Pichardo-Almarza Cesar, Diaz-Zuccarini Vanessa
Multiscale Cardiovascular Engineering Group (MUSE), Department of Mechanical Engineering, University College LondonLondon, United Kingdom.
Institute of Healthcare Engineering, University College LondonLondon, United Kingdom.
Front Pharmacol. 2017 Sep 13;8:635. doi: 10.3389/fphar.2017.00635. eCollection 2017.
Statins are one of the most prescribed drugs to treat atherosclerosis. They inhibit the hepatic HMG-CoA reductase, causing a reduction of circulating cholesterol and LDL levels. Statins have had undeniable success; however, the benefits of statin therapy crystallize only if patients adhere to the prescribed treatment, which is far away from reality since adherence decreases with time with around half of patients discontinue statin therapy within the first year. The objective of this work is to; firstly, demonstrate a formal methodology based on a hybrid, multiscale mathematical model used to study the effect of statin treatment on atherosclerosis under different patient scenarios, including cases where the influence of medication adherence is examined and secondly, to propose a flexible simulation framework that allows extensions or simplifications, allowing the possibility to design other complex simulation strategies, both interesting features for software development. Different mathematical modeling paradigms are used to present the relevant dynamic behavior observed in biological/physiological data and clinical trials. A combination of continuous and discrete event models are coupled to simulate the pharmacokinetics (PK) of statins, their pharmacodynamic (PD) effect on lipoproteins levels (e.g., LDL) and relevant inflammatory pathways whilst simultaneously studying the dynamic effect of flow-related variables on atherosclerosis progression. Different scenarios were tested showing the impact of: (1) patient variability: a virtual population shows differences in plaque growth for different individuals could be as high as 100%; (2) statin effect on atherosclerosis: it is shown how a patient with a 1-year statin treatment will reduce his plaque growth by 2-3% in a 2-year period; (3) medical adherence: we show that a patient missing 10% of the total number of doses could increase the plaque growth by ~1% (after 2 years) compared to the same "regular" patient under a 1-year treatment with statins. The results in this paper describe the effect of pharmacological intervention combined with biological/physiological or behavioral factors in atherosclerosis progression and treatment in specific patients. It also provides an exemplar of basic research that can be practically developed into an application software.
他汀类药物是治疗动脉粥样硬化最常用的处方药之一。它们抑制肝脏中的HMG-CoA还原酶，从而降低循环胆固醇和低密度脂蛋白水平。他汀类药物取得了不可否认的成功；然而，只有患者坚持规定的治疗，他汀类药物治疗的益处才能显现出来，但这与现实相差甚远，因为依从性会随着时间的推移而降低，大约一半的患者在第一年就会停止他汀类药物治疗。这项工作的目的是：首先，展示一种基于混合多尺度数学模型的形式化方法，用于研究他汀类药物治疗在不同患者情况下对动脉粥样硬化的影响，包括检查药物依从性影响的情况；其次，提出一个灵活的模拟框架，允许进行扩展或简化，从而有可能设计其他复杂的模拟策略，这两个特性对软件开发都很有意义。使用不同的数学建模范式来呈现生物/生理数据和临床试验中观察到的相关动态行为。连续模型和离散事件模型相结合，以模拟他汀类药物的药代动力学（PK）、它们对脂蛋白水平（如低密度脂蛋白）和相关炎症途径的药效学（PD）作用，同时研究血流相关变量对动脉粥样硬化进展的动态影响。测试了不同的情况，展示了以下影响：（1）患者变异性：一个虚拟人群显示，不同个体的斑块生长差异可能高达100%；（2）他汀类药物对动脉粥样硬化的作用：显示了接受1年他汀类药物治疗的患者在2年内其斑块生长将如何减少2 - 3%；（3）药物依从性：我们表明，与接受1年他汀类药物治疗的相同“常规”患者相比，错过总剂量10%的患者（2年后）斑块生长可能增加约1%。本文的结果描述了药物干预与生物/生理或行为因素相结合对特定患者动脉粥样硬化进展和治疗的影响。它还提供了一个基础研究的范例，该范例实际上可以开发成应用软件。
