PSI Biomarkers SIG Webinar: Biomarkers in Clinical Development
Date: Thursday 19th January 2023
Time: 14:00-15:30 GMT | 15:00-16:30 CET
Speakers: Deepak Parashar (University of Warwick), Nicole Krämer (Boehringer Ingelheim) and Guillaume Desachy (AstraZeneca).
Who is this event intended for? Everybody interested to learn more about the importance of biomarkers in clinical development.
What is the benefit of attending? Attendees will be able to contribute to the discussion on how to improve clinical development using biomarkers.
This event is free to attend, for both Members and Non-Members of PSI.
To register your place for this event, please click here.
In this webinar, you will learn more about recent advances in biomarker-based designs, machine learning for biomarkers and data repositories for biomarker use cases.
This webinar is organized by the PSI Biomarkers SIG.
Presentation 1: Enrichment designs with predictive biomarkers
Most biomarker-driven trial designs are based on the assumption that the biomarker is predictive of response to treatment.
Would you rather verify this assumption during the trial?
If so, which population and subpopulations would you test?
What regulatory issues might you encounter?
How would you setup your hypotheses?
At the PSI Biomarkers SIG, we are addressing these issues within the setting of adaptive enrichment designs for Phase II trials in oncology.
Presentation 2: Who said Machine Learning for Biomarkers?
What do you expect from an inspiring presentation on Machine Learning for Biomarkers?
1. Excitement, because you have learned about all the sophisticated Machine Learning methods out there?
2. Being impressed by success stories on Machine Learning & biomarkers?
3. Actionable guidance so that you can incorporate Machine Learning for Biomarkers into your own work?
We, the PSI Biomarkers SIG, want to give it a try and share our thoughts on the past, present and future of Machine Learning for Biomarkers with you.
Presentation 3: But where is the data?
You've discovered this cool new Machine Learning technique but have no data to try it on?
Or you have heard of a new groundbreaking kind of biomarkers?
Or you are like: it is time for me to get my hands dirty with RNA-seq data!
We’ve all been in such situations but then comes the harsh reality: finding publicly available biomarkers data is hard. Finding publicly available biomarkers data along with corresponding treatment response is even harder.
Changing this was the motivation of the PSI Biomarkers SIG to have a focus on data repositories.
During this presentation, we will share with you the fruit of this work!
Deepak Parashar is an Associate Professor at the University of Warwick, with research interests at the interface of mathematics, statistics, and cancer research. He studied for BSc (Honours) and MSc Physics from Delhi, Master of Advanced Study in Mathematics from Cambridge, and obtained a PhD in Mathematics from Aberdeen.
Deepak has been the lead statistician on numerous real-world clinical studies, clinical trials in cancer, with extensive statistical work on NHS datasets for survival studies. He has developed statistical methodology for personalised medicine in cancer, in particular, biomarker-guided adaptive designs, master protocols, addressing issues such as control of false positive error rates and treatment heterogeneity. His current research includes using real-world evidence in clinical trials, subgroup identification methods and novel trial designs, quantitative decision-making, and geometric representations of multidimensional clinical trial data. Deepak has held research positions in Leipzig, Swansea, Rome, Bonn, Cambridge, and is a Turing Fellow at The Alan Turing Institute for Data Science and Artificial Intelligence.
Nicole Krämer is a Senior Principal Statistician at Boehringer Ingelheim in Biberach, Germany. As a member of the Therapeutic Area & Methodology Statistics Group, she supports clinical development teams on strategy and methodology for biomarker analysis, translational medicine and early clinical development. Leveraging the power of biomarkers using Data Science is a very rewarding experience for Nicole. She uses her skills to drive forward early endpoint development, subgroup identification and dose optimization.
Nicole received her PhD in Machine Learning in 2006. Together with Guillaume Desachy, she chairs the EFSPI/PSI Biomarkers Special Interest Group. She is also a member of the EFSPI/PSI Subgroups Special Interest Group.
Since graduating from ENSAI (Biostatistics M. Sc.) 10 years ago, Guillaume has been immersing himself in precision medicine.
Data-driven, he is passionate about answering scientific questions and making sure we convey the right message to stakeholders, both internally & externally.
He feels very fortunate to have had the chance to work with various kinds of OMICs data and leverage the power of biomarkers to strengthen drug development.
He also feels incredibly lucky to have worked in a diverse set of settings, be it in academia (UCSF, U.S.), in a biotech (Enterome, France) or in the pharmaceutical industry (BMS, Servier & AstraZeneca, France & Sweden). He now works as a Statistical Science Director for AstraZeneca in Gothenburg, Sweden.
Apart from his day job at AstraZeneca, Guillaume teaches a course about OMICs data analysis at ENSAI (www.ensai.fr), is actively involved in the ENSAI alumni association (www.ensai.org) and is a mentor for Article 1, a non-profit organization promoting equal opportunity (https://article-1.eu/) and together with Nicole Krämer, he leads the EFSPI/PSI Biomarkers Special Interest Group (here is their podcast on the topic: https://bit.ly/3rqtA4I).
Whether it is to discuss about statistics, choices that you are making in your early career or any other subject, you can contact him via LinkedIn (https://www.linkedin.com/in/guillaume-desachy/).