Traditionally, Translational medicine aimed to improve the flow from laboratory research through clinical testing and evaluation to standard therapeutic practice. Translational Statistics facilitates the integration of biostatistics within clinical research and enhances communication of research findings in an accurate and accessible manner to diverse audiences. Statistical analyses has often focused on methodological approaches for the scientific aspects of the studies; translational statistics aims to make the scientific results useful in practice.
This Scientific Meeting will focus on blurring the hard line between non-clinical and clinical and move to a more iterative discussion and investigates innovative designs in early phases of drug development to increase efficiency of the development process whist also considering reproducibility, relevance, and communication.
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Translational Statistics: Relevance, Reproducibility, and Communication – John Hinde (University of Galway)
Translational Statistics: Relevance, Reproducibility, and Communication
(University of Galway)
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.
Translation, a two way street - A Case Study of Event Related Potentials (ERPs) as a Neuroscience Biomarker
Claire Brittain (Lilly)
In early clinical drug development, biomarkers capable of providing proof of mechanism are considered critical tools and can help reduce attrition during phase II clinical trials. However, with neuroscience drugs it’s common to use different measures in rats and humans e.g. Water mazes in rats and ADAS-Cog questionnaires for Alzheimer’s in humans. They are both excellent measures in their own right but translation can be greatly improved if you start by comparing apples with apples.
This gives us 3 options:
1. Ask volunteers to swim in circular tanks looking for a hidden platform
2. Teach rats to answer complex questions on their cognitive impairment
3. Find a new measure that can used in both species and thus allows us to compare directly
This presentation will take you through the journey of how we selected our biomarker (Auditory Evoked Related Potentials) and the considerations in designing and analysing the experiments between species. I hope to show what can be achieved if we blur the hard line between non-clinical and clinical and think of it more of an iterative discussion.
Statistics for Decision Processes: Transitions between Research and Development Phases - Richardus Vonk (Bayer)
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.
Improving Design, Evaluation and Analysis of Early Drug Development Studies (IDEAS)
Thomas Jaki (University of Lancaster)
Abstract: Drug development is a long and costly process which suffers from the major shortcoming that frequently failure is often only determined during the final stage. Advanced statistical methods for study design, evaluation and analysis, employed already at the early phases of drug development, have a great potential to increase the efficiency of the development process.
IDEAS is a European training network for 14 early stage researchers working on statistical methods for early drug development. The network is funded by the European Union’s Horizon 2020 research and innovation programme and comprises of 8 full partners and three associated partners at major European universities, the pharmaceutical industry, and consulting companies.
In this talk we will outline the structure of IDEAS and highlight two specific projects that are focusing on translation between pre-clinical and clinical studies.
The use of biomarkers in translating from pre-clinical to first in human trials in Immunology – Alun Bedding (Roche)
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.
Integrative modelling of experimental medicine clinical data shows that target engagement predicts clinically relevant biological effects – Fabio Rigat (GSK)
Abstract: Experimental Medicine studies measure multiple dependent parameters in a relatively small number of subjects. This situation, commonly known in Statistics as the “large p, small N” paradigm, calls for the use of multivariate inference to extract robust low-dimensional inferences for data interpretation and to support clinical decision making. This talk demonstrates that quadratic discriminant analysis, a simple and well established multivariate method, can be used in this context to identify a relation between target engagement (TE) of a therapeutic monoclonal antibody and the downstream changes in multiple biomarkers at the site of action. The multivariate distribution of all biomarker changes within each TE class (high or low) is used to estimate the Bayes-optimal allocation of each subject within each TE class. The accuracy of the resulting classification is found to be higher than that resulting from a random allocation, thereby establishing the statistical significance of a relation between TE and the biomarker changes. Leveraging this result, the probability distribution of the most clinically meaningful biomarker is used for planning of a proof of concept (PoC) trial endpoint focussing in on patient transitions across different disease statuses. The marginal transition probabilities showing patients’ improvement during the trial are estimated using a conjugate Bayesian Multinomial classification model.
Design of Drug Development Programs with Biomarkers: A stratified medicine approach – Paul Frewer (AZ)
Abstract: In oncology is it becoming increasing common to have targeted treatments based on a patient’s biomarker status. The talk will focus on types of designs which could be used in investigating a treatment with this intention. For example designs allowing assessment of both biomarker positive and negative populations and designs incorporating a number of different investigational medicines targeted at specific patients. There are advantages and challenges to the designs and we will focus on the statistical considerations to be aware of when developing the clinical plan.
Numbers are limited to 70 for this free event and therefore registration is a commitment to attend. Registration includes all refreshments and lunch. Registration for this meeting has now closed.
PSI, the European Federation of Statisticians in the Pharmaceutical Industry (EFSPI) and the Biopharmaceutical Section of the American Statistical Association (ASA) are jointly organising a webinar on Estimands in Practice. Speakers from regulatory authorities (FDA and EMA) and industry will present on their experience on this topic to date.
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