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Artificial Intelligence in Nanopharmaceutical Development: From Predictive Design to Clinical Translation
1Department of Engineering and Exact Sciences, Palotina Sector, Federal University of Paraná (UFPR), Palotina 85950-000, PR, Brazil.
Pharmaceutics
|June 26, 2026
Summary
Artificial intelligence (AI) enhances nanopharmaceutical development by enabling predictive design and optimizing nanocarrier performance. AI integration accelerates translation from lab to clinic, overcoming data and validation challenges for precision nanomedicine.
Area of Science:
- Nanomedicine and Pharmaceutical Sciences
- Computational Biology and Bioinformatics
- Materials Science and Engineering
Background:
- Nanopharmaceutical development traditionally relies on empirical screening, facing challenges with complex nonlinear relationships.
- Integrating artificial intelligence (AI) offers a shift towards data-driven, predictive, and translationally oriented nanocarrier design.
- AI is crucial for navigating the intricate interplay between material properties, biological interactions, and therapeutic outcomes in nanomedicine.
Purpose of the Study:
- To review the multifaceted applications of AI in advancing nanopharmaceutical development, from formulation to clinical translation.
- To synthesize current AI methodologies, including machine learning, deep learning, and hybrid models, for nanocarrier design and optimization.
- To identify key challenges and future directions for AI implementation in nanomedicine.
Main Methods:
- Review and synthesis of current AI applications in nanopharmaceutical research.
- Analysis of machine learning, deep learning, physics-informed modeling, and hybrid AI approaches.
- Examination of AI's role in critical quality attribute modeling, multi-objective optimization, and process analytical technology.
Main Results:
- AI supports rational nanocarrier design, predicts formulation-property relationships, and integrates diverse datasets.
- AI models are applied to predict nano-bio interactions, pharmacokinetics, toxicity, and immunogenicity.
- Current limitations include fragmented data, interpretability issues, and regulatory hurdles for AI adoption.
Conclusions:
- AI serves as a powerful optimization tool and a translational framework for nanopharmaceutical development.
- Overcoming challenges requires standardized data, explainable models, and robust validation for clinical translation.
- Future progress hinges on interdisciplinary collaboration, regulatory alignment, and clinically relevant AI applications.
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