A model‐based approach to predict individual weight loss with semaglutide in people with overweight or obesity
Bibliographic record
Abstract
AIMS: To determine the relationship between exposure and weight-loss trajectories for the glucagon-like peptide-1 analogue semaglutide for weight management. MATERIALS AND METHODS: Data from one 52-week, phase 2, dose-ranging trial (once-daily subcutaneous semaglutide 0.05-0.4 mg) and two 68-week phase 3 trials (once-weekly subcutaneous semaglutide 2.4 mg) for weight management in people with overweight or obesity with or without type 2 diabetes were used to develop a population pharmacokinetic (PK) model describing semaglutide exposure. An exposure-response model describing weight change was then developed using baseline demographics, glycated haemoglobin and PK data during treatment. The ability of the exposure-response model to predict 1-year weight loss based on weight data collected at baseline and after up to 28 weeks of treatment, was assessed using three independent phase 3 trials. RESULTS: Based on population PK, exposure levels over time consistently explained the weight-loss trajectories across trials and dosing regimens. The exposure-response model had high precision and limited bias for predicting body weight loss at 1 year in independent datasets, with increased precision when data from later time points were included in the prediction. CONCLUSION: An exposure-response model has been established that quantitatively describes the relationship between systemic semaglutide exposure and weight loss and predicts weight-loss trajectories for people with overweight or obesity who are receiving semaglutide doses up to 2.4 mg once weekly.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".