Dataset Information

Mavoglurant (AFQ056) for the treatment of levodopa-induced dyskinesia in patients with Parkinson's disease: a meta-analysis.

ABSTRACT:

Background

Mavoglurant (AFQ056), a selective metabotropic glutamate receptor 5 (mGluR5) inhibitor, was tested for t levodopa-induced dyskinesia (LID) in patients with Parkinson's Disease (PD). However, clinical trials showed inconsistent results regarding the efficacy of mavoglurant in treating LID in patients with Parkinson's disease (PD).

Methods

A computer literature search of PubMed, Scopus, Web of science, and Cochrane CENTRAL was conducted until March 2021. We selected relevant randomized controlled trials comparing mavoglurant to placebo. Study data were extracted and pooled as mean difference (MD) in the meta-analysis model.

Results

Six RCTs were included in this meta-analysis with a total of 485 patients. Mavoglurant was not significantly superior to placebo in terms of the "off-time" (MD -0.27 h, 95% CI -0.65 to 0.11), "on time" (MD 0.29 h, 95% CI -0.09 to 0.66), Lang-Fahn activities of daily living dyskinesia scale (MD -0.95, 95% CI -1.98 to 0.07), UPDRS-III (MD -0.51, 95% CI -1.66 to 0.65), or UPDRS-IV (MD -0.41, 95% CI -0.85 to 0.03). However, the pooled modified abnormal involuntary movement scale favored the mavoglurant group than the placebo group (MD -2.53, 95% CI -4.23 to -0.82).

Conclusions

This meta-analysis provides level one evidence that mavoglurant is not effective in treating the LID in patients with PD.

SUBMITTER: Negida A

PROVIDER: S-EPMC8342336 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Publications

Mavoglurant (AFQ056) for the treatment of levodopa-induced dyskinesia in patients with Parkinson's disease: a meta-analysis.

Negida Ahmed A Ghaith Hazem S HS Fala Salma Yousry SY Ahmed Hussien H Bahbah Eshak I EI Ebada Mahmoud Ahmed MA Aziz Mohamed Abd Elalem MAE

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 20210520 8

<h4>Background</h4>Mavoglurant (AFQ056), a selective metabotropic glutamate receptor 5 (mGluR5) inhibitor, was tested for t levodopa-induced dyskinesia (LID) in patients with Parkinson's Disease (PD). However, clinical trials showed inconsistent results regarding the efficacy of mavoglurant in treating LID in patients with Parkinson's disease (PD).<h4>Methods</h4>A computer literature search of PubMed, Scopus, Web of science, and Cochrane CENTRAL was conducted until March 2021. We selected relev ...[more]

PMID: 34014397

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Project description:ImportanceForty percent of Parkinson's disease patients develop levodopa-induced-dyskinesia (LiD) within 4 years of starting levodopa. The genetic basis of LiD remains poorly understood, and there have been few well powered studies.ObjectiveTo discover common genetic variants in the PD population that increase the probability of developing LiD.Design setting and participantsWe performed survival analyses to study the development of LiD in 5 separate longitudinal cohorts. We performed a meta-analysis to combine the results of genetic association from each study based on a fixed effects model weighting the effect sizes by the inverse of their standard error. The selection criteria was specific to each cohort. We studied individuals that were genotyped from each cohort and that passed our analysis specific inclusion criteria.Main outcomes and measuresWe measured the time for PD patients on levodopa treatment to develop LiD as defined by reaching a score higher or equal than 2 from the MDS-UPDRS part IV, item 1, which is equivalent to a range of 26%-50% of the waking time with dyskinesia. We carried out a genome-wide analysis of the hazard ratio and the association of genome-wide SNPs with the probability of developing LiD using cox proportional hazard models (CPH).ResultsThis study included 2,784 PD patients of European ancestry, of whom 14.6% developed LiD. Consistent with previous studies, we found female gender (HR = 1.35, SE = 0.11, P = 0.007) and younger age at onset (HR = 1.8, SE = 0.14, P = 2 × 10 -5 ) to increase the probability of developing LiD. We identified three loci significantly associated with time-to-LiD onset. rs72673189 on chromosome 1 (HR = 2.77, SE = 0.18, P = 1.53 × 10 -8 ) located in the LRP8 locus, rs189093213 on chromosome 4 (HR = 3.06,, SE = 0.19, P = 2.81 × 10 -9 ) in the non-coding RNA LINC02353 locus, and rs180924818 on chromosome 16 (HR = 3.13, SE = 0.20, P = 6.27 × 10 -9 ) in the XYLT1 locus. Subsequent colocalization analyses on chromosome 1 identified DNAJB4 as a candidate gene associated with LiD through a change in gene expression. We computed a PRS based on our GWAS meta-analysis and found high accuracy to stratify between PD-LID and PD (AUC 83.9). We also performed a stepwise regression analysis for baseline features selection associated with LiD status. We found baseline anxiety status to be significantly associated with LiD (OR = 1.14, SE = 0.03, P = 7.4 × 10 -5 ). Finally, we performed a candidate variant analysis and found that genetic variability in ANKK1 ( rs1800497 , Beta = 0.24, SE = 0.09, P = 8.89 × 10 -3 ) and BDNF ( rs6265 , Beta = 0.19, SE = 0.10, P = 4.95 × 10 -2 ) loci were significantly associated with time to LiD in our large meta-analysis.ConclusionIn this association study, we have found three novel genetic variants associated with LiD, as well as confirming reports that variability in ANKK1 and BDNF loci were significantly associated with LiD probability. A PRS nominated from our time-to-LiD meta-analysis significantly differentiated between PD-LiD and PD. In addition, we have found female gender, young PD onset and anxiety to be significantly associated with LiD.

Project description:Background: Levodopa (L-Dopa), representing the therapeutic gold standard for the treatment of Parkinson disease (PD), is associated with side effects like L-Dopa induced dyskinesia (LID). Although several non-genetic and genetic factors have been investigated for association with LID risk, contrasting results were reported and its genetic basis remain largely unexplored. Methods: In an Italian PD cohort (N = 460), we first performed stepwise multivariable Cox Proportional Hazard regressions modeling LID risk as a function of gender, PD familiarity, clinical subtype, weight, age-at-onset (AAO) and years-of-disease (YOD), L-Dopa dosage, severity scores, and scales assessing motor (UPDRS-III), cognitive (MoCA), and non-motor symptoms (NMS). Then we enriched the resulting model testing two variants-rs356219 and D4S3481-increasing the expression of the SNCA gene, previously suggested as a potential mechanism of LID onset. To account for more complex (non-linear) relations of these variables with LID risk, we built a survival random forest (SRF) algorithm including all the covariates mentioned above. Results: Among tested variables (N = 460 case-complete, 211 LID events; total follow-up 31,361 person-months, median 61 months), disease duration showed significant association (p < 0.005), with 6 (3-8)% decrease of LID risk per additional YOD. Other nominally significant associations were observed for gender-with women showing a 39 (5-82)% higher risk of LID-and AAO, with 2 (0.3-3)% decrease of risk for each year increase of PD onset. The SRF algorithm confirmed YOD as the most prominent feature influencing LID risk, with a variable importance of about 8% in the model. In genetic models, no statistically significant effects on incident LID risk was observed. Conclusions: This evidence supports a protective effect of late PD onset and gender (men) against LID risk and suggests a new independent protective factor, YOD. Moreover, it underlines the importance of personalized therapeutic protocols for PD patients in the future.

Dataset Information

Mavoglurant (AFQ056) for the treatment of levodopa-induced dyskinesia in patients with Parkinson's disease: a meta-analysis.

Background

Methods

Results

Conclusions

Publications

Mavoglurant (AFQ056) for the treatment of levodopa-induced dyskinesia in patients with Parkinson's disease: a meta-analysis.

Similar Datasets

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