664-P: Incorporating Treatment Pauses, Dosing Flexibility, and Education to Support GLP-1RA Therapy Persistence
Bibliographic record
Abstract
As the first oral GLP-1 receptor agonist (GLP-1RA), oral semaglutide (sema) may facilitate increased access to the benefits of GLP-1RA therapy in broader care settings. Understanding the management of GLP-1RA therapy when intolerability occurs, including gastrointestinal (GI) adverse events (AEs), is important to overcome potential barriers to treatment persistence. The PIONEER 6 trial (NCT02692716; N=3183) examined the efficacy and safety of oral sema in patients (pts) with T2D either ≥50 years with established cardiovascular (CV) or kidney disease, or ≥60 years with CV risk factors. In total, 27% of pts stopped taking oral sema at least once during the trial due to an AE, but only 12% permanently discontinued due to an AE. We thus evaluated medication management strategies within PIONEER 6 to assess their role in supporting treatment continuation. Pts on oral sema underwent dose escalation starting at 3 mg, increasing to 7 mg after 4 weeks, and 14 mg after 8 weeks. Investigators were permitted to reduce the dose if AEs developed and to re-escalate the dose once symptoms had resolved or diminished. Pts were educated as needed to address GI tolerability issues throughout the trial. Pts who discontinued treatment because of an AE were encouraged to resume treatment once willing or once the AE had ceased. The time off oral sema was considered a treatment pause. If treatment pauses were >21 days, re-escalation from a lower dose was recommended to mitigate GI AEs. Discontinuation of oral sema (temporary and permanent) mostly occurred during the initial dose escalation period. In total, 23% of pts receiving oral sema had ≥1 treatment pause, the majority of whom (72%) had just one pause. The median duration of treatment pause was 21 (IQR 7-51) days. Importantly, 75% of pts restarted oral sema after the first AE-related treatment discontinuation. These data highlight the role of treatment pauses, flexibility, and education in mitigating potential AEs to support treatment persistence on GLP-1RAs. Disclosure V. R. Aroda: Consultant; Self; Applied Therapeutics, Duke, Novo Nordisk, Pfizer Inc., Sanofi, Employee; Spouse/Partner; Janssen, Merck, Research Support; Self; Applied Therapeutics, Eli Lilly and Company, Fractyl, Medpace, Medpace, Novo Nordisk, Premier, Sanofi, Stock/Shareholder; Spouse/Partner; Janssen, Merck. R. Bauer: Employee; Self; Novo Nordisk A/S. A. L. Davies: Employee; Self; Novo Nordisk. E. B. Kreiner: Employee; Self; Novo Nordisk A/S, Stock/Shareholder; Self; Novo Nordisk A/S. P. J. Lin: Advisory Panel; Self; Novo Nordisk, Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Canada Inc., Sanofi. R. E. Pratley: Other Relationship; Self; Hanmi Pharmaceutical, Merck Sharp & Dohme Corp., Metavention, Monster Energy Company, Inc., Novo Nordisk, Pfizer Inc., Poxel SA, Sanofi, Scohia Pharma Inc., Sun Pharmaceutical Industries Ltd. S. C. Bain: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly Diabetes, Napp Pharmaceuticals, Novo Nordisk A/S, Sanofi, Consultant; Self; ADOCIA, Speaker’s Bureau; Self; Bayer AG, Medscape, WebMD LLC. Funding Novo Nordisk A/S
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".