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Mitigating the Resolution-Field of View Trade-Off for Comprehensive Microstructural Characterization with Advanced AI Methods.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Mitigating the Resolution-Field of View Trade-Off for Comprehensive Microstructural Characterization with Advanced AI Methods.
Συγγραφείς: Pokhrel AR; DigiM Solution LLC, 500 West Cummings Park, Suite 3650, Woburn, Massachusetts, 01801, USA., Das J; Office of Product Quality Research, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, White Oak, LS Building 64, 10903 New Hampshire Ave, Silver Spring, Maryland, 20993-002, USA.; FDA/CDER/OPQ/OPQR White Oak, LS Building 64, 10903 New Hampshire Ave, Silver Spring, Maryland, 20993-002, USA., Ke B; DigiM Solution LLC, 500 West Cummings Park, Suite 3650, Woburn, Massachusetts, 01801, USA., Ma L; DigiM Solution LLC, 500 West Cummings Park, Suite 3650, Woburn, Massachusetts, 01801, USA., Zhang S; DigiM Solution LLC, 500 West Cummings Park, Suite 3650, Woburn, Massachusetts, 01801, USA., Korang-Yeboah M; Office of Product Quality Research, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, White Oak, LS Building 64, 10903 New Hampshire Ave, Silver Spring, Maryland, 20993-002, USA. Maxwell.Korang-Yeboah@fda.hhs.gov.; FDA/CDER/OPQ/OPQR White Oak, LS Building 64, 10903 New Hampshire Ave, Silver Spring, Maryland, 20993-002, USA. Maxwell.Korang-Yeboah@fda.hhs.gov.
Πηγή: AAPS PharmSciTech [AAPS PharmSciTech] 2026 Sep 29; Vol. 27 (7). Date of Electronic Publication: 2026 Sep 29.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Springer Country of Publication: United States NLM ID: 100960111 Publication Model: Electronic Cited Medium: Internet ISSN: 1530-9932 (Electronic) Linking ISSN: 15309932 NLM ISO Abbreviation: AAPS PharmSciTech Subsets: MEDLINE
Imprint Name(s): Publication: New York : Springer
Original Publication: Arlington, VA : American Association of Pharmaceutical Scientists, c2000-
Ιατρικοί όροι (MeSH): Artificial Intelligence*, Image Processing, Computer-Assisted/methods ; Pharmaceutical Preparations/chemistry ; Chemistry, Pharmaceutical/methods ; Convolutional Neural Networks ; Generative Adversarial Networks ; Generative Artificial Intelligence ; Porosity
Περίληψη: Microstructural characterization of pharmaceutical drug products is essential for understanding process-microstructure-performance relationships and ensuring consistent product quality. However, quantitative characterization is limited by a trade-off between imaging resolution and field of view: high-resolution imaging captures fine structural detail but over small sample volumes, whereas lower-resolution imaging provides broader coverage while missing critical morphological features. To address this limitation, we developed and validated an integrated framework combining convolutional neural network (CNN)-based super-resolution with Generative Adversarial Network (GAN)-based microstructure synthesis. Lyophilized drug products imaged by X-ray microscopy at multiple resolutions served as the model system. We first demonstrated that imaging resolution is a governing factor in quantitative microstructural analysis: mean pore size showed a coefficient of variation of 40.2% across resolution levels, compared with only 0.47% attributable to spatial heterogeneity within the same sample. CNN-based upscaling using ESRGAN/BSRGAN recovered solid-wall structures and pore-size distributions lost after downsampling and restored effective diffusivity toward values measured in the original high-resolution data. In an independent validation using a separate formulation imaged at 5 and 10 µm per voxel, the upscaled images reproduced pore-size distributions and diffusivity profiles closer to the 5 µm reference and showed better recovery of CQA-relevant structural features than conventional bicubic interpolation, despite lower pixel-level image fidelity scores. GAN-based synthesis expanded the field of view four-fold from a small high-resolution seed region while preserving pore-size distributions and transport properties. Together, these findings demonstrate a scalable approach for improving microstructural characterization under practical imaging constraints.
(© 2026. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)
Competing Interests: Declarations. Ethical Approval and Informed Consent: This study did not involve human participants or animals. Therefore, ethical approval and informed consent were not required. Conflict of interest: The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Disclaimer: The opinions expressed in this work are solely those of the author(s) and should not be construed to represent FDA’s views or policies. The authors acknowledge the use of Elsa, a generative AI tool developed for internal use at the FDA, which was used solely to assist with language editing and readability. All scientific content, data interpretation, conclusions, and technical accuracy remain entirely the responsibility of the authors, who carefully reviewed and verified all material. An additional disclaimer concerning this work is that this was funded through an FDA contract is subject to FDA public access policy. The FDA has the right to make the author accepted manuscript publicly available in PubMed Central upon the official date of publication.
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Contributed Indexing: Keywords: artificial intelligence; generative AI; image processing; lyophilization; machine learning; microstructural characterization; upscaling
Substance Nomenclature: 0 (Pharmaceutical Preparations)
Entry Date(s): Date Created: 20260929 Date Completed: 20260929 Latest Revision: 20260929
Update Code: 20260930
DOI: 10.1208/s12249-026-03558-5
PMID: 42811251
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1530-9932
DOI:10.1208/s12249-026-03558-5