Quantitative analysis to guide orphan drug development.

L J Lesko

Abstract read

In one paragraph

Article in Clinical pharmacology and therapeutics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

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.

A Pharmacometric Approach to Substitute for a Conventional Dose-Finding Study in Rare Diseases: Example of Phase III Dose Selection for Emicizumab in Hemophilia A.Clinical pharmacokinetics · 2018
Trial

4 · The record

Corrections and comments

Commented on by
An aide memoire for model-based drug development--a new key for the interpretation of the erythromycin breath test--therapeutic index as a biomarker for smart drug development.2013

5 · Who and what money

Authors and funding

1 author.

L J LeskoDepartment of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, University of Florida, Orlando, FL, USA. llesko@cop.ufl.edu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of orphan drugs for rare diseases has made impressive strides in the past 10 years. There has been a surge in orphan drug designations, but new drug approvals have not kept up. This article presents a three-pronged hierarchical strategy for quantitative analysis of data at the descriptive, mechanistic, and systems levels of the biological system that could represent a standardized and rational approach to orphan drug development. Examples are provided to illustrate the concept.

Indexed as

Clinical Trials as TopicDrug ApprovalDrug DiscoveryHumansModels, BiologicalOrphan Drug ProductionResearch Design

Identifiers

PMID22739138

DOI10.1038/clpt.2012.80

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

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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.