Quantitative decision-making in clinical drug development incorporating biomarkers

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This MRC-funded doctoral training partnership (DTP) brings together cutting-edge molecular and analytical sciences with innovative computational approaches in data analysis to enable students to address important applied biomedical research questions in priority areas aligned with industry. This is a 4-year programme whose first year involves a series of taught modules and two laboratory-based research projects that lead to an MSc in Interdisciplinary Biomedical Research. The first two terms consist of a selection of taught modules that allow students to gain a solid grounding in multidisciplinary science. Students also attend a series of masterclasses led by academic and industry experts in areas of molecular, cellular and tissue dynamics, microbiology and infection, applied biomedical technologies and artificial intelligence and data science. During the third and summer terms students conduct two eleven-week research projects in labs of their choice. Project:Development of new drugs and therapies must pass three clinical milestones, Phase 1 (evaluation of safety), Phase 2 (initial evaluation of efficacy), and confirmatory Phase 3 trials. Each stage of this pipeline provides useful decision points to critically evaluate the accumulated data and make important decisions using sound quantitative methods. Given the high attrition rate, especially in early phases, it is paramount to make correct Go or No-Go decisions based on metrics relevant to that particular stage of development. Traditionally, these have relied on a hypothesis testing framework involving the use of statistical tests to achieve the relevant thresholds of significance based on p-values. However, given the high attrition rate especially in oncology, focusing on p-values alone can be counter-intuitive. Recently, alternative model-based approaches have been proposed based on using the upper and lower confidence intervals of the treatment effect such as the Target Value (TV) being the desired effect whereas the Lower Reference Value (LRV) representing the smallest clinically meaningful effect. Such methods provide a more robust categorisation of the strength of evidence of observed results into 3 outcomes: Go, No-Go decision and Consider zone. The proposed project will aim to formulate a generalisation of the decision-making criteria by further incorporating a biomarker that splits the patient population into two distinct subgroups. This will be achieved by simulation of a Phase II biomarker stratified oncology trial, whose operating characteristics will be studied in detail. This should improve quantitative decision-making using a variety of clinical endpoints and statistical designs across the drug development pipeline.