Application of a Dynamic Map for Learning, Communicating, Navigating, and Improving Therapeutic Development
Open accessAbstract
Drug discovery and development is commonly schematized as a “pipeline,” and, although appreciated by drug developers to be a useful oversimplification, this cartology may perpetuate inaccurate notions of straightforwardness and is of minimal utility for process engineering to improve efficiency. To create a more granular schema, a group of drug developers, researchers, patient advocates, and regulators developed a crowdsourced atlas of the steps involved in translating basic discoveries into health interventions, annotated with the steps that are particularly prone to difficulty or failure. This Drug Discovery, Development, and Deployment Map (4DM), provides a network view of the process, which will be useful for communication and education to those new to the field, orientation and navigation of individual projects, and prioritization of technology development and re‐engineering endeavors to improve efficiency and effectiveness. The 4DM is freely available for utilization, modification, and further development by stakeholders across the translational ecosystem.
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The R&D process is far from the linear pathway commonly described.
“The R&D process itself is recognized as far from the linear pathway commonly described (Fig 1).”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [7]
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“and development; f) regulatory review; g) post-marketing and h) medical practice deployment (2).”
Background
- Authors
- Wagner, J. A., Dahlem, A. M., Hudson, L. D., Terry, S. F., Altman, R. B., Gilliland, C. T., DeFeo, C., & Austin, C. P.
- Affiliations
- Takeda Pharmaceuticals International Co. Cambridge Massachusetts USA
- Eli Lilly (United States) (US)
- Critical Path Institute (US)
- Genetic Alliance (US)
- Stanford University (US)
- National Institutes of Health (US)
- Bethesda Maryland USA
- Corresponding author
- Austin, Christopher P.
- Venue
- Clinical and Translational Science
- Publisher
- Wiley
- Published
- 2017-12-22
- Volume
- 11
- Issue
- 2
- Pages
- 166-174
- ISSN
- 1752-8054 (ISSN-L)
- 1752-8062
- PMC
- PMC5866991
- OpenAlex
- W2779431579
- Cited by
- 45
- References
- 18
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 20 · 2022: 3 · 2021: 6 · 2020: 5 · 2019: 2 · 2018: 4 · 2017: 1
- FWCI
- 3.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Clinical and Translational Science (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health and Medical Research Impacts
- Social Sciences › Decision Sciences › Information Systems and Management › Interdisciplinary Research and Collaboration
- Health Sciences › Medicine › Reproductive Medicine › Science, Research, and Medicine
- Keywords
- drug discovery, drug development, treatment development, dynamic maps, translational research, process engineering, network visualization, patient advocacy, regulatory science
- MeSH terms
- Intersectoral Collaboration, Drug Development, Clinical Trials as Topic, Communication, Humans, Learning, Myositis Ossificans, Neuropeptide Y, Obesity, Pyrazoles, Pyrimidines, Research Design, United States, United States Food and Drug Administration, National Academies of Science, Engineering, and Medicine, U.S., Health and Medicine Division, Polycystic Kidney, Autosomal Dominant, Biomedical Technology, Drug Discovery, Translational Research, Biomedical
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