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Molecular Informatics
Full Paper

Exploiting Pharmacological Similarity to Identify Safety Concerns – Listen to What the Data Tells You

Daniel Muthas

Corresponding Author

E-mail address: Daniel.muthas@astrazeneca.com

Computational Toxicology, Global Safety Assessment, AstraZeneca R&D Mölndal, Pepparedsleden 1, SE‐43183 Mölndal, Sweden phone/fax:+46 31 7762157

Computational Toxicology, Global Safety Assessment, AstraZeneca R&D Mölndal, Pepparedsleden 1, SE‐43183 Mölndal, Sweden phone/fax:+46 31 7762157
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Scott Boyer

Computational Toxicology, Global Safety Assessment, AstraZeneca R&D Mölndal, Pepparedsleden 1, SE‐43183 Mölndal, Sweden phone/fax:+46 31 7762157

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First published: 14 January 2013
Cited by: 5
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Abstract

Whilst most new drugs are designed to act on a single target or a small number of targets, many do show broad pharmacological activity. In some cases this can be beneficial and necessary for efficacy and in others it can be detrimental, leading to increased safety liability. To probe off‐target pharmacology most drug discovery programs include screening against a broad panel of targets that represent known troublesome pharmacology. Hits against any one of these targets can then be subjected to a risk assessment for potential safety problems in preclinical or clinical studies. In addition, the secondary pharmacology profile can also be thought of as an alternative description of the compound and as such can be used as a method for assessing ‘similarity’. Consequently, inspection of the in vivo findings of pharmacological neighbors can give important insights into potential safety liabilities that are neither identified by pure chemical similarity searches nor by risk assessment on individual targets. Here we show that the pharmacological profile contains additional information as compared to chemical similarity, and also demonstrate how this can be used in the hazard assessment done during drug discovery and development.

Number of times cited according to CrossRef: 5

  • , The opportunities of mining historical and collective data in drug discovery, Drug Discovery Today, 20, 4, (422), (2015).
  • , Atom-Atom-Path similarity and Sphere Exclusion clustering: tools for prioritizing fragment hits, Journal of Cheminformatics, 10.1186/s13321-015-0056-8, 7, 1, (2015).
  • , Emerging Topics in Structure-Based Virtual Screening, Pharmaceutical Research, 30, 5, (1458), (2013).
  • , Polypharmacology – Foe or Friend?, Journal of Medicinal Chemistry, 10.1021/jm400856t, 56, 22, (8955-8971), (2013).
  • , A critical assessment of modeling safety-related drug attrition, MedChemComm, 4, 7, (1058), (2013).