Evidence map›Paper›PMID 39509464›Full record

ArticlePLoS computational biology2024

Beyond IC50-A computational dynamic model of drug resistance in enzyme inhibition treatment.

J Roadnight Sheehan, Astrid S de Wijn, Thales Souza Freire, Ran Friedman

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Review
  2. A computational dynamic model of combination treatment for type II inhibitors with asciminib.Protein science : a publication of the Protein Society · 2025
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

J Roadnight SheehanDepartment of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0000-0001-7169-6226
Astrid S de WijnDepartment of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0000-0003-4664-6811
Thales Souza FreireInstitute of Physics of the University of São Paulo, Department of General Physics, São Paulo, Brazil.
Ran FriedmanDepartment of Chemistry and Biomedical Sciences, Linnaeus University, Kalmar, Sweden.ORCID 0000-0001-8696-3104

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Resistance to therapy is a major clinical obstacle to treatment of cancer and communicable diseases. Drug selection in treatment of patients where the disease is showing resistance to therapy is often guided by IC50 or fold-IC50 values. In this work, through a model of the treatment of chronic myeloid leukaemia (CML), we contest using fold-IC50 values as a guide for treatment selection. CML is a blood cancer that is treated with Abl1 inhibitors, and is often seen as a model for targeted therapy and drug resistance. Resistance to the first-line treatment occurs in approximately one in four patients. The most common cause of resistance is mutations in the Abl1 enzyme. Different mutant Abl1 enzymes show resistance to different Abl1 inhibitors and the mechanisms that lead to resistance for various mutation and inhibitor combinations are not fully known, making the selection of Abl1 inhibitors for treatment a difficult task. We developed a model based on information of catalysis, inhibition and pharmacokinetics, and applied it to study the effect of three Abl1 inhibitors on mutants of the Abl1 enzyme. From this model, we show that the relative decrease of product formation rate (defined in this work as "inhibitory reduction prowess") is a better indicator of resistance than an examination of the size of the product formation rate or fold-IC50 values for the mutant. We also examine current ideas and practices that guide treatment choice and suggest a new parameter for selecting treatments that could increase the efficacy and thus have a positive impact on patient outcomes.

Indexed as

Drug Resistance, NeoplasmLeukemia, Myelogenous, Chronic, BCR-ABL PositiveProto-Oncogene Proteins c-ablAntineoplastic AgentsComputational BiologyComputer SimulationHumansInhibitory Concentration 50Models, BiologicalMutationProtein Kinase InhibitorsABL1 protein, humanAntineoplastic AgentsProtein Kinase InhibitorsProto-Oncogene Proteins c-abl

Identifiers

PMID39509464
PMCPMC11575782

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.