Association between terbinafine and arthralgia, fever and urticaria: symptoms or syndrome?

In vitro activity of Penicillium chrysogenum antifungal protein (PAF) and its combination with fluconazole against different dermatophytes

Strains of five dermatophyte species (Microsporum canis, Microsporum gypseum, Trichophyton mentagrophytes, Trichophyton rubrum and Trichophyton tonsurans) were selected for testing against Penicillium chrysogenum antifungal protein (PAF) and its combination with fluconazole (FCZ). Inhibition of microconidia germination and growth was detected with MICs of PAF ranging from 1.56 to 200 microg ml(-1) when it was used alone, or at constant concentration (100 microg ml(-1)) in combination with FCZ at from 0.25 to 32 microg ml(-1). The MICs for FCZ were found to be between 0.25 and 128 microg ml(-1). PAF caused a fungicidal effect at 200 microg ml(-1) and reduced growth at between 50 and 200 microg ml(-1). Total growth inhibition with fungistatic activity was detected at 64 microg ml(-1) of FCZ for M. gypseum, T. mentagrophytes, and T. tonsurans, and at 32 microg ml(-1) FCZ for M. canis and T. rubrum. PAF and FCZ acted synergistically and/or additively on all of the tested fungi except M. gypseum, where no interactions were detected.

Signal detection methodologies to support effective safety management

The increased focus on the safety of medical products, as well as the growing volume of available safety information, has created a need for objective quantitative approaches to supplement the medical review of individual case safety reports. Statistical algorithms can be used to identify trends and relationships in both clinical and postmarketing safety databases in support of safety signal detection. Powerful data visualization tools facilitate the medical review of the complex information generated by these methods. In addition, all these approaches need to be integrated into the daily practice of clinical safety and postmarketing pharmacovigilance.

Hepatic disorders in patients treated with COX-2 selective inhibitors or nonselective NSAIDs: A case/noncase analysis of spontaneous reports

BACKGROUND

Hepatic adverse events associated with the use of nonaspirin drugs and NSAIDs are uncommon, but the widespread use of these drugs may impact public health.

OBJECTIVE

We conducted a case/noncase analysis of spontaneous reports to compare the hepatic safety profile of cyclooxygenase (COX)-2 selective inhibitors with that of nonselective NSAIDs.

METHODS

This case/noncase analysis was conducted using the US Food and Drug Administration Freedom of Information (FDA/FOI) database (through quarter 1, 2003) and the World Health Organization Uppsala Monitoring Centre (WHO/UMC) database (through quarter 3, 2003). Council for International Organizations of Medical Sciences and WHO Adverse Reaction Terminology preferred terms were used to classify hepatic disorders with broad and specific case definitions. After reports involving established hepatotoxic drugs (bromfenac, nimesulide, sulindac) were excluded, the proportion of reports (PRs) of each case definition was calculated for each NSAID. Crude and adjusted reporting odds ratios (RORs) were used to compare the overall proportions of hepatic disorders and hepatic failure of celecoxib and rofecoxib versus nonselective NSAIDs.

RESULTS

A total of 158,539 and 185,253 reports of NSAIDs were identified in the FDA/FOI and WHO/UMC databases and 25% and 16%, respectively, involved other hepatotoxic drugs. The PRs of hepatic disorders for all COX-2 selective inhibitors and non-selective NSAIDs were 3.0% in the FDA/FOI database and 2.7% in the WHO/UMC database. In the FDA/FOI and WHO/UMC databases, respectively, mmesulide (16.7% and 14.4%), bromfenac (12.0% and 20.7%), diclofenac (8.1% and 4.7%), and sulindac (6.1 % and 9.9%) were reported to be associated with higher proportions of overall hepatic disorders compared with those of other NSAIDs. Crude and adjusted RORs for the prevalences of overall hepatic disorders and hepatic failure with celecoxib and rofecoxib versus the other NSAIDs were <1 (indicating that the proportion was not higher than that of the comparator) in both databases. The interpretation of the results was unchanged when bromfenac, nimesulide, and sulindac were excluded from the analysis.

CONCLUSIONS

In this case/noncase analysis, bromfenac, nimesulide, sulindac, and diclofenac had higher proportions of reports of hepatic disorders compared with those of other NSAIDs in the FDA/FOI and WHO/UMC databases. The analysis did not raise a safety concern for celecoxib or rofecoxib versus NSAIDs for overall hepatic disorders and hepatic failure.

Current use of anti-infectives in dermatology

Dermatologic diseases encompass a broad category of pathologic situations. Infection remains a significant aspect of the pathology faced in patient encounters, and it is natural to expect that anti-infectives play a major element in the armamentarium utilized by dermatologists. Aside from the treatment of the classic bacterial and fungal infections, there are now new uses for antiviral agents to help suppress recurrent disease, such as herpes simplex. There is also the novel approach of using anti-infectives, or agents that have been thought to have antimicrobial activity, to treat inflammatory diseases. This review describes anti-infectives, beginning with common antibiotics used to treat bacterial infections. The discussion will then cover the current use of antivirals. Finally, the description of antifungals will be separated, starting with the oral agents and ending with the topical antimycotics. The use of anti-infectives in tropical dermatology has been purposefully left out, and perhaps should be the subject of a separate review. Cutaneous bacterial infections consist chiefly of those microorganisms that colonize the skin, such as species of staphylococcus and streptococcus. Propionibacterium acnes and certain other anaerobes can be involved in folliculitis, pyodermas and in chronic conditions such as hidradenitis suppurativa.

Application of quantitative signal detection in the Dutch spontaneous reporting system for adverse drug reactions

The primary aim of spontaneous reporting systems (SRSs) is the timely detection of unknown adverse drug reactions (ADRs), or signal detection. Generally this is carried out by a systematic manual review of every report sent to an SRS. Statistical analysis of the data sets of an SRS, or quantitative signal detection, can provide additional information concerning a possible relationship between a drug and an ADR. We describe the role of quantitative signal detection and the way it is applied at the Netherlands Pharmacovigilance Centre Lareb. Results of the statistical analysis are implemented in the traditional case-by-case analysis. In addition, for data-mining purposes, a list of associations of ADRs and suspected drugs that are disproportionally present in the database is periodically generated. Finally, quantitative signal generation can be used to study more complex relationships, such as drug-drug interactions and syndromes. The results of quantitative signal detection should be considered as an additional source of information, complementary to the traditional analysis. Techniques for the detection of drug interactions and syndromes offer a new challenge for pharmacovigilance in the near future.

Use of measures of disproportionality in pharmacovigilance: three Dutch examples

Spontaneous reporting systems for suspected adverse drug reactions (ADRs) remain a cornerstone of pharmacovigilance. In The Netherlands 'the Netherlands Pharmacovigilance Foundation Lareb' maintains such a system. A primary aim in pharmacovigilance is the timely detection of either new ADRs or a change of the frequency of ADRs that are already known to be associated with the drugs involved, i.e. signal detection. Adequate signal detection solely based on the human intellect (case by case analysis or qualitative signal detection) is becoming time consuming given the increasingly large number of data, as well as less effective, especially in more complex associations such as drug-drug interactions, syndromes and when various covariates are involved. In quantitative signal detection measures that express the extent in which combinations of drug(s) and clinical event(s) are disproportionately present in the database of reported suspected ADRs are used to reveal associations of interest. Although the rationale and the methodology of the various quantitative approaches differ, they all share the characteristic that they express to what extent the number of observed cases differs from the number of expected cases. In this paper three Dutch examples are described in which a measure of disproportionality is used in quantitative signal detection in pharmacovigilance: (i) the association between antidepressant drugs and the occurrence of non-puerpural lactation as an example of an association between a single drug and a single event; (ii) the onset or worsening of congestive heart failure associated with the combined use of nonsteroidal anti-inflammatory drugs and diuretics as an example of an association between two drugs and a single event (drug-drug interaction); and the (iii) (co)-occurrence of fever, urticaria and arthralgia and the use of terbinafine as an example of an association between a single drug and multiple events (syndrome). We conclude that the use of quantitative measures in addition to qualitative analysis is a step forward in signal detection in pharmacovigilance. More research is necessary into the performance of these approaches, especially its predictive value, its robustness as well as into further extensions of the methodology.