Evidence map›Paper›PMID 32719954›Full record

ArticleThe AAPS journal2020

Integrated Multi-stakeholder Systems Thinking Strategy: Decision-making with Biopharmaceutics Risk Assessment Roadmap (BioRAM) to Optimize Clinical Performance of Drug Products.

Arzu Selen, Anette Müllertz, Filippos Kesisoglou, Rodney J Y Ho, Jack A Cook, Paul A Dickinson, Talia Flanagan

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In one paragraph

Article in The AAPS journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Arzu SelenUS Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Quality, Office of Testing and Research, 10903 New Hampshire Ave., Silver Spring, Maryland, 20993, USA. Arzu.Selen@fda.hhs.gov.
Anette MüllertzBioneer: FARMA, Department of Pharmacy, University of Copenhagen, 2100, Copenhagen, Denmark.
Filippos KesisoglouBiopharmaceutics, Pharmaceutical Sciences and Clinical Supply, Merck & Co, Inc., West Point, Pennsylvania, 19486, USA.
Rodney J Y HoUniversity of Washington, Seattle, Washington, 98195, USA.
Jack A CookClinical Pharmacology Department, Global Product Development, Pfizer, Inc., Groton, Connecticut, 06340, USA.
Paul A DickinsonSeda Pharmaceutical Development Services, Alderley Park, Alderley Edge, Cheshire, SK10 4TG, UK.
Talia FlanaganUCB Pharma S.A., Avenue de l'Industrie, 1420, Braine - l'Alleud, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Decision-making in drug development benefits from an integrated systems approach, where the stakeholders identify and address the critical questions for the system through carefully designed and performed studies. Biopharmaceutics Risk Assessment Roadmap (BioRAM) is such a systems approach for application of systems thinking to patient focused and timely decision-making, suitable for all stages of drug discovery and development. We described the BioRAM therapy-driven drug delivery framework, strategic roadmap, and integrated risk assessment instrument (BioRAM Scoring Grid) in previous publications (J Pharm Sci 103:3377-97, 2014; J Pharm Sci 105:3243-55, 2016). Integration of systems thinking with pharmaceutical development, manufacturing, and clinical sciences and health care is unique to BioRAM where the developed strategy identifies the system and enables risk characterization and balancing for the entire system. Successful decision-making process in BioRAM starts with the Blueprint (BP) meetings. Through shared understanding of the system, the program strategy is developed and captured in the program BP. Here, we provide three semi-hypothetical examples for illustrating risk-based decision-making in high and moderate risk settings. In the high-risk setting, which is a rare disease area, two completely alternate development approaches are considered (gene therapy and small molecule). The two moderate-risk examples represent varied knowledge levels and drivers for the programs. In one moderate-risk example, knowledge leveraging opportunities are drawn from the manufacturing knowledge and clinical performance of a similar drug substance. In the other example, knowledge on acute tolerance patterns for a similar mechanistic pathway is utilized for identifying markers to inform the drug release profile from the dosage form with the necessary "flexibility" for dosing. All examples illustrate implementation of the BioRAM strategy for leveraging knowledge and decision-making to optimize the clinical performance of drug products for patient benefit.

Indexed as

BiopharmaceuticsSystems AnalysisDecision MakingRisk Assessmentbiopharmaceuticsblueprint meetingsdecision-making for the systemintegrated risk assessmentsystems thinking

Identifiers

What OpenQuestion holds

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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.