Modeling promiscuity based on in vitro safety pharmacology profiling data.
This study describes a method for mining and modeling binding data obtained from a large panel of targets (in vitro safety pharmacology) to distinguish differences between promiscuous and selective compounds. Two naïve Bayes models for promiscuity and selectivity were generated and validated on a test set as well as publicly available drug databases. The model shows a higher score (lower promiscuity) for marketed drugs than for compounds in early development or compounds that failed during clinical development. Such models can be used in triaging high-throughput screening data or for lead optimization.
Azzaoui
Kamal
K
CPC/LFP/MLI, Novartis Institutes for Biomedical Research, Novartis Pharma AG, Postfach, 4002 Basel, Switzerland. kamal.azzaoui@novartis.com
Hamon
Jacques
J
Faller
Bernard
B
Whitebread
Steven
S
Jacoby
Edgar
E
Bender
Andreas
A
Jenkins
Jeremy L
JL
Urban
Laszlo
L
D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000639:trends; D016503:Drug Delivery Systems; D015195:Drug Design; D064420:Drug-Related Side Effects and Adverse Reactions; D008956:Models, Chemical; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000145:classification / Q000378:metabolism; D010600:Pharmacology / Q000379:methods* / Q000639:trends*; D013329:Structure-Activity Relationship
D004364:Pharmaceutical Preparations