PVmap™ of the Week

ProSanos has initiated a program to publicly provide a limited set of PVmaps™ generated from the FDA's Adverse Event database. A different map will be posted each week focusing on a drug and adverse event combination that is a current topic of discussion within the industry or in the published literature. For more information about PVmaps or the PVmap of the Week program, .

Asthma drugs and Churg-Strauss Syndrome (5/14/2007)
An article in Pharmacoepidemiology and Drug Safety discusses the possible relationship between asthma drug use and the development of Churg-Strauss syndrome (CSS), also known as allergic granulomatous angiitis.⁴⁴ The article describes a case-control study that was conducted in response to a finding in the AERS database of a strong association between leukotriene modifier use and CSS. The study concludes that this association is not statistically significant if the concomitant use of inhaled or oral corticosteroids is taken into account. Here we use PVmaps to illustrate how the information produced by data mining can be used to inform the design of a study such as the one described in the article.

We begin with an event-focused PVmap for allergic granulomatous angiitis. (An additional 46 cases appear under the MedDRA Preferred Term churg strauss syndrome. A full analysis rather than an illustration would incorporate these.) This is an Event-focused PVmap, where we use the flexibility of PVmaps to reverse the usual paradigm and ask, "What drugs are most highly associated with a particular adverse event?" rather than the other way around.

The publicly-available data in the PVmap comes from the FDA via its Adverse Event Reporting System (AERS) using data covering the period from 2001 through the first quarter of 2006. In this case, the points on the map represent drugs rather than adverse events. The drug most strongly associated with this adverse event term is montelukast (marketed as SINGULAIR^®). Many other drugs, nearly all of them asthma treatments, also show a strong statistical association with this condition.

Several factors complicate the interpretation of drug associations with CSS. Asthma is a precursor condition in CSS, so the presence of asthma drugs on this map cannot be interpreted as causal. Emphasizing that point, we see that the list of drugs associated with CSS on this map includes leukotriene modifiers, inhaled and oral corticosteroids, and even the bronchodilator theophylline (marketed as THEO-DUR^® and under other names). These are medications from diverse classes and different mechanisms of action so the data doesn't describe a clear pattern that even raises the possibility of a causal association.

A Potential Interactions PVmap can easily be generated for montelukast to identify potential bystander effects that confound the analysis. A Potential Interactions PVmap answers the question: "For a given drug/adverse event combination, what other drugs are reported as being taken at the time of that adverse event". It can be used to suggest potential drug interactions as well as bystander effects.

The Potential Interactions PVmap above shows a preponderance of inhaled steroids as well as asthma drugs of other classes that are being taken by patients at the same time as the occurrence of Montelukast and Allergic Granulomatous Angiitis. If the next step of follow-up for a signal involves the design of a case-control study, this type of map can contain valuable information about what concomitant-medication data needs to be collected. In this case, Harrold et al included steroid use as a factor in their analysis and showed that it was a sufficiently strong factor to fully explain the observed relationship between montelukast and CSS.

These maps emphasize the "hypothesis-generating" role of data mining in pharmacovigilance. This role is not limited to signal detection. Data mining can also be used to identify the factors that need to be incorporated into the design of an observational study, such as a case-control study, in situations where a safety signal is of sufficient importance to call for such a study. A Potential Interactions PVmap can indicate where potential confounders must be identified and accounted for in such a study. The maps here illustrate our capability to identify not just safety signals, but potential confounders as well.

Event-focused PVmaps
The first PVmap shown in the case study above is an Event-focused PVmap that allows you to visualize which drugs are most highly associated with a particular adverse event (rather than the other way around). In this case, the adverse event is the MedDRA term allergic granulomatous angiitis and the red dots represent drugs reported in the AERS database to be associated with this condition. On the horizontal axis of this graph is the reporting ratio, which compares the number of times that a drug is reported with the specified adverse event to the number expected due to chance alone. The vertical axis expresses the statistical significance of the finding. Dots above the horizontal blue line and to the right of the vertical blue line represent "significant signals". The drugs with the strongest association to allergic granulomatous angiitis appear at the top and to the right on the PVmap.

Potential Interactions PVmaps
The second map in the example above is a Potential Interactions PVmap, which allows you to visualize what concomitant drugs are significantly associated with a specified drug/adverse event combination. In this case, the drug/adverse event combination is the drug montelukast reported in association with CSS. The red dots on the first map represent concomitant drugs in use when the drug / adverse event combination montelukast / CSS occurred. On the horizontal axis of this graph is the reporting ratio, which compares the use of the concomitant drug along with montelukast / CSS with the use of the concomitant drug expected due to chance alone. The vertical axis expresses the statistical significance of the finding. Dots above the horizontal blue line and to the right of the vertical blue line represent "significant signals". The concomitant drugs that are most highly associated with the drug/event combination of interest appear at the top and to the right of the PVmap.

Sponsor companies have used ProSanos PVMaps for multiple therapeutic areas. To learn more about PVMaps projects in your therapeutic area or indication, please .

Disclaimers

1. Potential risks highlighted by drug safety analysis must be balanced against the clinical benefit attained by the use of a pharmaceutical product in a given clinical situation. Nothing in these analyses is intended to influence the practice of medicine, nor to weigh the benefits of one product over another.
2. Whether the reporting ratio of an adverse event is high enough to influence the decision to use a given product or products can only be determined by a complete analysis of the benefits, risks, and therapeutic alternatives.
3. Use of the publicly available FDA AERS data does not imply endorsement or agreement of the findings by the FDA Center for Drug Evaluation and Research.
4. There are many factors that can influence how the adverse events are reported in the AERS database and may impact the resulting safety signal. These include but are not limited to: publicity and media attention, litigation, length of time drug is on the market, whether the event in question has been previously attributed to the drug, the source of the report, etc.
5. AERS data must often be "cleaned" prior to analysis. This process may include de-duplication, reconciliation of misspelled product names, mapping of adverse events terms, and other manipulations which could introduce bias into the analysis.
6. PVmaps has been evaluated as a safety signal investigation tool for over two years.

References

44. Harrold LR, Patterson MK, Andrade SE, et al. Asthma drug use and the development of Churg-Strauss Syndrome (CSS). Pharmacoepidemiology and Drug Safety. Published online at Wiley Interscience (www.interscience.wiley.com) DOI: 10.1002/pds.1353 .