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Tessella

Analytics, Software Services, Consulting

Tessella and Neusentis, a research unit of Pfizer Limited, Team Recognised for Innovative Data Analysis Technique

Novel work analysing microneurography data wins Tessella Excellence Award

Award

Left to right: Dave Dungate – Tessella; Jason Miranda – Pfizer; Ruth McKernon – Pfizer; Huw Rees – Pfizer; Alan Gaby – Tessella

A Tessella project, working with a team of Neusentis scientists from Cambridge and other Pfizer scientists from Sandwich, respectively, has been recognised for novel work analysing microneurography data with a Tessella Excellence Award in the ‘Most Innovative Work’ category. The Tessella Excellence Awards were created to recognise outstanding work by people throughout the company along with their client counterparts, by giving them an opportunity to share the work they’ve done. The Tessella members of the team were presented with their own award, while, as the Tessella client, the Neusentis team were presented with an engraved glass plaque at a presentation held at the Neusentis Cambridge offices.

Tessella was contracted by Neusentis for their specific expertise in mathematical algorithm development to assist the Neusentis team in developing better methods for analysing microneurography data collected in pain projects. In basic terms, microneurography is the study of the transmission of electrical signals down a nerve fibre. In a typical experiment, the team electrically stimulate a nerve in the paw of an anaesthetised rat and then measure the response further along the nerve fibre, thus gauging how the signal is transmitted. Measuring the function of peripheral nerves gives a direct measure of drug effect if it is measured before and after administration. The problem with microneurography is that it generates vast amounts of data, with continuous recordings lasting up to several hours and including blocks of different stimulations that can be consecutive or a few minutes apart.

While the data set is very rich, some of the desired parameters have been extracted manually which is both time-consuming also not fully objective. In this project the team applied a range of algorithms normally used for image analysis, radar tracking and other disciplines to improve the signal to noise
ratio, identify the key features, extract and model the response curves, and provide the required key parameters from the massive and very complex data set.

The team at Neusentis are working, together with academia and other industry partners, on ‘Europain’, which is a public-private consortium funded by the European Innovative Medicines Initiative (IMI) aimed at improving the treatment of patients with chronic pain and includes microneurography in several work packages, both preclinical and clinical.