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Mapping opioid exposure through prescription data and postmortem analysis of opioid drugs in multiple tissues
Niamh Higgins1,2, Alan Wells3, Nimish Patel1,2
1Skaggs School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacy Practice and Sciences, University of California San Diego, La Jolla, California, USA.
Background And Purpose:
Although opioids are central to end of life (EoL) care, tissue-level opioid exposure remains poorly understood. The objective of this study was to characterize the relationship between prescription-derived morphine equivalent daily dose (MEDD) and measured morphine concentrations across multiple organs.
Experimental Approach:
We analysed data from the Last Gift cohort, a community-centred HIV research rapid autopsy programme. Cumulative MEDD for the final 7 and 30 days before death (MEDD-7, MEDD-30) was calculated using prescription data. Postmortem samples from multiple organs were analysed using ultra-high-performance liquid chromatography with tandem mass spectrometry to quantify opioids. Mixed-effects regression models and Pearson correlations evaluated relationships between MEDD and tissue morphine concentrations.
Key Results:
Among 261 samples from 27 participants (median age 65 years), 96% had ≥1 detectable opioid. Morphine was most frequently prescribed (78%), followed by fentanyl (41%), hydromorphone (37%), and oxycodone (33%). Median MEDD-7 and MEDD-30 were 940 (IQR 158-3481) and 3430 (IQR 563-9972), respectively. Morphine concentrations were highest in the ascending colon, kidney, duodenum, and liver, and lowest in adipose tissue and cerebrospinal fluid. Tissue morphine concentrations correlated with both MEDD-7 and MEDD-30 (e.g., medulla r = .80; spinal cord r = .77; parietal cortex r = .72; all p < .01). In adjusted mixed-effects models, each 10-fold increase in MEDD-7/-30 predicted a 3.7- to 3.8-fold increase in tissue morphine concentration.
Conclusion And Implications:
Prescription-based morphine exposure was strongly associated with morphine tissue concentrations across multiple organs, providing a quantitative framework for integrating pharmacologic data into EoL research.
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