In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids
Malaria is a parasitic tropical disease that kills around 600,000 patients every year. The emergence of resistant Plasmodium falciparum parasites to artemisinin-based combination therapies (ACTs) represents a significant public health threat, indicating the urgent need for new effective compounds to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-06-01
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| Series: | Molecules |
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| Online Access: | http://www.mdpi.com/1420-3049/21/7/853 |
| _version_ | 1819072316783460352 |
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| author | Birgit Viira Thibault Gendron Don Antoine Lanfranchi Sandrine Cojean Dragos Horvath Gilles Marcou Alexandre Varnek Louis Maes Uko Maran Philippe M. Loiseau Elisabeth Davioud-Charvet |
| author_facet | Birgit Viira Thibault Gendron Don Antoine Lanfranchi Sandrine Cojean Dragos Horvath Gilles Marcou Alexandre Varnek Louis Maes Uko Maran Philippe M. Loiseau Elisabeth Davioud-Charvet |
| author_sort | Birgit Viira |
| collection | DOAJ |
| description | Malaria is a parasitic tropical disease that kills around 600,000 patients every year. The emergence of resistant Plasmodium falciparum parasites to artemisinin-based combination therapies (ACTs) represents a significant public health threat, indicating the urgent need for new effective compounds to reverse ACT resistance and cure the disease. For this, extensive curation and homogenization of experimental anti-Plasmodium screening data from both in-house and ChEMBL sources were conducted. As a result, a coherent strategy was established that allowed compiling coherent training sets that associate compound structures to the respective antimalarial activity measurements. Seventeen of these training sets led to the successful generation of classification models discriminating whether a compound has a significant probability to be active under the specific conditions of the antimalarial test associated with each set. These models were used in consensus prediction of the most likely active from a series of curcuminoids available in-house. Positive predictions together with a few predicted as inactive were then submitted to experimental in vitro antimalarial testing. A large majority from predicted compounds showed antimalarial activity, but not those predicted as inactive, thus experimentally validating the in silico screening approach. The herein proposed consensus machine learning approach showed its potential to reduce the cost and duration of antimalarial drug discovery. |
| first_indexed | 2024-12-21T17:35:48Z |
| format | Article |
| id | doaj.art-0cb2773dd1f8491c9ae4f32679cffdec |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-12-21T17:35:48Z |
| publishDate | 2016-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-0cb2773dd1f8491c9ae4f32679cffdec2022-12-21T18:55:46ZengMDPI AGMolecules1420-30492016-06-0121785310.3390/molecules21070853molecules21070853In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial CurcuminoidsBirgit Viira0Thibault Gendron1Don Antoine Lanfranchi2Sandrine Cojean3Dragos Horvath4Gilles Marcou5Alexandre Varnek6Louis Maes7Uko Maran8Philippe M. Loiseau9Elisabeth Davioud-Charvet10Institute of Chemistry, University of Tartu, 50411 Tartu, EstoniaBioorganic and Medicinal Chemistry Team, UMR 7509 CNRS-Université de Strasbourg, European School of Chemistry, Polymers and Materials (ECPM), 25, rue Becquerel, Strasbourg F-67087, FranceBioorganic and Medicinal Chemistry Team, UMR 7509 CNRS-Université de Strasbourg, European School of Chemistry, Polymers and Materials (ECPM), 25, rue Becquerel, Strasbourg F-67087, FranceAntiparasitic Chemotherapy, Faculty of Pharmacy, BioCIS, UMR 8076 CNRS-Université Paris-Sud, Rue Jean-Baptiste Clément, Chatenay-Malabry F-92290, FranceLaboratoire de Chemoinformatique, UMR7140 CNRS-Université de Strasbourg, 1 rue Blaise Pascal, Strasbourg F-67000, FranceLaboratoire de Chemoinformatique, UMR7140 CNRS-Université de Strasbourg, 1 rue Blaise Pascal, Strasbourg F-67000, FranceLaboratoire de Chemoinformatique, UMR7140 CNRS-Université de Strasbourg, 1 rue Blaise Pascal, Strasbourg F-67000, FranceLaboratory of Microbiology, Parasitology and Hygiene (LMPH), Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, University of Antwerp, Universiteitsplein 1, Antwerp B-2610, BelgiumInstitute of Chemistry, University of Tartu, 50411 Tartu, EstoniaAntiparasitic Chemotherapy, Faculty of Pharmacy, BioCIS, UMR 8076 CNRS-Université Paris-Sud, Rue Jean-Baptiste Clément, Chatenay-Malabry F-92290, FranceBioorganic and Medicinal Chemistry Team, UMR 7509 CNRS-Université de Strasbourg, European School of Chemistry, Polymers and Materials (ECPM), 25, rue Becquerel, Strasbourg F-67087, FranceMalaria is a parasitic tropical disease that kills around 600,000 patients every year. The emergence of resistant Plasmodium falciparum parasites to artemisinin-based combination therapies (ACTs) represents a significant public health threat, indicating the urgent need for new effective compounds to reverse ACT resistance and cure the disease. For this, extensive curation and homogenization of experimental anti-Plasmodium screening data from both in-house and ChEMBL sources were conducted. As a result, a coherent strategy was established that allowed compiling coherent training sets that associate compound structures to the respective antimalarial activity measurements. Seventeen of these training sets led to the successful generation of classification models discriminating whether a compound has a significant probability to be active under the specific conditions of the antimalarial test associated with each set. These models were used in consensus prediction of the most likely active from a series of curcuminoids available in-house. Positive predictions together with a few predicted as inactive were then submitted to experimental in vitro antimalarial testing. A large majority from predicted compounds showed antimalarial activity, but not those predicted as inactive, thus experimentally validating the in silico screening approach. The herein proposed consensus machine learning approach showed its potential to reduce the cost and duration of antimalarial drug discovery.http://www.mdpi.com/1420-3049/21/7/853antimalarialquantitative structure-activity relationships (QSAR)curcuminoidMichael additionPlasmodium falciparumthioredoxin reductasein silico |
| spellingShingle | Birgit Viira Thibault Gendron Don Antoine Lanfranchi Sandrine Cojean Dragos Horvath Gilles Marcou Alexandre Varnek Louis Maes Uko Maran Philippe M. Loiseau Elisabeth Davioud-Charvet In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids Molecules antimalarial quantitative structure-activity relationships (QSAR) curcuminoid Michael addition Plasmodium falciparum thioredoxin reductase in silico |
| title | In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids |
| title_full | In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids |
| title_fullStr | In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids |
| title_full_unstemmed | In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids |
| title_short | In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids |
| title_sort | in silico mining for antimalarial structure activity knowledge and discovery of novel antimalarial curcuminoids |
| topic | antimalarial quantitative structure-activity relationships (QSAR) curcuminoid Michael addition Plasmodium falciparum thioredoxin reductase in silico |
| url | http://www.mdpi.com/1420-3049/21/7/853 |
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