Professor of Physiology at the University of Oxford
Peter Robbins is Professor of Physiology at the University of Oxford. He has a strong reputation for developing innovative technologies to address previously intractable questions in human physiology, particularly relating to respiratory and cardiovascular function. His interests include hypoxia biology and iron homeostasis. In these areas, he has had a long-term collaboration with Professor Sir Peter Ratcliffe, the 2019 Nobel Laureate in Physiology or Medicine.
He has held research contracts from Vifor Pharma and GSK, as well as receiving substantial funding from various government agencies and charities to support his work. Peter Robbins completed a doctorate in physiology in 1981 and qualified in clinical medicine in 1984. Both degrees were from the University of Oxford. He completed a degree in mathematics from the Open University in 1992. Alongside his academic interests, he has held senior managerial roles. These include running the finances and the investment portfolio of The Queen’s College, Oxford from 2001-2006, being a Charity Trustee and member of Council for the University of Oxford from 2007-2014, and being Head of the Department of Physiology, Anatomy and Genetics from 2011-2016.
The Department has been ranked the top department in the world for Physiology and Anatomy (QS2017 and QS2018 rankings).
Optimising surrogate outcome measures for respiratory trials
- The pharmaceutical industry should engage directly in optimising surrogate outcome measures for clinical trials. Medical device companies do not have the same economic incentive associated with success.
- Inhomogeneity in lung function is an attractive target for developing a surrogate outcome measure that is more sensitive to early disease and to therapeutic intervention than those associated with airway calibre.
- The current measure of inhomogeneity, the lung clearance index, has many failings that limit its attractiveness as a surrogate outcome measure. These failings are both conceptual and methodological.
- An approach using modern sensor technologies and computation can solve all the significant failings associated with the lung clearance index.
- Clinical examples will be given illustrating the benefits of this novel approach in chronic airways disease.