Reducing safety-related drug attrition: the use of in vitro pharmacological profiling
Abstract
In vitro pharmacological profiling is increasingly being used earlier in the drug discovery process to identify undesirable off-target activity profiles that could hinder or halt the development of candidate drugs or even lead to market withdrawal if discovered after a drug is approved. Here, for the first time, the rationale, strategies and methodologies for in vitro pharmacological profiling at four major pharmaceutical companies (AstraZeneca, GlaxoSmithKline, Novartis and Pfizer) are presented and illustrated with examples of their impact on the drug discovery process. We hope that this will enable other companies and academic institutions to benefit from this knowledge and consider joining us in our collaborative knowledge sharing.
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In vitro pharmacological profiling implemented earlier in the drug discovery process helped to identify some predictable undesirable off-target activity profiles.
“in vitro pharmacological profiling implemented earlier in the drug discovery process helped to identify some predictable undesirable off-target activity profiles, which would hinder drug candidate development or even lead to market withdrawal if discovered after drug approval3.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [3]
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- Authors
- Bowes, J., Brown, A. J., Hamon, J., Jarolimek, W., Sridhar, A., Waldron, G., & Whitebread, S.
- Affiliations
- AstraZeneca (United Kingdom) (GB)
- GlaxoSmithKline (United Kingdom) (GB)
- Novartis (Switzerland) (CH)
- GlaxoSmithKline (Italy) (IT)
- Pfizer (United Kingdom) (GB)
- Corresponding author
- Bowes, Joanne
- Brown, Andrew J.
- Hamon, Jacques
- Jarolimek, Wolfgang
- Sridhar, Arun
- Waldron, Gareth
- Whitebread, Steven
- Venue
- Nature Reviews Drug Discovery
- Published
- 2012-11-30
- Volume
- 11
- Issue
- 12
- Pages
- 909-922
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W2018453465
- Cited by
- 748
- References
- 104
- Citations by year
- 2026: 45 · 2025: 65 · 2024: 75 · 2023: 49 · 2022: 60 · 2021: 62 · 2020: 61 · 2019: 59 · 2018: 50 · 2017: 58 · 2016: 56 · 2015: 52 · 2014: 29 · 2013: 26
- FWCI
- 3.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Keywords
- off-target effects, drug attrition, drug safety
- MeSH terms
- Drug Industry, Risk Assessment, Drug Discovery, Drug-Related Side Effects and Adverse Reactions
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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