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Human-Based Immune Responsive In Vitro Infection Models for Validation of Novel TLR4 Antagonists Identified by Computational Discovery
Infectious diseases are still a major problem worldwide. This includes microbial infections, with a constant increase in resistance to the current anti-infectives employed. Toll-like receptors (TLRs) perform a fundamental role in pathogen recognition and activation of the innate immune response. Pro...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8876567/ https://www.ncbi.nlm.nih.gov/pubmed/35208698 http://dx.doi.org/10.3390/microorganisms10020243 |
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author | Merk, Helena Amran-Gealia, Tehila Finkelmeier, Doris Kohl, Christina Pichota, Isabelle Stern, Noa Rupp, Steffen Goldblum, Amiram Burger-Kentischer, Anke |
author_facet | Merk, Helena Amran-Gealia, Tehila Finkelmeier, Doris Kohl, Christina Pichota, Isabelle Stern, Noa Rupp, Steffen Goldblum, Amiram Burger-Kentischer, Anke |
author_sort | Merk, Helena |
collection | PubMed |
description | Infectious diseases are still a major problem worldwide. This includes microbial infections, with a constant increase in resistance to the current anti-infectives employed. Toll-like receptors (TLRs) perform a fundamental role in pathogen recognition and activation of the innate immune response. Promising new approaches to combat infections and inflammatory diseases involve modulation of the host immune system via TLR4. TLR4 and its co-receptors MD2 and CD14 are required for immune response to fungal and bacterial infection by recognition of microbial cell wall components, making it a prime target for drug development. To evaluate the efficacy of anti-infective compounds early on, we have developed a series of human-based immune responsive infection models, including immune responsive 3D-skin infection models for modeling fungal infections. By using computational methods: pharmacophore modeling and molecular docking, we identified a set of 46 potential modulators of TLR4, which were screened in several tests systems of increasing complexity, including immune responsive 3D-skin infection models. We could show a strong suppression of cytokine and chemokine response induced by lipopolysacharide (LPS) and Candida albicans for individual compounds. The development of human-based immune responsive assays provides a more accurate and reliable basis for development of new anti-inflammatory or immune-modulating drugs. |
format | Online Article Text |
id | pubmed-8876567 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88765672022-02-26 Human-Based Immune Responsive In Vitro Infection Models for Validation of Novel TLR4 Antagonists Identified by Computational Discovery Merk, Helena Amran-Gealia, Tehila Finkelmeier, Doris Kohl, Christina Pichota, Isabelle Stern, Noa Rupp, Steffen Goldblum, Amiram Burger-Kentischer, Anke Microorganisms Article Infectious diseases are still a major problem worldwide. This includes microbial infections, with a constant increase in resistance to the current anti-infectives employed. Toll-like receptors (TLRs) perform a fundamental role in pathogen recognition and activation of the innate immune response. Promising new approaches to combat infections and inflammatory diseases involve modulation of the host immune system via TLR4. TLR4 and its co-receptors MD2 and CD14 are required for immune response to fungal and bacterial infection by recognition of microbial cell wall components, making it a prime target for drug development. To evaluate the efficacy of anti-infective compounds early on, we have developed a series of human-based immune responsive infection models, including immune responsive 3D-skin infection models for modeling fungal infections. By using computational methods: pharmacophore modeling and molecular docking, we identified a set of 46 potential modulators of TLR4, which were screened in several tests systems of increasing complexity, including immune responsive 3D-skin infection models. We could show a strong suppression of cytokine and chemokine response induced by lipopolysacharide (LPS) and Candida albicans for individual compounds. The development of human-based immune responsive assays provides a more accurate and reliable basis for development of new anti-inflammatory or immune-modulating drugs. MDPI 2022-01-22 /pmc/articles/PMC8876567/ /pubmed/35208698 http://dx.doi.org/10.3390/microorganisms10020243 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Merk, Helena Amran-Gealia, Tehila Finkelmeier, Doris Kohl, Christina Pichota, Isabelle Stern, Noa Rupp, Steffen Goldblum, Amiram Burger-Kentischer, Anke Human-Based Immune Responsive In Vitro Infection Models for Validation of Novel TLR4 Antagonists Identified by Computational Discovery |
title | Human-Based Immune Responsive In Vitro Infection Models for Validation of Novel TLR4 Antagonists Identified by Computational Discovery |
title_full | Human-Based Immune Responsive In Vitro Infection Models for Validation of Novel TLR4 Antagonists Identified by Computational Discovery |
title_fullStr | Human-Based Immune Responsive In Vitro Infection Models for Validation of Novel TLR4 Antagonists Identified by Computational Discovery |
title_full_unstemmed | Human-Based Immune Responsive In Vitro Infection Models for Validation of Novel TLR4 Antagonists Identified by Computational Discovery |
title_short | Human-Based Immune Responsive In Vitro Infection Models for Validation of Novel TLR4 Antagonists Identified by Computational Discovery |
title_sort | human-based immune responsive in vitro infection models for validation of novel tlr4 antagonists identified by computational discovery |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8876567/ https://www.ncbi.nlm.nih.gov/pubmed/35208698 http://dx.doi.org/10.3390/microorganisms10020243 |
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