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Schultz B, DeLong LN, Masny A, Lentzen M, Raschka T, van Dijk D, Zaliani A, Fröhlich H. A machine learning method for the identification and characterization of novel COVID-19 drug targets. Sci Rep 2023; 13:7159. [PMID: 37137934 PMCID: PMC10156718 DOI: 10.1038/s41598-023-34287-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 04/27/2023] [Indexed: 05/05/2023] Open
Abstract
In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. In order to contribute to this effort, we present GuiltyTargets-COVID-19 ( https://guiltytargets-covid.eu/ ), a machine learning supported web tool to identify novel candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with a lung tissue specific protein-protein interaction network, we demonstrate that GuiltyTargets-COVID-19 is capable of (i) prioritizing meaningful target candidates and assessing their druggability, (ii) unraveling their linkage to known disease mechanisms, (iii) mapping ligands from the ChEMBL database to the identified targets, and (iv) pointing out potential side effects in the case that the mapped ligands correspond to approved drugs. Our example analyses identified 4 potential drug targets from the datasets: AKT3 from both the bulk and single cell RNA-Seq data as well as AKT2, MLKL, and MAPK11 in the single cell experiments. Altogether, we believe that our web tool will facilitate future target identification and drug development for COVID-19, notably in a cell type and tissue specific manner.
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Affiliation(s)
- Bruce Schultz
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt, Augustin, Germany
- Fraunhofer Center for Machine Learning, Sankt, Germany
| | - Lauren Nicole DeLong
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt, Augustin, Germany
- Artificial Intelligence and its Applications Institute, University of Edinburgh School of Informatics, 10 Crichton St, Edinburgh, EH8 9AB, UK
| | - Aliaksandr Masny
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt, Augustin, Germany
- Fraunhofer Center for Machine Learning, Sankt, Germany
| | - Manuel Lentzen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt, Augustin, Germany
- University of Bonn, Bonn-Aachen Center for IT (b-it), Friedrich Hirzebruch-Allee 6, 53115, Bonn, Germany
| | - Tamara Raschka
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt, Augustin, Germany
- University of Bonn, Bonn-Aachen Center for IT (b-it), Friedrich Hirzebruch-Allee 6, 53115, Bonn, Germany
- Fraunhofer Center for Machine Learning, Sankt, Germany
| | - David van Dijk
- Center for Biomedical Data Science, Yale School of Medicine, Yale University, 333 Cedar Street, New Haven, CT, 06510, USA
| | - Andrea Zaliani
- Fraunhofer Institute for Translational Medicine and Pharmacologie (ITMP), Drug Discovery Research ScreeningPort, VolksparkLabs, Schnackenburgallee 114, 22535, Hamburg, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt, Augustin, Germany.
- University of Bonn, Bonn-Aachen Center for IT (b-it), Friedrich Hirzebruch-Allee 6, 53115, Bonn, Germany.
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Nandi S, Nayak BS, Khede MK, Saxena AK. Repurposing of Chemotherapeutics to Combat COVID-19. Curr Top Med Chem 2022; 22:2660-2694. [PMID: 36453483 DOI: 10.2174/1568026623666221130142517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/16/2022] [Accepted: 10/06/2022] [Indexed: 12/05/2022]
Abstract
Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is a novel strain of SARS coronavirus. The COVID-19 disease caused by this virus was declared a pandemic by the World Health Organization (WHO). SARS-CoV-2 mainly spreads through droplets sprayed by coughs or sneezes of the infected to a healthy person within the vicinity of 6 feet. It also spreads through asymptomatic carriers and has negative impact on the global economy, security and lives of people since 2019. Numerous lives have been lost to this viral infection; hence there is an emergency to build up a potent measure to combat SARS-CoV-2. In view of the non-availability of any drugs or vaccines at the time of its eruption, the existing antivirals, antibacterials, antimalarials, mucolytic agents and antipyretic paracetamol were used to treat the COVID-19 patients. Still there are no specific small molecule chemotherapeutics available to combat COVID-19 except for a few vaccines approved for emergency use only. Thus, the repurposing of chemotherapeutics with the potential to treat COVID-19 infected people is being used. The antiviral activity for COVID-19 and biochemical mechanisms of the repurposed drugs are being explored by the biological assay screening and structure-based in silico docking simulations. The present study describes the various US-FDA approved chemotherapeutics repositioned to combat COVID-19 along with their screening for biological activity, pharmacokinetic and pharmacodynamic evaluation.
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Affiliation(s)
- Sisir Nandi
- Department of Pharmaceutical Chemistry, Global Institute of Pharmaceutical Education and Research, Affiliated to Uttarakhand Technical University, Kashipur, 244713, India
| | - Bhabani Shankar Nayak
- Department of Pharmaceutics, Institute of Pharmacy and Technology, Salipur, Affiliated to Biju Patnaik University of Technology, Odisha, 754202, India
| | - Mayank Kumar Khede
- Department of Pharmaceutics, Institute of Pharmacy and Technology, Salipur, Affiliated to Biju Patnaik University of Technology, Odisha, 754202, India
| | - Anil Kumar Saxena
- Department of Pharmaceutical Chemistry, Global Institute of Pharmaceutical Education and Research, Affiliated to Uttarakhand Technical University, Kashipur, 244713, India
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Fusar-Poli P, Manchia M, Koutsouleris N, Leslie D, Woopen C, Calkins ME, Dunn M, Tourneau CL, Mannikko M, Mollema T, Oliver D, Rietschel M, Reininghaus EZ, Squassina A, Valmaggia L, Kessing LV, Vieta E, Correll CU, Arango C, Andreassen OA. Ethical considerations for precision psychiatry: A roadmap for research and clinical practice. Eur Neuropsychopharmacol 2022; 63:17-34. [PMID: 36041245 DOI: 10.1016/j.euroneuro.2022.08.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/04/2022] [Accepted: 08/05/2022] [Indexed: 12/14/2022]
Abstract
Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.
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Affiliation(s)
- Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy; Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy; Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada
| | | | | | | | - Monica E Calkins
- Neurodevelopment and Psychosis Section and Lifespan Brain Institute of Penn/CHOP, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - Michael Dunn
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore
| | - Christophe Le Tourneau
- Institut Curie, Department of Drug Development and Innovation (D3i), INSERM U900 Research unit, Paris-Saclay University, France
| | - Miia Mannikko
- European Federation of Associations of Families of People with Mental Illness (EUFAMI), Leuven, Belgium
| | - Tineke Mollema
- Global Alliance of Mental Illness Advocacy Networks-Europe (GAMIAN), Brussels, Belgium
| | - Dominic Oliver
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Eva Z Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Alessio Squassina
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Italy
| | - Lucia Valmaggia
- South London and Maudsley NHS Foundation Trust, London, UK; Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Psychiatry, KU Leuven, Belgium
| | - Lars Vedel Kessing
- Copenhagen Affective disorder Research Center (CADIC), Psychiatric Center Copenhagen, Denmark; Department of clinical Medicine, University of Copenhagen, Denmark
| | - Eduard Vieta
- Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Christoph U Correll
- The Zucker Hillside Hospital, Department of Psychiatry, Northwell Health, Glen Oaks, NY, USA; Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Center for Psychiatric Neuroscience; The Feinstein Institutes for Medical Research, Manhasset, NY, USA; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Gregorio Marañón; Health Research Institute (IiGSM), School of Medicine, Universidad Complutense de Madrid; Biomedical Research Center for Mental Health (CIBERSAM), Madrid, Spain
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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Schultz B, Wehr M, Witters H, Escher S, Jacobs M. P01-03 Integration of adverse outcome pathways with knowledge graphs. Toxicol Lett 2022. [DOI: 10.1016/j.toxlet.2022.07.247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Lage-Rupprecht V, Schultz B, Dick J, Namysl M, Zaliani A, Gebel S, Pless O, Reinshagen J, Ellinger B, Ebeling C, Esser A, Jacobs M, Claussen C, Hofmann-Apitius M. A hybrid approach unveils drug repurposing candidates targeting an Alzheimer pathophysiology mechanism. Patterns (N Y) 2022; 3:100433. [PMID: 35510183 DOI: 10.1016/j.patter.2021.100433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/30/2021] [Accepted: 12/23/2021] [Indexed: 01/04/2023]
Abstract
The high number of failed pre-clinical and clinical studies for compounds targeting Alzheimer disease (AD) has demonstrated that there is a need to reassess existing strategies. Here, we pursue a holistic, mechanism-centric drug repurposing approach combining computational analytics and experimental screening data. Based on this integrative workflow, we identified 77 druggable modifiers of tau phosphorylation (pTau). One of the upstream modulators of pTau, HDAC6, was screened with 5,632 drugs in a tau-specific assay, resulting in the identification of 20 repurposing candidates. Four compounds and their known targets were found to have a link to AD-specific genes. Our approach can be applied to a variety of AD-associated pathophysiological mechanisms to identify more repurposing candidates. Drug-repurposing approach that combines in silico analyses and in vitro screenings A drug- and mechanism-oriented model, the Human Brain Pharmacome (HBP) was created The HBP was used to mine data related to drugs and targets to generate a hypothesis Experimental evidence validated predicted drug-target combinations
Owing to current setbacks in the discovery and development of novel treatments tackling Alzheimer disease (AD), a re-evaluation of research and development (R&D) strategies is underway. Here, we present a holistic pharmacological approach that combines drug-target information with knowledge graphs that represent essential pathophysiology mechanisms. The resulting Human Brain Pharmacome (HBP) embeds hundreds of relevant drug-target interactions in the context of disease mechanisms governing AD. We demonstrate how such a tool can be used to aid AD research by identifying already-approved drugs that have the potential to treat the disease, thereby bypassing the expensive and time-consuming task of researching and developing a new drug. In our study, we identified new drug-target combinations and provided mechanistic explanations that help to improve our understanding of AD pathology and support future development of effective therapeutic strategies.
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Linden T, Hanses F, Domingo-Fernández D, DeLong LN, Kodamullil AT, Schneider J, Vehreschild MJGT, Lanznaster J, Ruethrich MM, Borgmann S, Hower M, Wille K, Feldt T, Rieg S, Hertenstein B, Wyen C, Roemmele C, Vehreschild JJ, Jakob CEM, Stecher M, Kuzikov M, Zaliani A, Fröhlich H. Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases. Artif Intell Life Sci 2021; 1:100020. [PMID: 34988543 PMCID: PMC8677630 DOI: 10.1016/j.ailsci.2021.100020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 11/22/2021] [Accepted: 11/22/2021] [Indexed: 02/08/2023]
Abstract
Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center 'Lean European Open Survey on SARS-CoV-2-infected patients' (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.
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Affiliation(s)
- Thomas Linden
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- University of Bonn, Bonn-Aachen International Center for IT, Friedrich Hirzebruch-Allee 6, 53115 Bonn, Germany
| | - Frank Hanses
- Emergency Department, University Hospital Regensburg, 93053 Regensburg, Germany
- Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Germany
| | - Daniel Domingo-Fernández
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Lauren Nicole DeLong
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- University of Bonn, Bonn-Aachen International Center for IT, Friedrich Hirzebruch-Allee 6, 53115 Bonn, Germany
| | - Alpha Tom Kodamullil
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Jochen Schneider
- Technical University of Munich, School of Medicine, University Hospital rechts der Isar, Department of Internal Medicine II, 81675 Munich, Germany
| | - Maria J G T Vehreschild
- Department II of Internal Medicine, Infectious Diseases, University Hospital Frankfurt, Goethe University, 60590 Frankfurt, Germany
| | - Julia Lanznaster
- Department of Internal Medicine II, Hospital Passau, Innstraße 76, 94032 Passau, Germany
| | - Maria Madeleine Ruethrich
- Institute for Infection Medicine and Hospital Hygiene, University Hospital Jena, 07743 Jena, Germany
| | - Stefan Borgmann
- Department of Infectious Diseases and Infection Control, Hospital Ingolstadt, 85049 Ingolstadt, Germany
| | - Martin Hower
- Department of Pneumology, Infectious Diseases and Intensive Care, Klinikum Dortmund gGmbH, Hospital of University Witten / Herdecke, 44137 Dortmund, Germany
| | - Kai Wille
- University Clinic for Haematology, Oncology, Haemostaseology and Palliative Care, Johannes Wesling Medical Centre Minden, 32429 Minden, Germany
| | - Torsten Feldt
- Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University Düsseldorf, Moorenstrasse 5, 40225 Düsseldorf, Germany
| | - Siegbert Rieg
- Department of Medicine II, University Hospital Freiburg, 79110 Freiburg, Germany
| | - Bernd Hertenstein
- Department of Medicine II, University Hospital Freiburg, 79110 Freiburg, Germany
| | - Christoph Wyen
- Christoph Wyen, Praxis am Ebertplatz Cologne, 50668 Cologne, Germany
| | - Christoph Roemmele
- Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, 86156 Augsburg, Germany
| | - Jörg Janne Vehreschild
- Department II of Internal Medicine, Infectious Diseases, University Hospital Frankfurt, Goethe University, 60590 Frankfurt, Germany
| | - Carolin E M Jakob
- Department I for Internal Medicine, University Hospital of Cologne, University of Cologne, 50931 Cologne, Germany
| | - Melanie Stecher
- Fraunhofer Institute for Translational Medicine and Pharmacologie (ITMP), VolksparkLabs, Schnackenburgallee 114, 22535 Hamburg, Germany
| | - Maria Kuzikov
- Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Germany
| | - Andrea Zaliani
- Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Germany
| | - Holger Fröhlich
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- University of Bonn, Bonn-Aachen International Center for IT, Friedrich Hirzebruch-Allee 6, 53115 Bonn, Germany
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