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Article ; Online: New Computational Approaches Aimed at the Prediction of More Selective and Active Drugs.

Concu, Riccardo

2020 Volume 20, Issue 18, Page(s) 1581

MeSH term(s)	Computational Biology ; Drug Development ; Humans ; Machine Learning ; Pharmaceutical Preparations/chemical synthesis ; Pharmaceutical Preparations/chemistry
Chemical Substances	Pharmaceutical Preparations
Language	English
Publishing date	2020-08-26
Publishing country	United Arab Emirates
Document type	Editorial ; Introductory Journal Article
ZDB-ID	2064823-6
ISSN	1873-4294 ; 1568-0266
ISSN (online)	1873-4294
ISSN	1568-0266
DOI	10.2174/156802662018200630150100
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Article ; Online: Recent Advances in Computer Aided Drug Design.

Concu, Riccardo / Goyal, Amit K / Gupta, Umesh

Current topics in medicinal chemistry

2023 Volume 23, Issue 1, Page(s) 30

MeSH term(s)	Drug Design ; Computer-Aided Design
Language	English
Publishing date	2023-02-22
Publishing country	United Arab Emirates
Document type	Editorial
ZDB-ID	2064823-6
ISSN	1873-4294 ; 1568-0266
ISSN (online)	1873-4294
ISSN	1568-0266
DOI	10.2174/156802662301230113160655
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Article: Computational Biology: A New Frontier in Applied Biology.

Toma, Milan / Concu, Riccardo

Biology

2021 Volume 10, Issue 5

Abstract: All living things are related to one another [ ... ]. ...

Abstract	All living things are related to one another [...].
Language	English
Publishing date	2021-04-27
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2661517-4
ISSN	2079-7737
ISSN	2079-7737
DOI	10.3390/biology10050374
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Article ; Online: MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions.

Concu, Riccardo / Cordeiro, Maria Natália Dias Soeiro / Pérez-Pérez, Martín / Fdez-Riverola, Florentino

Molecules (Basel, Switzerland)

2023 Volume 28, Issue 3

Abstract: Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the ...

Abstract	Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.
MeSH term(s)	Quantitative Structure-Activity Relationship ; Software ; Neural Networks, Computer ; Machine Learning ; Internet
Language	English
Publishing date	2023-01-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	1413402-0
ISSN	1420-3049 ; 1431-5165 ; 1420-3049
ISSN (online)	1420-3049
ISSN	1431-5165 ; 1420-3049
DOI	10.3390/molecules28031182
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Article: New Mechanistic Insights on Carbon Nanotubes’ Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches

González-Durruthy, Michael / Concu, Riccardo / Ruso, Juan M / Cordeiro, M. Natália D. S

Biology. 2021 Feb. 25, v. 10, no. 3

2021

Abstract: Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and ... ...

Abstract	Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = −9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = −6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = −5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine.
Keywords	Gibbs free energy ; H-transporting ATP synthase ; carbon nanotubes ; crystallography ; hydrophobicity ; ligands ; mitochondria ; nanomedicine ; oligomycin ; prediction ; toxicity ; van der Waals forces
Language	English
Dates of publication	2021-0225
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	2661517-4
ISSN	2079-7737
ISSN	2079-7737
DOI	10.3390/biology10030171
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Alignment-Free Method to Predict Enzyme Classes and Subclasses.

Concu, Riccardo / Cordeiro, M Natália D S

International journal of molecular sciences

2019 Volume 20, Issue 21

Abstract: The Enzyme Classification (EC) number is a numerical classification scheme for enzymes, established using the chemical reactions they catalyze. This classification is based on the recommendation of the Nomenclature Committee of the International Union of ...

Abstract	The Enzyme Classification (EC) number is a numerical classification scheme for enzymes, established using the chemical reactions they catalyze. This classification is based on the recommendation of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology. Six enzyme classes were recognised in the first Enzyme Classification and Nomenclature List, reported by the International Union of Biochemistry in 1961. However, a new enzyme group was recently added as the six existing EC classes could not describe enzymes involved in the movement of ions or molecules across membranes. Such enzymes are now classified in the new EC class of translocases (EC 7). Several computational methods have been developed in order to predict the EC number. However, due to this new change, all such methods are now outdated and need updating. In this work, we developed a new multi-task quantitative structure-activity relationship (QSAR) method aimed at predicting all 7 EC classes and subclasses. In so doing, we developed an alignment-free model based on artificial neural networks that proved to be very successful.
MeSH term(s)	Algorithms ; Computational Biology/methods ; Databases, Factual ; Enzymes/chemistry ; Enzymes/classification ; Enzymes/metabolism ; Linear Models ; Machine Learning ; Nonlinear Dynamics ; Peptidyl Transferases ; Proteins/chemistry ; Proteins/genetics ; Quantitative Structure-Activity Relationship ; Sensitivity and Specificity
Chemical Substances	Enzymes ; Proteins ; Peptidyl Transferases (EC 2.3.2.12)
Language	English
Publishing date	2019-10-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms20215389
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Developing a Multi-target Model to Predict the Activity of Monoamine Oxidase A and B Drugs.

Concu, Riccardo / González-Durruthy, Michael / Cordeiro, Maria Natália D S

Current topics in medicinal chemistry

2020 Volume 20, Issue 18, Page(s) 1593–1600

Abstract: Introduction: Monoamine oxidase inhibitors (MAOIs) are compounds largely used in the treatment of Parkinson's disease (PD), Alzheimer's disease and other neuropsychiatric disorders since they are closely related to the MAO enzymes activity. The two ... ...

Abstract	Introduction: Monoamine oxidase inhibitors (MAOIs) are compounds largely used in the treatment of Parkinson's disease (PD), Alzheimer's disease and other neuropsychiatric disorders since they are closely related to the MAO enzymes activity. The two isoforms of the MAO enzymes, MAO-A and MAO-B, are responsible for the degradation of monoamine neurotransmitters and due to this, relevant efforts have been devoted to finding new compounds with more selectivity and less side effects. One of the most used approaches is based on the use of computational approaches since they are time and money-saving and may allow us to find a more relevant structure-activity relationship. Objective: In this manuscript, we will review the most relevant computational approaches aimed at the prediction and development of new MAO inhibitors. Subsequently, we will also introduce a new multitask model aimed at predicting MAO-A and MAO-B inhibitors. Methods: The QSAR multi-task model herein developed was based on the use of the linear discriminant analysis. This model was developed gathering 5,759 compounds from the public dataset Chembl. The molecular descriptors used was calculated using the Dragon software. Classical statistical tests were performed to check the validity and robustness of the model. Results: The herein proposed model is able to correctly classify all the 5,759 compounds. All the statistical performed tests indicated that this model is robust and reproducible. Conclusion: MAOIs are compounds of large interest since they are largely used in the treatment of very serious illness. These inhibitors may lose efficacy and produce severe side effects. Due to this, the development of selective MAO-A or MAO-B inhibitors is crucial for the treatment of these diseases and their effects. The herein proposed multi-target QSAR model may be a relevant tool in the development of new and more selective MAO inhibitors.
MeSH term(s)	Drug Development ; Humans ; Models, Molecular ; Monoamine Oxidase/metabolism ; Monoamine Oxidase Inhibitors/chemical synthesis ; Monoamine Oxidase Inhibitors/chemistry ; Monoamine Oxidase Inhibitors/pharmacology ; Structure-Activity Relationship
Chemical Substances	Monoamine Oxidase Inhibitors ; Monoamine Oxidase (EC 1.4.3.4) ; monoamine oxidase A, human (EC 1.4.3.4.)
Language	English
Publishing date	2020-06-03
Publishing country	United Arab Emirates
Document type	Journal Article
ZDB-ID	2064823-6
ISSN	1873-4294 ; 1568-0266
ISSN (online)	1873-4294
ISSN	1568-0266
DOI	10.2174/1568026620666200603121224
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article: New Mechanistic Insights on Carbon Nanotubes' Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches.

González-Durruthy, Michael / Concu, Riccardo / Ruso, Juan M / Cordeiro, M Natália D S

Biology

2021 Volume 10, Issue 3

Abstract	Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = -9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = -6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = -5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine.
Language	English
Publishing date	2021-02-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2661517-4
ISSN	2079-7737
ISSN	2079-7737
DOI	10.3390/biology10030171
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Looking for New Inhibitors for the Epidermal Growth Factor Receptor.

Concu, Riccardo / Cordeiro, M Natalia D S

Current topics in medicinal chemistry

2018 Volume 18, Issue 3, Page(s) 219–232

Abstract: Epidermal Growth Factor Receptor (EGFR) is still the main target of the Head and Neck Squamous Cell Cancer (HNSCC) because its overexpression has been detected in more than 90% of this type of cancer. This overexpression is usually linked with more ... ...

Abstract	Epidermal Growth Factor Receptor (EGFR) is still the main target of the Head and Neck Squamous Cell Cancer (HNSCC) because its overexpression has been detected in more than 90% of this type of cancer. This overexpression is usually linked with more aggressive disease, increased resistance to chemotherapy and radiotherapy, increased metastasis, inhibition of apoptosis, promotion of neoplastic angiogenesis, and, finally, poor prognosis and decreased survival. Due to this reason, the main target in the search of new drugs and inhibitors candidates is to downturn this overexpression. Quantitative Structure-Activity Relationship (QSAR) is one of the most widely used approaches while looking for new and more active inhibitors drugs. In this contest, a lot of authors used this technique, combined with others, to find new drugs or enhance the activity of well-known inhibitors. In this paper, on one hand, we will review the most important QSAR approaches developed in the last fifteen years, spacing from classical 1D approaches until more sophisticated 3D; the first paper is dated 2003 while the last one is from 2017. On the other hand, we will present a completely new QSAR approach aimed at the prediction of new EGFR inhibitors drugs. The model presented here has been developed over a dataset consisting of more than 1000 compounds using various molecular descriptors calculated with the DRAGON 7.0© software.
MeSH term(s)	Carcinoma, Squamous Cell/drug therapy ; Dose-Response Relationship, Drug ; Drug Screening Assays, Antitumor ; Head and Neck Neoplasms/drug therapy ; Humans ; Molecular Dynamics Simulation ; Protein Kinase Inhibitors/chemistry ; Protein Kinase Inhibitors/pharmacology ; Quantitative Structure-Activity Relationship ; Receptor, Epidermal Growth Factor/antagonists & inhibitors ; Receptor, Epidermal Growth Factor/metabolism
Chemical Substances	Protein Kinase Inhibitors ; EGFR protein, human (EC 2.7.10.1) ; Receptor, Epidermal Growth Factor (EC 2.7.10.1)
Language	English
Publishing date	2018
Publishing country	United Arab Emirates
Document type	Journal Article ; Review
ZDB-ID	2064823-6
ISSN	1873-4294 ; 1568-0266
ISSN (online)	1873-4294
ISSN	1568-0266
DOI	10.2174/1568026618666180329123023
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Article ; Online: Cetuximab and the Head and Neck Squamous Cell Cancer.

Concu, Riccardo / Cordeiro, M Natalia D S

Current topics in medicinal chemistry

2018 Volume 18, Issue 3, Page(s) 192–198

Abstract: The Head and Neck Squamous Cell Cancer (HNSCC) is the most common type of head and neck cancer (more than 90%), and all over the world more than a half million people have been developing this cancer in the last years. This type of cancer is usually ... ...

Abstract	The Head and Neck Squamous Cell Cancer (HNSCC) is the most common type of head and neck cancer (more than 90%), and all over the world more than a half million people have been developing this cancer in the last years. This type of cancer is usually marked by a poor prognosis with a really significant morbidity and mortality. Cetuximab received early favor as an exciting and promising new therapy with relatively mild side effect, and due to this, received authorization in 2004 from the European Medicines Agency (EMA) and in 2006 from the Food and Drug Association (FDA) for the treatment of patients with squamous cell cancer of the head and neck in combination with radiation therapy for locally advanced disease. In this work we will review the application and the efficacy of the Cetuximab in the treatment of the HNSCC.
MeSH term(s)	Antineoplastic Agents/chemistry ; Antineoplastic Agents/therapeutic use ; Carcinoma, Squamous Cell/drug therapy ; Cetuximab/chemistry ; Cetuximab/therapeutic use ; Drug Screening Assays, Antitumor ; Head and Neck Neoplasms/drug therapy ; Humans ; Structure-Activity Relationship
Chemical Substances	Antineoplastic Agents ; Cetuximab (PQX0D8J21J)
Language	English
Publishing date	2018
Publishing country	United Arab Emirates
Document type	Journal Article ; Review
ZDB-ID	2064823-6
ISSN	1873-4294 ; 1568-0266
ISSN (online)	1873-4294
ISSN	1568-0266
DOI	10.2174/1568026618666180112162412
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