Gradual versus binary drug licensing process

Oye, colleagues in Clinical Pharmacology and Therapeutics
February 16, 2012

CAMBRIDGE, MA—A review paper published online in Clinical Pharmacology and Therapeutics promotes further exploration of adaptive licensing (AL) to identify serious safety issues earlier, improve the efficacy of drug therapies in use, and reduce the number of patients exposed to risks.

Under the present system, licensing decisions are based primarily on demonstrations of safety and efficacy of drugs in tightly controlled clinical trials using relatively homogeneous populations of patients that are free from complicating conditions. However, once approved, most drugs are used in uncontrolled settings by large heterogeneous populations of patients with confounding factors. As a consequence, it is not surprising that with some frequency projections of safety and efficacy based on such trials do not hold for the same drugs in real world use.

Hans-Georg Eichler, Kenneth Oye and colleagues review eight proposals for AL, including Health Canada's Progressive Licensing Project, the MIT Center for Biomedical Innovation's New Drug Development Paradigms, and the European Medicines Agency Road Map to 2015. In addition , the authors identify general considerations successful AL programs need to address, including acknowledgment of acceptable levels of uncertainty, improved public communication of drug safety and efficacy, increased prescription control, and additional surveillance and data collection. Finally, the authors flag unresolved issues that require further research.

This work represents an unusually integrative joint project linking MIT Center for Biomedical Innovation and the MIT Center for International Studies. The paper on AL of pharmaceuticals is part of a larger set of projects, including prior work by Lawrence McCray, Kenneth Oye and Arthur Petersen on adaptive regulation in environment, health and safety and current work by the Program on Emerging Technologies on adaptive management of risks associated with emerging technologies such as synthetic biology supported by NSF Synthetic Biology Engineering Research Center.

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