Big Data

For Better Hearts


Interview with Claire Baudier





Dr Claire Baudier  

Claire is a Data Scientist at Servier Paris, France. She is a key member of case study 5“Identify novel druggable targets using proteomics and genomics” working group in the IMI BigData@Heart (BD@H) project. 


Q: Claire, please tell us a bit about yourself: 

A: I was born in France and I have been raised in New Caledonia, which is a French island in the Pacific Ocean. When I was 18, I came back to Europe where I pursued my studies as an Engineer. I specialised with a master’s degree in Systems Biology and Bioinformatics at the University of Amsterdam, and did my PhD at the University Paris-Saclay. I then joined Servier as a Data Scientist to work on several case studies of the IMI BD@H project. 


Q: How did you become interested in systems biology and bioinformatics? 

A: During my studies as an Engineer in France, I had the opportunity to follow a double degree abroad. I was particularly interested in Biology and was looking for a master’s degree program related to Life Sciences. At that time, my engineering school was establishing a partnership with the University of Amsterdam, a city I love! A Master of Systems Biology and Bioinformatics, which gathered three subjects that I enjoy: Mathematics, Informatics, and Biology, drew my attention. After its completion, I continued to work at the intersection of these three fields in the industry sector. 


Q: When did you get involved in BD@H?  

A: I have been involved in BD@H since I joined Servier as a Data Scientist in June 2019. More specifically, I am conducting research in BD@H case study 1 “Comparison of the real-world HF (heart failure) patients to HF trial patients to guide future trials” and case study 5 “Identify novel druggable targets using proteomics and genomics”. 


Q: Can you describe your work in BD@H? 

A: I mainly work on BD@H case study 5 at Servier. We use the Mendelian randomization method first to re-demonstrate the efficacy of beta-blockers on heart failure, as a proof of concept before applying it to the discovery of novel drug targets.  

The Mendelian randomization - a method used in Epidemiology to demonstrate whether there is an effect between an exposure, for example, blood pressure, and an outcome, usually a disease, such as myocardial infarction or heart failure – can be used to help to predict the efficacy of drugs, using data from Genome-wide association studies (GWAS).  

In my daily activity, I use large cardiovascular GWAS such as blood pressure, heart rate, and heart failure to perform these analyses. GWAS are particularly useful to find links between genetic variations and some human-specific traits that contribute to common, complex diseases, such as heart diseases or cancer. GWAS are based on the sequencing of 100,000 to 1,000,000 study participants and help researchers in identifying new genetic association to develop better strategies to detect, treat, and prevent the diseases.  

We started with a pilot study to re-demonstrate the protective effect of the beta-1 adrenergic receptor inhibition by beta-blockers on heart failure using Mendelian Randomization. To do so, we used genetic variants of the ADRB1 gene which encodes the beta-1 adrenergic receptor (the target of beta-blockers) and that specifically impact blood pressure. The mendelian randomization analysis predicted that lowering blood pressure through beta-1 adrenergic receptor inhibition decreases the risk of heart failure. 


Q: Tell us about the latest results of the sub-study on redemonstrating beta-blockers efficacy on HF through Mendelian randomization in simple words. 

A: After having re-demonstrated the protective effect of blood pressure lowering through the blocking of the beta-1 adrenergic receptor by beta-blockers on heart failure, we decided to extend our study to other adrenergic receptors, including alpha 1 and 2 as well as beta 2 and 3 receptors. We investigated their effects on heart failure, coronary heart disease, and ventricular dimensions.  

I cannot add additional details at the moment, but if you are interested in our latest results, please be aware that the main results will be presented at the AHA 2020 - American Heart Association Conference in November 2020 and a paper will follow.  


Q: Why is this BD@H sub-study important? What does it mean for HF patients? (in lay language) 

A: 80% of drugs tested in clinic fail to get to market, mostly due to a lack of efficacy or safety issues. By performing Mendelian randomization analysis, we are able to predict the efficacy and some side effects of the candidate drugs and select the most promising ones, saving healthcare stakeholders’ time and money. While the method may not be applicable to all drug targets, it will help shortening drug development and bringing new efficient treatments to treat heart failure into the market. Therefore, patients can get more rapidly new drugs with a reinforced safety.  

Mendelian randomization is a revolutionary statistical method that comes from economics in the second half of the 20th century. It has been applied since 2004-2005 in epidemiology to unlock the potential of big data for the benefit of citizens.  

All these new methods, computational power, and the access to large data sets are key elements that will improve patients’ lives in the coming years.  A data-sharing culture should be promoted. As a data scientist, I can confirm that we carefully follow the GDPR regulation and that we use data in a responsible, secure, and trusted way to advance research. 


Q: How is working at Servier and being involved in the IMI BD@H project? 

A: Being a Data Scientist at Servier, an international leading pharmaceutical company is a great opportunity. I have learned a lot about drug development, working together with senior colleagues with relevant expertise (they are always available to support you here at Servier), and developing technical skills. Furthermore, being involved in a Public-Private Partnership such as BD@H, gave me the chance to gain a deeper understanding of biological, statistical aspects, and new methods, including the Mendelian Randomization method and GWAS. I highly appreciated working with international teams from the Netherlands, UK, Spain, Germany, Sweden, and Switzerland, learning how to effectively collaborate remotely.   


Q: What are your future plans? 

A: I have a contract with Servier to work on BD@H till the end of the year. In 2021, I would like to continue my career, getting a Data Science related position in a Pharmaceutical or in a MedTech Company. 

I enjoyed working at Servier, contributing to advance the research in new treatments. For the first time, I was involved in close-to-market research activities and I saw the impact of our studies in serving patient needs.  

Published on: 11/02/2020