Abstract
Parkinson disease (PD) is characterized by a clear beneficial motor response to levodopa (LD) treatment. However, with disease progression and longer LD exposure, drug-related motor fluctuations usually occur. Recognition of the individual relationship between LD concentration and its effect may be difficult, due to the complexity and variability of the mechanisms involved. This work proposes an innovative procedure for the automatic estimation of LD pharmacokinetics and pharmacodynamics parameters, by a biologically-inspired mathematical model. An original issue, compared with previous similar studies, is that the model comprises not only a compartmental description of LD pharmacokinetics in plasma and its effect on the striatal neurons, but also a neurocomputational model of basal ganglia action selection. Parameter estimation was achieved on 26 patients (13 with stable and 13 with fluctuating LD response) to mimic plasma LD concentration and alternate finger tapping frequency along four hours after LD administration, automatically minimizing a cost function of the difference between simulated and clinical data points. Results show that individual data can be satisfactorily simulated in all patients and that significant differences exist in the estimated parameters between the two groups. Specifically, the drug removal rate from the effect compartment, and the Hill coefficient of the concentration-effect relationship were significantly higher in the fluctuating than in the stable group. The model, with individualized parameters, may be used to reach a deeper comprehension of the PD mechanisms, mimic the effect of medication, and, based on the predicted neural responses, plan the correct management and design innovative therapeutic procedures.
Highlights
The neurocomputational model of basal ganglia, levodopa pharmacokinetics and pharmacodynamics, as well as the parameter estimation procedure are written in Matlab
Matlab codes and data are available in ModelDB (Basal Ganglia and Levodopa Pharmacodynamics model for parameter estimation in Parkinson disease (PD) (Ursino et al 2020)) at http://modeldb.yale.edu/261624
The dashed red lines with asterisks represent patient data, while the continuous blue lines with open circles are model simulation results obtained with optimal parameter values
Summary
Parameter estimation in patients with parkinson disease kinetic-dynamic test, M. The neurocomputational model of basal ganglia, levodopa pharmacokinetics and pharmacodynamics, as well as the parameter estimation procedure are written in Matlab. Matlab codes and data are available in ModelDB (Basal Ganglia and Levodopa Pharmacodynamics model for parameter estimation in PD (Ursino et al 2020)) at http://modeldb.yale.edu/261624
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