Therapeutic Approaches to Restore Dopamine Homeostasis and Alleviate Motor Symptoms in Parkinson’s Disease – a Narrative Review
Bibliographic record
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disorder, characterized by progressive dopaminergic neuronal loss in the substantia nigra pars compacta, leading to motor impairments such as bradykinesia, tremors, and rigidity. Current therapeutic strategies aim to restore dopamine homeostasis and alleviate motor symptoms through pharmacological, surgical, and non-pharmacological interventions; however, there is no current treatments that treat PD as a whole. This narrative review highlights three primary pharmacological approaches: (1) dopamine replacement with levodopa, the gold standard therapy, which is effective but associated with long-term complications such as motor fluctuations and dyskinesias; (2) dopamine receptor agonists, which offer an alternative to levodopa but exhibit increased non-motor side effects; and (3) inhibitors of dopamine degradation enzymes, including monoamine oxidase B (MAO-B) and catechol-O-methyltransferase (COMT) inhibitors, which prolong dopamine availability and reduce motor fluctuations. Emerging strategies focus on multifunctional compounds targeting both MAO-B and COMT, offering neuroprotection and improved dopaminergic stability. Despite advancements in PD management, an urgent need remains for novel therapeutics that provide sustained symptom relief with minimal side effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".