Department of Mechanical Engineering, University of Connecticut, Storrs, CT, USA.
Department of Computer Science & Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, UK; Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA.
Comput Methods Programs Biomed. 2023 Apr;231:107419. doi: 10.1016/j.cmpb.2023.107419. Epub 2023 Feb 18.
Osteoarthritis (OA) is a pervasive and debilitating disease, wherein degeneration of cartilage features prominently. Despite extensive research, we do not yet understand the cause or progression of OA. Studies show biochemical, mechanical, and biological factors affect cartilage health. Mechanical loads influence synthesis of biochemical constituents which build and/or break down cartilage, and which in turn affect mechanical loads. OA-associated biochemical profiles activate cellular activity that disrupts homeostasis. To understand the complex interplay among mechanical stimuli, biochemical signaling, and cartilage function requires integrating vast research on experimental mechanics and mechanobiology-a task approachable only with computational models. At present, mechanical models of cartilage generally lack chemo-biological effects, and biochemical models lack coupled mechanics, let alone interactions over time.
We establish a first-of-its kind virtual cartilage: a modeling framework that considers time-dependent, chemo-mechano-biologically induced turnover of key constituents resulting from biochemical, mechanical, and/or biological activity. We include the "minimally essential" yet complex chemical and mechanobiological mechanisms. Our 3-D framework integrates a constitutive model for the mechanics of cartilage with a novel model of homeostatic adaptation by chondrocytes to pathological mechanical stimuli, and a new application of anisotropic growth (loss) to simulate degradation clinically observed as cartilage thinning.
Using a single set of representative parameters, our simulations of immobilizing and overloading successfully captured loss of cartilage quantified experimentally. Simulations of immobilizing, overloading, and injuring cartilage predicted dose-dependent recovery of cartilage when treated with suramin, a proposed therapeutic for OA. The modeling framework prompted us to add growth factors to the suramin treatment, which predicted even better recovery.
Our flexible framework is a first step toward computational investigations of how cartilage and chondrocytes mechanically and biochemically evolve in degeneration of OA and respond to pharmacological therapies. Our framework will enable future studies to link physical activity and resulting mechanical stimuli to progression of OA and loss of cartilage function, facilitating new fundamental understanding of the complex progression of OA and elucidating new perspectives on causes, treatments, and possible preventions.
骨关节炎(OA)是一种普遍且使人虚弱的疾病,其中软骨退化尤为突出。尽管进行了广泛的研究,但我们仍不了解 OA 的病因或进展。研究表明,生化、机械和生物学因素会影响软骨健康。机械负荷会影响生化成分的合成,这些成分会构建和/或分解软骨,进而影响机械负荷。与 OA 相关的生化特征会激活细胞活动,破坏体内平衡。要了解机械刺激、生化信号和软骨功能之间的复杂相互作用,需要整合关于实验力学和机械生物学的大量研究——这是一项只有借助计算模型才能完成的任务。目前,软骨的力学模型通常缺乏化学-生物学效应,而生化模型则缺乏耦合力学,更不用说随时间的相互作用了。
我们建立了一个前所未有的虚拟软骨模型:一个建模框架,该框架考虑了关键成分的时变、化学机械生物诱导的更替,这些更替是由生化、机械和/或生物学活动引起的。我们包括了“最小必要”但复杂的化学和机械生物学机制。我们的 3D 框架将软骨力学的本构模型与软骨细胞对病理性机械刺激的稳态适应的新模型以及用于模拟临床上观察到的软骨变薄等退化的各向异性生长(损失)的新应用结合在一起。
使用一组代表性参数,我们对固定和过载的模拟成功捕捉到了实验量化的软骨损失。对固定、过载和损伤软骨的模拟预测,在用 OA 治疗药物苏拉明治疗时,软骨会出现剂量依赖性的恢复。该建模框架促使我们在苏拉明治疗中添加生长因子,这预测了更好的恢复。
我们的灵活框架是朝着计算研究软骨和软骨细胞在 OA 退变中如何在机械和生化方面演变以及对药物治疗的反应迈出的第一步。我们的框架将使未来的研究能够将身体活动和由此产生的机械刺激与 OA 的进展和软骨功能丧失联系起来,促进对 OA 复杂进展的新的基本理解,并阐明病因、治疗方法和可能的预防措施的新视角。