Computer Model May Help in Better Tailoring Levodopa Dose to Patient’s Needs

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by Forest Ray PhD |

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A computational model of how Parkinson’s disease evolves over time may help guide physicians in tailoring levodopa doses to a patient’s needs at given disease stages.

Levodopa works to ease the slowness or difficulty in moving, known as bradykinesia, experienced by many people with Parkinson’s. But its effectiveness wanes as the disease progresses and adjustments are needed. Yet too high a dose can cause the uncontrollable, jerky movements known as dyskinesia.

Researchers at the University of Montreal, in Quebec, working with colleagues, sought a way to optimize treatment and better balance these symptoms.

Their study, “Nonlinear pharmacodynamics of Levodopa through Parkinson’s disease progression,” was published in the journal Chaos.

A progressive loss of dopamine-producing (dopaminergic) neurons in the brain characterizes Parkinson’s. Levodopa helps to compensate for this loss by being metabolized into dopamine.

In Parkinson’s early stages, dopamine-producing neurons can prolong levodopa’s effect by storing dopamine to be released as needed. As more dopaminergic neurons die in a process called denervation, however, this buffering capacity is lost. Levodopa’s effectiveness begins to vary in patients as their disease progresses.

“It is as if levodopa fills a leaky reservoir that becomes leakier with denervation,” the researchers wrote.

An accurate model that describes this effect could prove valuable in determining how best to manage a patient’s use of this therapy.

The researchers created a computational model to predict how dopamine dynamics change with nerve cell loss. After determining that this model compared well to real-world patient data, they applied it to simulate a patient tapping a finger several hours after taking levodopa, a common clinical assessment of bradykinesia.

This test showed that as dopaminergic nerve cells were lost, dopamine production fell even with increasing doses of levodopa, causing finger tapping frequency to drop.

This confirmed their suspicions that as nerve cell death progresses dopamine concentrations decline in these neurons, eventually reaching a point that no amount of levodopa can correct. Lower doses of levodopa no longer treat bradykinesia, while higher amounts of the medicine cause dyskinesia.

The challenge to physicians is knowing how much levodopa is too much at the right moment.

The authors hope that the insights provided by their model can help in better tailoring individual treatments. Combined with monitoring devices that periodically measure a patient’s finger tapping frequency after taking levodopa, for instance, their model might help estimate a more effective dose for that person.

“Understanding the nonlinear dynamics that underly Parkinson’s disease therapy is critical to optimize the current treatment, to fit individualized needs, and to find information about alternative therapies,” the researchers wrote.

Added Florence Véronneau-Veilleux, PhD, the study’s lead author in a press release, “[w]ith an optimization algorithm, we can find the optimal regimen — dose and time — according to different criteria” for specific patients.