Randomized controlled trial data for successful new drug application for rare diseases in the United States

Abstract Background Randomized controlled trial (RCT) data have important implications in drug development. However, the feasibility and cost of conducting RCTs lower the motivation for drug development, especially for rare diseases. We investigated the potential factors associated with the need for...

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Main Authors: Yosuke Kubota, Mamoru Narukawa
Format: Article
Language:English
Published: BMC 2023-04-01
Series:Orphanet Journal of Rare Diseases
Subjects:
Online Access:https://doi.org/10.1186/s13023-023-02702-9
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author Yosuke Kubota
Mamoru Narukawa
author_facet Yosuke Kubota
Mamoru Narukawa
author_sort Yosuke Kubota
collection DOAJ
description Abstract Background Randomized controlled trial (RCT) data have important implications in drug development. However, the feasibility and cost of conducting RCTs lower the motivation for drug development, especially for rare diseases. We investigated the potential factors associated with the need for RCTs in the clinical data package for new drug applications for rare diseases in the United States (US). This study focused on 233 drugs with orphan drug designations approved in the US between April 2001 and March 2021. Univariable and multivariable logistic regression analyses were conducted to investigate the association between the presence or absence of RCTs in the clinical data package for new drug applications. Results Multivariable logistic regression analysis showed that the severity of the disease outcome (odds ratio [OR] 5.63, 95% confidence interval [CI] 2.64–12.00), type of drug usage (odds ratio [OR] 2.95, 95% confidence interval [CI] 1.80–18.57), and type of primary endpoint (OR 5.57, 95% CI 2.57–12.06) were associated with the presence or absence of RCTs. Conclusions Our results indicated that the presence or absence of RCT data in the clinical data package for successful new drug application in the US was associated with three factors: severity of disease outcome, type of drug usage, and type of primary endpoint. These results highlight the importance of selecting target diseases and potential efficacy variables to optimize orphan drug development.
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spelling doaj.art-3c0d5f335d17437da9c86c13a8bc64f62023-04-23T11:27:55ZengBMCOrphanet Journal of Rare Diseases1750-11722023-04-011811710.1186/s13023-023-02702-9Randomized controlled trial data for successful new drug application for rare diseases in the United StatesYosuke Kubota0Mamoru Narukawa1Department of Clinical Medicine (Pharmaceutical Medicine), Graduate School of Pharmaceutical Sciences, Kitasato UniversityDevelopment, Astellas Pharma IncAbstract Background Randomized controlled trial (RCT) data have important implications in drug development. However, the feasibility and cost of conducting RCTs lower the motivation for drug development, especially for rare diseases. We investigated the potential factors associated with the need for RCTs in the clinical data package for new drug applications for rare diseases in the United States (US). This study focused on 233 drugs with orphan drug designations approved in the US between April 2001 and March 2021. Univariable and multivariable logistic regression analyses were conducted to investigate the association between the presence or absence of RCTs in the clinical data package for new drug applications. Results Multivariable logistic regression analysis showed that the severity of the disease outcome (odds ratio [OR] 5.63, 95% confidence interval [CI] 2.64–12.00), type of drug usage (odds ratio [OR] 2.95, 95% confidence interval [CI] 1.80–18.57), and type of primary endpoint (OR 5.57, 95% CI 2.57–12.06) were associated with the presence or absence of RCTs. Conclusions Our results indicated that the presence or absence of RCT data in the clinical data package for successful new drug application in the US was associated with three factors: severity of disease outcome, type of drug usage, and type of primary endpoint. These results highlight the importance of selecting target diseases and potential efficacy variables to optimize orphan drug development.https://doi.org/10.1186/s13023-023-02702-9Rare diseasesOrphan drugsClinical trialsRandomizationEfficacy endpointLogistic regression analysis
spellingShingle Yosuke Kubota
Mamoru Narukawa
Randomized controlled trial data for successful new drug application for rare diseases in the United States
Orphanet Journal of Rare Diseases
Rare diseases
Orphan drugs
Clinical trials
Randomization
Efficacy endpoint
Logistic regression analysis
title Randomized controlled trial data for successful new drug application for rare diseases in the United States
title_full Randomized controlled trial data for successful new drug application for rare diseases in the United States
title_fullStr Randomized controlled trial data for successful new drug application for rare diseases in the United States
title_full_unstemmed Randomized controlled trial data for successful new drug application for rare diseases in the United States
title_short Randomized controlled trial data for successful new drug application for rare diseases in the United States
title_sort randomized controlled trial data for successful new drug application for rare diseases in the united states
topic Rare diseases
Orphan drugs
Clinical trials
Randomization
Efficacy endpoint
Logistic regression analysis
url https://doi.org/10.1186/s13023-023-02702-9
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