Analysis of the Risks and Benefits of New Chemical Entities Approved by the US Food and Drug Administration (FDA) and Subsequently Withdrawn From the US Market

Abstract

Benefit-risk evaluations of drugs have been conducted since the introduction of modern regulatory systems more than 50 years ago. Such judgments are typically made on the basis of qualitative or semiquantitative approaches, often without the aid of quantitative assessment methods, the latter having often been applied asymmetrically to place emphasis on benefit more so than harm. In an effort to preliminarily evaluate the utility of lives lost or saved, or quality-adjusted life-years (QALY) lost and gained as a means of quantitatively assessing the potential benefits and risks of a new chemical entity, we focused our attention on the unique scenario in which a drug was initially approved based on one set of data, but later withdrawn from the market based on a second set of data. In this analysis, a dimensionless risk to benefit ratio was calculated in each instance, based on the risk and benefit quantified in similar units. The results indicated that FDA decisions to approve the drug corresponded to risk to benefit ratios less than or equal to 0.136, and that decisions to withdraw the drug from the US market corresponded to risk to benefit ratios greater than or equal to 0.092. The probability of FDA approval was then estimated using logistic regression analysis. The results of this analysis indicated that there was a 50% probability of FDA approval if the risk to benefit ratio was 0.121, and that the probability approaches 100% for values much less than 0.121, and the probability approaches 0% for values much greater than 0.121. The large uncertainty in these estimates due to the small sample size and overlapping data may be addressed in the future by applying the methodology to other drugs.

Predicting Regulatory Product Approvals Using a Proposed Quantitative Version of FDA’s Benefit–Risk Framework to Calculate Net-Benefit Score and Benefit–Risk Ratio

“…These include probabilistic decision analysis [ 4 ], use of spatial planes [ 5 ], Bayesian approaches [ 6 ], patient preferences [ 7 , 8 ], incremental net health benefit using simulation data [ 9 ], number-needed-to-treat (NNT) and number-needed-to-harm (NNH) [ 10 ], and a variety of other quantitative approaches [ 11 ]. Some notable studies involved the benefit–risk analysis of cancer-related endpoints of pivotal studies from 20 + products for non-small cell lung cancer [ 12 ], multiple myeloma [ 13 ], and melanoma [ 14 ], and another compared the quantitative profile of new chemical entities that were initially approved but subsequently withdrawn from the market [ 15 ]. While many approaches highlighted the features of the product’s benefits and risks and specifically the results of the pivotal studies, none of the reviewed publications factored in the critical contributions of risk management, defined in the BRT, as part of the overall decision-making process.…”