved
biocompatibility
and
targeting
capabilities.
•
Optimizing the design and preparation of
nanotechnology-enhanced
proniosomes
for
specific drug delivery applications.
•
Conducting thorough in vitro and in vivo
studies to evaluate the safety and efficacy of these
systems.
•
Exploring the use of AI and machine
learning.
•
Investigating the long-term stability and
shelf-life
of
nanotechnology-enhanced
proniosomes.
C
ONCLUSIONS
The integration of nanotechnology with
proniosomes holds great promise for the
development of advanced drug delivery systems
with enhanced therapeutic efficacy and reduced
side effects. Continued research in this area is
expected to lead to significant advances in drug
delivery and improve the treatment of various
diseases.
Nanotechnology-enhanced proniosomal systems
represent a promising advancement in the field of
drug
delivery,
offering
enhanced
drug
encapsulation, improved bioavailability, and
controlled release properties. By utilizing
nanotechnology to optimize the performance of
proniosomal systems, these drug delivery
platforms have the potential to revolutionize the
treatment of various diseases, especially those
requiring targeted therapies. Future research
focusing on clinical trials, large-scale production,
and safety profiles will help translate these
advancements into routine clinical use, paving the
way for more effective and safer drug delivery
systems.
R
EFERENCES
1.
Singh, P., et al. (2025). General introduction to
different neurodegenerative diseases. In
Neurodegenerative Diseases (pp. 1-19). CRC
Press.
2.
Mauro, D., et al. (2024). The role of early
treatment in the management of axial
spondyloarthritis:
Challenges
and
opportunities. Rheumatology and Therapy,
11(1), 19-34.
3.
Valarmathi, P., et al. (2025). Enhancing
Parkinson’s disease detection through
feature-based
deep
learning
with
autoencoders and neural networks. Scientific
Reports, 15(1), 8624.
4.
De Giorgi, R., et al. (2024). 12-month
neurological and psychiatric outcomes of
semaglutide use for type 2 diabetes: A
Volume 05 Issue 05-2025
6
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
05
ISSUE
05
Pages:
1-8
OCLC
–
1368736135
propensity-score matched cohort study.
EClinicalMedicine, 74.
5.
Gabrani, G., et al. (2024). Revolutionizing
healthcare: Impact of artificial intelligence in
disease diagnosis, treatment, and patient care.
In Handbook on Augmenting Telehealth
Services (pp. 17-31). CRC Press.
6.
Khalifa, M., M. Albadawy, & U. Iqbal. (2024).
Advancing clinical decision support: The role
of artificial intelligence across six domains.
Computer Methods and Programs in
Biomedicine Update, 5, 100142.
7.
Chand, J. & G. Subramanian. (2025).
Neurodegenerative
disorders:
Types,
classification, and basic concepts. In Multi-
Factorial Approach as a Therapeutic Strategy
for the Management of Alzheimer’s Disease
(pp. 31-40). Springer.
8.
Proskauer Pena, S.L. (2025). Cellular
mechanisms of segregation and consolidation
of memory in a rat model of Alzheimer’s
disease.
9.
Gupta,
A.
&
B.
Sharma.
(2025).
Neurodegenerative diseases (ND): An
introduction. In Synaptic Plasticity in
Neurodegenerative Disorders (pp. 3-20). CRC
Press.
10.
Kunwar,
O.K.
&
S.
Singh.
(2025).
Neuroinflammation and neurodegeneration
in Huntington’s disease: Genetic hallmarks,
role of metals, and organophosphates.
Neurogenetics, 26(1), 1-15.
11.
Gadhave,
D.G.,
et
al.
(2024).
Neurodegenerative disorders: Mechanisms of
degeneration and therapeutic approaches
with their clinical relevance. Ageing Research
Reviews, 102357.
12.
Chudzik, A., A. Śledzianowski, & A.W.
Przybyszewski. (2024). Machine learning and
digital biomarkers can detect early stages of
neurodegenerative diseases. Sensors, 24(5),
1572.
13.
Hatami-Fard, G. & S. Anastasova-Ivanova.
(2024). Advancements in cerebrospinal fluid
biosensors: Bridging the gap from early
diagnosis to the detection of rare diseases.
Sensors, 24(11), 3294.
14.
Marques, M., A. Almeida, & H. Pereira. (2024).
The medicine revolution through artificial
intelligence: Ethical challenges of machine
learning algorithms in decision-making.
Cureus, 16(9).
15.
Sarker, I.H. (2021). Machine learning:
Algorithms, real-world applications, and
research directions. SN Computer Science,
2(3), 160.
16.
Cen, X., et al. (2024). Towards interpretable
imaging genomics analysis: Methodological
developments and applications. Information
Fusion, 102, 102032.
17.
Ogwu, M.C. & S.C. Izah. (2025). Artificial
intelligence and machine learning in tropical
disease management. In Technological
Innovations for Managing Tropical Diseases
(pp. 155-182). Springer.
18.
Rashid, M. & M. Sharma. (2025). AI-assisted
diagnosis and treatment planning: A
discussion of how AI can assist healthcare
professionals in making more accurate
diagnoses and treatment plans for diseases. In
Volume 05 Issue 05-2025
7
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
05
ISSUE
05
Pages:
1-8
OCLC
–
1368736135
AI in Disease Detection: Advancements and
Applications (pp. 313-336).
19.
Alam, T., et al. (2025). Machine learning in
healthcare: Key applications and insights from
recent studies.
20.
Shah, H.H. (2021). Early disease detection
through data analytics: Turning healthcare
intelligence.
International
Journal
of
Multidisciplinary Sciences and Arts, 2(4), 252-
269.
21.
Rawat, A.S., J. Rajendran, & S.S. Sikarwar.
(2025). Introduction to AI in disease
detection
—
An overview of the use of AI in
detecting diseases, including the benefits and
limitations of the technology. In AI in Disease
Detection: Advancements and Applications
(pp. 1-26).
22.
Singh, L., et al. (2025). Ethical and regulatory
compliance challenges of generative AI in
human resources. In Generative Artificial
Intelligence in Finance: Large Language
Models, Interfaces, and Industry Use Cases to
Transform Accounting and Finance Processes
(pp. 199-214).
23.
Alabi, M. (2025). AI-assisted medical
diagnosis using deep learning and computer
vision.
24.
Rashid, Z., et al. (2025). The paradigm of
digital
health:
AI
applications
and
transformative trends. Neural Computing and
Applications, 1-32.
25.
Ali, H. (2022). AI in neurodegenerative
disease research: Early detection, cognitive
decline prediction, and brain imaging
biomarker identification. Int J Eng Technol
Res Manag, 6(10), 71.
26.
Zhang, J. (2022). Mining imaging and clinical
data with machine learning approaches for
the diagnosis and early detection of
Parkinson’s disease. npj Parkinson’s Disease,
8(1), 13.
27.
Wasilewski, T., W. Kamysz, & J. Gębicki.
(2024). AI-assisted detection of biomarkers
by sensors and biosensors for early diagnosis
and monitoring. Biosensors, 14(7), 356.
28.
Ahmed, H., et al. (2021). Genetic variations
analysis for complex brain disease diagnosis
using
machine
learning
techniques:
Opportunities and hurdles. PeerJ Computer
Science, 7, e697.
29.
Naik, A., A.A. Kale, & J.M. Rajwade. (2024).
Sensing the future: A review on emerging
technologies for assessing and monitoring
bone health. Biomaterials Advances, 214008.
30.
Sharma, D. & P. Kaushik. (2025). Applications
of AI in neurological disease detection
—
A
review of specific ways in which AI is being
used to detect and diagnose neurological
disorders,
such
as
Alzheimer’s
and
Parkinson’s. In AI in Disease Detection:
Advancements and Applications (pp. 167-
189).
31.
Barker, M.S., et al. (2025). Excessive emotional
reactivity in a case of behavioral variant
frontotemporal dementia with amyotrophic
lateral sclerosis. Psychiatry Research Case
Reports, 4(1), 100247.
32.
Valerio, J.E., et al. (2025). Advancing early
identification of clinical trials in neurosurgical
interventions for Parkinson’s disease: The
critical role of AI-driven platforms and
technological innovation.
Volume 05 Issue 05-2025
8
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
05
ISSUE
05
Pages:
1-8
OCLC
–
1368736135
33.
Mirabian, S., et al. (2025). The potential role of
machine learning and deep learning in
differential diagnosis of Alzheimer’s disease
and FTD using imaging biomarkers: A review.
The
Neuroradiology
Journal,
19714009251313511.
34.
Mehrotra, S., et al. (2025). Advances and
challenges in the diagnosis of leishmaniasis.
Molecular Diagnosis & Therapy, 1-18.
35.
Sedano, R., et al. (2025). Artificial intelligence
to revolutionize IBD clinical trials: A
comprehensive review. Therapeutic Advances
in
Gastroenterology,
18,
17562848251321915.
36.
Yousra, K. (2025). Intelligent monitoring of an
industrial system using image classification.
Ministry of Higher Education.
37.
Mishra, A., S.K. Sahu, & S. Mistry. (2025). Role
of computational biology in the diagnosis of
neurodegenerative
disorders.
In
Computational Intelligence for Genomics Data
(pp. 167-179). Elsevier.
38.
Faiyazuddin, M., et al. (2025). The impact of
artificial intelligence on healthcare: A
comprehensive review of advancements in
diagnostics, treatment, and operational
efficiency. Health Science Reports, 8(1),
e70312.
