Traditional drug licensing approaches are based on binary decisions. At the moment of licensing, an experimental therapy is presumptively transformed into a fully vetted, safe, efficacious therapy. By contrast, adaptive licensing (AL) approaches are based on stepwise learning under conditions of acknowledged uncertainty, with iterative phases of data gathering and regulatory evaluation. This approach allows approval to align more closely with patient needs for timely access to new technologies and for data to inform medical decisions. The concept of AL embraces a range of perspectives. Some see AL as an evolutionary step, extending elements that are now in place. Others envision a transformative framework that may require legislative action before implementation. This article summarizes recent AL proposals; discusses how proposals might be translated into practice, with illustrations in different therapeutic areas; and identifies unresolved issues to inform decisions on the design and implementation of AL.

The current process of benefit–risk assessment of medicines relies primarily on intuitive expert judgment. Frameworks are needed for transparent, rational and defensible decision making that benefits patients, drug developers, and decision makers. The Benefit Risk Action Team framework is a set of processes and tools for selecting, organizing, summarizing, and interpreting data that is relevant to decisions based on benefit–risk assessments. It provides a standardized yet flexible platform for incorporating study outcomes and preference weights as well as for communicating the rationales for decisions. Clinical Pharmacology & Therapeutics (2011) 89 2, 312–315. doi:

One-year mortality after acute myocardial infarction continues to decrease, and changes in the prognostic value of traditional methods of risk stratification have occurred.

The BRAT Framework is a set of flexible processes and tools that provides a structured approach to pharmaceutical benefit-risk decision making in drug development and post approval settings. A work in progress, it consists of six steps that produce representations of key tradeoffs, with appropriate documentation of the rationale for decisions and the assumptions made in their development. This article describes insights, gained from case studies, into the Framework's performance in a variety of constructed benefit-risk scenarios, focusing on a hypothetical example of a triptan for migraine. The scenarios described illustrate the challenges inherent in arriving at many of the regulatory decisions, including obtaining data for matching populations for all outcomes, finding data of consistent quality, addressing correlated outcomes (e.g., elevated liver function tests and hepatitis rates), dealing with rare but serious adverse events (AEs), and understanding and making decisions based on information for many outcomes simultaneously. The Framework provides a structure for organizing, interpreting, and communicating relevant information, including heterogeneity in results and the quality and level of uncertainty of data, in order to facilitate benefit-risk decisions.

SAECG adjustments for sex, age, and MI location did not improve sensitivity and specificity but produced a more uniform predictive performance. The proposed criteria are based only on QRSd, because late potentials (VRMS and LAS) did not discriminate patients with sudden death. Duration of high-level activity during QRS (QRSd-LAS) can predict AEs, suggesting that the arrhythmogenic substate involves a large mass of myocardium.