An advanced and well-established four-day residential course designed to:
• Provide an interface between the computer analysis of PK and PD data and physiological concepts.
• Equip delegates, through lecture sessions, with an advanced understanding of all aspects of the subject, including pharmacodynamic theory, interpretation of computer output, practical experimental design, discrimination between rival models and combining data of different sources.
• Give delegates the unique opportunity of access to the WinNonlin modeling package to undertake hands-on exercises on real-life case studies - allowing delegates to apply the concepts learnt in lecture sessions to an extensive number of real-life problems and data-sets. Users of software other than WinNonlin will also benefit from the methods discussed in the lectures and hands-on sessions.
• Allow delegates one-to-one time with the expert course tutors in problem-solving sessions. Participants are encouraged to bring their own kinetic/dynamic data.
• Provide reference material for use after the course through a full resource pack and textbook relevant to predictive science.
• Allow delegates to network
• Provide an interface between the computer analysis of PK and PD data and physiological concepts.
• Equip delegates, through lecture sessions, with an advanced understanding of all aspects of the subject, including pharmacodynamic theory, interpretation of computer output, practical experimental design, discrimination between rival models and combining data of different sources.
• Give delegates the unique opportunity of access to the WinNonlin modeling package to undertake hands-on exercises on real-life case studies - allowing delegates to apply the concepts learnt in lecture sessions to an extensive number of real-life problems and data-sets. Users of software other than WinNonlin will also benefit from the methods discussed in the lectures and hands-on sessions.
• Allow delegates one-to-one time with the expert course tutors in problem-solving sessions. Participants are encouraged to bring their own kinetic/dynamic data.
• Provide reference material for use after the course through a full resource pack and textbook relevant to predictive science.
• Allow delegates to network
