Although levodopa remains the most efficacious symptomatic therapy for Parkinson disease (PD), management of levodopa treatment during the advanced stages of the disease is extremely challenging. This difficulty is a result of levodopa’s short half-life, a progressive narrowing of the therapeutic window, and major inter-patient and intra-patient variations in the dose–response relationship. Therefore, a suitable alternative to repeated oral administration of levodopa is being sought. Recent research efforts have focused on the development of novel levodopa delivery strategies and wearable physical sensors that track symptoms and disease progression. However, the need for methods to monitor the levels of levodopa present in the body in real time has been overlooked. Advances in chemical sensor technology mean that the development of wearable and mobile biosensors for continuous or frequent levodopa measurements is now possible. Such levodopa monitoring could help to deliver personalized and timely medication dosing to alleviate treatment-related fluctuations in the symptoms of PD. Therefore, with the aim of optimizing therapeutic management of PD and improving the quality of life of patients, we share our vision of a future closed-loop autonomous wearable ‘sense-and-act’ system. This system consists of a network of physical and chemical sensors coupled with a levodopa delivery device and is guided by effective big data fusion algorithms and machine learning methods.

尽管左旋多巴仍然是治疗帕金森病 (PD) 最有效的对症疗法，但在疾病晚期进行左旋多巴治疗极具挑战性。这种困难是由于左旋多巴的半衰期短、治疗窗逐渐变窄以及剂量-反应关系在患者间和患者体内的主要差异所致。因此，正在寻找一种合适的替代方法来替代重复口服左旋多巴。最近的研究工作集中在开发新型左旋多巴递送策略和跟踪症状和疾病进展的可穿戴物理传感器。然而，人们忽视了对实时监测体内左旋多巴水平的方法的需求。化学传感器技术的进步意味着现在可以开发用于连续或频繁测量左旋多巴的可穿戴和移动生物传感器。这种左旋多巴监测有助于提供个性化和及时的药物剂量，以减轻与治疗相关的 PD 症状波动。因此，为了优化 PD 的治疗管理和提高患者的生活质量，我们分享了我们对未来闭环自主可穿戴“感知和行动”系统的愿景。该系统由物理和化学传感器网络与左旋多巴输送装置组成，并以有效的大数据融合算法和机器学习方法为指导。这种左旋多巴监测有助于提供个性化和及时的药物剂量，以减轻与治疗相关的 PD 症状波动。因此，为了优化 PD 的治疗管理和提高患者的生活质量，我们分享了我们对未来闭环自主可穿戴“感知和行动”系统的愿景。该系统由物理和化学传感器网络与左旋多巴输送装置组成，并以有效的大数据融合算法和机器学习方法为指导。这种左旋多巴监测有助于提供个性化和及时的药物剂量，以减轻与治疗相关的 PD 症状波动。因此，为了优化 PD 的治疗管理和提高患者的生活质量，我们分享了我们对未来闭环自主可穿戴“感知和行动”系统的愿景。该系统由物理和化学传感器网络与左旋多巴输送装置组成，并以有效的大数据融合算法和机器学习方法为指导。