Linked Life Data

16257374

Source:http://linkedlifedata.com/resource/pubmed/id/16257374

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
22
pubmed:dateCreated
2005-10-31
pubmed:abstractText
The process of discovering and developing new drugs is long, costly and risk-laden. Faced with a wealth of newly discovered compounds, industrial scientists need to target resources carefully to discern the key attributes of a drug candidate and to make informed decisions. Here, we describe a quantitative approach to modelling the risk associated with drug development as a tool for scenario analysis concerning the probability of success of a compound as a potential pharmaceutical agent. We bring together the three strands of manufacture, clinical effectiveness and financial returns. This approach involves the application of a Bayesian Network. A simulation model is demonstrated with an implementation in MS Excel using the modelling engine Crystal Ball.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1359-6446
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
10
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1520-6
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Quantitative risk modelling for new pharmaceutical compounds.
pubmed:affiliation
School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK.
pubmed:publicationType
Journal Article, Review