Abstract
Introduction
Injecting drug users often extract morphine from slow-release oral tablets, potentially leading to harmful particle contamination upon injection. This study assesses the efficiency of morphine extraction and particle content of filtrates produced by various methods employed by drug users in Trondheim, Norway. The findings provide important insights that can inform harm-reduction services and healthcare providers in efforts to reduce injection-related morbidity among people who already inject drugs.
Methods
Four extraction methods were evaluated using 60 mg Dolcontin tablets: Method A (no coating removal, 3-minute boiling), Method B (coating removal, crushing, 3-minute boiling), Method C (coating removal, 3-minute boiling), and Method D (coating removal, 10-minute boiling). Resulting solutions were filtered using cotton balls, and morphine content was quantified using LC-MS/MS. Particle content of filtrates was analyzed using slide scanning, deep learning-based particle segmentation, and QuPath image analysis software.
Results
Morphine recovery ranged from 81.2% (Method D) to 91.3% (Method B). Method A yielded a significant presence of small insoluble particles (<100 μm), while Method B yielded the highest density of the largest particles (>500 μm). Method C exhibited the highest density of medium-sized particles (100-500 μm). Method D generated the fewest particles across all size categories.
Conclusion
The extraction methods used by injecting drug users result in significant variability in morphine recovery and particle content of filtrates. Method D (10-minute vs. 3-minute boiling) demonstrated the highest efficiency in particle reduction, with only 10% less morphine recovery. Lack of coat removal significantly increases the number of primarily small (<100 μm) fragments. These findings highlight the importance of evidence-based harm-reduction measures to mitigate risks associated with injecting tablet-derived solutions. The results may support harm-reduction counselling and service design aimed at reducing particulate exposure and related complications, without endorsing or facilitating drug use.
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Data availability
The scanned slides, raw MS-data and segmentation masks for the particle content analyses are available from the corresponding author upon reasonable requests.
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Acknowledgements
We would like to acknowledge the technical help of scanning and preparation for scanning by Cellular & Molecular Imaging Core Facility (CMIC), NTNU.
Funding
Open access funding provided by NTNU Norwegian University of Science and Technology (incl St. Olavs Hospital - Trondheim University Hospital). This research was partly funded by Research Fund for the Center for Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital.
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HSP performed the image analysis and statistical analysis of images data and participated in the preparation and scanning of samples. POMG designed and performed morphine extraction and participated in the preparation and scanning of samples. KM contributed to the design of the study, morphine extraction, the preparation of the manuscript, and contributed to the preparation and scanning of samples. TOAA contributed to the design of the study. All authors contributed to the writing and editing of the manuscript.
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Pettersen, H.S., Gundersen, P.O.M., Aamo, T.O. et al. Deep learning analysis of particle content in extracted slow-release morphine: longer boiling reduces large fragments while retaining morphine extraction. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35870-2
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DOI: https://doi.org/10.1038/s41598-026-35870-2
