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Study · Not a test of the peptide alone · 2025

4 results

Synthetic target trial emulation and predictive modeling of amylin-pathway therapies for obesity and type 2 diabetes.

Al-Harbi FA, Alsaif AK, Almutairi AG, Alshehri HJ, Aleidan EA, Alabdulaaly GS, Alanazi ME, Azzam AY

Metabolism open31 Oct 2025Cited by 1 (Europe PMC)

PMID41255585 (opens in a new tab)PMCIDPMC12621565 (opens in a new tab)DOI10.1016/j.metop.2025.100414 (opens in a new tab)PepBankPUB-41255585
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Summary

IntroductionAmylin-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.MethodsFollowing 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.ResultsSynthetic 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 (I2_DL = 0 %, τ2 = 0) and moderate variation for discontinuation (I2_DL = 13 %, τ2 = 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.ConclusionsSynthetic 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.
CC BYText from Europe PMC. Highlighted sentences are the source of results.

Results from this study4

No result from this study comes from a test of the peptide.

Studies that did not test the peptide alone4

In this study, the peptide was a lab tool or a carrier, part of a mix, or the subject of a lab method. These results are not evidence about taking the peptide.

  1. 01Computer modelNot a test of Cagrilintide aloneIn silicoEfficacy outcome

    In a synthetic target trial emulation built from published randomised trials, virtual head-to-head modelling predicted that CagriSema was superior to subcutaneous amycretin at matched timepoints.

    In plain wordsIn a computer model built from published trials, CagriSema was predicted to be better than subcutaneous amycretin at matched timepoints.

    Peptide
    Cagrilintide
    Outcome
    Not applicablePredicted CagriSema superiority over subcutaneous amycretin
    Study

    n: not reported in source

    Synthetic individual patient data and virtual head-to-head model
    Comparator
    Amycretin subcutaneous
    Outcome group
    Overall treatment effect · Metabolic and weight

    Measurements

    Posterior probability of superiority
    > 0.95 probability
    CagriSema vs amycretin subcutaneous

    Source sentences

    Summary
    Virtual head-to-head modeling confirmed CagriSema superiority over amycretin subcutaneous at matched timepoints (posterior probability >0.95).
    1 context sentence
    Summary · context
    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.

    In this study, Cagrilintide was a lab tool or a carrier, part of a mix, or the subject of a lab method. This result is not evidence about taking Cagrilintide.

  2. 02Computer modelNot a test of Amycretin aloneIn silicoEfficacy outcome

    In virtual comparisons, models predicted lower effectiveness for subcutaneous amycretin than CagriSema at matched timepoints, with posterior probability >0.95.

    In plain wordsIn computer models, subcutaneous amycretin had lower predicted effectiveness than CagriSema at matched timepoints.

    Peptide
    Amycretin
    Outcome
    DecreaseModeled comparative effectiveness
    Study

    n: not reported in source

    Synthetic individual patient data and computational predictive models
    Comparator
    CagriSema
    Outcome group
    Body weight · Metabolic and weight

    Measurements

    Posterior probability
    > 0.95 probability
    CagriSema superiority over subcutaneous amycretin

    Source sentences

    Summary
    Virtual head-to-head modeling confirmed CagriSema superiority over amycretin subcutaneous at matched timepoints (posterior probability >0.95).
    1 context sentence
    Summary · context
    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.

    In this study, Amycretin was a lab tool or a carrier, part of a mix, or the subject of a lab method. This result is not evidence about taking Amycretin.

  3. 03Computer modelNot a test of Amycretin aloneIn silicoActivity measurement

    Computational dose-response models identified amycretin ED80 at 8.88 mg subcutaneous, with a 95 % confidence interval of 7.12-11.08 mg.

    In plain wordsIn computer models, the estimated amycretin ED80 was 8.88 mg by subcutaneous administration.

    Peptide
    Amycretin
    Outcome
    Not applicableModeled ED80 exposure
    Study

    n: not reported in source

    Synthetic individual patient data and computational predictive models
    Outcome group
    Receptor activity and mechanism · Pharmacology

    Measurements

    ED80
    8.88 mg
    subcutaneous amycretin
    ED80 confidence interval
    7.12 to 11.08 mg
    95 % CI

    Source sentences

    Summary
    Dose-response modeling identified optimal amycretin exposures (ED80: 8.88 mg subcutaneous, 95 % CI: 7.12-11.08)
    1 context sentence
    Summary · context
    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.

    In this study, Amycretin was a lab tool or a carrier, part of a mix, or the subject of a lab method. This result is not evidence about taking Amycretin.

  4. 04Computer modelNot a test of Amycretin aloneIn silicoOther

    Computational models identified an amycretin therapeutic window at 10-20 mg, balancing efficacy plateau against gastrointestinal adverse-event and discontinuation thresholds.

    In plain wordsIn computer models, the amycretin therapeutic window balanced predicted effectiveness against gastrointestinal side effects and treatment discontinuation.

    Peptide
    Amycretin
    Outcome
    Not applicableModeled benefit-risk therapeutic window
    Study

    n: not reported in source

    Synthetic individual patient data and computational predictive models
    Outcome group
    Adverse events · Whole body

    Measurements

    Therapeutic window
    10 to 20 mg
    amycretin benefit-risk frontier
    GI-AE threshold
    < 75%
    modeled therapeutic window
    Discontinuation threshold
    < 20%
    modeled therapeutic window

    Source sentences

    Summary
    with benefit-risk frontier analysis delineating a therapeutic window at 10-20 mg balancing efficacy plateau against tolerability thresholds (GI-AE <75 %, discontinuation <20 %).
    1 context sentence
    Summary · context
    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.

    In this study, Amycretin was a lab tool or a carrier, part of a mix, or the subject of a lab method. This result is not evidence about taking Amycretin.

AI wrote and checked these results. AI can make mistakes, so check important results against the source.

Version history

PepBank keeps every version of the data on this study. Status changes from Retraction Watch, Europe PMC and Crossref are listed with their source.

  1. Versions 4 to 6Current

    28 Oct 2026, 02:03 UTC

    The same notice was added 3 times, first on 27 Oct 2026, 18:20 UTC. The status did not change between them.

    Study results updated

  2. Version 3

    27 Oct 2026, 16:32 UTC

    Study data updated · source update from Europe PMC

  3. Version 2

    9 Oct 2026, 18:17 UTC

    Study results updated

  4. Version 1

    9 Oct 2026, 03:04 UTC

    First added · source update from Europe PMC

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