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SUMMARY:Innovative statistical approaches for studies in anti-infective dr
 ug combination development - Alun Bedding (Roche Products)
DTSTART:20170503T181500Z
DTEND:20170503T203000Z
UID:TALK65843@talks.cam.ac.uk
CONTACT:Peter Watson
DESCRIPTION:Combination trials tend to be seen as the pre-dominance of onc
 ology\; however\, in other therapeutic areas they have many benefits.  The
  treatments for many viral diseases are effective and safe\, however\, in 
 some there is still a clinical unmet need.  Levels of successful vaccinati
 on have reduced the need for treatments in some viral diseases\, however\,
  where there is a need\, current therapies show low efficacy and poor tole
 rability.  Monotherapies are seen to be partially effective\, however\, it
  is thought that combinations would provide better efficacy\, whilst still
  maintaining a good safety profile.\n\nThe development of combinations in 
 anti-infective drugs rely more on combining two new molecular entities rat
 her than adding onto an existing standard of care.  This presents many pro
 blems\; however\, some learning from oncology can help with the developmen
 t of these combinations.\n\nIn this presentation I will show how using an 
 adaptive platform study\, combined with Bayesian methods will increase the
  chances of effective combinations coming to market.   Platform trials\, w
 here many treatment arms are compared to a common control are seen as an e
 fficient way of testing new therapies.  The use of Bayesian stopping rules
  for futility allow for the stopping of ineffective arms.  The use of borr
 owing also adds to the efficiency.  These ideas may be coupled with the us
 e of seamless Phase II/III designs to further increase efficiency in movin
 g towards the registration of new combination therapies.
LOCATION:Amgen Ltd\, Cambridge Science Park
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