School of Mechanical, Medical and Process Engineering, Queensland University of Technology, QLD 4000, Australia.
Departmento de Ingeniería Mecánica y Fabricación, Universidad de Sevilla, Seville 41092, Spain.
Bone. 2024 Sep;186:117140. doi: 10.1016/j.bone.2024.117140. Epub 2024 Jun 3.
Osteoporosis (OP) is a chronic progressive bone disease which is characterised by reduction of bone matrix volume and changes in the bone matrix properties which can ultimately lead to bone fracture. The two major forms of OP are related to aging and/or menopause. With the worldwide increase of the elderly population, particularly age-related OP poses a serious health issue which puts large pressure on health care systems. A major challenge for development of new drug treatments for OP and comparison of drug efficacy with existing treatments is due to current regulatory requirements which demand testing of drugs based on bone mineral density (BMD) in phase 2 trials and fracture risk in phase 3 trials. This requires large clinical trials to be conducted and to be run for long time periods, which is very costly. This, together with the fact that there are already many drugs available for treatment of OP, makes the development of new drugs inhibitive. Furthermore, an increased trend of the use of different sequential drug therapies has been observed in OP management, such as sequential anabolic-anticatabolic drug treatment or switching from one anticatabolic drug to another. Running clinical trials for concurrent and sequential therapies is neither feasible nor practical due to large number of combinatorial possibilities. In silico mechanobiological pharmacokinetic-pharmacodynamic (PK-PD) models of OP treatments allow predictions beyond BMD, i.e. bone microdamage and degree of mineralisation can also be monitored. This will help to inform clinical drug usage and development by identifying the most promising scenarios to be tested clinically (confirmatory trials rather than exploratory only trials), optimise trial design and identify subgroups of the population that show benefit-risk profiles (both good and bad) that are different from the average patient. In this review, we provide examples of the predictive capabilities of mechanobiological PK-PD models. These include simulation results of PMO treatment with denosumab, implications of denosumab drug holidays and coupling of bone remodelling models with calcium and phosphate systems models that allows to investigate the effects of co-morbidities such as hyperparathyroidism and chronic kidney disease together with calcium and vitamin D status on drug efficacy.
骨质疏松症(OP)是一种慢性进行性骨骼疾病,其特征是骨基质体积减少和骨基质特性改变,最终导致骨折。OP 的两种主要形式与衰老和/或绝经有关。随着全球老年人口的增加,特别是与年龄相关的 OP 给医疗保健系统带来了严重的健康问题。开发新的 OP 药物治疗方法并比较现有治疗方法的疗效的主要挑战是由于当前的监管要求,这些要求要求在 2 期试验中基于骨密度(BMD)和 3 期试验中骨折风险测试药物。这需要进行大型临床试验并进行长时间运行,这非常昂贵。再加上已经有许多药物可用于 OP 的治疗,这使得新药的开发受到抑制。此外,在 OP 管理中已经观察到不同序贯药物治疗方法的使用趋势增加,例如序贯合成代谢-抗分解代谢药物治疗或从一种抗分解代谢药物转换为另一种。由于组合可能性众多,同时进行序贯治疗的临床试验既不可行也不切实际。OP 治疗的计算机模拟机械生物学药代动力学-药效学(PK-PD)模型允许超出 BMD 的预测,即也可以监测骨微损伤和矿化程度。这将有助于通过确定最有希望进行临床测试的方案(确证性试验而不是仅探索性试验)来告知临床药物使用和开发,优化试验设计并确定显示受益风险特征(好与坏)与平均患者不同的人群亚组。在这篇综述中,我们提供了机械生物学 PK-PD 模型的预测能力的示例。这些包括使用地舒单抗治疗 PMO 的模拟结果、地舒单抗药物假期的影响以及将骨重建模型与钙和磷酸盐系统模型耦合,这允许研究共病(如甲状旁腺功能亢进和慢性肾病)以及钙和维生素 D 状态对药物疗效的影响。