ReviewTherapeutic innovation & regulatory science2023
A Structured Benefit-Risk Assessment Operating Model for Investigational Medicinal Products in the Pharmaceutical Industry.
Review in Therapeutic innovation & regulatory science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Incorporating Patient Perspectives into Structured Benefit-Risk Assessment: A Drug Development Framework Recommendation.Therapeutic innovation & regulatory science · 2026Review
- Methodological comparisons for benefit-risk assessment in complex decision-making: applications and insights from a case study.Frontiers in drug safety and regulation · 2026Article
- Peripheral and central nervous system toxicities of Bruton tyrosine kinase Inhibitors: a real-world pharmacovigilance study.Frontiers in medicine · 2026Article
- Article
- A Survey to Assess the Current Status of Structured Benefit-Risk Assessment in the Global Drug and Medical Device Industry.Therapeutic innovation & regulatory science · 2024Article
- Planning Benefit-Risk Assessments Using Visualizations.Therapeutic innovation & regulatory science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Robust and transparent formal benefit-risk (BR) analyses for medicinal products represent a means to better understand the appropriate use of medicinal products, and to maximize their value to prescribers and patients. Despite regulatory and social imperatives to conduct structured BR (sBR) assessments, and the availability of a plethora of methodological tools, there exists large variability in the uptake and execution of sBR assessments among pharmaceutical companies. As such, in this paper we present an sBR assessment framework developed and implemented within a large global pharmaceutical company that aims to guide the systematic assessment of BR across the continuum of drug development activities, from first-time-in-human studies through to regulatory submission. We define and emphasize the concepts of Key Clinical Benefits and Key Safety Risks as the foundation for BR analysis. Furthermore, we define and foundationally employ the concepts of sBR and a Core Company BR position as the key elements for our BR framework. We outline 3 simple stages for how to perform the fundamentals of an sBR analysis, along with an emphasis on the weighting of Key Clinical Benefits and Key Safety Risks, and a focus on any surrounding uncertainties. Additionally, we clarify existing definitions to differentiate descriptive, semi-quantitative, and fully quantitative BR methodologies. By presenting our framework, we wish to stimulate productive conversation between industry peers and health authorities regarding best practice in the BR field. This paper may also help facilitate the pragmatic implementation of sBR methodologies for organizations without an established framework for such assessments.
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Registered trials
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.