Evidence map›Paper›PMID 32053711›Full record

ArticlePloS one2020

In-silico simulated prototype-patients using TPMS technology to study a potential adverse effect of sacubitril and valsartan.

Guillem Jorba, Joaquim Aguirre-Plans, Valentin Junet, Cristina Segú-Vergés, José Luis Ruiz, Albert Pujol, Narcís Fernández-Fuentes, José Manuel Mas, Baldo Oliva

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

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  12. AnMolecular genetics and metabolism reports · 2023
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  15. Article
  16. Frontiers in psychiatry · 2023
    Article
  17. Frontiers in neuroscience · 2023
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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

9 authors.

Guillem JorbaAnaxomics Biotech SL, Barcelona, Catalonia, Spain.
Joaquim Aguirre-PlansStructural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.ORCID 0000-0002-1971-1350
Valentin JunetAnaxomics Biotech SL, Barcelona, Catalonia, Spain.
Cristina Segú-VergésAnaxomics Biotech SL, Barcelona, Catalonia, Spain.
José Luis RuizAnaxomics Biotech SL, Barcelona, Catalonia, Spain.
Albert PujolAnaxomics Biotech SL, Barcelona, Catalonia, Spain.
Narcís Fernández-FuentesDepartment of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Vic, Catalonia, Spain.
José Manuel MasAnaxomics Biotech SL, Barcelona, Catalonia, Spain.
Baldo OlivaStructural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.ORCID 0000-0003-0702-0250

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unveiling the mechanism of action of a drug is key to understand the benefits and adverse reactions of a medication in an organism. However, in complex diseases such as heart diseases there is not a unique mechanism of action but a wide range of different responses depending on the patient. Exploring this collection of mechanisms is one of the clues for a future personalized medicine. The Therapeutic Performance Mapping System (TPMS) is a Systems Biology approach that generates multiple models of the mechanism of action of a drug. Each molecular mechanism generated could be associated to particular individuals, here defined as prototype-patients, hence the generation of models using TPMS technology may be used for detecting adverse effects to specific patients. TPMS operates by (1) modelling the responses in humans with an accurate description of a protein network and (2) applying a Multilayer Perceptron-like and sampling strategy to find all plausible solutions. In the present study, TPMS is applied to explore the diversity of mechanisms of action of the drug combination sacubitril/valsartan. We use TPMS to generate a wide range of models explaining the relationship between sacubitril/valsartan and heart failure (the indication), as well as evaluating their association with macular degeneration (a potential adverse effect). Among the models generated, we identify a set of mechanisms of action associated to a better response in terms of heart failure treatment, which could also be associated to macular degeneration development. Finally, a set of 30 potential biomarkers are proposed to identify mechanisms (or prototype-patients) more prone of suffering macular degeneration when presenting good heart failure response. All prototype-patients models generated are completely theoretical and therefore they do not necessarily involve clinical effects in real patients. Data and accession to software are available at http://sbi.upf.edu/data/tpms/.

Indexed as

AminobutyratesAngiotensin Receptor AntagonistsBiomarkersBiphenyl CompoundsComputer SimulationDrug CombinationsHeartHeart FailureHumansNeprilysinSoftwareStroke VolumeSystems BiologyTetrazolesValsartanVentricular Function, LeftAminobutyratesAngiotensin Receptor AntagonistsBiomarkersBiphenyl CompoundsDrug CombinationsNeprilysinsacubitril and valsartan sodium hydrate drug combinationTetrazolesValsartan

Identifiers

PMID32053711
PMCPMC7018085

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Registered trials

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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.