Classification of l-DOPA pharmacokinetics shapes and creating a predictive model

Abstract

BackgroundIt is known that the pharmacokinetics (PK) of levodopa (LD) varies considerably. Difference in PK shapes is expected to affect drug efficacy and development of dyskinesia. In this study, the authors aimed to explore correlations between PK series data of LD and clinical characteristics and dyskinesia in patients with Parkinson's disease (PD). MethodsWe studied 270 PD patients who underwent PK assessment after administration of LD/carbidopa (100/10 mg) in non-compartmental analysis. The patients were grouped according to similarities in time series data of blood LD concentration. Each group was analyzed with respect to clinical characteristics and PK parameters. We created a model to predict PK patterns based on these findings. ResultsPD patients were divided into three groups by clustering analysis: blood LD concentration of the patients in Groups 1 (n = 129), 3 (n = 44) and 2 (n = 97) rose rapidly, relatively slowly and at an intermediate rate, respectively. There were no statistically significant differences in patient characteristics except age among the three groups (one-way ANOVA). Multivariate analysis showed that frequency of dyskinesias in Group 1 was significantly higher than that in Group 2. We successfully created a PK pattern prediction model based on body weight and blood LD concentration at 15 and 30 min after administration. ConclusionsThe PK series data of LD was classified into three patterns. The rapid absorption was associated with dyskinesias. Patients’ PK patterns were successfully predicted based on their body weight and two-point LD concentration.