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OSE Seminar by Dr. Adrian Perez Galvan of Vertex Pharmaceuticals on Bringing down the uncertainty: how physics can generate insights in early drug discovery

Departmental News

Dr. Adrian Perez Galvan seminar image

Posted: April 4, 2018

Date: Wednesday, April 4, 2018 

Time:  11:00 AM to Noon 

Location:  CHTM, Room 101 

Map to CHTM: 

Parking passes are available at the receptionist's desk.


Discovering and developing new medicines is an extremely challenging, lengthy, and expensive process with approvals of new drugs by the Food and Drug Administration below 10%. A major problem is that drug discovery remains fundamentally rooted in empiricism, so that outcomes in the pharmaceutical industry are realized only after clinical testing in humans. Any tool that can reduce the uncertainty in our understanding of the behavior of the drug prior to its application to humans will not only make this process more efficient but will also help bring much needed medicines to patients faster.

In this talk I will present how Vertex Pharmaceuticals has successfully incorporated practices and techniques from more traditional physics-based companies, like semiconductor or telecommunication industries, to build instruments that study the behavior of a particular compound in a biological relevant environment. In addition I will also show how deep knowledge of the biology intrinsic to the system understudy coupled with a sound understanding of the laws of physics can help design powerful probes of the compound-target interaction.

Speaker's Bio:

Adrian Perez Galvan is a member of the Instrumentation Group at Vertex Pharmaceuticals. He received his Ph.D. in experimental atomic physics at the University of Maryland, College Park doing precision laser spectroscopy of laser-cooled francium for studies of the electroweak force. He has held post-doctoral appointments at the California Institute of Technology and Argonne National Laboratory trapping ultra-cold neutrons and radioactive ions, respectively for searches of physics beyond the Standard Model.