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  1. Article ; Online: Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas.

    Kishk, Ali / Pires Pacheco, Maria / Heurtaux, Tony / Sauter, Thomas

    Briefings in bioinformatics

    2024  Volume 25, Issue 3

    Abstract: Gliomas are the most common type of malignant brain tumors, with glioblastoma multiforme (GBM) having a median survival of 15 months due to drug resistance and relapse. The treatment of gliomas relies on surgery, radiotherapy and chemotherapy. Only 12 ... ...

    Abstract Gliomas are the most common type of malignant brain tumors, with glioblastoma multiforme (GBM) having a median survival of 15 months due to drug resistance and relapse. The treatment of gliomas relies on surgery, radiotherapy and chemotherapy. Only 12 anti-brain tumor chemotherapies (AntiBCs), mostly alkylating agents, have been approved so far. Glioma subtype-specific metabolic models were reconstructed to simulate metabolite exchanges, in silico knockouts and the prediction of drug and drug combinations for all three subtypes. The simulations were confronted with literature, high-throughput screenings (HTSs), xenograft and clinical trial data to validate the workflow and further prioritize the drug candidates. The three subtype models accurately displayed different degrees of dependencies toward glutamine and glutamate. Furthermore, 33 single drugs, mainly antimetabolites and TXNRD1-inhibitors, as well as 17 drug combinations were predicted as potential candidates for gliomas. Half of these drug candidates have been previously tested in HTSs. Half of the tested drug candidates reduce proliferation in cell lines and two-thirds in xenografts. Most combinations were predicted to be efficient for all three glioma types. However, eflornithine/rifamycin and cannabidiol/adapalene were predicted specifically for GBM and low-grade glioma, respectively. Most drug candidates had comparable efficiency in preclinical tests, cerebrospinal fluid bioavailability and mode-of-action to AntiBCs. However, fotemustine and valganciclovir alone and eflornithine and celecoxib in combination with AntiBCs improved the survival compared to AntiBCs in two-arms, phase I/II and higher glioma clinical trials. Our work highlights the potential of metabolic modeling in advancing glioma drug discovery, which accurately predicted metabolic vulnerabilities, repurposable drugs and combinations for the glioma subtypes.
    MeSH term(s) Humans ; Glioma/drug therapy ; Glioma/metabolism ; Glioma/pathology ; Cannabidiol/therapeutic use ; Cannabidiol/pharmacology ; Brain Neoplasms/drug therapy ; Brain Neoplasms/metabolism ; Brain Neoplasms/pathology ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Animals ; Models, Biological ; Cell Line, Tumor ; Organophosphorus Compounds/therapeutic use ; Organophosphorus Compounds/pharmacology
    Chemical Substances Cannabidiol (19GBJ60SN5) ; Organophosphorus Compounds
    Language English
    Publishing date 2024-05-03
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2068142-2
    ISSN 1477-4054 ; 1467-5463
    ISSN (online) 1477-4054
    ISSN 1467-5463
    DOI 10.1093/bib/bbae199
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: A dynamic multi-tissue model to study human metabolism.

    Martins Conde, Patricia / Pfau, Thomas / Pires Pacheco, Maria / Sauter, Thomas

    NPJ systems biology and applications

    2021  Volume 7, Issue 1, Page(s) 5

    Abstract: Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood and urine metabolite dynamics, the integration of ...

    Abstract Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood and urine metabolite dynamics, the integration of multiple metabolically active tissues is necessary. We developed a dynamic multi-tissue model, which recapitulates key properties of human metabolism at the molecular and physiological level based on the integration of transcriptomics data. It enables the simulation of the dynamics of intra-cellular and extra-cellular metabolites at the genome scale. The predictive capacity of the model is shown through the accurate simulation of different healthy conditions (i.e., during fasting, while consuming meals or during exercise), and the prediction of biomarkers for a set of Inborn Errors of Metabolism with a precision of 83%. This novel approach is useful to prioritize new biomarkers for many metabolic diseases, as well as for the integration of various types of personal omics data, towards the personalized analysis of blood and urine metabolites.
    MeSH term(s) Biomarkers/blood ; Biomarkers/urine ; Computational Biology/methods ; Computer Simulation ; Humans ; Metabolomics/methods ; Models, Biological ; Organ Specificity/genetics ; Organ Specificity/physiology ; Systems Biology/methods
    Chemical Substances Biomarkers
    Language English
    Publishing date 2021-01-22
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2056-7189
    ISSN (online) 2056-7189
    DOI 10.1038/s41540-020-00159-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling.

    Kishk, Ali / Pacheco, Maria Pires / Sauter, Thomas

    iScience

    2021  Volume 24, Issue 11, Page(s) 103331

    Abstract: The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected ... ...

    Abstract The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov).
    Language English
    Publishing date 2021-10-23
    Publishing country United States
    Document type Journal Article
    ISSN 2589-0042
    ISSN (online) 2589-0042
    DOI 10.1016/j.isci.2021.103331
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: scFASTCORMICS: A Contextualization Algorithm to Reconstruct Metabolic Multi-Cell Population Models from Single-Cell RNAseq Data.

    Pacheco, Maria Pires / Ji, Jimmy / Prohaska, Tessy / García, María Moscardó / Sauter, Thomas

    Metabolites

    2022  Volume 12, Issue 12

    Abstract: Tumours are composed of various cancer cell populations with different mutation profiles, phenotypes and metabolism that cause them to react to drugs in diverse manners. Increasing the resolution of metabolic models based on single-cell expression data ... ...

    Abstract Tumours are composed of various cancer cell populations with different mutation profiles, phenotypes and metabolism that cause them to react to drugs in diverse manners. Increasing the resolution of metabolic models based on single-cell expression data will provide deeper insight into such metabolic differences and improve the predictive power of the models. scFASTCORMICS is a network contextualization algorithm that builds multi-cell population genome-scale models from single-cell RNAseq data. The models contain a subnetwork for each cell population in a tumour, allowing to capture metabolic variations between these clusters. The subnetworks are connected by a union compartment that permits to simulate metabolite exchanges between cell populations in the microenvironment. scFASTCORMICS uses Pareto optimization to simultaneously maximise the compactness, completeness and specificity of the reconstructed metabolic models. scFASTCORMICS is implemented in MATLAB and requires the installation of the COBRA toolbox, rFASTCORMICS and the IBM CPLEX solver.
    Language English
    Publishing date 2022-12-02
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662251-8
    ISSN 2218-1989
    ISSN 2218-1989
    DOI 10.3390/metabo12121211
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: TESTING INFORMED SIR BASED EPIDEMIOLOGICAL MODEL FOR COVID-19 IN LUXEMBOURG

    Sauter, Thomas / Pires Pacheco, Maria

    medRxiv

    Abstract: The interpretation of the number of COVID-19 cases and deaths in a country or region is strongly dependent on the number of performed tests. We developed a novel SIR based epidemiological model (SIVRT) which allows the country-specific integration of ... ...

    Abstract The interpretation of the number of COVID-19 cases and deaths in a country or region is strongly dependent on the number of performed tests. We developed a novel SIR based epidemiological model (SIVRT) which allows the country-specific integration of testing information and other available data. The model thereby enables a dynamic inspection of the pandemic and allows estimating key figures, like the number of overall detected and undetected COVID-19 cases and the infection fatality rate. As proof of concept, the novel SIVRT model was used to simulate the first phase of the pandemic in Luxembourg. An overall number of infections of 13.000 and an infection fatality rate of 1,3% was estimated, which is in concordance with data from population-wide testing. Furthermore based on the data as of end of May 2020 and assuming a partial deconfinement, an increase of cases is predicted from mid of July 2020 on. This is consistent with the current observed rise and shows the predictive potential of the novel SIVRT model.
    Keywords covid19
    Language English
    Publishing date 2020-07-25
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.07.21.20159046
    Database COVID19

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  6. Article ; Online: A framework model to integrate sources and pathways in the assessment of river water pollution.

    Bessa Santos, Regina Maria / Farias do Valle Junior, Renato / Abreu Pires de Melo Silva, Maytê Maria / Tarlé Pissarra, Teresa Cristina / Carvalho de Melo, Marília / Valera, Carlos Alberto / Leal Pacheco, Fernando António / Sanches Fernandes, Luís Filipe

    Environmental pollution (Barking, Essex : 1987)

    2024  Volume 347, Page(s) 123661

    Abstract: Metal and nutrient pollution, soil erosion, and alterations in climate and hydrology are prevalent issues that impact the water quality of riverine systems. However, integrated approaches to assess and isolate causes and paths of river water pollution ... ...

    Abstract Metal and nutrient pollution, soil erosion, and alterations in climate and hydrology are prevalent issues that impact the water quality of riverine systems. However, integrated approaches to assess and isolate causes and paths of river water pollution are scarce, especially in the case of watersheds impacted by multiple hazardous activities. Therefore, a framework model for investigating the multiple sources of river water pollution was developed. The chosen study area was the Paraopeba River basin located in the Minas Gerais, Brazil. Besides multiple agriculture, industrial, and urban pollution sources, this region was profoundly affected by the rupture of the B1 tailings dam (in January 2019) at the Córrego do Feijão mine, resulting in the release of metal-rich waste. Considering this situation, thirty-nine physicochemical and hydromorphological parameters were examined in the Paraopeba River basin, in the 2019-2023 period. The analysis involved various statistical techniques, including bivariate and multivariate methods such as correlation analysis, principal component analysis, and clustering. The Paraopeba River was mainly impacted by metal contamination resulting from the dam collapse, whereas nutrient contamination, mainly from urban and industrial discharges, predominantly affected its tributaries. Additionally, the elevated concentrations of aluminum, iron, nitrate, and sulfate in both main river and tributaries can be attributed to diffuse and point source pollution. In terms of hydromorphology and soil type, the interaction between woody vegetation and erosion-resistant soils, especially latosols, contributes to the stability of riverbanks in the main river. Meanwhile, in the tributaries, the presence of neosols and sparse vegetation in urbanized areas promoted riverbank erosion potentially amplifying pollution. While the study was conducted in a particular watershed, the findings are based on a methodology that can be applied universally. Hence, the insights on surface water quality from this research can be a valuable resource for researchers studying watersheds with diverse pollution sources.
    MeSH term(s) Rivers ; Environmental Monitoring/methods ; Water Pollutants, Chemical/analysis ; Water Pollution/analysis ; Water Quality ; Soil
    Chemical Substances Water Pollutants, Chemical ; Soil
    Language English
    Publishing date 2024-02-26
    Publishing country England
    Document type Journal Article
    ZDB-ID 280652-6
    ISSN 1873-6424 ; 0013-9327 ; 0269-7491
    ISSN (online) 1873-6424
    ISSN 0013-9327 ; 0269-7491
    DOI 10.1016/j.envpol.2024.123661
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: DCcov

    Ali Kishk / Maria Pires Pacheco / Thomas Sauter

    iScience, Vol 24, Iss 11, Pp 103331- (2021)

    Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling

    2021  

    Abstract: Summary: The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells ... ...

    Abstract Summary: The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov).
    Keywords Virology ; Pharmaceutical science ; Bioinformatics ; Science ; Q
    Language English
    Publishing date 2021-11-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Drug Target Prediction Using Context-Specific Metabolic Models Reconstructed from rFASTCORMICS.

    Bintener, Tamara / Pacheco, Maria Pires / Kishk, Ali / Didier, Jeff / Sauter, Thomas

    Methods in molecular biology (Clifton, N.J.)

    2022  Volume 2535, Page(s) 221–240

    Abstract: Metabolic modeling is a powerful computational tool to analyze metabolism. It has not only been used to identify metabolic rewiring strategies in cancer but also to predict drug targets and candidate drugs for repurposing. Here, we will elaborate on the ... ...

    Abstract Metabolic modeling is a powerful computational tool to analyze metabolism. It has not only been used to identify metabolic rewiring strategies in cancer but also to predict drug targets and candidate drugs for repurposing. Here, we will elaborate on the reconstruction of context-specific metabolic models of cancer using rFASTCORMICS and the subsequent prediction of drugs for repurposing using our drug prediction workflow.
    MeSH term(s) Antineoplastic Agents/therapeutic use ; Computational Biology ; Drug Repositioning ; Humans ; Neoplasms/drug therapy ; Workflow
    Chemical Substances Antineoplastic Agents
    Language English
    Publishing date 2022-07-22
    Publishing country United States
    Document type Journal Article
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-0716-2513-2_17
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Towards the network-based prediction of repurposed drugs using patient-specific metabolic models.

    Pacheco, Maria Pires / Bintener, Tamara / Sauter, Thomas

    EBioMedicine

    2019  Volume 43, Page(s) 26–27

    MeSH term(s) Algorithms ; Computational Biology/methods ; Drug Repositioning ; Humans ; Models, Biological
    Language English
    Publishing date 2019-04-09
    Publishing country Netherlands
    Document type Letter
    ZDB-ID 2851331-9
    ISSN 2352-3964
    ISSN (online) 2352-3964
    DOI 10.1016/j.ebiom.2019.04.017
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Towards the routine use of in silico screenings for drug discovery using metabolic modelling.

    Bintener, Tamara / Pacheco, Maria Pires / Sauter, Thomas

    Biochemical Society transactions

    2020  Volume 48, Issue 3, Page(s) 955–969

    Abstract: Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the ... ...

    Abstract Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.
    MeSH term(s) Algorithms ; Animals ; Computer Simulation ; Databases, Genetic ; Drug Delivery Systems ; Drug Discovery/methods ; Drug Repositioning ; Gene Deletion ; Genome, Human ; Humans ; Metabolic Networks and Pathways ; Models, Chemical ; Neoplasms/drug therapy ; Oligonucleotide Array Sequence Analysis
    Language English
    Publishing date 2020-06-03
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 184237-7
    ISSN 1470-8752 ; 0300-5127
    ISSN (online) 1470-8752
    ISSN 0300-5127
    DOI 10.1042/BST20190867
    Database MEDical Literature Analysis and Retrieval System OnLINE

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