Levodopa in Parkinson’s Disease: A Review of Population Pharmacokinetics/Pharmacodynamics Analysis
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease is the second most common neurodegenerative disorder after Alzheimer's disease. Although levodopa remains the single effective agent in the management of Parkinson's disease, the accurate determination of this optimal dosage is complicated by marked between-subject and between-occasion variability in this population. This review presents a synthesis of the population pharmacokinetic and pharmacodynamic models of levodopa described in Parkinson's disease. METHODS: A literature search was conducted from the PubMed database, from their inception through April 2016, using the following terms: levodopa, pharmacokinetic(s), pharmacodynamic(s) population, model(ling) and nonlinear mixed effect. Articles were excluded if they were not pertinent. References of all selected articles were also evaluated. RESULTS: A total of 12 articles were finally retained. The following covariates were selected as interindividual variability factors: body weight, age, sex, creatinine clearance and levodopa dose. The clinical response versus effect site concentration relationship was described with different sigmoidal Emax models. Different pharmacodynamic effects were described: UPDRS, Tapping, Dyskinesia, CURSΣ and treatment response scale. DISCUSSION: This review allows us to realize interpretation of a patient's clinical picture and confirmed the appropriateness of the pharmacokinetic-pharmacodynamic modeling for levodopa. External evaluation of previous published models should be also continued to evaluate these previous studies. New pharmacokinetic and/or pharmacodynamic population modelling studies could be consider to improve future models and decrease variability, to better understand the evolution of patients with Parkinson's disease treated by levodopa. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".