Computational design of treatment strategies for proactive therapy on atopic dermatitis using optimal control theory.
Computational design of treatment strategies for proactive therapy on atopic dermatitis using optimal control theory.
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DOI：
10.1098/rsta.2016.0285
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发表时间：
2017-06-28
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影响因子：
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通讯作者：
Tanaka RJ
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文献类型：
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作者：
Christodoulides P;Hirata Y;Domínguez-Hüttinger E;Danby SG;Cork MJ;Williams HC;Aihara K;Tanaka RJ
特应性皮炎（AD）是一种常见的慢性皮肤病，其特征在于反复发生的皮肤炎症和弱的皮肤屏障，并且已知是其他过敏性疾病（如哮喘）的前兆。AD影响全球高达25%的儿童，并且发病率持续上升。在治疗选择、效力、持续时间和频率方面，最佳治疗策略仍存在不确定性。本研究的目的是开发一种计算方法，以设计最佳的治疗策略，临床推荐的“积极治疗”的AD。积极治疗的目的是防止复发耀斑一旦疾病已得到初步控制。通常情况下，这是通过使用抗炎治疗，如一个有效的局部皮质类固醇集中几周来“得到控制”，然后通过间歇性每周治疗，以抑制亚临床炎症“保持控制”。使用我们最近提出的AD发病机制的混合数学模型，我们计算得出的最佳治疗策略，个人虚拟患者队列，递归地解决最优控制问题，使用差分进化算法。我们的模拟结果表明，这种方法可以告知最佳个性化治疗方案的设计，包括局部皮质类固醇和润肤剂的应用，根据每周医院就诊时观察到的患者的疾病状态。我们展示了我们的方法应用于临床环境的潜力和差距。这篇文章是“医学中的数学方法：神经科学，心脏病学和病理学”主题问题的一部分。
Atopic dermatitis (AD) is a common chronic skin disease characterized by recurrent skin inflammation and a weak skin barrier, and is known to be a precursor to other allergic diseases such as asthma. AD affects up to 25% of children worldwide and the incidence continues to rise. There is still uncertainty about the optimal treatment strategy in terms of choice of treatment, potency, duration and frequency. This study aims to develop a computational method to design optimal treatment strategies for the clinically recommended ‘proactive therapy’ for AD. Proactive therapy aims to prevent recurrent flares once the disease has been brought under initial control. Typically, this is done by using an anti-inflammatory treatment such as a potent topical corticosteroid intensively for a few weeks to ‘get control’, followed by intermittent weekly treatment to suppress subclinical inflammation to ‘keep control’. Using a hybrid mathematical model of AD pathogenesis that we recently proposed, we computationally derived the optimal treatment strategies for individual virtual patient cohorts, by recursively solving optimal control problems using a differential evolution algorithm. Our simulation results suggest that such an approach can inform the design of optimal individualized treatment schedules that include application of topical corticosteroids and emollients, based on the disease status of patients observed on their weekly hospital visits. We demonstrate the potential and the gaps of our approach to be applied to clinical settings. This article is part of the themed issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’.