Liposome Particle Size Prediction by In‐Line Process Analytical Technology (PAT)‐Integrated Machine Learning
Precise control of liposome size is critical for drug delivery. We developed an in-line PAT-integrated machine learning model that predicts particle size with high accuracy (root mean square error 7.18 nm) using limited experimental data. By integrating physicochemical membrane characteristics, the model demonstrates generalization (root mean square error 7.53 nm) and interpretability, establishing a practical framework for advanced liposome particle size control.
Authors
- Nozomi Morishita Watanabe (ORCID: https://orcid.org/0000-0002-3461-3660)
- Seonghyeon Eom (ORCID: https://orcid.org/0009-0008-6178-7640)
- Ho‐Sup Jung (ORCID: https://orcid.org/0000-0001-5991-2256)
- Hiroshi Umakoshi (ORCID: https://orcid.org/0000-0002-9241-853X)
- Junghu Lee
- Noriko Yoshimoto (ORCID: https://orcid.org/0000-0002-3171-9953)
- Moon Kyu Kwak (ORCID: https://orcid.org/0000-0002-8902-7685)
Institutions
- Tech University of Korea (KR)
- Osaka University of Economics (JP)
- Yamaguchi University (JP)
- Biocon (Switzerland) (CH)
- Kyungpook National University (KR)
- The University of Osaka (JP)
Publication Details
- Journal
- Small Methods
- Published
- 2026-05-01
- DOI
- https://doi.org/10.1002/smtd.70663
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation of Korea
- Ministry of SMEs and Startups
- Japan Society for the Promotion of Science