A machine learning workflow to accelerate the design of <i>in vitro</i> release tests from liposomes
A machine learning workflow was developed to predict liposome drug release profiles, enabling accelerated design of in vitro release tests.
Authors
- Maryam Parhizkar (ORCID: https://orcid.org/0000-0002-4930-7308)
- James Mann
- Mischa Zelzer (ORCID: https://orcid.org/0000-0002-6086-2206)
- Magnus Röding (ORCID: https://orcid.org/0000-0002-5956-9934)
- Heather Mead (ORCID: https://orcid.org/0000-0003-2752-7059)
- Vasiliki Paraskevopoulou (ORCID: https://orcid.org/0000-0001-7065-7209)
- Cameron Alexander (ORCID: https://orcid.org/0000-0001-8337-1875)
- Daniel Yanes
- Jamie Twycross
Institutions
- University of Nottingham (GB)
- AstraZeneca (Netherlands) (NL)
- AstraZeneca (Poland) (PL)
- AstraZeneca (Sweden) (SE)
- University College London (GB)
- Chalmers University of Technology (SE)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5dd00112a
- Citations
- 1
- Primary Topic
- Biosensors and Analytical Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.67
Funders
- Engineering and Physical Sciences Research Council
- Division of Mathematical Sciences