Larger Versus Smaller Clinical Safety Databases for Regulatory Approval of Drugs.

August 7, 2008 By Vijayalakshmi Kunadian MBBS MD MRCP [mailto:vkunadian@perfuse.org]

North Carolina: A recent extensive statistical analysis from Duke University suggests that large database prior to approval of drugs can be a cost-effective means to reduce adverse events in the post approval phase.

Most clinical trials utilizing new pharmacological agents are statistically powered to identify significant difference in the primary efficacy endpoints rather than adverse events associated with the drugs. These adverse events do not come to light until after the drugs are in the post approval phase. Several drugs including Vioxx have been removed from the shelves in the post approval phase due to the occurrence of serious adverse events in the United States.

Reed and other researchers from Duke University developed a model to determine the magnitude of the expected benefit of using larger versus smaller clinical safety databases for regulatory approval of a hypothetical new drug measured by the avoidance of adverse drug events. Clinical trials using cyclooxygenase-2 inhibitors (COX-2) that had reports of cardiovascular and cerebrovascular adverse events were used to estimate model parameters. Three analyses were performed in the model: Base-case scenario, sensitivity analysis and cost-effectiveness analysis.

In the overall model, the investigators assumed that 10 million patients would be treated with the hypothetical drug with 0.5% (odds ratio 2.5 with the study drug) annual incidence of adverse events. Based on these, the statistical power was 76% for 2000 patients (57,000 adverse events avoided in the target population) as compared with 96% for 4000 patients (72, 000 adverse events avoided in the target population).

In the sensitivity analysis, when the odds ratio increased, the differences between the small and large database in detecting adverse events was small. Likewise when the background risk increased, the ability to detect adverse events between the small and large database reached maximum statistical power. In the cost-effectiveness analysis, when the sample size increased from 2000 to 8000, the incremental cost-effectiveness ratio was ~$70,700/life-year saved and this increased to ~$361,100/life-year saved when the sample size was increased from 4000 to 8000.

From their analysis, the researchers extrapolate three scenarios resulting from a regulatory policy requiring larger preapproval safety databases. First, when the small databases are adequately powered to detect the increased risk of an adverse event, the use of a large database would unnecessarily delay the availability of drugs in the market and the potential benefits to the patients. Second, when the small database is inadequately powered, large database would be of benefit in identifying adverse events before the approval of the drugs. Third, when both small and large database are underpowered, this can lead to increased adverse events in the post approval phase that were not detected prior to drug approval. High risk patients might experience more adverse events compared with low risk patients.

Hence while designing a clinical trial the authors propose several approaches to identify the potential adverse events associated with the study. This consists of standardizing the method of reporting adverse events of the approved drugs, weighing the potential advantages and disadvantages of regulatory requirements for larger safety databases and strengthening the surveillance efforts in the post approval phase. The authors also recommend that in addition to considering the safety and efficacy of a drug, other factors such as legal, ethical, political and economic issues should also be taken into consideration in the regulatory approval of drugs.

In an accompanying editorial, Dr Garber (Stanford University, California) notes that “the work of Reed and colleagues represents a step toward the comprehensive, formal analysis needed to make rational decisions about drug safety”.

Source

 * 1) http://content.healthaffairs.org/cgi/reprint/hlthaff.27.5.w360v2
 * 2) http://content.healthaffairs.org/cgi/reprint/hlthaff.27.5.w371v1