jrnls.org logo
Submit

BROWSE ALL JOURNALS

SEE ALL 19 JOURNALS
International Journal of Pharmaceutical InvestigationNOW IN
Review

The Impact of Nanoparticulate Drug Delivery Systems on Cancer Therapy and the Role of Artificial Intelligence

Santosh Kumar Dash1★, Ashutosh Padhan1, Abdul Sayeed Khan1, Tusara Kanta Behera1, Sachhidananda Mohapatra1, Sulagna Parta1, Ranjan Kumar Sahoo2★ Corresponding author

  1. 1Department of Pharmaceutics, The Pharmaceutical College, Barpali, Odisha, INDIA.
  2. 2Department of Pharmaceutical chemistry, The Pharmaceutical College, Barpali, Odisha, INDIA.

CORRESPONDENCE

Santosh Kumar Dash

Professor, Department of Pharmaceutics, The Pharmaceutical College, Barpali, Odisha, INDIA.

clickskdash@gmail.com

Received: 02-09-2025; Revised: 22-10-2025; Accepted: 18-12-2025.

Volume 16, Issue 2 · pp. 536–540 · PUBLISHED Apr-Jun 2026 · DOI: 10.5530/ijpi.20260109

View on IJPI original site ↗

ABSTRACT

Cancer remains a leading cause of global mortality. Conventional treatments such as chemotherapy, radiation, and surgery often lead to severe side effects and limited effectiveness. Nanoparticulate Drug Delivery Systems (NDDS) offer novel strategies to enhance drug targeting, solubility, and treatment outcomes. This review discusses the types, mechanisms, advantages, challenges, and future directions of NDDS in cancer therapy. The role of Artificial Intelligence (AI) in optimizing drug formulations, targeting strategies, and real-time monitoring is also explored. NDDS improve drug solubility, circulation time, and targeted delivery while minimizing side effects. Mechanisms such as the Enhanced Permeability and Retention (EPR) effect, active targeting, and controlled release enhance therapeutic efficacy. AI assists in drug design, nanoparticle characterization, personalized therapy, and resistance prediction. NDDS, supported by AI-driven approaches, have significant potential to revolutionize cancer treatment. Addressing biocompatibility, scalability, regulatory hurdles, and tumor heterogeneity remains essential for wider clinical adoption.

KEYWORDS

0% READ

FULL TEXT

INTRODUCTION

The intricate nature of cancer biology and the diversity of tumors present major obstacles to successful treatment, even with advancements in early detection and therapy, numerous tumors continue to be diagnosed at later stages, resulting in unfavorable outcomes. Although chemotherapy and radiation therapy have shown some success, they often result in the indiscriminate spread of drugs, which can harm healthy cells and diminish the effectiveness of the treatment (Hanahan and Weinberg, 2011). Nanoparticle-based drug delivery methods have come to the forefront as an effective way to overcome these challenges. Therapeutic drugs, such as immune-modulating agents, gene therapies, or chemotherapeutics, can be encapsulated in these systems as nanoparticles that can be designed for targeted release and selective accumulation at tumor locations (Barenholz, 2007). The integration of Artificial Intelligence (AI) in the creation, enhancement, and usage of nano-particulate drug delivery systems within clinical environments is significantly transforming this field (Liu et al., 2016). Nano-particulate drug delivery systems offer significant therapeutic advantages including enhanced targeting, controlled release, and reduced side effects; while facing challenges such as toxicity, formulation complexity, and scalability. The integration of AI into these systems is accelerating advances by enabling precision targeting, optimization of nanoparticle design, and real-time control of drug release. This review examines the therapeutic benefits, design challenges, and potential applications of nano-particulate drug delivery systems. It also explores how AI is advancing these technologies.

Nanoparticulate Drug Delivery Systems: Overview and Classification

Structure and Functionality

Nanoparticulate drug delivery systems consist of nanoparticles engineered for targeted therapeutic outcomes. These nanoparticles include liposomes, micelles, dendrimers, polymeric nanoparticles, and inorganic nanoparticles, each possessing distinct characteristics. Factors such as size, surface charge, and functionalization play crucial roles in influencing their interactions with biological systems and the efficacy of drug delivery (Panyam and Labhasetwar, 2003; Prabhu et al., 2021).

Types of Nanoparticulate Systems in Cancer Therapy

Liposomes

These are spherical structures formed from phospholipid layers that can encapsulate both water-soluble and fat-soluble medications. Liposomes can enhance the stability of drugs, prolong their duration in the body, and reduce adverse effects. Customized liposomal formulations can be modified with ligands or antibodies to specifically target and deliver drugs to cancer cells (Allen and Cullis, 2013; Gabizon and Barenholz, 1993).

Polymeric Nanoparticles

Nanoparticles that encapsulate drugs are composed of biodegradable polymers such as Poly (Lactic-Co-Glycolic Acid) (PLGA). These devices can aim at tumor areas by utilizing the Enhanced Permeability and Retention (EPR) effect, allowing for controlled release and extended circulation times. In summary, PLGA-based nanoparticles leverage the EPR effect for targeted tu

mor drug delivery, enabling sustained, regulated release and prolonged circulation, making them a leading platform for cancer therapy and other biomedical applications (Desai, 2012; Nair and Dhandapani, 2019).

Dendrimers

These macromolecules possess a unique structure and exhibit a highly branched, tree-like appearance. Their versatility allows for the attachment of various medications, targeting ligands, and imaging agents, which enhances drug solubility, targeting ability, and effectiveness (Duncan and Izzo, 2005; Shcharbin et al., 2019).

Inorganic Nanoparticles

These nanoparticles, such as gold nanoparticles and silica nanoparticles, are used for drug delivery and imaging. Their surfaces can be modified to enhance drug binding, and they provide extra advantages in diagnostic applications due to their optical characteristics (Jiang et al., 2016; Jain et al., 2008).

Mechanisms of Action in Cancer Therapy

The effectiveness of NDDS in cancer treatment comes from their ability to leverage the physiological properties of tumors, enhancing drug delivery. These mechanisms include:

Enhanced Permeability and Retention (EPR) Effect

Tumors show dysfunctional blood vessels and inadequate lymphatic drainage, leading to the preferential accumulation of nanoparticles within the tumor microenvironment. This occurrence is referred to as the EPR effect and constitutes a key principle that facilitates targeted drug delivery using nanoparticles (Jain, 1987).

Passive Targeting

Nanoparticles have the ability to passively gather at tumor locations as a result of the Enhanced Permeability and Retention (EPR) effect, enabling them to evade quick elimination from the bloodstream, which enhances their therapeutic effectiveness (Maeda et al., 2000).

Active Targeting

Nanoparticles can be modified with particular ligands, antibodies, or peptides that identify receptors or antigens that are overexpressed on cancer cells. This improves the precision and selectivity of drug delivery, minimizing harm to healthy tissues (Allen and Cullis, 2013).

Controlled Release

Nanoparticles can be designed to deliver drugs in reaction to certain triggers like pH levels, temperature variations, or specific enzymes. This targeted release enhances the drug's pharmacokinetics, keeping therapeutic levels at the tumor location for longer durations (Gaumet et al., 2008).

Combination Therapy

Nanoparticles are capable of co-delivering various therapeutic agents, including chemotherapeutics, immunotherapeutics, and gene therapies, which allows for combination therapies that improve treatment effectiveness and address resistance mechanisms (Jain, 2000).

Therapeutic Benefits of Nanoparticulate Drug Delivery Systems

Improved Drug Solubility and Bioavailability

Numerous anticancer medications exhibit low solubility, which restricts their therapeutic effectiveness. Utilizing nanoparticles can enhance the solubility and stability of these medications, thereby improving their bioavailability and therapeutic efficacy (Sahoo et al., 2007).

Reduced Toxicity

By targeting drug delivery directly to the tumor, NDDS decreases the contact of healthy tissues with harmful chemotherapy drugs, consequently lessening side effects like nausea, immune system suppression, and loss of hair (Moghimi et al., 2005).

Targeted Drug Delivery

Nanoparticles can effectively transport medications directly to cancer cells, resulting in increased drug levels at the tumor location, which enhances the chances of successful treatment while protecting healthy cells (Bartlett and Davis, 2006).

Longer Circulation Time

Nanoparticles can be designed to avoid detection by the immune system, resulting in longer circulation periods and enhanced drug concentration at the tumor location. This is especially advantageous for medications that have a brief half-life (Allen and Martin, 2018).

Theranostic Applications

Nanoparticles present the opportunity for integrated therapy and diagnostics (theranostics), allowing for simultaneous administration of medication and imaging. This capability can be especially valuable for tracking treatment responses and tumor development in real-time (Mitragotri et al., 2014).

The Role of Artificial Intelligence in NDDS

Artificial intelligence is increasingly essential for the creation and improvement of nanoparticulate drug delivery systems aimed at cancer treatment shown in Figure 1. The incorporation of AI in this area improves drug formulation, design, and targeting methods. Key functions of AI encompass:

Figure 1: The role of artificial intelligence in NDDS, illustrating drug design, synthesis, targeting, monitoring, and personalized therapy applications.

Drug Design and Optimization

Machine learning algorithms are capable of forecasting the ideal ratio of drugs to carriers, as well as the size and surface properties of nanoparticles. AI models can also replicate how nanoparticles interact with biological systems, allowing for predictions about the pharmacokinetics and pharmacodynamics of the nanoparticles loaded with drugs (Wani and Ahsan, 2020).

Nanoparticle Synthesis and Characterization

The use of AI for automating the creation and characterization of nanoparticles is on the rise. By examining extensive datasets derived from nanoparticle attributes (such as dimensions, morphology, and surface charge), AI can discern patterns and enhance synthesis methods to ensure uniform quality (Chaudhary et al., 2020).

Personalized Cancer Therapy

Artificial intelligence algorithms can examine individual patient data, such as genomic profiles, to create customized nanoparticle formulations suited to the distinct features of the patient's tumor. This approach can enhance treatment results by ensuring that the drug delivery system is specifically crafted to target the unique biomarkers or genetic mutations of the tumor (Chaudhary et al., 2020).

Predicting Tumor Response

Artificial intelligence can be utilized in medical imaging and data analysis to anticipate the response of tumors to nanoparticle-based drug delivery. By examining the diversity within tumors and patterns of drug distribution, AI can project the effectiveness of treatments, enabling healthcare providers to modify treatment strategies in real-time (Yang et al., 2021).

Targeting and Precision Medicine

Computational models driven by AI can discern particular receptors or antigens that are overexpressed in specific types of cancer, which aids in the functionalization of nanoparticles for better targeting. This enhances the accuracy of therapies using nanoparticles, reducing off-target effects and improving the chances of successfully eliminating tumors (Lee et al., 2021).

Monitoring and Real-time Feedback

By utilizing AI to evaluate extensive datasets from clinical trials or patient treatment records, real-time insights can be generated regarding the effectiveness and safety of NDDS. This facilitates ongoing refinement of treatment plans and prompts identification of possible adverse effects (Khosravi et al., 2021).

Drug Resistance Prediction

Artificial intelligence can help to elucidate the processes contributing to drug resistance in cancer. By combining genetic information with treatment results, AI models can forecast the chances of resistance to a particular medication, enabling researchers to create nanoparticles that evade resistance mechanisms or simultaneously deliver agents that counteract resistance (Sun et al., 2020).

Challenges and Limitations

Although there are significant benefits, numerous obstacles persist in the creation and clinical use of nanoparticle-based drug delivery systems:

Biocompatibility and Toxicity

The long-term safety of nanoparticles is still a topic of concern. Nanoparticles have the potential to trigger immune responses or accumulate in essential organs, which can result in toxicity (Lai et al., 2020).

Scalability and Manufacturing

Producing nanoparticles in large quantities while ensuring uniformity and quality control poses significant challenges. It is crucial to standardize production methods for successful clinical application (Zang et al., 2020).

Regulatory Challenges

Because of their unique characteristics, the regulatory approval process for nanoparticulate systems is more intricate than that for traditional medications. Demonstrating their safety and effectiveness through thorough preclinical and clinical trials poses a considerable challenge (Mei et al., 2020).

Tumor Heterogeneity

Tumors exhibit significant heterogeneity, and the EPR effect might not be consistent among all tumor types. Such differences can result in less effective drug delivery in some instances (Huang et al., 2019).

Cost

The production and creation of nanoparticles can be expensive, posing a major challenge for the advancement of cost-effective treatments (Sur et al., 2015).

Future Perspectives

Nanoparticulate drug delivery systems, when paired with AI technology, possess significant potential to transform cancer treatment. Upcoming advancements are expected to concentrate on:

Personalized Nanomedicine

Customizing nanoparticles according to a specific patient's cancer characteristics may improve the effectiveness of the treatment (Gajbhiye et al., 2020).

AI-driven Nanoparticle Optimization:

AI will continue to play a key role in optimizing nanoparticle formulations, improving targeting strategies, and predicting treatment outcomes with greater precision (Vagner et al., 2018).

Smart Nanoparticles

Nanoparticles that respond to specific biological stimuli (e.g., changes in pH, temperature, or enzymes) for controlled and localized drug release are being developed, and AI models will help identify the best conditions for these systems (Wang et al., 2021).

CONCLUSION

Nanoparticulate drug delivery systems present a promising approach to address many limitations found in traditional cancer treatments. By enhancing drug solubility, targeting accuracy, and minimizing side effects, NDDS represents a groundbreaking method for tailored cancer care. The incorporation of AI further expedites the advancement of these systems, allowing for more efficient, individualized, and accurate treatments. Nevertheless, obstacles such as biocompatibility, scalability, and regulatory challenges need to be resolved before these systems can be broadly implemented in clinical environments. Ongoing research and innovation in nanomedicine and AI will be crucial for realizing the complete potential of nanoparticulate systems in cancer therapy.

REFERENCES

As published

Showing references and in-text citations exactly as published.

  1. 1.Allen, T. M.; Cullis, P. R.. Liposomal drug delivery systems: From concept to clinical applications. Advanced Drug Delivery Reviews. 2013;65(1):36–48. https://doi.org/10.1016/j.addr.2012.09.037DOIGOOGLE SCHOLAR
  2. 2.Allen, T. M.; Martin, F.. Liposomal drug delivery systems in the treatment of cancer. Advanced Drug Delivery Reviews. 2018;67(1):74–83. https://doi.org/10.1016/j.addr.2018.01.007DOIGOOGLE SCHOLAR
  3. 3.Barenholz, Y.. Liposome application: Problems and prospects. Nanomedicine: Nanotechnology, Biology and Medicine. 2007;3(1):75–80. https://doi.org/10.1016/j.nano.2006.11.008DOIGOOGLE SCHOLAR
  4. 4.Bartlett, D. W.; Davis, M. E.. Impact of tumor-specific targeting on the uptake and pharmacodynamics of liposomal anticancer drugs. Cancer Journal. 2006;12(4):309–318. https://doi.org/10.1097/00130404-200607000-00011DOIGOOGLE SCHOLAR
  5. 5.Chaudhary, A.; El Bayoumi, T.; Feroz, S. et al. Machine learning algorithms in drug delivery: Recent advances and future perspectives. Journal of Drug Delivery Science and Technology. 2020;58:Article 101679. https://doi.org/10.1016/j.jddst.2020.101679DOIGOOGLE SCHOLAR
  6. 6.Duncan, R.; Izzo, L.. Dendrimer biocompatibility and toxicity. Advanced Drug Delivery Reviews. 2005;57(15):2215–2237. https://doi.org/10.1016/j.addr.2005.09.019DOIGOOGLE SCHOLAR
  7. 7.Fan, Y.; Lin, L.; Yin, F.; Zhu, Y.; Shen, M.; Wang, H. et al. Scalable production of nanoparticles for drug delivery: Current status and future challenges. Nano Today. 2020;33:Article 100899. https://doi.org/10.1016/j.nantod.2020.100899DOIGOOGLE SCHOLAR
  8. 8.Gabizon, A.; Barenholz, Y.. Liposome formulations of anthracyclines: Therapeutic efficacy and toxicity. Journal of Clinical Oncology. 1993;11(1):4–9. https://doi.org/10.1200/JCO.1993.11.1.4DOIGOOGLE SCHOLAR
  9. 9.Gajbhiye, V.; Gajbhiye, K.; Kim, S.. Personalized cancer therapy: Role of nanotechnology in cancer diagnosis and therapy. Materials Science and Engineering. Part C. 2020;112:Article 110895. https://doi.org/10.1016/j.msec.2020.110895DOIGOOGLE SCHOLAR
  10. 10.Gaumet, M.; Vargas, A.; Gauthier, M. et al. Nanoparticles for drug delivery: The importance of nanoparticle properties. Nanomedicine. 2008;3(2):119–128. https://doi.org/10.1016/j.nano.2008.01.002DOIGOOGLE SCHOLAR
  11. 11.Hanahan, D.; Weinberg, R. A.. Hallmarks of cancer: The next generation. Cell. 2011;144(5):646–674. https://doi.org/10.1016/j.cell.2011.02.013DOIGOOGLE SCHOLAR
  12. 12.Huang, Y.; Feng, M.; Xu, J. et al. Tumor heterogeneity and nanoparticle delivery in cancer therapy. Nano Letters. 2019;19(6):4087–4097. https://doi.org/10.1021/acs.nanolett.9b00953DOIGOOGLE SCHOLAR
  13. 13.Jain, R. A.. The manufacturing techniques of various drug-loaded biodegradable poly(lactic acid) and poly(lactic-co-glycolic acid) nanoparticles. Advanced Drug Delivery Reviews. 2000;47(1):25–45. https://doi.org/10.1016/S0169-409X(00DOIGOOGLE SCHOLAR
  14. 14.Jain, R. K.. Transport of molecules in the tumor interstitium: A review. Cancer Research. 1987;47(12):3039–3051.GOOGLE SCHOLAR
  15. 15.Jain, T. K.; Reddy, M. K.; Yadav, S. et al. Dendrimers in cancer therapy: A review. Cancer Letters. 2008;268(2):172–182. https://doi.org/10.1016/j.canlet.2008.03.047DOIGOOGLE SCHOLAR
  16. 16.Jiang, W.; Wang, X.; Zhang, L. et al. Inorganic nanoparticles for cancer therapy: Opportunities and challenges. Nano Today. 2016;11(3):309–321. https://doi.org/10.1016/j.nantod.2016.04.006DOIGOOGLE SCHOLAR
  17. 17.Khosravi, P.; Niazi, M.; Salehahmadi, Z. et al. Artificial intelligence in cancer treatment: Opportunities and challenges. Artificial Intelligence in Medicine. 2021;113:Article 102073. https://doi.org/10.1016/j.artmed.2020.102073DOIGOOGLE SCHOLAR
  18. 18.Lai, Z.; Bano, S.; Ghosh, S. et al. Nanoparticles in drug delivery: Applications and challenges. Science Progress. 2020;103(1):36850419892352. https://doi.org/10.1177/0036850419892352DOIGOOGLE SCHOLAR
  19. 19.Lee, S.; Lee, S.; Kim, M. et al. Applications of artificial intelligence in cancer therapy and precision medicine. Computational Biology and Chemistry. 2021;89:Article 107398. https://doi.org/10.1016/j.compbiolchem.2020.107398DOIGOOGLE SCHOLAR
  20. 20.Liu, Y.; Guo, C.; Sui, L. et al. A new approach of targeted drug delivery system: Nanoparticles in cancer therapy. Cancer Nanotechnology. 2016;15(3):47–56. https://doi.org/10.1007/s12951-016-0189-8DOIGOOGLE SCHOLAR
  21. 21.Maeda, H.; Wu, J.; Sawa, T.; Matsumura, Y.; Hori, K.. Tumor vascular permeability and the EPR effect in macromolecular therapeutics: A review. Journal of Controlled Release. 2000;65(1–2):271–284. https://doi.org/10.1016/S0168-3659(99DOIGOOGLE SCHOLAR
  22. 22.Mitragotri, S.; Burke, P. A.; Langer, R.. Overcoming the challenges of drug delivery: The future of nanomedicine. Nature Reviews Drug Discovery. 2014;13(9):607–622. https://doi.org/10.1038/nrd4364DOIGOOGLE SCHOLAR
  23. 23.Moghimi, S. M.; Hunter, A. C.; Murray, J. C.. Nanomedicine: Current status and future prospects. FASEB Journal. 2005;19(3):311–330. https://doi.org/10.1096/fj.04-2747revDOIGOOGLE SCHOLAR
  24. 24.Nair, P.; Dhandapani, K.. Polymeric nanoparticles for cancer drug delivery. International Journal of Pharmaceutical Sciences and Research. 2019;10(4):1409–1417. https://doi.org/10.13040/IJPSR.0975-8232.10(4DOIGOOGLE SCHOLAR
  25. 25.Panyam, J.; Labhasetwar, V.. Biodegradable nanoparticles for drug and gene delivery to cells and tissues. Advanced Drug Delivery Reviews. 2003;55(3):329–347. https://doi.org/10.1016/S0169-409X(02DOIGOOGLE SCHOLAR
  26. 26.Prabhu, S.; Kumar, N.; Prabaharan, M.. Nanoparticles for drug delivery in cancer therapy: A review. Journal of Drug Delivery Science and Technology. 2021;64:Article 102689. https://doi.org/10.1016/j.jddst.2021.102689DOIGOOGLE SCHOLAR
  27. 27.Sagaama, A.; Issaoui, N.. Design, molecular docking analysis of an anti-inflammatory drug, computational analysis and intermolecular interactions energy studies of 1-benzothiophene-2-carboxylic acid. Computational Biology and Chemistry. 2020;88:Article 107348. https://doi.org/10.1016/j.compbiolchem.2020.107348DOIGOOGLE SCHOLAR
  28. 28.Sahoo, S. K.; Parveen, S.; Panda, J. J.. The present and future of nanomedicine in cancer therapy. Nanomedicine: Nanotechnology, Biology and Medicine. 2007;3(1):20–31. https://doi.org/10.1016/j.nano.2006.11.008DOIGOOGLE SCHOLAR
  29. 29.Shcharbin, D.; Solovev, S.; Bykov, A. et al. Biocompatibility and application of dendrimers in cancer therapy. Journal of Nanoscience and Nanotechnology. 2019;19(10):5724–5734. https://doi.org/10.1166/jnn.2019.16607DOIGOOGLE SCHOLAR
  30. 30.Sur, S.; Stumpf, C. A.; Dewhirst, M. W.. Cost-effectiveness of nanoparticle-based drug delivery systems in cancer therapy. Nanomedicine. 2015;10(12):1909–1917. https://doi.org/10.2217/nnm.15.29DOIGOOGLE SCHOLAR
  31. 31.Vagner, J.; Lemieux, P.; Peralta, M. et al. Artificial intelligence in drug delivery and development. Advanced Drug Delivery Reviews. 2018;130:1–11. https://doi.org/10.1016/j.addr.2018.07.002DOIGOOGLE SCHOLAR
  32. 32.Wang, H.; Lane, K.; Lou, Z.; Parker, S.; Placke, M.. Genotoxicity and carcinogenicity risk assessment of prucalopride, a selective 5-hydroxytryptamine 4 receptor agonist. Regulatory Toxicology and Pharmacology. 2020;112:Article 104586. https://doi.org/10.1016/j.yrtph.2020.104586DOIGOOGLE SCHOLAR
  33. 33.Wang, X.; Jiang, Y.; Li, Y. et al. Smart nanoparticles in cancer therapy. Journal of Drug Targeting. 2021;29(3):245–259. https://doi.org/10.1080/1061186X.2020.1859292DOIGOOGLE SCHOLAR
  34. 34.Wani, M. R.; Ahsan, H.. Artificial intelligence and machine learning for pharmaceutical research and development. Pharmaceutical Research. 2020;37(2):102–115. https://doi.org/10.1007/s11095-020-02729-7DOIGOOGLE SCHOLAR
  35. 35.Yang, Z.; Zhao, S.; Zhang, Y. et al. Artificial intelligence in cancer diagnosis and treatment: Machine learning and deep learning applications. Journal of Cancer Research and Clinical Oncology. 2021;147(5):1145–1163. https://doi.org/10.1007/s00432-020-03328-2DOIGOOGLE SCHOLAR
  36. 36.Zhang, Y.; Liu, J.; Chen, Y. et al. Machine learning and artificial intelligence in the design of nanoparticles for cancer therapy. Science Advances. 2021;7(12):Article eabf3789. https://doi.org/10.1126/sciadv.abf3789DOIGOOGLE SCHOLAR

Cite this article

SELECT FORMAT

Dash, S. K., Padhan, A., Khan, A. S., Behera, T. K., Mohapatra, S., Parta, S., & Sahoo, R. K. (2026). The Impact of Nanoparticulate Drug Delivery Systems on Cancer Therapy and the Role of Artificial Intelligence. International Journal of Pharmaceutical Investigation, 16(2), 536–540. https://doi.org/10.5530/ijpi.20260109