Alpha-dihydroergocryptine in the treatment of <i>de novo</i>
 parkinsonian patients: results ofa multicentre, randomized, double-blind, placebo-controlled study

Bergamasco, B.; Frattola, L.; Muratorio, A; Piccoli, Fernanda V.R.; Mailland, F.; Parnetti, Lucilla

doi:10.1034/j.1600-0404.2000.90295a.x

Cited by 32 publications

(25 citation statements)

References 9 publications

(9 reference statements)

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“…The variability in the maximum placebo effect was assumed to be 50% greater than the variability in the drug effects. The parameters for the dropout model were derived by iteratively changing the parameters of the hazard model until the predictions visually resembled the reported data [12,[22][23][24]26,[28][29][30].…”

Section: Model Parametersmentioning

confidence: 99%

“…For the purposes of shorter duration trials with more frequent observations we have chosen to include a separate placebo response component. This allowed us to better describe the observations in several shortterm trials [12,[22][23][24][25][26][27].…”

Section: Placebo Effect1mentioning

confidence: 99%

“…z Estimated using data from [12,[22][23][24]26,[28][29][30]. analyses and was compiled using Compaq Visual Fortran version 6.6A (Hewlett Packard, Palo Alto, CA).…”

Section: Computationmentioning

confidence: 99%

“…The placebo effect parameters were estimated from mean placebo data [12,[22][23][24][25][26][27]. Based on the predictions for levodopa [15] in the ELLDOPA trial we have assumed a combined slope and offset effect.…”

Section: Model Parametersmentioning

confidence: 99%

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Washout and delayed start designs for identifying disease modifying effects in slowly progressive diseases using disease progression analysis

Ploeger

Holford

2008

Pharmaceutical Statistics

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Treatment of a slowly progressive disease, such as Parkinson's and Alzheimer's disease should slow down or even reverse progression of the disease. For estimating the actual treatment effect a clinical trial should allow the time course of treatment effects to be distinguished from those due to natural disease progression. A symptomatic treatment effect has a rapid onset, which will disappear after treatment cessation, whereas disease modifying effects are slow and persistent. Our objective was to compare the power of washout and delayed start designs for distinguishing symptomatic and disease modifying effects using disease progression modelling. The effect of dropout due to worsening disease status on the power was also considered.The change in the Unified Parkinson's Disease Rating Scale score over time was simulated, assuming that a treatment has a combined symptomatic and disease modifying effect. The simulated data were then fitted with the combined effect model and also with alternative models with either symptomatic or disease modifying effects alone. The power was derived from the number of times that the true model was correctly distinguished from the alternative models.Including a washout period in which the subjects are followed up after cessation of treatment substantially increases the power to distinguish different treatment effect types (washout>80%; delayed start<60%). Dropout of 40%-50% during the trial has a small effect on power (7% decrease). Under the assumptions of the simulation we found a clear advantage from using the washout design compared with a delayed start design.

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Section: Model Parametersmentioning

confidence: 99%

Section: Placebo Effect1mentioning

confidence: 99%

“…z Estimated using data from [12,[22][23][24]26,[28][29][30]. analyses and was compiled using Compaq Visual Fortran version 6.6A (Hewlett Packard, Palo Alto, CA).…”

Section: Computationmentioning

confidence: 99%

Section: Model Parametersmentioning

confidence: 99%

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Washout and delayed start designs for identifying disease modifying effects in slowly progressive diseases using disease progression analysis

Ploeger

Holford

2008

Pharmaceutical Statistics

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“…Based on one Level-I, placebo-controlled trial 5 , DHEC is EFFI-CACIOUS in the management of de novo patients with PD. However, efficacy beyond 3 months is not established at this time.…”

Section: Monotherapymentioning

confidence: 99%

DA agonists - Ergot derivaties: Dihydroergocryptine (DHEC)

Goetz¹,

Köller²,

Poewe³

et al. 2002

Mov Disord.

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DHEC is a dihydro-derivative of ergocryptine acting as a D2 agonist and a partial D1 agonist. Therefore, DHEC has a pharmacodynamic profile quite comparable to that of bromocriptine. Like all ergotamine derivatives, DHEC has effects on serotonergic and adrenergic receptors.1,2 DHEC improves the symptoms of the MPTP-treated monkey model of Parkinson's disease (PD). In healthy volunteers, its effects on D2 receptors reduce prolactin plasma levels, and induce nausea and hypotension. Preclinical data suggest that DHEC may have neuroprotective properties. 3,4PHARMACOKINETICS DHEC, like other ergot derivatives, has linear kinetics. Its oral bioavailability after first pass effect is low (below 5%). It has linear metabolism with generation of active metabolites, is eliminated through feces, and has no interference with L-dopa kinetics.

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Dopamine agonist therapy in early Parkinson's disease

Stowe

Ives

Clarke

et al. 2008

Cochrane Database of Systematic Reviews

145

108

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This meta-analysis confirms that motor complications are reduced with dopamine agonists compared to levodopa, but also establishes that other important side-effects are increased and symptom control is poorer with agonists. Larger, long-term comparative trials assessing patient-rated quality of life are needed to assess more reliably the balance of benefits and risks of dopamine agonists compared to levodopa.

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Alpha-dihydroergocryptine in the treatment of de novo parkinsonian patients: results ofa multicentre, randomized, double-blind, placebo-controlled study

Abstract: The results indicate that alpha-dihydroergocryptine is safe and effective in improving symptoms of de novo parkinsonian patients.

Cited by 32 publications

References 9 publications

Washout and delayed start designs for identifying disease modifying effects in slowly progressive diseases using disease progression analysis

Washout and delayed start designs for identifying disease modifying effects in slowly progressive diseases using disease progression analysis

DA agonists - Ergot derivaties: Dihydroergocryptine (DHEC)

Dopamine agonist therapy in early Parkinson's disease

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