Synthetic target trial emulation and predictive modeling of amylin-pathway therapies for obesity and type 2 diabetes
Bibliographic record
Abstract
Amylin-pathway therapies represent a novel therapeutic class for obesity and type 2 diabetes, however head-to-head comparative data and long-term outcome predictions remain limited. We conducted target trial emulation and computational predictive modeling aiming to predict future trial outcomes and comparative effectiveness across the amylin-pathway development program. Following PRISMA 2020 and TARGET framework guidelines, we search in the current literature for eligible trials and extracted data from seven randomized controlled trials (N=5,786 participants) of amylin-pathway therapies published up to September 2025. We reconstructed high-precision synthetic individual patient data (IPD) and developed computational models for virtual head-to-head comparisons, dose-response optimization, longitudinal trajectory prediction, and trial simulation. Network meta-analysis integrated evidence across CagriSema, cagrilintide, and amycretin formulations. Synthetic IPD reconstruction achieved >99% fidelity to source trials, validated through leave-trial-out cross-validation (efficacy RMSE: 2.9% points, calibration slope: 0.61; discontinuation RMSE: 0.18, slope: 1.08). Virtual head-to-head modeling confirmed CagriSema superiority over amycretin subcutaneous at matched timepoints (posterior probability >0.95). Dose-response modeling identified optimal amycretin exposures (ED80: 8.88 mg subcutaneous, 95% CI: 7.12-11.08), with benefit-risk frontier analysis delineating a therapeutic window at 10-20 mg balancing efficacy plateau against tolerability thresholds (GI-AE <75%, discontinuation <20%). Longitudinal kinetics showed plateau timing at 52-68 weeks for obesity outcomes and 24-32 weeks for glycemic endpoints. Heterogeneity analysis revealed complete resolution for GI adverse events (I 2 _DL = 0%, τ 2 = 0) and moderate variation for discontinuation (I 2 _DL = 13%, τ 2 = 0.03) after logit-scale correction with proper within-arm variance weighting. Machine learning models predicted treatment response with 82-87% accuracy using baseline characteristics. Synthetic target trial emulation with structured validation (leave-trial-out, posterior predictive checks, simulation-based calibration) demonstrated promising evidence for amylin-pathway development optimization. Benefit-risk frontier analysis identified an optimal 10-20 mg subcutaneous therapeutic window, and heterogeneity quantification through maximum a posteriori (MAP) predictive interval provides design-ready estimates for confirmatory trials requiring around 800-1,200 participants per arm for 90% power.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".