Cargando…

Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology

Endometriosis is a chronic inflammatory hormone-dependent condition associated with pelvic pain and infertility, characterized by the growth of ectopic endometrium outside the uterus. Given its still unknown etiology, treatments usually aim at diminishing pain and/or achieving pregnancy. Despite som...

Descripción completa

Detalles Bibliográficos
Autores principales: Malvezzi, Helena, Marengo, Eliana Blini, Podgaec, Sérgio, Piccinato, Carla de Azevedo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425005/
https://www.ncbi.nlm.nih.gov/pubmed/32787880
http://dx.doi.org/10.1186/s12967-020-02471-0
_version_ 1783570415516385280
author Malvezzi, Helena
Marengo, Eliana Blini
Podgaec, Sérgio
Piccinato, Carla de Azevedo
author_facet Malvezzi, Helena
Marengo, Eliana Blini
Podgaec, Sérgio
Piccinato, Carla de Azevedo
author_sort Malvezzi, Helena
collection PubMed
description Endometriosis is a chronic inflammatory hormone-dependent condition associated with pelvic pain and infertility, characterized by the growth of ectopic endometrium outside the uterus. Given its still unknown etiology, treatments usually aim at diminishing pain and/or achieving pregnancy. Despite some progress in defining mode-of-action for drug development, the lack of reliable animal models indicates that novel approaches are required. The difficulties inherent to modeling endometriosis are related to its multifactorial nature, a condition that hinders the recreation of its pathology and the identification of clinically relevant metrics to assess drug efficacy. In this review, we report and comment endometriosis models and how they have led to new therapies. We envision a roadmap for endometriosis research, integrating Artificial Intelligence, three-dimensional cultures and organ-on-chip models as ways to achieve better understanding of physiopathological features and better tailored effective treatments.
format Online
Article
Text
id pubmed-7425005
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-74250052020-08-16 Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology Malvezzi, Helena Marengo, Eliana Blini Podgaec, Sérgio Piccinato, Carla de Azevedo J Transl Med Review Endometriosis is a chronic inflammatory hormone-dependent condition associated with pelvic pain and infertility, characterized by the growth of ectopic endometrium outside the uterus. Given its still unknown etiology, treatments usually aim at diminishing pain and/or achieving pregnancy. Despite some progress in defining mode-of-action for drug development, the lack of reliable animal models indicates that novel approaches are required. The difficulties inherent to modeling endometriosis are related to its multifactorial nature, a condition that hinders the recreation of its pathology and the identification of clinically relevant metrics to assess drug efficacy. In this review, we report and comment endometriosis models and how they have led to new therapies. We envision a roadmap for endometriosis research, integrating Artificial Intelligence, three-dimensional cultures and organ-on-chip models as ways to achieve better understanding of physiopathological features and better tailored effective treatments. BioMed Central 2020-08-12 /pmc/articles/PMC7425005/ /pubmed/32787880 http://dx.doi.org/10.1186/s12967-020-02471-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Review
Malvezzi, Helena
Marengo, Eliana Blini
Podgaec, Sérgio
Piccinato, Carla de Azevedo
Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology
title Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology
title_full Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology
title_fullStr Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology
title_full_unstemmed Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology
title_short Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology
title_sort endometriosis: current challenges in modeling a multifactorial disease of unknown etiology
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425005/
https://www.ncbi.nlm.nih.gov/pubmed/32787880
http://dx.doi.org/10.1186/s12967-020-02471-0
work_keys_str_mv AT malvezzihelena endometriosiscurrentchallengesinmodelingamultifactorialdiseaseofunknownetiology
AT marengoelianablini endometriosiscurrentchallengesinmodelingamultifactorialdiseaseofunknownetiology
AT podgaecsergio endometriosiscurrentchallengesinmodelingamultifactorialdiseaseofunknownetiology
AT piccinatocarladeazevedo endometriosiscurrentchallengesinmodelingamultifactorialdiseaseofunknownetiology