
The European Medicines Agency (EMA) Management Board has nominated Emer Cooke as its new executive director.
Cooke, an Irish national, currently serves as the director of regulation and prequalification department at the World Health Organization, where she leads health technologies regulation efforts. Her department also is responsible for working with member states and international partners to monitor the quality, safety, efficacy, and performance of health technologies.
Emer Cooke. Image courtesy of the European Medicine Agency.The board selected Cooke from a shortlist of candidates during a virtual session on 25 June. The nomination process includes a statement and a question-and-answer session. The nominee needs to receive at least 24 votes out of the 36 board members.
Next, Cooke will make a statement to the European Parliament's Committee on Environment, Public Health and Food Safety on 13 July. She will then be appointed executive director pending a letter from the president of the European Parliament to the EMA Management Board.
Cooke has 30 years of experience in international regulatory roles, including work for the pharmaceutical unit of the European Commission and the EMA. She received a pharmacy degree, Master of Science degree, and a Master of Business Administration degree from Trinity College in Dublin.










![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)








