PubTransformer

A site to transform Pubmed publications into these bibliographic reference formats: ADS, BibTeX, EndNote, ISI used by the Web of Knowledge, RIS, MEDLINE, Microsoft's Word 2007 XML.

Adverse Effect Predictions Based on Computational Toxicology Techniques and Large-scale Databases.

Abstract  Understanding the features of chemical structures related to the adverse effects of drugs is useful for identifying potential adverse effects of new drugs. This can be based on the limited information available from post-marketing surveillance, assessment of the potential toxicities of metabolites and illegal drugs with unclear characteristics, screening of lead compounds at the drug discovery stage, and identification of leads for the discovery of new pharmacological mechanisms. This present paper describes techniques used in computational toxicology to investigate the content of large-scale spontaneous report databases of adverse effects, and it is illustrated with examples. Furthermore, volcano plotting, a new visualization method for clarifying the relationships between drugs and adverse effects via comprehensive analyses, will be introduced. These analyses may produce a great amount of data that can be applied to drug repositioning.
PMID
Related Publications

Accessing, using, and creating chemical property databases for computational toxicology modeling.

Getting the most out of PubChem for virtual screening.

Use of toxicological information in drug design.

In silico toxicology for the pharmaceutical sciences.

Authors

Mayor MeshTerms
Keywords

adverse effect

computational toxicology

database

prediction model

quantitative structure-activity relationship

Journal Title yakugaku zasshi : journal of the pharmaceutical society of japan
Publication Year Start




PMID- 29386432
OWN - NLM
STAT- In-Process
LR  - 20180201
IS  - 1347-5231 (Electronic)
IS  - 0031-6903 (Linking)
VI  - 138
IP  - 2
DP  - 2018
TI  - [Adverse Effect Predictions Based on Computational Toxicology Techniques and
      Large-scale Databases].
PG  - 185-190
LID - 10.1248/yakushi.17-00174-4 [doi]
AB  - Understanding the features of chemical structures related to the adverse effects 
      of drugs is useful for identifying potential adverse effects of new drugs. This
      can be based on the limited information available from post-marketing
      surveillance, assessment of the potential toxicities of metabolites and illegal
      drugs with unclear characteristics, screening of lead compounds at the drug
      discovery stage, and identification of leads for the discovery of new
      pharmacological mechanisms. This present paper describes techniques used in
      computational toxicology to investigate the content of large-scale spontaneous
      report databases of adverse effects, and it is illustrated with examples.
      Furthermore, volcano plotting, a new visualization method for clarifying the
      relationships between drugs and adverse effects via comprehensive analyses, will 
      be introduced. These analyses may produce a great amount of data that can be
      applied to drug repositioning.
FAU - Uesawa, Yoshihiro
AU  - Uesawa Y
AD  - Department of Clinical Pharmaceutics, Meiji Pharmaceutical University.
LA  - jpn
PT  - English Abstract
PT  - Journal Article
PL  - Japan
TA  - Yakugaku Zasshi
JT  - Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan
JID - 0413613
OTO - NOTNLM
OT  - adverse effect
OT  - computational toxicology
OT  - database
OT  - prediction model
OT  - quantitative structure-activity relationship
EDAT- 2018/02/02 06:00
MHDA- 2018/02/02 06:00
CRDT- 2018/02/02 06:00
PHST- 2018/02/02 06:00 [entrez]
PHST- 2018/02/02 06:00 [pubmed]
PHST- 2018/02/02 06:00 [medline]
AID - 10.1248/yakushi.17-00174-4 [doi]
PST - ppublish
SO  - Yakugaku Zasshi. 2018;138(2):185-190. doi: 10.1248/yakushi.17-00174-4.