Green nanoparticles in drug delivery: Translational challenges and AI solutions
5 affiliations
- 1Department of Chemical Engineering, University of Manchester, Manchester M13 9PL, UK
- 2Department of Chemical and Petrochemical Engineering, University of Nizwa, Nizwa 616, Oman
- 3Oil Exploration Company, Ministry of Oil – Iraq, Port Said St, Baghdad 10064, Iraq
- 4Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Department of Chemistry, Shantou University, Shantou, China
- 5College of Engineering Oil and Gas, University of Technology Iraq, Baghdad 10066, Iraq
Abstract
Green nanotechnology offers a sustainable and highly promising alternative for the biosynthesis of nanoparticles (NPs) tailored for advanced drug delivery systems, leveraging plant and microbial extracts due to their high biocompatibility and low systemic toxicity. However, the transition of these biogenic platforms from laboratory proof-of-concept to clinical-grade therapeutics is fundamentally bottlenecked by raw material heterogeneity, poor batch-to-batch reproducibility, a lack of process standardization, and the unpredictable clinical efficacy of the passive enhanced permeability and retention (EPR) effect. To address these systemic barriers, this review provides a critical translational analysis reframed around advanced computational engineering and digital design paradigms. We introduce a comparative Translation Readiness Matrix that evaluates six major green nanoparticle platforms across six distinct clinical criteria, systematically isolating current safety and regulatory gaps. Crucially, this review explores the paradigm shift driven by the integration of Artificial Intelligence (AI) and Quality by Digital Design (QbDD) frameworks. We evaluate how machine learning architectures optimize high-dimensional formulation spaces, how graph neural networks predict protein corona dynamics, and how predictive modeling enhances patient stratification to mitigate EPR variability. Furthermore, we analyze advanced fabrication methodologies, specifically microfluidics, continuous flow synthesis, and real-time AI-integrated quality control that stabilize hydrodynamic synthesis environments and transition manufacturing from empirical batch optimization to continuous quality assurance. By linking biogenic synthesis with intelligent digital architectures, this review delivers a definitive engineering blueprint to transform green nanoparticles from fragmented laboratory concepts into reproducible, regulatory-compliant industrial realities.
Related
- Cell Membrane-Coated Nanoparticles as Biomimetic Drug Delivery Systems: Design, Therapeutic Applications, and Translational Challenges.
- Advanced Drug Delivery in Pediatric Neurology: Developmental Barriers, Nanocarrier Strategies, and Translational Challenges.
- Nanoparticle-Mediated Brain-Targeted Drug Delivery for Methamphetamine Use Disorder: Advances, Challenges and Translational Perspectives.
- Green Synthesis of Silver Nanoparticles Using Ammoides pusilla Extract: Optimization, Characterization, and Antimicrobial Activity
- Green synthesis of Nonea lutea-mediated gold nanoparticles with multifunctional biomedical and environmental applications
BibTeX
@article{alhinai2026green,
title = {{Green nanoparticles in drug delivery: Translational challenges and AI solutions}},
author = {Abdulhakim Alhinai and Raghad M. Al-Maliki and Hicham Meskher and Shahnaz Bassim},
journal = {Next Materials},
year = {2026},
month = aug,
doi = {10.1016/j.nxmate.2026.103081},
url = {https://doi.org/10.1016/j.nxmate.2026.103081},
note = {CC BY},
}
RIS
TY - JOUR
TI - Green nanoparticles in drug delivery: Translational challenges and AI solutions
AU - Abdulhakim Alhinai
AU - Raghad M. Al-Maliki
AU - Hicham Meskher
AU - Shahnaz Bassim
PY - 2026
DA - 2026/08/12
JO - Next Materials
DO - 10.1016/j.nxmate.2026.103081
UR - https://doi.org/10.1016/j.nxmate.2026.103081
AB - Green nanotechnology offers a sustainable and highly promising alternative for the biosynthesis of nanoparticles (NPs) tailored for advanced drug delivery systems, leveraging plant and microbial extracts due to their high biocompatibility and low systemic toxicity. However, the transition of these biogenic platforms from laboratory proof-of-concept to clinical-grade therapeutics is fundamentally bottlenecked by raw material heterogeneity, poor batch-to-batch reproducibility, a lack of process standardization, and the unpredictable clinical efficacy of the passive enhanced permeability and retention (EPR) effect. To address these systemic barriers, this review provides a critical translational analysis reframed around advanced computational engineering and digital design paradigms. We introduce a comparative Translation Readiness Matrix that evaluates six major green nanoparticle platforms across six distinct clinical criteria, systematically isolating current safety and regulatory gaps. Crucially, this review explores the paradigm shift driven by the integration of Artificial Intelligence (AI) and Quality by Digital Design (QbDD) frameworks. We evaluate how machine learning architectures optimize high-dimensional formulation spaces, how graph neural networks predict protein corona dynamics, and how predictive modeling enhances patient stratification to mitigate EPR variability. Furthermore, we analyze advanced fabrication methodologies, specifically microfluidics, continuous flow synthesis, and real-time AI-integrated quality control that stabilize hydrodynamic synthesis environments and transition manufacturing from empirical batch optimization to continuous quality assurance. By linking biogenic synthesis with intelligent digital architectures, this review delivers a definitive engineering blueprint to transform green nanoparticles from fragmented laboratory concepts into reproducible, regulatory-compliant industrial realities.
SN - 2949-8228
ER -