期刊
FRONTIERS IN MEDICINE
卷 9, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fmed.2022.807994
关键词
osteoporosis; chronic kidney disease; artificial intelligence; mathematical modeling; in silico clinical trials
资金
- Department of Veterans Affairs Merit Review Board [I01 CX001614/CX/CSRD VA/United States]
- John S. Fordtran Endowed Professorship, University of Texas Southwestern Medical Center
Chronic kidney disease (CKD) leads to severe bone loss, but the incomplete understanding of the pathophysiology and complexity of the disease hampers fracture prevention. This review proposes the use of mathematical modeling and artificial intelligence techniques to accelerate clinical trials and improve understanding of CKD osteoporosis. It also discusses the potential application of individualized precision medical therapy using principles of quantitative systems pharmacology, model predictive control, and reinforcement learning.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据