Evidence map›Paper›PMID 32152206›Full record

ArticleDiabetes2020

Connecting Rodent and Human Pharmacokinetic Models for the Design and Translation of Glucose-Responsive Insulin.

Jing Fan Yang, Xun Gong, Naveed A Bakh, Kelley Carr, Nelson F B Phillips, Faramarz Ismail-Beigi, Michael A Weiss, Michael S Strano

Abstract read
In one paragraph

Article in Diabetes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Examining Type 1 Diabetes Mathematical Models Using Experimental Data.International journal of environmental research and public health · 2022
    Article
  9. Review
  10. Review
  11. Review: Glucose-sensitive insulin.Molecular metabolism · 2021
    Review
  12. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Jing Fan YangDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA.
Xun GongDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA.
Naveed A BakhDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA.
Kelley CarrDepartment of Biochemistry, Case Western Reserve University, Cleveland, OH.
Nelson F B PhillipsDepartment of Biochemistry, Case Western Reserve University, Cleveland, OH.
Faramarz Ismail-BeigiDepartment of Biochemistry, Case Western Reserve University, Cleveland, OH.ORCID 0000-0002-1154-5482
Michael A WeissDepartment of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN.
Michael S StranoDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA strano@mit.edu.ORCID 0000-0003-2944-808X

Funding

Non-Standard Protein Design in Molecular Endocrinology: Insulin and GlucagonR01DK040949 · NIDDK · UNIVERSITY OF CHICAGO · PI CHERRINGTON, ALAN D, GEORGIADIS, MILLIE M · 1989 to 2024
$8.4M
NIDDK NIH HHS R01 DK040949
6 · The paper itself

Abstract

Despite considerable progress, development of glucose-responsive insulins (GRIs) still largely depends on empirical knowledge and tedious experimentation-especially on rodents. To assist the rational design and clinical translation of the therapeutic, we present a Pharmacokinetic Algorithm Mapping GRI Efficacies in Rodents and Humans (PAMERAH) built upon our previous human model. PAMERAH constitutes a framework for predicting the therapeutic efficacy of a GRI candidate from its user-specified mechanism of action, kinetics, and dosage, which we show is accurate when checked against data from experiments and literature. Results from simulated glucose clamps also agree quantitatively with recent GRI publications. We demonstrate that the model can be used to explore the vast number of permutations constituting the GRI parameter space and thereby identify the optimal design ranges that yield desired performance. A design guide aside, PAMERAH more importantly can facilitate GRI's clinical translation by connecting each candidate's efficacies in rats, mice, and humans. The resultant mapping helps to find GRIs that appear promising in rodents but underperform in humans (i.e., false positives). Conversely, it also allows for the discovery of optimal human GRI dynamics not captured by experiments on a rodent population (false negatives). We condense such information onto a "translatability grid" as a straightforward, visual guide for GRI development.

Indexed as

AlgorithmsAnimalsGlucose Clamp TechniqueHumansHypoglycemic AgentsInsulinMiceRatsHypoglycemic AgentsInsulin

Identifiers

PMID32152206
PMCPMC8176262

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.