Evidence map›Paper›PMID 16296872›Full record

ReviewDrugs2005

Issues for clinical drug development in neurodegenerative diseases.

Michel Dib

Abstract readReview
PubMed Publisher
In one paragraph

Review in Drugs, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.2field-weighted citation impact, top 39% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Michel DibService de Neurologie, Hôpital de la Salpêtrière, Paris, France. dib.michel@wanadoo.fr
Pitié-Salpêtrière Hospital · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurodegenerative diseases pose specific challenges for drug development. These diseases typically have a slow and variable clinical course, an insidious onset, and symptom expression is only observed when a significant proportion of neurons are already lost. It is important to identify vulnerability factors and other determinants of clinical course in order to be able in the future to select patient populations for clinical trials with a predictable prognosis. The neurodegenerative process itself is not amenable to direct observation and, thus, cannot be monitored in clinical trials. For this reason, surrogate biomarkers are required for use as outcome parameters. In this respect, magnetic resonance imaging has proved valuable for assessing disease activity and progression in multiple sclerosis. Rating scales are of use as outcome measures but, as these generally measure symptom severity, they are most appropriate for use in assessing symptomatic treatments. Survival has been used with success as an outcome measure in trials in amyotrophic lateral sclerosis, where disease progression is rapid. The optimal outcome measure, the sample size required and the treatment duration need to be chosen in relation to the phase of the disease. Potential new treatments can be chosen based upon new knowledge of the genetics and physiopathology of neurodegenerative diseases and, in some cases, screened in transgenic mouse models, although it should be recognised that the validity of these models in terms of treatment response has yet to be established empirically.

Indexed as

AnimalsBiomarkersClinical Trials as TopicGenetic Predisposition to DiseaseHumansNeurodegenerative DiseasesNeuroprotective AgentsSurvival AnalysisTreatment OutcomeBiomarkersNeuroprotective Agents

Identifiers

PMID16296872
OpenAlexW2103775005

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.