Adverse event profile of tigecycline: data mining of the public version of the U.S. Food and Drug Administration adverse event reporting system.

The recent emergence of multidrug-resistant pathogens and/or pharmacokinetics-pharmacodynamics considerations may result in off-label use of a certain class of antibacterials, including tigecycline. This study was performed to clarify the safety profile of tigecycline in the user-derived manner and to compare it with the prescribing information provided by the manufacturer. Numerous spontaneous adverse event reports (AERs) submitted to the U.S. Food and Drug Administration (FDA) were analyzed after a revision of arbitrary drug names and the deletion of duplicated submissions. Standardized official pharmacovigilance tools were used for quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Based on 22017956 co-occurrences, i.e., drug-adverse event pairs, found in 1644220 AERs from 2004 to 2009, 248 adverse events were suggested as tigecycline-associated ones. Adverse events with a relatively high frequency included nausea, vomiting, pancreatitis, hepatic failure, hypoglycemia, and increase in levels of alanine aminotransferase, bilirubin, alkaline phosphatase, aspartate aminotransferase, and gamma-glutamyltransferase. It is noted that cholestasis, jaundice, an increase in International Normalized Ratio, and Stevens-Johnson syndrome were also, although they were infrequent. The adverse events suggested were in agreement with information provided by the manufacturer, suggesting that off-label use hardly results in unexpected adverse events, presumably due to usage with extreme caution.

[1]  W. DuMouchel,et al.  Novel Statistical Tools for Monitoring the Safety of Marketed Drugs , 2007, Clinical pharmacology and therapeutics.

[2]  Yasushi Okuno,et al.  Statin-Associated Muscular and Renal Adverse Events: Data Mining of the Public Version of the FDA Adverse Event Reporting System , 2011, PloS one.

[3]  M. Lindquist,et al.  A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions , 2002, Pharmacoepidemiology and drug safety.

[4]  M. Falagas,et al.  Tigecycline for the treatment of multidrug-resistant (including carbapenem-resistant) Acinetobacter infections: a review of the scientific evidence. , 2008, The Journal of antimicrobial chemotherapy.

[5]  Yasushi Okuno,et al.  Adverse Event Profiles of Platinum Agents: Data Mining of the Public Version of the FDA Adverse Event Reporting System, AERS, and Reproducibility of Clinical Observations , 2011, International journal of medical sciences.

[6]  H. Giamarellou Multidrug-resistant Gram-negative bacteria: how to treat and for how long. , 2010, International journal of antimicrobial agents.

[7]  A. Bate,et al.  Quantitative signal detection using spontaneous ADR reporting , 2009, Pharmacoepidemiology and drug safety.

[8]  G. Keating,et al.  Tigecycline: in community-acquired pneumonia. , 2008, Drugs.

[9]  N. Woodford,et al.  Emergence of a new antibiotic resistance mechanism in India, Pakistan, and the UK: a molecular, biological, and epidemiological study , 2010, The Lancet. Infectious diseases.

[10]  A. Bate,et al.  A Bayesian neural network method for adverse drug reaction signal generation , 1998, European Journal of Clinical Pharmacology.

[11]  S. Evans,et al.  Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports , 2001, Pharmacoepidemiology and drug safety.

[12]  Yasushi Okuno,et al.  Adverse Event Profiles of 5-Fluorouracil and Capecitabine: Data Mining of the Public Version of the FDA Adverse Event Reporting System, AERS, and Reproducibility of Clinical Observations , 2011, International journal of medical sciences.

[13]  M. Falagas,et al.  Treatment of Acinetobacter infections , 2010, Expert opinion on pharmacotherapy.

[14]  A Lawrence Gould,et al.  Practical pharmacovigilance analysis strategies. , 2003, Pharmacoepidemiology and drug safety.

[15]  J. Bartlett,et al.  Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America. , 2009, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[16]  R. O’Neill,et al.  Use of Screening Algorithms and Computer Systems to Efficiently Signal Higher-Than-Expected Combinations of Drugs and Events in the US FDA’s Spontaneous Reports Database , 2002, Drug safety.

[17]  H. Yabuuchi,et al.  Platinum Agent-Induced Hypersensitivity Reactions: Data Mining of the Public Version of the FDA Adverse Event Reporting System, AERS , 2011, International journal of medical sciences.

[18]  Comparisons and Applications of Quantitative Signal Detections for Adverse Drug Reactions (ADRs): An Empirical Study Based On The Food And Drug Administration (FDA) Adverse Event Reporting System (AERS) And A Large Medical Claims Database , 2008 .

[19]  M. Falagas,et al.  Clinical significance of the pharmacokinetic and pharmacodynamic characteristics of tigecycline. , 2009, Current drug metabolism.

[20]  H. Giamarellou,et al.  Multidrug-Resistant Gram-Negative Infections , 2009, Drugs.

[21]  Yasushi Okuno,et al.  Hypersensitivity reactions to anticancer agents: Data mining of the public version of the FDA adverse event reporting system, AERS , 2011, Journal of experimental & clinical cancer research : CR.

[22]  H. B. Fung,et al.  Tigecycline: a glycylcycline antimicrobial agent. , 2006, Clinical therapeutics.

[23]  N. Gordon,et al.  A review of clinical and microbiological outcomes following treatment of infections involving multidrug-resistant Acinetobacter baumannii with tigecycline. , 2009, The Journal of antimicrobial chemotherapy.

[24]  H. Giamarellou,et al.  Multidrug-Resistant Gram-Negative Infections are the Treatment Options? , 2009 .