Dataset Information

PIPELINEs: Creating Comparable Clinical Knowledge Efficiently by Linking Trial Platforms.

ABSTRACT: Adaptive, seamless, multisponsor, multitherapy clinical trial designs executed as large scale platforms, could create superior evidence more efficiently than single-sponsor, single-drug trials. These trial PIPELINEs also could diminish barriers to trial participation, increase the representation of real-world populations, and create systematic evidence development for learning throughout a therapeutic life cycle, to continually refine its use. Comparable evidence could arise from multiarm design, shared comparator arms, and standardized endpoints-aiding sponsors in demonstrating the distinct value of their innovative medicines; facilitating providers and patients in selecting the most appropriate treatments; assisting regulators in efficacy and safety determinations; helping payers make coverage and reimbursement decisions; and spurring scientists with translational insights. Reduced trial times and costs could enable more indications, reduced development cycle times, and improved system financial sustainability. Challenges to overcome range from statistical to operational to collaborative governance and data exchange.

SUBMITTER: Trusheim MR

PROVIDER: S-EPMC5142736 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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PIPELINEs: Creating Comparable Clinical Knowledge Efficiently by Linking Trial Platforms.

Trusheim M R MR Shrier A A AA Antonijevic Z Z Beckman R A RA Campbell R K RK Chen C C Flaherty K T KT Loewy J J Lacombe D D Madhavan S S Selker H P HP Esserman L J LJ

Clinical pharmacology and therapeutics 20161019 6

Adaptive, seamless, multisponsor, multitherapy clinical trial designs executed as large scale platforms, could create superior evidence more efficiently than single-sponsor, single-drug trials. These trial PIPELINEs also could diminish barriers to trial participation, increase the representation of real-world populations, and create systematic evidence development for learning throughout a therapeutic life cycle, to continually refine its use. Comparable evidence could arise from multiarm design ...[more]

PMID: 27643536

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Project description:BackgroundMedical school curricula strives to teach as much material as can be retained in a limited amount of time. A common "gold standard" resource used building curricula are medical objectives suggested by national societies. Unfortunately these objectives suffer from several functional limitations such as limited accessibility to society members, non-searchable formats (such as nested tables or pdf images), and inability to compare and search across societal objectives for redundancy or gaps. The shift towards integrated curriculums in medical school also highlights the need to access suggested content across classical discipline categories.Main bodyWe have codified recommendations from national societies in the United States for medical school objectives in a common tabular format and developed an open access database which can be searched across disciplines and societies. A front end website that allows for searching objectives by keyword while filtering on society or discipline was created. The objectives returned from the initial search can be subsearched by a second term. There is a large range in the format, age, breadth, quantity, and quality of objectives from different societies. Some unique disciplines have overlapping suggested content though most of the content does seem "binnable" by discipline. The choice of metadata for objectives from each given society was also very inconsistent.ConclusionA free and searchable database of medical content to deliver during medical school has been developed with over 13,000 objectives from 18 societies and 22 disciplines at http://data.medobjectives.marian.edu/ . The normalization of the different disciplines' objectives into a common database allows a platform to standardize objectives moving forward. Future work could include adding user accounts to access the database, submission of new objectives, voting up and down suggested objectives, and adding "answers" mapped to objectives. Keyword tagging could allow import of content (e.g. PowerPoints) and outputting of suggested objectives, which would also allow comparison of curriculum across medical schools.

Project description:BackgroundAdvancements in Next Generation Sequencing (NGS) technologies regarding throughput, read length and accuracy had a major impact on microbiome research by significantly improving 16S rRNA amplicon sequencing. As rapid improvements in sequencing platforms and new data analysis pipelines are introduced, it is essential to evaluate their capabilities in specific applications. The aim of this study was to assess whether the same project-specific biological conclusions regarding microbiome composition could be reached using different sequencing platforms and bioinformatics pipelines.ResultsChicken cecum microbiome was analyzed by 16S rRNA amplicon sequencing using Illumina MiSeq, Ion Torrent PGM, and Roche 454 GS FLX Titanium platforms, with standard and modified protocols for library preparation. We labeled the bioinformatics pipelines included in our analysis QIIME1 and QIIME2 (de novo OTU picking [not to be confused with QIIME version 2 commonly referred to as QIIME2]), QIIME3 and QIIME4 (open reference OTU picking), UPARSE1 and UPARSE2 (each pair differs only in the use of chimera depletion methods), and DADA2 (for Illumina data only). GS FLX+ yielded the longest reads and highest quality scores, while MiSeq generated the largest number of reads after quality filtering. Declines in quality scores were observed starting at bases 150-199 for GS FLX+ and bases 90-99 for MiSeq. Scores were stable for PGM-generated data. Overall microbiome compositional profiles were comparable between platforms; however, average relative abundance of specific taxa varied depending on sequencing platform, library preparation method, and bioinformatics analysis. Specifically, QIIME with de novo OTU picking yielded the highest number of unique species and alpha diversity was reduced with UPARSE and DADA2 compared to QIIME.ConclusionsThe three platforms compared in this study were capable of discriminating samples by treatment, despite differences in diversity and abundance, leading to similar biological conclusions. Our results demonstrate that while there were differences in depth of coverage and phylogenetic diversity, all workflows revealed comparable treatment effects on microbial diversity. To increase reproducibility and reliability and to retain consistency between similar studies, it is important to consider the impact on data quality and relative abundance of taxa when selecting NGS platforms and analysis tools for microbiome studies.

Dataset Information

PIPELINEs: Creating Comparable Clinical Knowledge Efficiently by Linking Trial Platforms.

Publications

PIPELINEs: Creating Comparable Clinical Knowledge Efficiently by Linking Trial Platforms.

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