In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha

Speck‐Planche, Alejandro; Kleandrova, Valeria V.; Scotti, Marcus Tullius

doi:10.3390/biom11121832

Cited by 11 publications

(12 citation statements)

References 86 publications

(153 reference statements)

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“…Here, we are providing the physicochemical and structural interpretations of the D ( GTI ) cj descriptors in the PTML-MLP models. To support such interpretations, we employed certain graphics that illustrated the significance of each D ( GTI ) cj descriptor in the form of sensitivity values (SV) while extracting information on the propensities (increment or diminution) in the value of each D ( GTI ) cj descriptor [ 37 , 63 , 80 ]. Such propensities indicate how to vary the values of the D ( GTI ) cj descriptors to enhance the desired activity, which, in our case, is the improvement of the inhibitory activity against both the PANC-related proteins and the PANC cell lines.…”

Section: Resultsmentioning

confidence: 99%

“…In Equation (4), n ( cj ) has been defined in Equation (2) and represents the number of cases/chemicals assayed by considering the same element of the experimental condition cj [ 63 ], which were annotated as active (in the training set). Similarly, N T ( cj ), considering the same condition cj , represents the total number of cases/chemicals in the training set.…”

Section: Methodsmentioning

confidence: 99%

“…The creation and application of the two PTML-MLP models developed in this work involved steps such as splitting the dataset in the training and test series, selecting the most suitable D ( GTI ) cj descriptors using the software IMMAN v1.0 [ 64 ], analysis of the correlations among the D ( GTI ) cj descriptors via the Pearson correlation coefficient ( PCC ) [ 65 ], generation of the models using the program STATISTICA v13.5.0.17 [ 66 ], analysis of the applicability domain of each PTML-MLP model, interpretation of the D ( GTI ) cj descriptors, selection of suitable molecular fragments, and virtual design. All these steps have been described comprehensively in seminal works [ 37 , 63 , 67 , 68 , 69 ]. In any case, when selecting the best D ( GTI ) cj descriptors to subsequently build the PTML-MLP models, the mutual information differential Shannon’s entropy (MI-DSE) [ 70 ] and the Jeffreys information [ 71 , 72 ] were applied as criteria for descriptor selection; such criteria permitted the selection of at least one D ( GTI ) cj descriptor per each element of the experimental condition cj , which was a mandatory condition to develop the PTML-MLP models.…”

Section: Methodsmentioning

confidence: 99%

“…In Equation (2), GTI refers to any of the molecular descriptors discussed above, i.e., SM(PP)k, X(s)o, Xv(s)o, e(s)o, NTI, and ASqm(x)T. The meanings of the terms avg(GTI)cj and n(cj) have already been explained in detail in a recent work [63]. This means that Equation (2) was applied to each element of the experimental conditions cj (that is, ma, tg, and ei) separately.…”

Section: Bioactivity Data and Molecular Descriptorsmentioning

confidence: 99%

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PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors

Kleandrova

Speck‐Planche

2022

Biomedicines

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Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwide and exhibits a remarkably poor prognosis. To date, discovering anti-PANC agents remains a very complex and expensive process. Computational approaches can accelerate the search for anti-PANC agents. We report for the first time two models that combined perturbation theory with machine learning via a multilayer perceptron network (PTML-MLP) to perform the virtual design and prediction of molecules that can simultaneously inhibit multiple PANC cell lines and PANC-related proteins, such as caspase-1, tumor necrosis factor-alpha (TNF-alpha), and the insulin-like growth factor 1 receptor (IGF1R). Both PTML-MLP models exhibited accuracies higher than 78%. Using the interpretation from one of the PTML-MLP models as a guideline, we extracted different molecular fragments desirable for the inhibition of the PANC cell lines and the aforementioned PANC-related proteins and then assembled some of those fragments to form three new molecules. The two PTML-MLP models predicted the designed molecules as potentially versatile anti-PANC agents through inhibition of the three PANC-related proteins and multiple PANC cell lines. Conclusions: This work opens new horizons for the application of the PTML modeling methodology to anticancer research.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Bioactivity Data and Molecular Descriptorsmentioning

confidence: 99%

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PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors

Kleandrova

Speck‐Planche

2022

Biomedicines

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“…When calculating the D [ GTI ] cj descriptors, 76 the a priori probabilities ps ( cj ) were defined according to the following mathematical formalism: …”

Section: Methodsmentioning

confidence: 99%

Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles

Speck‐Planche

Kleandrova

2022

ACS Omega

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Respiratory viruses are infectious agents, which can cause pandemics. Although nowadays the danger associated with respiratory viruses continues to be evidenced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the virus responsible for the current COVID-19 pandemic, other viruses such as SARS-CoV-1, the influenza A and B viruses (IAV and IBV, respectively), and the respiratory syncytial virus (RSV) can lead to globally spread viral diseases. Also, from a biological point of view, most of these viruses can cause an organ-damaging hyperinflammatory response known as the cytokine storm (CS). Computational approaches constitute an essential component of modern drug development campaigns, and therefore, they have the potential to accelerate the discovery of chemicals able to simultaneously inhibit multiple molecular and nonmolecular targets. We report here the first multicondition model based on quantitative structure–activity relationships and an artificial neural network (mtc-QSAR-ANN) for the virtual design and prediction of molecules with dual pan-antiviral and anti-CS profiles. Our mtc-QSAR-ANN model exhibited an accuracy higher than 80%. By interpreting the different descriptors present in the mtc-QSAR-ANN model, we could retrieve several molecular fragments whose assembly led to new molecules with drug-like properties and predicted pan-antiviral and anti-CS activities.

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Molecular insights into the anti‐inflammatory activity of fermented pineapple juice using multimodal computational studies

Tallei,

Fatimawali,

Adam

et al. 2023

Archiv der Pharmazie

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Pineapple has been recognized for its potential to enhance health and well‐being. This study aimed to gain molecular insights into the anti‐inflammatory properties of fermented pineapple juice using multimodal computational studies. In this study, pineapple juice was fermented using Lactobacillus paracasei, and the solution underwent liquid chromatography‐mass spectrometry analysis. Network pharmacology was applied to investigate compound interactions and targets. In silico methods assessed compound bioactivities. Protein–protein interactions, network topology, and enrichment analysis identified key compounds. Molecular docking explored compound–receptor interactions in inflammation regulation. Molecular dynamics simulations were conducted to confirm the stability of interactions between the identified crucial compounds and their respective receptors. The study revealed several compounds including short‐chain fatty acids, peptides, dihydroxyeicosatrienoic acids, and glycerides that exhibited promising anti‐inflammatory properties. Leucyl–leucyl–norleucine and Leu–Leu–Tyr exhibited robust and stable interactions with mitogen‐activated protein kinase 14 and IκB kinase β, respectively, indicating their potential as promising therapeutic agents for inflammation modulation. This proposition is grounded in the pivotal involvement of these two proteins in inflammatory signaling pathways. These findings provide valuable insights into the anti‐inflammatory potential of these compounds, serving as a foundation for further experimental validation and exploration. Future studies can build upon these results to advance the development of these compounds as effective anti‐inflammatory agents.

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In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha

Cited by 11 publications

References 86 publications

PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors

PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors

Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles

Molecular insights into the anti‐inflammatory activity of fermented pineapple juice using multimodal computational studies

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