Faculté de Pharmacie, Université de Montréal, Montréal, QC, Canada.
Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, 40136, Italy.
J Pharmacokinet Pharmacodyn. 2021 Feb;48(1):133-148. doi: 10.1007/s10928-020-09723-y. Epub 2020 Oct 21.
Levodopa is considered the gold standard treatment of Parkinson's disease. Although very effective in alleviating symptoms at their onset, its chronic use with the progressive neuronal denervation in the basal ganglia leads to a decrease in levodopa's effect duration and to the appearance of motor complications. This evolution challenges the establishment of optimal regimens to manage the symptoms as the disease progresses. Based on up-to-date pathophysiological and pharmacological knowledge, we developed an integrative model for Parkinson's disease to evaluate motor function in response to levodopa treatment as the disease progresses. We combined a pharmacokinetic model of levodopa to a model of dopamine's kinetics and a neurocomputational model of basal ganglia. The parameter values were either measured directly or estimated from human and animal data. The concentrations and behaviors predicted by our model were compared to available information and data. Using this model, we were able to predict levodopa plasma concentration, its related dopamine concentration in the brain and the response performance of a motor task for different stages of disease.
左旋多巴被认为是治疗帕金森病的金标准。尽管它在缓解疾病初期症状方面非常有效,但随着基底神经节中神经元的进行性丧失,其慢性使用会导致左旋多巴作用持续时间的缩短,并出现运动并发症。这种演变挑战了建立最佳方案来管理疾病进展时的症状。基于最新的生理病理学和药理学知识,我们开发了一个综合的帕金森病模型,以评估随着疾病进展,对左旋多巴治疗的运动功能反应。我们将左旋多巴的药代动力学模型与多巴胺动力学模型和基底神经节的神经计算模型相结合。参数值要么直接测量,要么根据人体和动物数据进行估计。我们模型预测的浓度和行为与现有信息和数据进行了比较。使用该模型,我们能够预测不同疾病阶段的左旋多巴血浆浓度、其在大脑中的相关多巴胺浓度以及运动任务的反应性能。