Understanding the Benefits and Risks of Pharmaceuticals Workshop Summary (2007) / Chapter Skim
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2 Regulatory Assessment
2 Regulatory Assessment
Pages 10-31
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PROBLEMS AND POTENTIAL SOLUTIONS FOR REGULATORY ASSESSMENT OF BENEFIT AND RISK1 Panelists discussed how benefit and risk data are currently collected and evaluated prior to a drug gaining regulatory approval from the FDA. The challenges to completing the assessment were identified as follows: • Lack of a systematic, consistent, and transparent approach to benefit–risk analysis; • Uncertainty regarding how to balance risk and benefit; • Insufficient knowledge about the risks of drugs at the time of their launch; • Conflict of interest (for example, experts consult with industry and 1This section is based on the presentations of Peter Tollman,The Boston Consulting Group; Louis Garrison, University of Washington; Alan Garber, Stanford University; Steven Galson, FDA; David Slavin, Pfizer; Larry Lesko, FDA; Brian Strom, University of Pennsylvania; Douglas Throckmorton, FDA; and Jeffrey Leiden, Abbott Laboratories.
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Quantification, however, has its own set of challenges: • Quantitatively capturing a complex drug benefit–risk profile; • Quantitatively characterizing drug benefit–risk for individuals because of variation among patients in terms of both physiology and preferences; • Updating benefit–risk assessments with new information through the drug life cycle; • Addressing the inherent uncertainty in benefit–risk measurement; • Addressing disagreement about the role that cost should play in benefit–risk calculations; • Addressing the cost of adopting a quantitative framework and its potential adverse effect on innovation; and • Effectively presenting and communicating quantitative information.
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Challenges in Developing a Common Methodology Dr. Galson argued that developing a common methodology for assessing efficacy and safety throughout the regulatory life cycle of a drug poses an enormous challenge because our understanding can change substantially over its course.
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The premarketing safety database, which included approximately 3,000 patients in two dose-ranging trials, revealed only limited dose-dependent adverse events. After launch, however, when the population of patients who were exposed to the drug increased rapidly, there was a severe increase in labeled gastrointestinal (GI)
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fig 2-2 QUANTITATIVE APPROACHES A QALY-Based Approach to Benefit–Risk Modeling Committees often take a piecemeal approach when weighing benefit and risk evidence and making approval decisions.
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Dr. Garrison explained that for most new drugs, estimating QALYs requires using models to synthesize information and extrapolate beyond what is traditionally collected in Phase III trials.
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Participants noted that the calculation of QALYs requires modeling and extrapolation beyond the information typically collected in Phase III trials. Since modeling requires making assumptions -- for example, how surrogate outcomes translate into clinical benefit -- the uncertainty surrounding those assumptions must be addressed.
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Lesko argued that quantitative tools are attractive because they are complementary to conventional tools, which have certain limitations. For example, when Phase III data are analyzed with conventional tools, the change in response from baseline to end of study is compared between treatment and placebo groups.
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It was demonstrated through quantitative analysis that linking kidney function exposure and dose adjustment would provide a reduction in the risk in individual patients and subgroups of patient. While the learning process with Zometa was not used to design additional clinical trials (via simulations)
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Strom remarked that subjective judgments are being made throughout the entire benefit–risk assessment life cycle and it is naïve to think that we can quantify these subjective judgments. Pharmaceutical companies make subjective judgments about whether to develop a drug; advisory boards and regulators make subjective judgments about whether to approve a drug; physicians make subjective judgments about whether to prescribe a drug; and patients make subjective judgments about whether to take a drug.
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Throckmorton argued that integrating quantitative benefit–risk assessment into the early drug development process would enable all stakeholders to make better decisions. Companies could make earlier and more informed go or no-go decisions, the FDA and other regulators could make more informed early approval decisions, and patients could make better decisions about treatment options.
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Novel drugs for diseases with large unmet need have less well-defined regulatory paths, longer development times, less well-defined and often smaller markets, less well-defined safety issues, and high liability risks. These challenges make pharmaceutical executives more reluctant to initiate clinical development programs for novel drugs.
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Industry would benefit from the incentive to pursue innovative products for smaller markets (for example, by not spending $100 million or $200 million on products that would be too risky and expensive to push through the regulatory process) ; guaranteed market exclusivity; and further R&D made possible by the revenue from provisionally approved products.
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Dr. Throckmorton's suggestion that benefit–risk data be used in clinical trial simulation early during development raised questions about how the uncertainty surrounding unknown risks would be handled.
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. The anticipated role of the Red Book was that it would provide a framework for a well-integrated riskassessment process that could quantitatively characterize chemical hazards in a way that was objective and "separate and distinct" from decision making.
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has involved lack of disclosure, not actual outcome. Transparency provides the means to reconstruct scientific analyses to determine where objective quantitative methods end and professional judgment begins, and it allows for a better understanding of the uncertainty in analyses and conclusions.
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Samet reemphasized the long history of chemical risk assessment and how the Red Book provided a much-needed formal framework for addressing environmental health risk questions. He discussed what is widely considered one of the best human data-based quantitative chemical risk assessments conducted thus far: radon exposure.
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Finally, both the "natural unit" and common metric estimates should be reported. While these demands complicate pharmaceutical drug efficacy assessments and increase uncertainty, he said that there are ways to deal with the uncertainty.
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CRISIS IN CREDIBILITY6 A recurring topic of discussion over the course of the two-day workshop was loss of public trust in the U.S. drug safety system and wide 6This section is based on the presentations of Brian Strom, University of Pennsylvania; David Slavin, Pfizer Inc.; and Lynn Goldman, Johns Hopkins University.
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The effect of these limitations is that the public misunderstands drug safety, believing that postmarketing discovery of adverse drug reactions means that "somebody messed up." In reality, almost all postmarketing safety issues involve rare adverse events that could not have been detected prior to marketing. This misunderstanding, coupled with growing concern about drug safety has led to overreaction, increased premarketing requirements, and delayed access to new drugs.
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She noted that there are few sources of funding for pharmacology research through the National Institutes of Health (NIH) , FDA, or other federal sources, and funding is not allotted for independent data assessments, consumer surveys, or efforts to communicate with consumers about pharmaceutical risks.
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Dr. Leiden mentioned a recent article reporting that there has not been a single case of research fraud caused by these financial conflicts of interest between industry and academia (Stossel 2005)
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