GUILDify v2.0: A Tool to Identify Molecular Networks Underlying Human Diseases, Their Comorbidities and Their Druggable Targets

Aguirre‐Plans, Joaquim; Piñero, Janet; Sanz, Ferrán; Furlong, Laura I.; Fernández-Fuentes, Narcís; Oliva, Baldo; Güney, Emre

doi:10.1016/j.jmb.2019.02.027

Cited by 35 publications

(37 citation statements)

References 36 publications

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“…To corroborate these biomarkers, we tested how many of them are found using a different approach also based on the use of functional networks. For this purpose, we used GUILDify v2.0 [23], a web server that extends the information of disease-gene associations through the protein-protein interactions network. GUILDify scores proteins according to their proximity with the genes associated with a disease (seeds).…”

Section: Analysis Of Proposed Biomarkers With Guildifymentioning

confidence: 99%

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

et al. 2020

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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/.

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Section: Analysis Of Proposed Biomarkers With Guildifymentioning

confidence: 99%

“…We then compared these sets of MoAs and proposed a list of biomarkers to identify potential cases of MD when using sacubitril/valsartan. Simultaneously, we used GUILDify v2.0 web server [23] as an alternative approach to compare the biomarkers proposed by TPMS and reinforce the results.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…There are already several examples of repurposed drugs identified using TPMS approach that have been validated in vitro and in vivo [ 8 , 9 , 12 – 18 ] with one of them advancing to clinical trials. Simultaneously, GUILDify v2.0 web server [ 19 ] has been used as an alternative method to corroborate the promising combinations.…”

Section: Introductionmentioning

confidence: 99%

In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm

et al. 2020

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From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, nonsevere symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.

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“…In the following sections, we analysed and compared the four groups of MoAs and searched for biomarkers that can potentially identify MD as an adverse effect of the drug (and consequently classify patients). We used the web server GUILDify v2.0 15 to analyse the comorbidity of both phenotypes and analyse the relevance of the proposed biomarkers in a different context.…”

Section: Resultsmentioning

confidence: 99%

“…We compare these MoAs and propose biomarkers to identify potential cases of MD when using sacubitril/valsartan. Finally, we use GUILDify v2.0 web server 15 to analyse the comorbidity of both phenotypes and analyse the relevance of the proposed biomarkers with a different approach.…”

Section: Introductionmentioning

confidence: 99%

TPMS technology to infer biomarkers of macular degeneration prognosis in in silico simulated prototype-patients under the study of heart failure treatment with sacubitril and valsartan

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Aguirre‐Plans

Segú-Vergés

et al. 2019

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Unveiling the mechanism of action of a drug is key to understand the benefits and adverse reactions of drug(s) 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 personalised medicine. The Therapeutic Performance Mapping System (TPMS) is a Systems Biology approach that generates multiple models of the mechanism of action of a drug. This is achieved by (1) modelling the responses in human with an accurate description of the protein networks and (2) applying a Multilayer Perceptron-like and sampling method 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 range of mechanism of action models explaining the relationship between sacubitril/valsartan and heart failure (the indication), as well as evaluating their relationship with macular degeneration (a common/recurrent adverse effect). We found that a lower response in terms of heart failure treatment is more associated to macular degeneration development, although good response mechanisms can also associate to the adverse effect. A set of 30 potential biomarkers are proposed to identify mechanisms (or patients) more prone to suffering macular degeneration when presenting good heart failure response. As each molecular mechanism can be particular not only of cells but also individuals, we conclude that the study of the collection of models generated using TPMS technology can be used to detect adverse effects personalized to patients.

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GUILDify v2.0: A Tool to Identify Molecular Networks Underlying Human Diseases, Their Comorbidities and Their Druggable Targets

Cited by 35 publications

References 36 publications

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

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

In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm

TPMS technology to infer biomarkers of macular degeneration prognosis in in silico simulated prototype-patients under the study of heart failure treatment with sacubitril and valsartan

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