Effective visualization of integrated knowledge and data to enable informed decisions in drug development and translational medicine
Open accessAbstract
Integrative understanding of preclinical and clinical data is imperative to enable informed decisions and reduce the attrition rate during drug development. The volume and variety of data generated during drug development have increased tremendously. A new information model and visualization tool was developed to effectively utilize all available data and current knowledge. The Knowledge Plot integrates preclinical, clinical, efficacy and safety data by adding two concepts: knowledge from the different disciplines and protein binding.Internal and public available data were gathered and processed to allow flexible and interactive visualizations. The exposure was expressed as the unbound concentration of the compound and the treatment effect was normalized and scaled by including expert opinion on what a biologically meaningful treatment effect would be.The Knowledge Plot has been applied both retrospectively and prospectively in project teams in a number of different therapeutic areas, resulting in closer collaboration between multiple disciplines discussing both preclinical and clinical data. The Plot allows head to head comparisons of compounds and was used to support Candidate Drug selections and differentiation from comparators and competitors, back translation of clinical data, understanding the predictability of preclinical models and assays, reviewing drift in primary endpoints over the years, and evaluate or benchmark compounds in due diligence comparing multiple attributes.The Knowledge Plot concept allows flexible integration and visualization of relevant data for interpretation in order to enable scientific and informed decision-making in various stages of drug development. The concept can be used for communication, decision-making, knowledge management, and as a forward and back translational tool, that will result in an improved understanding of the competitive edge for a particular project or disease area portfolio. In addition, it also builds up a knowledge and translational continuum, which in turn will reduce the attrition rate and costs of clinical development by identifying poor candidates early.
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The new version of the guideline does not introduce any specific outline of nonclinical safety studies needed to support first-in-human studies, because relevant information is provided by ICH guidelines.
“The new version of the guideline does not introduce any specific outline of the nonclinical safety studies needed to support FIH studies, since the relevant information is provided by the well-established overarching nonclinical ICH guidelines (ICH M3 [16], S6 [21] and S9 [22]).”
From Commentary on the <scp>EMA Guideline</scp> on strategies to identify and mitigate risks for first‐in‐human and early clinical trials with investigational medicinal products (2018) · cites this paper as [16]
The Results passages discuss visualization of preclinical safety data and early clinical studies, not the content of a revised guideline. Searches for first-in-human nonclinical safety requirements and ICH M3, S6, and S9 found no passage addressing why a guideline omits an outline of those studies. Page numbers were not available in our copy. (LLM, conf=0.77)
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- MeSH http://www.ncbi.nlm.nih.gov/mesh
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- Authors
- Brynne, L., Bresell, A., & Sjögren, N.
- Affiliations
- AstraZeneca (Sweden) (SE)
- Corresponding author
- Brynne, Lena
- ORCID
- Brynne, Lena
- Venue
- Journal of Translational Medicine
- Published
- 2013
- Volume
- 11
- Issue
- 1
- Pages
- 250
- ISSN
- 1479-5876 (ISSN-L)
- PMC
- PMC3842641
- OpenAlex
- W2149280153
- Cited by
- 9
- References
- 19
- Citations by year
- 2025: 1 · 2023: 1 · 2018: 2 · 2016: 1 · 2015: 3 · 2014: 1
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Translational Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- drug development, data visualization, translational medicine, drug attrition, expert opinion, knowledge management, decision support
- MeSH terms
- Alzheimer Disease, Decision Making, Drug Evaluation, Preclinical, Humans, Drug Design, Treatment Outcome, Knowledge, Translational Research, Biomedical
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