Post-marketing assessment of neuropsychiatric adverse events in influenza patients treated with oseltamivir: an updated review.

A 2008 review by our group concluded that the risk of neuropsychiatric adverse events (NPAEs) in influenza patients was not increased by oseltamivir exposure, and did not identify any mechanism by which oseltamivir or its metabolites could cause or worsen such events. The current article reviews new information on this topic. Between September 16, 2007 and May 15, 2010, 1,805 spontaneously-reported NPAEs were identified in 1,330 patients receiving oseltamivir: 767 (42.5%) from Japan, 296 (16.4%) from the USA, and 742 (41.1%) from other countries. NPAEs were more common in children: 1,072 (59.4%) events were in those aged ≤16 years. NPAEs often occurred within 48 h of treatment initiation (953 events; 52.8%). Nearly half of the events were serious in nature (838; 46.4%). The three largest categories of events were abnormal behavior (457 events, 25.3%), miscellaneous psychiatric events (370; 20.5%), and delusions/perceptual disturbances (316 events, 17.5%). A total of 1,545 events (85.6%) in eight different categories were considered to be delirium or delirium-like. Twenty-eight suicide-related events were reported. A US healthcare claims database analysis showed that the risk of NPAEs in 7,798 oseltamivir-treated patients was no higher than that in 10,411 patients not on antivirals, but a study on oseltamivir and abnormal behavior in Japan was less conclusive. NPAE frequency in oseltamivir-exposed Japanese and Taiwanese children with influenza was the same as in unexposed children. New analysis of the UK General Practice Research Database showed that the relative adjusted risk of NPAEs in influenza patients was 2.18-times higher than in the general population. Other epidemiology studies report frequent occurrence of encephalitis and similar disorders in influenza patients independently of oseltamivir exposure. The new data support the findings of the original assessment. Evidence suggests that influenza-related encephalopathies are caused by influenza-induced inflammatory responses, but more work is needed to confirm the underlying mechanisms.

Detecting signals of drug-drug interactions in a spontaneous reports database.

AIMS: The spontaneous reports database is widely used for detecting signals of ADRs. We have extended the methodology to include the detection of signals of ADRs that are associated with drug-drug interactions (DDI). In particular, we have investigated two different statistical assumptions for detecting signals of DDI. METHODS: Using the FDA's spontaneous reports database, we investigated two models, a multiplicative and an additive model, to detect signals of DDI. We applied the models to four known DDIs (methotrexate-diclofenac and bone marrow depression, simvastatin-ciclosporin and myopathy, ketoconazole-terfenadine and torsades de pointes, and cisapride-erythromycin and torsades de pointes) and to four drug-event combinations where there is currently no evidence of a DDI (fexofenadine-ketoconazole and torsades de pointes, methotrexade-rofecoxib and bone marrow depression, fluvastatin-ciclosporin and myopathy, and cisapride-azithromycine and torsade de pointes) and estimated the measure of interaction on the two scales. RESULTS: The additive model correctly identified all four known DDIs by giving a statistically significant (P < 0.05) positive measure of interaction. The multiplicative model identified the first two of the known DDIs as having a statistically significant or borderline significant (P < 0.1) positive measure of interaction term, gave a nonsignificant positive trend for the third interaction (P = 0.27), and a negative trend for the last interaction. Both models correctly identified the four known non interactions by estimating a negative measure of interaction. CONCLUSIONS: The spontaneous reports database is a valuable resource for detecting signals of DDIs. In particular, the additive model is more sensitive in detecting such signals. The multiplicative model may further help qualify the strength of the signal detected by the additive model.