ReviewPharmaceutical medicine2021
Increasing Efficiency and Cost-Effectiveness by Automating the Authoring of the Development Safety Update Report.
Review in Pharmaceutical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed, 2 citations in OpenAlex.
No citing paper in PubMed yet.
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionA Development Safety Update Report (DSUR) is a comprehensive review of safety information collected during an annual reporting period for an investigational drug. The creation of a DSUR is a resource-intensive process, requiring effective communication among, and collaboration with, various functional stakeholders.
objectiveTo increase the efficiency and cost-effectiveness of generating a DSUR with the use of an electronic authoring platform. This computerized platform auto-populates the DSUR template using available source data.
methodRequirements for the DSUR authoring tool were developed, and available data sources were mapped to respective sections of a company DSUR template. The DSUR authoring tool, mined, scanned, and extracted the relevant data from multiple-input source documents using Natural Language Processing logic. The data were then auto-populated into the template, as required by regulatory guidance ICH-E2F, with different text options based on questions and answers. The time- and cost-savings gained through automation were analyzed for DSURs authored during 2017-2019.
resultsThe DSUR contains 31 sections (20 main sections and 11 sub-sections), of which 9 were fully automated, 12 were partially automated, and 10 were not automated in this release of the automation tool. Use of the DSUR automation tool resulted in a time-savings of approximately 25% per report, corresponding to a cost saving of US$4550 per report.
conclusionDeployment of the automation tool reduced the time and cost required to manually produce DSURs, as well as improved the quality, compliance, and consistency of company-prepared DSURs. This tool enhanced the potential of the organization to generate larger volumes of DSURs in a timely manner, without requiring additional resources or compromising quality. This or a similar authoring automation tool could be employed in the future to generate other regulatory submission documents, which also require various source documents and cross-functional contributions.
Indexed as
Identifiers
What OpenQuestion holds
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.