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Biomarkers and Translational Tools (BTT) Community


Community Goals

Scientific Expertise: 
  • The Biomarkers and Translational Tools (BTT) Community facilitates interactions among experts across multidisciplinary fields to foster translational science with a focus on individuals via patient care and society via healthcare. The interactions are through social media, email blasts, and teleconferences with focused scientific content.
Catalyst for Innovation: 
  • The BTT Community is a catalyst for innovation by sponsoring a range of scientific programs at the Annual Meeting focused on biomarker and translational tools.  
Influence and Impact
  • The BTT Community is the lead group engaged in promoting the use of biomarkers and other translational tools for optimizing drug development and patient care.
Education and Communication: 
  • The BTT Community offers educational opportunities via the Annual Meeting and other fora.
Organizational Effectiveness: 
  • Leadership within BTT will be expanded by establishing a Network Steering Committee.  
  • The BTT Community has regularly scheduled teleconferences and utilizes social media to engage members throughout the year.
  • The BTT Community fosters the career of its members by assisting in recognizing members for individual achievements and contributions to ASCPT, sponsoring educational programs, and providing opportunities for networking activities throughout an individual’s career.   
Community News
Please log in as a member to access more Community News and members-only content.
Community Webinar
Recent Webinar
Title: Prediction of Clinical Outcome After 4 & 8 Weeks of Citalopram/Escitalopram Therapy for Major Depression: A Data-Driven Machine Learning Approach
Speakers: Arjun Athreya and Drew Neavin
Description: Major depressive disorder (MDD) is a serious illness that inflicts millions of people annually.  However, there are currently no biomarkers that can be used to diagnose patients. Further, one-third of MDD patients do not respond to a given antidepressant medication and there are currently no effective methods that can be used to predict patient outcomes prior to antidepressant treatment.  Previous machine learning methods have used clinical data, but did not include biological data in order to attempt to predict patient outcomes.
Please sign in as a member to access the video below. 

Leadership


Chair
Richard A. Graham, PhD
Theravance Biopharma

Richard Graham (Rick) received his Bachelor’s and Master’s degree in Biochemistry from Iowa State University and his Doctorate of Philosophy degree in Pharmaceutical Sciences from the University of North Carolina at Chapel Hill.  Rick has a strong background in basic research with particular expertise in drug metabolism clinical pharmacology.  Full bio

   

Vice Chair
Ana Caroline Costa Sa

Ana Caroline Costa Sa (Carol) received her Bachelor’s degree of Pharmacy from the University of Brasilia, Brazil, and her Master’s degree in Genetics and Parasite Biology from the Oswaldo Cruz Foundation, Rio de Janeiro, Brazil. Carol has recently concluded her PhD in Genetics and Genomics at the University of Florida. Read her full bio.

   
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