Cargando…
Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease
Chronic kidney disease (CKD) leads to clinically severe bone loss, resulting from the deranged mineral metabolism that accompanies CKD. Each individual patient presents a unique combination of risk factors, pathologies, and complications of bone disease. The complexity of the disorder coupled with o...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8990896/ https://www.ncbi.nlm.nih.gov/pubmed/35402468 http://dx.doi.org/10.3389/fmed.2022.807994 |
_version_ | 1784683474824724480 |
---|---|
author | Gaweda, Adam E. Lederer, Eleanor D. Brier, Michael E. |
author_facet | Gaweda, Adam E. Lederer, Eleanor D. Brier, Michael E. |
author_sort | Gaweda, Adam E. |
collection | PubMed |
description | Chronic kidney disease (CKD) leads to clinically severe bone loss, resulting from the deranged mineral metabolism that accompanies CKD. Each individual patient presents a unique combination of risk factors, pathologies, and complications of bone disease. The complexity of the disorder coupled with our incomplete understanding of the pathophysiology has significantly hampered the ability of nephrologists to prevent fractures, a leading comorbidity of CKD. Much has been learned from animal models; however, we propose in this review that application of multiple techniques of mathematical modeling and artificial intelligence can accelerate our ability to develop relevant and impactful clinical trials and can lead to better understanding of the osteoporosis of CKD. We highlight the foundational work that informed our current model development and discuss the potential applications of our approach combining principles of quantitative systems pharmacology, model predictive control, and reinforcement learning to deliver individualized precision medical therapy of this highly complex disorder. |
format | Online Article Text |
id | pubmed-8990896 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89908962022-04-09 Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease Gaweda, Adam E. Lederer, Eleanor D. Brier, Michael E. Front Med (Lausanne) Medicine Chronic kidney disease (CKD) leads to clinically severe bone loss, resulting from the deranged mineral metabolism that accompanies CKD. Each individual patient presents a unique combination of risk factors, pathologies, and complications of bone disease. The complexity of the disorder coupled with our incomplete understanding of the pathophysiology has significantly hampered the ability of nephrologists to prevent fractures, a leading comorbidity of CKD. Much has been learned from animal models; however, we propose in this review that application of multiple techniques of mathematical modeling and artificial intelligence can accelerate our ability to develop relevant and impactful clinical trials and can lead to better understanding of the osteoporosis of CKD. We highlight the foundational work that informed our current model development and discuss the potential applications of our approach combining principles of quantitative systems pharmacology, model predictive control, and reinforcement learning to deliver individualized precision medical therapy of this highly complex disorder. Frontiers Media S.A. 2022-03-25 /pmc/articles/PMC8990896/ /pubmed/35402468 http://dx.doi.org/10.3389/fmed.2022.807994 Text en Copyright © 2022 Gaweda, Lederer and Brier. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Gaweda, Adam E. Lederer, Eleanor D. Brier, Michael E. Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease |
title | Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease |
title_full | Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease |
title_fullStr | Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease |
title_full_unstemmed | Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease |
title_short | Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease |
title_sort | use of artificial intelligence to identify new mechanisms and approaches to therapy of bone disorders associated with chronic kidney disease |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8990896/ https://www.ncbi.nlm.nih.gov/pubmed/35402468 http://dx.doi.org/10.3389/fmed.2022.807994 |
work_keys_str_mv | AT gawedaadame useofartificialintelligencetoidentifynewmechanismsandapproachestotherapyofbonedisordersassociatedwithchronickidneydisease AT lederereleanord useofartificialintelligencetoidentifynewmechanismsandapproachestotherapyofbonedisordersassociatedwithchronickidneydisease AT briermichaele useofartificialintelligencetoidentifynewmechanismsandapproachestotherapyofbonedisordersassociatedwithchronickidneydisease |