Amélie Marsot1, Romain Guilhaumou, Jean Philippe Azulay, Olivier Blin. 1. Service de Pharmacologie Clinique et Pharmacovigilance, AP-HM, Pharmacologie intégrée et interface clinique et industrielle, Institut des Neurosciences Timone - AMU-CNRS 7289, Aix Marseille Université, 13385 Marseille.
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.
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.
Authors: Katie M Yang; Katherine V Blue; Haleigh M Mulholland; Meghna P Kurup; Cynthia A Kelm-Nelson; Michelle R Ciucci Journal: Behav Brain Res Date: 2017-11-03 Impact factor: 3.332
Authors: Héctor Hernández-Parra; Hernán Cortés; José Arturo Avalos-Fuentes; María Del Prado-Audelo; Benjamín Florán; Gerardo Leyva-Gómez; Javad Sharifi-Rad; William C Cho Journal: J Nanobiotechnology Date: 2022-09-15 Impact factor: 9.429