AI-driven design and optimization of nanoparticle-based drug delivery systems
Article Sidebar
Main Article Content
Abstract
Nanoparticle-based drug delivery systems represent a transformative advancement in targeted therapeutics, providing meticulous drug delivery, enhanced bioavailability, and diminished side effects. However, designing nanoparticles (NPs) optimal for specific drugs and diseases remains a complex challenge. The advancements in artificial intelligence (AI) have provided innovative approaches to design and optimize these systems, improving their efficacy and adaptability. This review encompasses the integration of AI in the conceptualization and development of NP drug delivery systems, signifying its potential to revolutionize the field. The review discusses the different AI methods such as machine learning, neural networks, and optimization algorithms that simplify the fabrication of NPs with tailored characteristics such as size, surface chemistry, and drug release profiles. AI can also standardize these characteristics to enhance drug loading capacity, targeting specificity, and controlled release at the chosen site of action. AI-based predictive modeling enables the quick screening of numerous parameters, thus quickening the discovery of optimal NP configurations tailored to specific therapeutic needs. Furthermore, the review also discusses the case studies where AI has efficaciously forecasted NP behavior in biological environments, crucial for enhanced targeting and diminished off-target effects. The amalgamation of AI and nanotechnology not only streamlines the drug development process but also paves the way for personalized medicine. The review also entails the different challenges associated with implementing AI in this field, such as data quality, algorithm transparency, and regulatory specifications. By utilizing AI, researchers and healthcare providers can unlock new potentials in novel drug delivery systems, ultimately advancing the precision and effectiveness of treatments for various diseases. Finally, the review discusses the future directions of AI-based NP design, highlighting its benefits to transform drug delivery and augment patient outcomes.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Adir, O., Poley, M., Chen, G., Froim, S., Krinsky, N., Shklover, J., Shainsky-Roitman, J., Lammers, T., and Schroeder, A. (2020). Integrating artificial intelligence and nanotechnology for precision cancer medicine. Advanced Materials, 32(13), e1901989.
Afzal, O., Altamimi, A. S. A., Nadeem, M. S., Alzarea, S. I., Almalki, W. H., Tariq, A., Mubeen, B., Murtaza, B. N., Iftikhar, S., Riaz, N., and Kazmi, I. (2022). Nanoparticles in drug delivery: From history to therapeutic applications. Nanomaterials, 12(24), 4494.
Altammar, K. A. (2023). A review on nanoparticles: Characteristics, synthesis, applications, and challenges. Frontiers in Microbiology, 14, 1155622.
Aumklad, P., Suriyaamporn, P., Panomsuk, S., Pamornpathomkul, B., and Opanasopit, P. (2024). Artificial intelligence-aided rational design and prediction model for progesterone-loaded self-microemulsifying drug delivery system formulations. Science, Engineering and Health Studies, 18, 24050002.
Bhatt, S., Sharma, J. B., Kamboj, R., Kumar, M., Saini, V., and Mandge, S. (2021). Design and optimization of febuxostat-loaded nano lipid carriers using full factorial design. Turkish Journal of Pharmaceutical Sciences, 18(1), 61–67.
Bhatt, S., Sharma, J., Singh, M., and Saini, V. (2018). Solid lipid nanoparticles: A promising technology for delivery of poorly water-soluble drugs. ACTA Pharmaceutica Sciencia, 56(3), 16.
Chaurawal, N., Quadir, S. S., Joshi, G., Barkat, M. A., Alanezi, A. A., and Raza, K. (2024). Development of fucoidan/polyethyleneimine based sorafenib-loaded self-assembled nanoparticles with machine learning and DoE-ANN implementation: Optimization, characterization, and in-vitro assessment for the anticancer drug delivery. International Journal of Biological Macromolecules, 279(Pt 1), 135123.
Chamsai, B., Samprasit, W., Opanasopit, P., Benjasirimongkol, P., and Sriamornsak, P. (2020). Types of solid lipids on physical stability of resveratrol-loaded nanostructures lipid carriers. Key Engineering Materials, 859, 203–207.
Chou, W. C., Chen, Q., Yuan, L., Cheng, Y. H., He, C., Monteiro-Riviere, N. A., Riviere, J. E., and Lin, Z. (2023). An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice. Journal of Controlled Release, 361, 53–63.
Dalwadi, S., Thakkar, V., and Prajapati, B. (2025). Optimizing neuroprotective nano-structured lipid carriers for transdermal delivery through artificial neural network. Pharmaceutical Nanotechnology, 13(1), 184–198.
Das, K. P., and J, C. (2022). Nanoparticles and convergence of artificial intelligence for targeted drug delivery for cancer therapy: Current progress and challenges. Frontiers in Medical Technology, 4, 1067144.
Dawoud, M. H., Mannaa, I. S., Abdel-Daim, A., and Sweed, N. M. (2023). Integrating artificial intelligence with quality by design in the formulation of lecithin/chitosan nanoparticles of a poorly water-soluble drug. AAPS PharmSciTech, 24(6), 169.
Ding, D. Y., Zhang, Y., Jia, Y., and Sun, J. (2023). Machine learning-guided lipid nanoparticle design for mRNA delivery. arXiv preprint arXiv:2308.01402.
Eldadamony, N. M., Ghoniem, A. A., Al-Askar, A. A., Attia, A. A., El-Hersh, M. S., Elattar, K. M., Alrdahi, H., and Saber, W. I. A. (2024). Optimization of pullulan production by Aureobasidium pullulans using semi-solid-state fermentation and artificial neural networks: Characterization and antibacterial activity of pullulan impregnated with Ag-TiO2 nanocomposite. International Journal of Biological Macromolecules, 269(Pt 1), 132109.
Hamilton, S., and Kingston, B. R. (2024). Applying artificial intelligence and computational modeling to nanomedicine. Current Opinion in Biotechnology, 85, 103043.
Hirun, M., Mahadlek, J., Limmatvapirat, S., Sriamornsak, P., Yonemochi, E., Furuishi, T., and Kraisit, P. (2024). Fabrication and characterization of pectin films containing solid lipid nanoparticles (SLNs) for buccal delivery of fluconazole. International Journal of Molecular Sciences, 25(10), 5413.
Hsu, C.-Y., Rheima, A. M., Kadhim, M. M., Ahmed, N. N., Mohammed, S. H., Abbas, F. H., Abed, Z. T., Mahdi, Z. M., Abbas, Z. S., Hachim, S. K., Ali, F. K., Mahmoud, Z. H., and Kianfar, E. (2023). An overview of nanoparticles in drug delivery: Properties and applications. South African Journal of Chemical Engineering, 46, 233–270.
Huanbutta, K., Sriamornsak, P., Suwanpitak, K., Klinchuen, N., Deebugkum, T., Teppitak, V., and Sangnim, T. (2023). Key fabrications of chitosan nanoparticles for effective drug delivery using flow chemistry reactors. International Journal of Nanomedicine, 18, 7889–7900.
Jariwala, N., Putta, C. L., Gatade, K., Umarji, M., Rahman, S. N. R., Pawde, D. M., Sree, A., Kamble, A. S., Goswami, A., Chakraborty, P., and Shunmugaperumal, T. (2023). Intriguing of pharmaceutical product development processes with the help of artificial intelligence and deep/machine learning or artificial neural network. Journal of Drug Delivery Science and Technology, 87, 104751.
Javanmard, S. (2024). Revolutionizing medical practice: The impact of artificial intelligence (AI) on healthcare. OA Journal of Applied Science and Technology, 2(1), 1–16.
Khayati, G. R. (2020). A predictive model on size of silver nanoparticles prepared by green synthesis method using hybrid artificial neural network-particle swarm optimization algorithm. Measurement, 151, 107199.
Li, Y., Liu, L., Zhao, X., Zhou, S., Wu, X., Lai, Y., Chen, Z., Chen, J., and Xing, X. (2024). Deep learning-assisted characterization of nanoparticle growth processes: Unveiling SAXS structure evolution. Radiation Detection Technology and Methods, 8, 1712–1728.
Lin, Y., Chen, X., Wang, K., Liang, L., and Zhang, H. (2024). An overview of nanoparticle-based delivery platforms for mRNA vaccines for treating cancer. Vaccines, 12(7), 727.
Lin, Z., Chou, W.-C., Cheng, Y.-H., He, C., Monteiro-Riviere, N. A., and Riviere, J. E. (2022). Predicting nanoparticle delivery to tumors using machine learning and artificial intelligence approaches. International Journal of Nanomedicine, 17, 1365–1379.
Mak, K.-K., and Pichika, M. R. (2019). Artificial intelligence in drug development: Present status and future prospects. Drug Discovery Today, 24(3), 773–780.
Manchun, S., Dass, C. R., and Sriamornsak, P. (2012). Targeted therapy for cancer using pH-responsive nanocarrier systems. Life Sciences, 90(11–12), 381–387.
May, M. (2022). Why drug delivery is the key to new medicines. Nature Medicine, 28(6), 1100–1102.
Mendes, B. B., Zhang, Z., Conniot, J., Sousa, D. P., Ravasco, J. M. J. M., Onweller, L. A., and Conde, J. (2024). A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research. Nature Nanotechnology, 19(6), 867–878.
Minphimai, R., Piriyaprasarth, S., and Sriamornsak, P. (2015). Application of artificial neural networks for prediction of ibuprofen yield from ultrasound-assisted anti-solvent crystallization. Advanced Materials Research, 1060, 145–148.
Mitchell, M. J., Billingsley, M. M., Haley, R. M., Wechsler, M. E., Peppas, N. A., and Langer, R. (2021). Engineering precision nanoparticles for drug delivery. Nature Reviews Drug Discovery, 20(2), 101–124.
Moussa, H. G., Husseini, G. A., Ahmad, S. E., and Awad, N. (2020). The use of artificial neural networks to control the concentration of a model drug released acoustically. Emergent Materials, 3(4), 503–513.
Nuhn, L. (2023). Artificial intelligence assists nanoparticles to enter solid tumours. Nature Nanotechnology, 18(6), 550–551.
Ortiz-Perez, A., van Tilborg, D., van der Meel, R., Grisoni, F., and Albertazzi, L. (2024). Machine learning-guided high throughput nanoparticle design. Digital Discovery. 3, 1280–1291.
Oviedo, L. R., Oviedo, V. R., Martins, M. O., Fagan, S. B., and da Silva, W. L. (2022). Nanoarchitectonics: The role of artificial intelligence in the design and application of nanoarchitectures. Journal of Nanoparticle Research, 24(8), 157.
Patel, R. P., Ladani, R., Dadhaniya, B., and Prajapati, B. G. (2015). In situ gel delivery systems for chlordiazepoxide using artificial neural network. International Journal of Pharma Research and Health Sciences, 1, 10–22.
Perumal, S., Atchudan, R., and Lee, W. (2022). A review of polymeric micelles and their applications. Polymers, 14(12), 2510.
Piriyaprasarth, S., Sriamornsak, P., Juttulapa, M., and Puttipipatkhachorn, S. (2011). Modeling of drug release from matrix tablets with process variables of microwave-assisted modification of arrowroot starch using artificial neural network. Advanced Materials Research, 152–153, 1700–1703.
Prajapati, J. B., Paliwal, H., Saikia, S., Prajapati, B. G., Prajapati, D. N., Philip, A. K., and Faiyazuddin, M. (2023). Impact of AI on drug delivery and pharmacokinetics: The present scenario and future prospects. In A Handbook of Artificial Intelligence in Drug Delivery (Philip, A., Shahiwala, A., Rashid, M., and Faiyazuddin, Md., Eds.), pp. 443–465. Cambridge, MA: Academic Press.
Prasad, R. D., Charmode, N., Shrivastav, O. P., Prasad, S. R., Moghe, A., Sarvalkar, P. D., and Prasad, N. R. (2021). A review on concept of nanotechnology in veterinary medicine. ES Food and Agroforestry, 4, 28–60.
Qi, Q., and Wang, Z. (2024). Machine learning-based models to predict aquatic ecological risk for engineered nanoparticles: using hazard concentration for 5% of species as an endpoint. Environmental Science and Pollution Research International, 31(17), 25114–25128.
Rajendra Kumar, P., Madhav Rao, B., Maddireddy, C. K. R., Thakor, S., Vaja, C. R., Prakash, K., and Jain, P. (2024). Dielectric characterization and machine learning-based predictions in polymer composites with mixed nanoparticles. Journal of Macromolecular Science, Part B, 1–15.
Ramović Hamzagić, A., Gazdić Janković, M., Cvetković, D., Nikolić, D., Nikolić, S., Milivojević Dimitrijević, N., Kastratović, N., Živanović, M., Miletić Kovačević, M., and Ljujić, B. (2024). Machine learning model for prediction of development of cancer stem cell subpopulation in tumurs subjected to polystyrene nanoparticles. Toxics, 12(5), 354.
Rizvi, S. A. A., and Saleh, A. M. (2018). Applications of nanoparticle systems in drug delivery technology. Saudi Pharmaceutical Journal, 26(1), 64–70.
Saikia, S., Prajapati, J. B., Prajapati, B. G., Padma, V. V., and Pathak, Y. V. (2022). The role of artificial intelligence in therapeutic drug monitoring and clinical toxicity. In Recent Advances in Therapeutic Drug Monitoring and Clinical Toxicology (Amponsah, S. K., and Pathak, Y. V., Eds.), pp. 67–85. Cham: Springer.
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160.
Selmani, A., Kovačević, D., and Bohinc, K. (2022). Nanoparticles: From synthesis to applications and beyond. Advances in Colloid and Interface Science, 303, 102640.
Shabanzadeh, P., Senu, N., Shameli, K., and Tabar, M. M. (2013). Artificial intelligence in numerical modeling of silver nanoparticles prepared in montmorillonite interlayer space. Journal of Chemistry, 2013(1), 305713.
Shabanzadeh, P., Yusof, R., and Shameli, K. (2015). Modeling of biosynthesized silver nanoparticles in Vitex negundo L. extract by artificial neural network. RSC Advances, 5(106), 87277–87285.
Sriamornsak, P., and Waijanya, S. (2021). Applying information and computer technologies in various industrial sectors. Science, Engineering and Health Studies, 15, 21010004.
Sriamornsak, P., and Waijanya, S. (2022). Machine learning and digital engineering in Southeast Asia. Science, Engineering and Health Studies, 16, 22010004.
Stillman, N. R., Balaz, I., Tsompanas, M.-A., Kovacevic, M., Azimi, S., Lafond, S., Adamatzky, A., and Hauert, S. (2021). Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment. npj Computational Materials, 7(1), 150.
Suriyaamporn, P., Pamornpathomkul, B., Wongprayoon, P., Rojanarata, T., Ngawhirunpat, T., and Opanasopit, P. (2024). The artificial intelligence and design of experiment assisted in the development of progesterone-loaded solid-lipid nanoparticles for transdermal drug delivery. Pharmacia, 71(1), 1–12.
Suwanpitak, K., Huanbutta, K., Weeranoppanant, N., Sriamornsak, P., Panpipat, C., and Sangnim, T. (2024). Optimization of lipid-based nanoparticles formulation loaded with biological product using a novel design vortex tube reactor via flow chemistry. International Journal of Nanomedicine, 19, 8729–8750.
Tan, P., Chen, X., Zhang, H., Wei, Q., and Luo, K. (2023). Artificial intelligence aids in development of nanomedicines for cancer management. Seminars in Cancer Biology, 89, 61–75.
Tao, H., Wu, T., Aldeghi, M., Wu, T. C., Aspuru-Guzik, A., and Kumacheva, E. (2021). Nanoparticle synthesis assisted by machine learning. Nature Reviews Materials, 6(8), 701–716.
Tenchov, R., Bird, R., Curtze, A. E., and Zhou, Q. (2021). Lipid nanoparticles: From liposomes to mRNA vaccine delivery, a landscape of research diversity and advancement. ACS Nano, 15(11), 16982–17015.
Vora, L. K., Gholap, A. D., Jetha, K., Thakur, R. R. S., Solanki, H. K., and Chavda, V. P. (2023). Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics, 15(7), 1916.
Wu, D., Lu, J., Zheng, N., Elsehrawy, M. G., Alfaiz, F. A., Zhao, H., Alqahtani, M. S., and Xu, H. (2024). Utilizing nanotechnology and advanced machine learning for early detection of gastric cancer surgery. Environmental Research, 245, 117784.
Xu, Y., Jiang, W. J., Bai, Y. Y., Yang, Y. J., and Zhang, Z. L. (2024). Artificial intelligence-assisted multiparameter size discrimination of silver nanoparticles through electrochemical collision. Analytical Chemistry, 96(16), 6195–6201.
Yacoub, A. S., Ammar, H. O., Ibrahim, M., Mansour, S. M., and El Hoffy, N. M. (2022). Artificial intelligence-assisted development of in situ forming nanoparticles for arthritis therapy via intra-articular delivery. Drug Delivery, 29(1), 1423–1436.
Yang, W., Liang, H., Ma, S., Wang, D., and Huang, J. (2019). Gold nanoparticle based photothermal therapy: Development and application for effective cancer treatment. Sustainable Materials and Technologies, 22, e00109.
Yuan, Y., Wu, Y., Cheng, J., Yang, K., Xia, Y., Wu, H., and Pan, X. (2024). Applications of artificial intelligence to lipid nanoparticle delivery. Particuology, 90, 88–97.
Zohuri, B., and Behgounia, F. (2023). Application of artificial intelligence driving nano-based drug delivery system. In A Handbook of Artificial Intelligence in Drug Delivery (Philip, A., Shahiwala, A., Rashid, M., and Faiyazuddin, M., Eds.), pp. 145–212. Cambridge, MA: Academic Press.