BrainPharm: Computational model - Basal Ganglia and Levodopa Pharmacodynamics model for parameter estimation in PD (Ursino et al 2020)

Computational model

Data

Name

Basal Ganglia and Levodopa Pharmacodynamics model for parameter estimation in PD (Ursino et al 2020)

Submitter name

Mauro Ursino

Pipeline ID

Model File

Final programs [193311]

Notes

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.

Model Neurons

More Cells

Abstract rate-based neuron Show Other

Model Currents

Model Receptors

D1 Show Other
D2 Show Other

Model Type

Connectionist Network Show Other

Model Concept

Parkinson's Show Other
Parameter Fitting Show Other
Hebbian plasticity Show Other
Reinforcement Learning Show Other

Simulator software

MATLAB Show Other

Model papers

Ursino M, Magosso E, Lopane G, Calandra-Buonaura G, Cortelli P, Contin M (2020) Show Other

Gene

hg folder name (applicable when in ModelDB hg)

Region Organism

Basal ganglia Show Other

Model ID Number

Gap Junctions

Implemented by

Ursino, Mauro [mauro.ursino at unibo.it] Show Other
Magosso, Elisa [elisa.magosso at unibo.it] Show Other

Public Submitter Email Address

mauro.ursino@unibo.it

Other Neuron

Model Neurotransmitters

Dopamine Show Other
Acetylcholine Show Other

Other Receptor

Other Current

Other Neurotransmitter

Other Model Type

Other Concept

Other Simulator

Other Implementer

Magosso Elisa (elisa.magosso at unibo.it)

Alternative Version

Citation

"MATHEMATICAL MODELING AND PARAMETER ESTIMATION OF LEVODOPA MOTOR RESPONSE IN PATIENTS WITH PARKINSON DISEASE" by Ursino M., Magosso E., et al., accepted with revision by Plos One.

Default File Notes

Other Gene

Simulation Platform ID

Has Model View

False

Component

RAM

Hide Auto-launch button

False

Run Protocols

ICG Channel Details

Species

Other categories referring to Basal Ganglia and Levodopa Pharmacodynamics model for parameter estimation in PD (Ursino et al 2020)

Revisions:	6
Last Time:	1/31/2020 5:23:23 PM
Reviewer:	Tom Morse - MoldelDB admin
Owner:	Tom Morse - MoldelDB admin