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Updated: Jun 26, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
Prescription cascades associated with acetylcholinesterase inhibitors use: a high-throughput sequence symmetry
Danielle Newby1, Sai Sumedha Bobba2, Berta Raventós3,4
1Health Data Sciences (HDS), Translational Sciences, Botnar Research Centre, University of Oxford, Oxford, UK.
Background:
Acetylcholinesterase inhibitors (AChEIs), are commonly prescribed for dementia and can cause adverse drug events that may lead to new prescriptions, known as prescription cascades. We aimed to identify potential AChEI-induced prescription cascades using high-throughput sequence symmetry analysis (SSA).
Methods:
Patients aged ≥18 years with 365 days of prior observation initiating AChEIs (donepezil, rivastigmine, or galantamine) were identified from the Clinical Practice Research Datalink GOLD (2002-2022). We screened 510 drug classes and 1213 individual ingredients initiated within ±180 days of AChEI initiation (365 days in sensitivity analyses). Crude and adjusted sequence ratios (ASRs) were calculated, and positive signals were reviewed for clinical plausibility.
Results:
We identified 66 155 AChEI initiators (median age 81 years [IQR 76-85]; 62.8% female). Among ATC classes and individual ingredients, 51 and 46 signals were positive with 28 (55%) and 22 (48%) classified as potential prescription cascades after review. Gastrointestinal drugs showed positive signals including antipropulsives (ASR 1.50 [99% CI 1.28-1.75]), loperamide (ASR 1.52 [1.30-1.77]) and cyclizine (ASR 2.10 [1.72-2.59]). Positive signals were also observed nervous system drugs such as benzodiazepine derivatives (ASR 1.83 [1.55-2.16]) and respiratory drugs including corticosteroids (ASR 1.66 [1.33-2.08]) and glucocorticoids (ASR 1.54 [1.30-1.83]). Most positive signals remained in sensitivity analysis.
Conclusions:
These findings suggest potential AChEI-related prescription cascades consistent with gastrointestinal, neuropsychiatric, dermatological and respiratory adverse effects. While findings require further validation, this study demonstrates the utility of high-throughput signal detection to support pharmacovigilance in high-risk populations.
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