{"entity": "publication", "iuid": "7e7744db512546ceb715891ecdd9f266", "timestamp": "2026-08-22T07:48:24.087Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/7e7744db512546ceb715891ecdd9f266.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/7e7744db512546ceb715891ecdd9f266"}}, "title": "Microfluidic Synthesis of Indomethacin-Loaded PLGA Microparticles Optimized by Machine Learning.", "authors": [{"family": "Damiati", "given": "Safa A", "initials": "SA"}, {"family": "Damiati", "given": "Samar", "initials": "S"}], "type": "journal article", "published": "2021-09-22", "journal": {"title": "Front Mol Biosci", "issn": "2296-889X", "volume": "8", "pages": "677547", "issn-l": null}, "abstract": "Several attempts have been made to encapsulate indomethacin (IND), to control its sustained release and reduce its side effects. To develop a successful formulation, drug release from a polymeric matrix and subsequent biodegradation need to be achieved. In this study, we focus on combining microfluidic and artificial intelligence (AI) technologies, alongside using biomaterials, to generate drug-loaded polymeric microparticles (MPs). Our strategy is based on using Poly (D,L-lactide-co-glycolide) (PLGA) as a biodegradable polymer for the generation of a controlled drug delivery vehicle, with IND as an example of a poorly soluble drug, a 3D flow focusing microfluidic chip as a simple device synthesis particle, and machine learning using artificial neural networks (ANNs) as an in silico tool to generate and predict size-tunable PLGA MPs. The influence of different polymer concentrations and the flow rates of dispersed and continuous phases on PLGA droplet size prediction in a microfluidic platform were assessed. Subsequently, the developed ANN model was utilized as a quick guide to generate PLGA MPs at a desired size. After conditions optimization, IND-loaded PLGA MPs were produced, and showed larger droplet sizes than blank MPs. Further, the proposed microfluidic system is capable of producing monodisperse particles with a well-controllable shape and size. IND-loaded-PLGA MPs exhibited acceptable drug loading and encapsulation efficiency (7.79 and 62.35%, respectively) and showed sustained release, reaching approximately 80% within 9 days. Hence, combining modern technologies of machine learning and microfluidics with biomaterials can be applied to many pharmaceutical applications, as a quick, low cost, and reproducible strategy.", "doi": "10.3389/fmolb.2021.677547", "pmid": "34631792", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8493061"}, {"db": "pii", "key": "677547"}], "notes": [], "created": "2026-08-21T12:59:50.074Z", "modified": "2026-08-21T12:59:50.085Z"}