Madrasi Kumpal, Li Fang, Kim Myong-Jin, Samant Snehal, Voss Stephen, Kehoe Theresa, Bashaw E Dennis, Ahn Hae Young, Wang Yaning, Florian Jeffy, Schmidt Stephan, Lesko Lawrence J, Li Li
Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, University of Florida, Gainesville, FL, USA.
J Clin Pharmacol. 2018 May;58(5):572-585. doi: 10.1002/jcph.1071. Epub 2018 Feb 27.
Osteoporosis is a disorder of the bones in which they are weakened to the extent that they become more prone to fracture. There are various forms of osteoporosis: some of them are induced by drugs, and others occur as a chronic progressive disorder as an individual gets older. As the median age of the population rises across the world, the chronic form of the bone disease is drawing attention as an important worldwide health issue. Developing new treatments for osteoporosis and comparing them with existing treatments are complicated processes due to current acceptance by regulatory authorities of bone mineral density (BMD) and fracture risk as clinical end points, which require clinical trials to be large, prolonged, and expensive to determine clinically significant impacts in BMD and fracture risk. Moreover, changes in BMD and fracture risk are not always correlated, with some clinical trials showing BMD improvement without a reduction in fractures. More recently, bone turnover markers specific to bone formation and resorption have been recognized that reflect bone physiology at a cellular level. These bone turnover markers change faster than BMD and fracture risk, and mathematically linking the biomarkers via a computational model to BMD and/or fracture risk may help in predicting BMD and fracture risk changes over time during the progression of a disease or when under treatment. Here, we discuss important concepts of bone physiology, osteoporosis, treatment options, mathematical modeling of osteoporosis, and the use of these models by the pharmaceutical industry and the Food and Drug Administration.
骨质疏松症是一种骨骼疾病,其骨骼会被削弱到更易骨折的程度。骨质疏松症有多种形式:其中一些是由药物引起的,另一些则是随着个体年龄增长而出现的慢性进行性疾病。随着全球人口年龄中位数的上升,这种慢性骨病作为一个重要的全球健康问题正受到关注。由于监管机构目前将骨矿物质密度(BMD)和骨折风险作为临床终点,开发骨质疏松症的新疗法并将其与现有疗法进行比较是复杂的过程,这需要进行大规模、长时间且昂贵的临床试验,以确定对BMD和骨折风险具有临床显著影响。此外,BMD的变化与骨折风险并不总是相关的,一些临床试验显示BMD有所改善,但骨折并未减少。最近,已经认识到反映细胞水平骨生理学的特定于骨形成和骨吸收的骨转换标志物。这些骨转换标志物的变化比BMD和骨折风险更快,通过计算模型将生物标志物与BMD和/或骨折风险进行数学关联,可能有助于预测疾病进展期间或治疗过程中随时间推移的BMD和骨折风险变化。在此,我们讨论骨生理学、骨质疏松症、治疗选择、骨质疏松症的数学建模以及制药行业和食品药品监督管理局对这些模型的使用等重要概念。