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Study efficiency is critical in the competitive Phase I oncology environment where a large number of (NMEs) are all competing for the same medical resources.
There is a growing trend to evaluate predictive biomarkers in enriched patient populations in early phases of clinical trial. Novel biomarker-driven, adaptive clinical trial designs can facilitate rapid evaluation of drugs, help validate multiple predictive biomarkers, minimize exposure of patients to ineffective therapies, and potentially allow accelerated drug approval in molecularly defined populations.
Enrichment is defined as the prospective use of any patient characteristic – including demographic, pathophysiologic, historical, and genetic – to select patients for a study population in which detection of a drug effect is more likely than it would be in an unselected population. While the main reason for enrichment is study efficiency (increasing the chance of success, often with a smaller sample size,) it also provides major benefits of individualization, directing treatment where it will do the most good and sparing people who cannot respond to potential harm.
There are three broad categories of enrichment: noise reduction (seeks to eliminate needless variability to improve the likelihood of distinguishing between two treatments); predictive (selects individuals who are likely to respond); and prognostic (selects high-risk patients for risk reduction studies.) The latter two enrichment approaches have an impact on “generalizability” of the results.
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