PMID- 19422098
OWN - NLM
STAT- MEDLINE
DCOM- 20090724
LR  - 20191111
IS  - 1096-0295 (Electronic)
IS  - 0273-2300 (Linking)
VI  - 54
IP  - 1
DP  - 2009 Jun
TI  - Identification of structure-activity relationships for adverse effects of 
      pharmaceuticals in humans: Part B. Use of (Q)SAR systems for early detection of 
      drug-induced hepatobiliary and urinary tract toxicities.
PG  - 23-42
AB  - This report describes the development of quantitative structure-activity 
      relationship (QSAR) models for predicting rare drug-induced liver and urinary 
      tract injury in humans based upon a database of post-marketing adverse effects 
      (AEs) linked to approximately 1600 chemical structures. The models are based upon 
      estimated population exposure using AE proportional reporting ratios. Models were 
      constructed for 5 types of liver injury (liver enzyme disorders, cytotoxic 
      injury, cholestasis and jaundice, bile duct disorders, gall bladder disorders) 
      and 6 types of urinary tract injury (acute renal disorders, nephropathies, 
      bladder disorders, kidney function tests, blood in urine, urolithiases). 
      Identical training data sets were configured for 4 QSAR programs (MC4PC, 
      MDL-QSAR, BioEpisteme, and Predictive Data Miner). Model performance was 
      optimized and was shown to be affected by the AE scoring method and the ratio of 
      the number of active to inactive drugs. The best QSAR models exhibited an overall 
      average 92.4% coverage, 86.5% specificity and 39.3% sensitivity. The 4 QSAR 
      programs were demonstrated to be complementary and enhanced performance was 
      obtained by combining predictions from 2 programs (average 78.4% specificity, 
      56.2% sensitivity). Consensus predictions resulted in better performance as 
      judged by both internal and external validation experiments.
FAU - Matthews, Edwin J
AU  - Matthews EJ
AD  - US Food and Drug Administration, Center for Drug Evaluation and Research, Office 
      of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff 
      (ICSAS), Silver Spring, MD 20993, USA. edwin.matthews@fda.hhs.gov
FAU - Ursem, Carling J
AU  - Ursem CJ
FAU - Kruhlak, Naomi L
AU  - Kruhlak NL
FAU - Benz, R Daniel
AU  - Benz RD
FAU - Sabate, David Aragones
AU  - Sabate DA
FAU - Yang, Chihae
AU  - Yang C
FAU - Klopman, Gilles
AU  - Klopman G
FAU - Contrera, Joseph F
AU  - Contrera JF
LA  - eng
PT  - Journal Article
PL  - Netherlands
TA  - Regul Toxicol Pharmacol
JT  - Regulatory toxicology and pharmacology : RTP
JID - 8214983
RN  - 0 (Pharmaceutical Preparations)
SB  - IM
MH  - *Adverse Drug Reaction Reporting Systems
MH  - Biliary Tract Diseases/chemically induced/*diagnosis
MH  - Chemical and Drug Induced Liver Injury/*diagnosis/etiology
MH  - Cluster Analysis
MH  - Databases, Factual
MH  - *Drug-Related Side Effects and Adverse Reactions
MH  - Early Diagnosis
MH  - Endpoint Determination
MH  - Humans
MH  - Models, Biological
MH  - Pharmaceutical Preparations/administration & dosage/*chemistry
MH  - Product Surveillance, Postmarketing
MH  - Quantitative Structure-Activity Relationship
MH  - Software
MH  - United States
MH  - United States Food and Drug Administration
MH  - Urologic Diseases/chemically induced/*diagnosis
EDAT- 2009/05/08 09:00
MHDA- 2009/07/25 09:00
CRDT- 2009/05/08 09:00
PHST- 2009/05/08 09:00 [entrez]
PHST- 2009/05/08 09:00 [pubmed]
PHST- 2009/07/25 09:00 [medline]
AID - S0273-2300(09)00013-0 [pii]
AID - 10.1016/j.yrtph.2009.01.009 [doi]
PST - ppublish
SO  - Regul Toxicol Pharmacol. 2009 Jun;54(1):23-42. doi: 10.1016/j.yrtph.2009.01.009.