Postmarketing hepatic adverse event experience with PEGylated/non-PEGylated drugs: a disproportionality analysis.

Objective: To compare reporting frequencies of hepatic adverse events between PEGylated and non-PEGylated formulations of active medicinal compounds in spontaneous reporting systems using a data mining algorithm (DMA).

Methods: Statistical DMAs are being promoted as a means of identifying drug-event combinations that are disproportionately reported in large spontaneous reporting systems databases, a critical data source for pharmacovigilance. After a review of case reports of hepatotoxicity with PEGylated drugs possibly associated with the polyethylene glycol moiety, we carried out a retrospective disproportionality analysis of WHO's multinational drug safety database for events related to hepatic dysfunction comparing PEGylated versus non-PEGylated formulations of four active moieties. A threshold of posterior interval (PI) 95% lower limit >0 was used to define a signal of disproportionate reporting with a drug and an event and 90% PIs of the information component were compared to identify statistical differences between the two compounds.

Results: On the basis of a total of 18 477 cases containing at least one of the drug pairs, we found disproportionate reporting for hepatic-related events with both PEGylated and non-PEGylated formulations. Overlapping of 90% PIs of the information components, however, suggested that there was no statistically significant difference between the frequency of hepatic injury reported with PEGylated versus non-PEGylated drug formulations.

Conclusion: We did not find significant indicators of differential reporting of hepatic injury between PEGylated and non-PEGylated drug formulations in this exploratory analysis using one DMA. The analysis also suggests that comparative disproportionality methodology although not in itself determinative, could be one useful component of a risk management plan for monitoring the postmarketing experience of drug delivery systems that uses multiple methods and data streams.