Introduction The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.Methods Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.Results Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.Discussion The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches / Niarakis, Anna; Ostaszewski, Marek; Mazein, Alexander; Kuperstein, Inna; Kutmon, Martina; Gillespie, Marc E; Funahashi, Akira; Acencio, Marcio Luis; Hemedan, Ahmed; Aichem, Michael; Klein, Karsten; Czauderna, Tobias; Burtscher, Felicia; Yamada, Takahiro G; Hiki, Yusuke; Hiroi, Noriko F; Hu, Finterly; Pham, Nhung; Ehrhart, Friederike; Willighagen, Egon L; Valdeolivas, Alberto; Dugourd, Aurelien; Messina, Francesco; Esteban-Medina, Marina; Peña-Chilet, Maria; Rian, Kinza; Soliman, Sylvain; Aghamiri, Sara Sadat; Puniya, Bhanwar Lal; Naldi, Aurélien; Helikar, Tomáš; Singh, Vidisha; Fernández, Marco Fariñas; Bermudez, Viviam; Tsirvouli, Eirini; Montagud, Arnau; Noël, Vincent; Ponce-de-Leon, Miguel; Maier, Dieter; Bauch, Angela; Gyori, Benjamin M; Bachman, John A; Luna, Augustin; Piñero, Janet; Furlong, Laura I; Balaur, Irina; Rougny, Adrien; Jarosz, Yohan; Overall, Rupert W; Phair, Robert; Perfetto, Livia; Matthews, Lisa; Rex, Devasahayam Arokia Balaya; Orlic-Milacic, Marija; Gomez, Luis Cristobal Monraz; De Meulder, Bertrand; Ravel, Jean Marie; Jassal, Bijay; Satagopam, Venkata; Wu, Guanming; Golebiewski, Martin; Gawron, Piotr; Calzone, Laurence; Beckmann, Jacques S; Evelo, Chris T; D'Eustachio, Peter; Schreiber, Falk; Saez-Rodriguez, Julio; Dopazo, Joaquin; Kuiper, Martin; Valencia, Alfonso; Wolkenhauer, Olaf; Kitano, Hiroaki; Barillot, Emmanuel; Auffray, Charles; Balling, Rudi; Schneider, Reinhard. - In: FRONTIERS IN IMMUNOLOGY. - ISSN 1664-3224. - 14:(2023), pp. 1-24. [10.3389/fimmu.2023.1282859]

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Perfetto, Livia;Valencia, Alfonso;
2023

Abstract

Introduction The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.Methods Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.Results Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.Discussion The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.
2023
SARS-CoV-2; disease maps; dynamic models; large-scale community effort; mechanistic models; systems biology; systems medicine
01 Pubblicazione su rivista::01a Articolo in rivista
Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches / Niarakis, Anna; Ostaszewski, Marek; Mazein, Alexander; Kuperstein, Inna; Kutmon, Martina; Gillespie, Marc E; Funahashi, Akira; Acencio, Marcio Luis; Hemedan, Ahmed; Aichem, Michael; Klein, Karsten; Czauderna, Tobias; Burtscher, Felicia; Yamada, Takahiro G; Hiki, Yusuke; Hiroi, Noriko F; Hu, Finterly; Pham, Nhung; Ehrhart, Friederike; Willighagen, Egon L; Valdeolivas, Alberto; Dugourd, Aurelien; Messina, Francesco; Esteban-Medina, Marina; Peña-Chilet, Maria; Rian, Kinza; Soliman, Sylvain; Aghamiri, Sara Sadat; Puniya, Bhanwar Lal; Naldi, Aurélien; Helikar, Tomáš; Singh, Vidisha; Fernández, Marco Fariñas; Bermudez, Viviam; Tsirvouli, Eirini; Montagud, Arnau; Noël, Vincent; Ponce-de-Leon, Miguel; Maier, Dieter; Bauch, Angela; Gyori, Benjamin M; Bachman, John A; Luna, Augustin; Piñero, Janet; Furlong, Laura I; Balaur, Irina; Rougny, Adrien; Jarosz, Yohan; Overall, Rupert W; Phair, Robert; Perfetto, Livia; Matthews, Lisa; Rex, Devasahayam Arokia Balaya; Orlic-Milacic, Marija; Gomez, Luis Cristobal Monraz; De Meulder, Bertrand; Ravel, Jean Marie; Jassal, Bijay; Satagopam, Venkata; Wu, Guanming; Golebiewski, Martin; Gawron, Piotr; Calzone, Laurence; Beckmann, Jacques S; Evelo, Chris T; D'Eustachio, Peter; Schreiber, Falk; Saez-Rodriguez, Julio; Dopazo, Joaquin; Kuiper, Martin; Valencia, Alfonso; Wolkenhauer, Olaf; Kitano, Hiroaki; Barillot, Emmanuel; Auffray, Charles; Balling, Rudi; Schneider, Reinhard. - In: FRONTIERS IN IMMUNOLOGY. - ISSN 1664-3224. - 14:(2023), pp. 1-24. [10.3389/fimmu.2023.1282859]
File allegati a questo prodotto
File Dimensione Formato  
Niarakis_Drug-target_2023.pdf

accesso aperto

Note: Articolo rivista
Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 6.38 MB
Formato Adobe PDF
6.38 MB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1718957
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 6
  • ???jsp.display-item.citation.isi??? ND
social impact