Gaweda Adam E, Lederer Eleanor D, Brier Michael E
Division of Nephrology, Department of Medicine, University of Louisville School of Medicine, Louisville, KY, United States.
Medical Services, VA North Texas Health Sciences Center, Dallas, TX, United States.
Front Med (Lausanne). 2022 Mar 25;9:807994. doi: 10.3389/fmed.2022.807994. eCollection 2022.
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.
慢性肾脏病（CKD）会导致临床上严重的骨质流失，这是由CKD伴随的矿物质代谢紊乱所致。每位患者都呈现出骨病危险因素、病理状况及并发症的独特组合。这种疾病的复杂性，加上我们对其病理生理学的不完全理解，严重阻碍了肾病学家预防骨折（CKD的一种主要合并症）的能力。从动物模型中我们已经了解到很多；然而，在本综述中我们提出，应用多种数学建模和人工智能技术可以加快我们开展相关且有影响力的临床试验的能力，并能使我们更好地理解CKD的骨质疏松症。我们强调为当前模型开发提供依据的基础工作，并讨论我们结合定量系统药理学原理、模型预测控制和强化学习方法的潜在应用，以提供针对这种高度复杂疾病的个体化精准医疗。
