Christodoulides Panayiotis, Hirata Yoshito, Domínguez-Hüttinger Elisa, Danby Simon G, Cork Michael J, Williams Hywel C, Aihara Kazuyuki, Tanaka Reiko J
Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.
Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan.
Philos Trans A Math Phys Eng Sci. 2017 Jun 28;375(2096). doi: 10.1098/rsta.2016.0285.
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'.
特应性皮炎（AD）是一种常见的慢性皮肤病，其特征为皮肤炎症反复发作且皮肤屏障功能薄弱，并且已知是哮喘等其他过敏性疾病的先兆。全球多达25%的儿童受AD影响，且发病率持续上升。在治疗选择、效力、持续时间和频率方面，最佳治疗策略仍不明确。本研究旨在开发一种计算方法，为临床上推荐的AD“积极治疗”设计最佳治疗策略。积极治疗旨在在疾病初步得到控制后预防复发。通常，这是通过密集使用强效外用皮质类固醇等抗炎治疗数周以“控制病情”，然后进行每周一次的间歇性治疗以抑制亚临床炎症来“维持控制”。利用我们最近提出的AD发病机制的混合数学模型，我们通过使用差分进化算法递归求解最优控制问题，为各个虚拟患者队列计算得出了最佳治疗策略。我们的模拟结果表明，这种方法可为最佳个体化治疗方案的设计提供参考，该方案包括根据患者每周门诊观察到的疾病状况应用外用皮质类固醇和润肤剂。我们展示了我们的方法应用于临床环境的潜力和差距。本文是主题为“医学中的数学方法：神经科学、心脏病学和病理学”的特刊的一部分。
