Statistics for Decision Processes: Transitions between Research and Development Phases - Richardus Vonk (Bayer)

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Translational medicine, often described as “bench to bedside", promotes the convergence of basic and clinical research disciplines. It aims to improve the flow from laboratory research through clinical testing and evaluation to standard therapeutic practice. This transfer of knowledge informs both clinicians and patients of the benefits and risks of therapies.

In an analogous fashion, we propose the concept of Translational Statistics to facilitate the integration of biostatistics within clinical research and enhance communication of research findings in an accurate and accessible manner to diverse audiences (e.g. policy makers, patients and the media). Much reporting of statistical analyses often focuses on methodological approaches for the scientific aspects of the studies; translational statistics aims to make the scientific results useful in practice.

In this talk we will consider some general principles for translational statistics that include reproducibility, relevance, and communication. We will also consider how modern web-based computing allows the simple development of interactive dynamic tools for communicating and exploring research findings. Various examples will be used to illustrate these ideas.

The high costs and long duration of clinical development, paired with high levels of attrition, require the quantification of the (un-)certainty when moving from preclinical animal research
to clinical development. One other, less often observed area of translation is the translation between the different development phases, where through the course of development the focus moves towards the target population. In the management of the transition risks, biomarkers may play an important role.

Further to the regulatory requirements, statistics and statistical thinking are integral parts of the internal decision making processes, particularly in early clinical development. This presentation offers an overview of the current challenges, and then concentrates on innovative statistical methods that facilitate the transitional efforts. We review the role of Bayesian methodology in this endeavour. We provide examples from the area of biomarker development, early clinical trials and proof of concept situations.

Abstract:  In the study of immunology the use of specific biomarkers of the immune system is critical to early develop of compounds to treat auto-immune disorders such as type I diabetes and ulcerative colitis.  Whilst one biomarker may be elevated for a positive response another may also be elevated, which might lead to a safety concern.  It is important to use these understand the dose response of the drug, with respect to these biomarkers, with the hypothesis that they will translate into a clinical effect.

This talk will discuss how a drug program can be developed in the area to ensure proper understanding of the dose response curve.  This is first done from the translation of pre-clinical discovery into the first time in human study.  The importance of this cannot be underestimated given drugs of this nature have the potential to cause a cytotoxic storm if doses too high.  Thoughts around addressing this will be presented.