Tufts Center for the Study of Drug Development

About: Tufts Center for the Study of Drug Development is a based out in . It is known for research contribution in the topics: Drug development & Clinical trial. The organization has 78 authors who have published 258 publications receiving 16047 citations.

Impact of Comparative Effectiveness Research on Drug Development Strategy and Innovation

Abstract: Food and Drug Administration approval of a new drug, or new indication for an existing drug, is a necessary but increasingly insufficient condition for market access in the United States. Payers evaluate a drug’s clinical safety and effectiveness profile in relation to existing standards of care. The collected evidence is referred to here as comparative effectiveness research (CER). Augmented with evidence from cost-effectiveness and budget-impact studies, CER elucidates a drug’s value, which, in turn, helps inform payer pricing and reimbursement decisions. Such decisions can reach back to impact clinical development programs and hence future innovation, as they indicate key value parameters that payers, health-care providers, and patients are seeking in new pharmaceutical products. In this chapter, we address what CER delivers in terms of information regarding the value of pharmaceutical products, to whom that information is directed, and the impact that CER can have on drug development strategy and biomedical innovation.

Is Earlier Signal Detection Always Better

Abstract: We read with interest the paper of Lerch et al. [1], which describes a significant yield of true safety signals upon ‘‘resignaling’’, i.e. re-examination of data regarding a drugevent combination that was previously reviewed and considered not to represent a true safety signal. Early detection of true safety signals has been the Holy Grail of pharmacovigilance, and the ability to detect signals early is frequently used as the criterion by which signaldetection algorithms are evaluated and compared. This was the case for signal detection algorithms which made use of spontaneous reports [2, 3], and this criterionwas carried over into subsequent work on algorithms which detect safety signals in electronic health records [4–6]. Time to detection was a key criterion in the recent Observational Medical Outcomes Partnership algorithm competition [7]. The Lerch et al. paper leads us to question this approach, in that a significant number of new safety signals appeared only upon a second assessment with additional data present. Are our signal-detection algorithms now so sensitive that they routinely detect signals before there is adequate accumulated data to properly assess them? We must keep in mind that signal-management is a multi-step process, involving detection, assessment, and decision-making to change a label. If a signal for a given drug-event combination is detected ‘‘too early’’, leading to an inconclusive assessment, it may then go into a monitoring queue where it will eventually re-signal and be reassessed. The overall time from detection until the event appears on the drug label may actually be longer than it would have been if a less-sensitive signal detection algorithm had been used. Reporting patterns may also change over time. Early reports may be sporadic and relatively uninformative. If reporters perceive a pattern of association between a drug and an event and perceive they are contributing to medical knowledge [8], they may report more consistently and provide the information necessary to describe the association to market authorization holders and regulatory agencies. Truly optimum signal detection would identify drugevent combinations at exactly the point where there is sufficient cumulative data to make a proper medical and scientific assessment of whether or not a real adverse drug reaction has been identified—no sooner, no later. Future evaluations of signal-detection algorithms may need to include the total time from detection to label change, accounting for re-signaling and re-assessment. This of course complicates and delays the evaluation process considerably, and surrogate measures may need to be developed which reward early detection, but only up to a point. Certain fruits aren’t ready to be harvested until they have had time to ripen. Does the same principle need to be applied to signal detection in pharmacovigilance?