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Study · Not a test of the peptide alone · 2024

4 results

Benchmarking the medication efficiency and technological progress of diabetes drugs.

Zhang H, Wang C, Xu T, Liu L, Ban X, Liu W, Yan C, Han X

Frontiers in public health8 Nov 2024

PMID39583077 (opens in a new tab)PMCIDPMC11582034 (opens in a new tab)DOI10.3389/fpubh.2024.1396832 (opens in a new tab)PepBankPUB-39583077
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Summary

BackgroundDiabetes poses a serious global challenge, given its increasing prevalence, detrimental effects on public health, and substantial economic burden. Since 1950s, tens of drugs have been approved by the United States (US) Food and Drug Administration (FDA). In the past decade, the medical community and regulatory agencies have moved away from the glucose-centric paradigm and increasingly call for a holistic approach to assess different treatments' benefits and harms.ObjectiveThis study aimed to assess the medication efficiency and technological progress of Type 2 Diabetes (T2D) drugs, by considering their physiological outcomes, including both benefits (i.e., glucose lowering and weight loss) and adverse effects (mortality), relative to dosing frequency.MethodsTo derive medication efficiency, this study utilized data from the US FDA and prominent meta-analyses. Given that both the benefits and adverse effects of medications are multidimensional, this study employed a nonparametric frontier method, the data envelopment analysis (DEA) model, to integrate these factors into a measure of medication efficiency. Physiological outcomes could assume both positive and negative values. Adverse effects were regarded undesirable outputs. The DEA model was built under the framework of directional distance function and was able to handle negative and undesirable values which naturally arose in the case of T2D medications.ResultsThe paper presented a ranking of 20 T2D drugs in terms of medication efficiency. Three of them were able to attain the highest medication efficiency, all of which were in the GLP-1 class, including oral Semaglutide, subcutaneous Semaglutide and Dulaglutide. However, the other two GLP-1 drugs, Lixisenatide and Liraglutide, were less efficient. The average medication efficiency of drugs approved post-2010 was significantly higher than pre-2010 drugs. High dose frequency, low HbA1c reduction and insignificant weight loss were the main driving factors behind inefficiencies. Overall, medication efficiency provided an alternative perspective on treatment effectiveness other than conventional measures such as cost-effectiveness.
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 Dulaglutide (Trulicity) aloneIn silicoOther

    In a diabetes drug efficiency model, dulaglutide attained the highest medication efficiency.

    In plain wordsIn computer models, dulaglutide attained the highest medication efficiency among the evaluated diabetes drugs.

    Peptide
    Dulaglutide (Trulicity)
    Outcome
    IncreaseMedication efficiency ranking
    Study

    n: not reported in source

    Outcome group
    Cost-effectiveness · Pharmacology

    Source sentences

    Summary
    Three of them were able to attain the highest medication efficiency, all of which were in the GLP-1 class, including oral Semaglutide, subcutaneous Semaglutide and Dulaglutide.
    2 context sentences
    Summary · context
    To derive medication efficiency, this study utilized data from the US FDA and prominent meta-analyses.
    Summary · context
    Given that both the benefits and adverse effects of medications are multidimensional, this study employed a nonparametric frontier method, the data envelopment analysis (DEA) model, to integrate these factors into a measure of medication efficiency.

    In this study, Dulaglutide (Trulicity) 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 Dulaglutide (Trulicity).

  2. 02Computer modelNot a test of Semaglutide aloneIn silicoOther

    In a diabetes drug efficiency model, oral semaglutide attained the highest medication efficiency.

    In plain wordsIn computer models, oral semaglutide attained the highest medication efficiency among the evaluated diabetes drugs.

    Peptide
    Semaglutide
    Outcome
    IncreaseOral medication efficiency ranking
    Study

    n: not reported in source

    Outcome group
    Cost-effectiveness · Pharmacology

    Source sentences

    Summary
    Three of them were able to attain the highest medication efficiency, all of which were in the GLP-1 class, including oral Semaglutide, subcutaneous Semaglutide and Dulaglutide.
    2 context sentences
    Summary · context
    To derive medication efficiency, this study utilized data from the US FDA and prominent meta-analyses.
    Summary · context
    Given that both the benefits and adverse effects of medications are multidimensional, this study employed a nonparametric frontier method, the data envelopment analysis (DEA) model, to integrate these factors into a measure of medication efficiency.

    In this study, Semaglutide 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 Semaglutide.

  3. 03Computer modelNot a test of Semaglutide aloneIn silicoOther

    In a diabetes drug efficiency model, subcutaneous semaglutide attained the highest medication efficiency.

    In plain wordsIn computer models, subcutaneous semaglutide attained the highest medication efficiency among the evaluated diabetes drugs.

    Peptide
    Semaglutide
    Outcome
    IncreaseSubcutaneous medication efficiency ranking
    Study

    n: not reported in source

    Outcome group
    Cost-effectiveness · Pharmacology

    Source sentences

    Summary
    Three of them were able to attain the highest medication efficiency, all of which were in the GLP-1 class, including oral Semaglutide, subcutaneous Semaglutide and Dulaglutide.
    2 context sentences
    Summary · context
    To derive medication efficiency, this study utilized data from the US FDA and prominent meta-analyses.
    Summary · context
    Given that both the benefits and adverse effects of medications are multidimensional, this study employed a nonparametric frontier method, the data envelopment analysis (DEA) model, to integrate these factors into a measure of medication efficiency.

    In this study, Semaglutide 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 Semaglutide.

  4. 04Computer modelNot a test of Liraglutide aloneIn silicoOther

    In a diabetes drug efficiency model, liraglutide was less efficient than the highest-efficiency GLP-1 drugs.

    In plain wordsIn computer models, liraglutide was less efficient than the highest-efficiency GLP-1 drugs.

    Peptide
    Liraglutide
    Outcome
    DecreaseMedication efficiency ranking
    Study

    n: not reported in source

    Comparator
    Highest-efficiency GLP-1 drugs
    Outcome group
    Cost-effectiveness · Pharmacology

    Source sentences

    Summary
    However, the other two GLP-1 drugs, Lixisenatide and Liraglutide, were less efficient.
    3 context sentences
    Summary · context
    Three of them were able to attain the highest medication efficiency, all of which were in the GLP-1 class, including oral Semaglutide, subcutaneous Semaglutide and Dulaglutide.
    Summary · context
    To derive medication efficiency, this study utilized data from the US FDA and prominent meta-analyses.
    Summary · context
    Given that both the benefits and adverse effects of medications are multidimensional, this study employed a nonparametric frontier method, the data envelopment analysis (DEA) model, to integrate these factors into a measure of medication efficiency.

    In this study, Liraglutide 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 Liraglutide.

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. Version 5Current

    27 Oct 2026, 16:36 UTC

    Study data updated · source update from Europe PMC

  2. Version 4

    22 Oct 2026, 02:25 UTC

    Study results updated

  3. Versions 2 to 3

    21 Oct 2026, 18:59 UTC

    The same notice was added 2 times, first on 9 Oct 2026, 03:06 UTC. The status did not change between them.

    Study data updated · source update from Europe PMC

  4. Version 1

    9 Oct 2026, 03:05 UTC

    First added · source update from Europe PMC

Subjects

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