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SUMMARY:Classifying drugs by their arrhythmogenic risk using multiscale mo
 deling and machine learning - Francisco Sahli Costabal (Stanford Universit
 y\; Pontificia Universidad Católica de Chile)
DTSTART:20190606T160000Z
DTEND:20190606T163000Z
UID:TALK125644@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:An   undesirable side effect of drugs are cardiac arrhythmias\
 , in particular a   condition called torsades de pointes. Current paradigm
 s for drug safety   evaluation are costly\, lengthy\, and conservative\, a
 nd impede efficient drug   development. Here we combine multiscale experim
 ent and simulation\,   high-performance computing\, and machine learning t
 o create an easy-to-use   risk assessment diagram to quickly and reliable 
 stratify the pro-arrhythmic   potential of new and existing drugs. We capi
 talize on recent developments in   machine learning and integrate informat
 ion across ten orders of magnitude in   space and time to provide a holist
 ic picture of the effects of drugs\, either   individually or in combinati
 on with other drugs. We show\, both experimentally   and computationally\,
  that drug-induced arrhythmias are dominated by the   interplay of two cur
 rents with opposing effects: the rapid delayed rectifier   potassium curre
 nt and the L-type calcium current. Using Gaussian process   classification
 \, we create a classifier that stratifies safe and arrhythmic   domains fo
 r any combinations of these two currents. We demonstrate that our   classi
 fier correctly identifies the risk categories of 23 common drugs\,   exclu
 sively on the basis of their concentrations at 50% current block. Our   ne
 w risk assessment diagram explains under which conditions blocking the   L
 -type calcium current can delay or even entirely suppress arrhythmogenic  
  events. Using machine learning in drug safety evaluation can provide a mo
 re   accurate and comprehensive mechanistic assessment of the pro-arrhythm
 ic   potential of new drugs. Our study shapes the way towards establishing
    science-based criteria to accelerate drug development\, design safer dr
 ugs\,   and reduce heart rhythm disorders.
LOCATION:Seminar Room 1\, Newton Institute
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