University of Nottingham

 Cyclops feasibility projects on cancer


Principal investigator: Professor Cameron Alexander, University of Nottingham.


Project start year: 2017


Co-investigators: Professor Helen Byrne - University of Oxford; Professor Ioan Notingher & Dr Serhiy Korposh & Professor Steve Morgan - University of Nottingham; Professor Poulam Patel - Nottingham University Hospitals NHS Trust.


Other partners: Dr Kristofer Thurecht, University of Queensland, Australia.

“SPI-CLOPS” (Surface Polymer Imprinted Closed Loop Optical Patient Sensors) for Dose Detection and Prevention of Cancer Resistance

Lay summary

The treatment of melanoma has improved dramatically since the introduction of drugs that interfere with disease specific pathways. However, the development of resistance to these drugs is a major cause of concern, as it leads to treatment failure and poor patient outcomes. Early stage detection of resistance to cancer drugs could revolutionise therapeutic regimens for melanoma and other cancers. This proposal sets out the first stages in a new healthcare technology which could enable clinicians to monitor the efficacy of cancer drugs in appropriate time scales, and detect the first signs of resistance thus indicating the optimum time to administer combination therapies.

Specifically, the project will develop advanced fibre-optic sensors with polymer coatings which allow simultaneous detection of drug levels in a cancer model and the onset of resistance pathways. Our vision is for sensing electronics and polymer materials science to be combined with mathematical modelling and 3D tumour mimics thus providing the critical proof-of-concept data prior to in vivo studies. We have assembled a multi-disciplinary, and international team with expertise linking physical and engineering science to advanced clinical oncology to tackle this vital unmet societal and medical need.  Ultimately we envisage a fully automated system in which drug delivery and tumour properties are monitored so that the appropriate dose can be delivered at the appropriate time.



Principal Investigator: Dr Leandro Pecchia, University of Warwick.


Project start year: 2018


Co-Investigators: Professor Helen Byrne - University of Oxford.


Other partners: Dr Pasquale Innominato - Warwick Medical School; Prof Stephen Fôn Hughes - BCUHB, North Wales & North West Urological Research Centre; Prof Mike Chappell & Dr Vishwesh Kulkarni - University of Warwick.

Closed-loop control for optimising chemotherapy infusion.

Lay summary

“Inner clock adapts our physiology to the dramatically different phases of the day, […] regulating critical functions such as behaviour, hormone levels, sleep, body temperature and metabolism”. This phenomenon is known as the biological clock or circadian rhythm and in 2017 its discoverers were awarded the Nobel Prize in medicine. Due to circadian rhythms, the efficacy and side effects of chemotherapy change throughout the day. Chemotherapy in turn alters circadian rhythm. This creates a closed-loop requiring control.

While circadian rhythms can be monitored using blood/salivary/urine hormone tests, such tests are not practical at home and do not provide continuous real-time monitoring. Combining artificial intelligence (machine learning and deep learning) and signal processing with commercial sensors embedded in smartwatches or clothes that measure physiological and behavioural attributes (features/variables) offers unprecedented and as yet unexplored opportunities to monitor circadian rhythms in real time.

To the best of our knowledge, this project will be the first to:1) Develop models for real time monitoring of circadian rhythms;2) Model how circadian rhythms affect response to chemotherapy;3) Answer fundamental questions that will pave the way for developing a (personalised) closed-loop control system in which the delivery of chemotherapy is related to a patient’s circadian cycle.This project could revolutionise the administration of chemotherapy, improve patient responses, and reduce side effects and costs. 



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