Identification of IBD using an electronic e-nose

1. Identification of IBD using an electronic e-nose Covington JA3,, Westinbrink E3, Nwokolo C1, Bardhan KD4, Arasaradnam RP1,2 1University Hospital Coventry & Warwickshire & 2Clinical Sciences Research Institute, Medical School, University of Warwick 3School of Engineering, University of Warwick, 4Rotherham NHS Trust 11th BROAD Meeting, Los Angeles – March 2013 1

2. Distal UC; proximal constipation PL, age 64 11 weeks after PEG: 4 / 20 markers Before PEG: 13 / 20 markers Cow 100% Horse 100% Cow 50% Saw dust 50% --horse-oid! 2

3. Composite animal Volatile Organic Compounds (VOCs) - chromatogram profile (GC) Pilot – Animal Studies Chicken = Black Horse = Pink Cow = Blue Time Quantity: Measured as ppm or calculated as area under curve (AUC) 3

4. Sniffing diseases..… Can we smell these chemcical? (>5 senses)

5. ‘Smell’ @ Warwick Life in ‘Smell’ Persaud & Dodd Nature 1982 First research group dedicated to the sense of smell First company making artificial olfaction instruments First commercial products manufactured here… Long history of smell research…

6. What can we analyse? Urine/Faecal/Breath samples from animals & patients Chemical analysis of odours emanating from the sample – essentially sniffing! 6

7. Volatile organic compounds (VOCs) • Disease alters gut flora - altered fermentation patterns which alters the composition of gases emitted from urine • Organic compounds that have high vapour pressure at normal room temperature. • Mainly from colonic fermentation by gut bacteria and partly from physiological metabolic processes • Released in breath, urine, faeces, blood • A potential diagnostic biomarker in IBD

8. Technologies used Ion Mobility Spectrometry Electronic nose GCMS

9. Study design 62 subjects – 3 groups Urine collected and analysed using E-nose and FAIMS Data analysed using Principal Component analysis

10. E-nose sensor response

11. Differentiating IBD using E-nose The maximum response minus the minimum response is used as a feature for data analysis

12. Results – E nose Discriminant Function (DF) Analysis of Fox 4000 data

13. FAIMS – Field Asymetric Ion Mobility Spectrometry • Simple fast analysis of vapours and gases • Detects chemicals in complex mixtures • Identifies by mobility (ion movement through an electric field) • Mobility determined by molecule size and mass

14. FAIMS Data • 4,000 variables from Wavelet transform • Cluster Analysis to identify key variables • Use 20 for identification 1st 500 variables Control – Disease state

15. Study design 105 subjects – 2 groups Urine collected and analysed using FAIMS Data analysed using Principal Component analysis

16. Results – FAIMS (n = 105) • 40 Crohns • 40 Ulcerative Colitis • 25 Controls Each sample re-classified based on remaining samples

17. Results – FAIMS flare vs remission

18. Separation of IBD and controls with Gas Chromatography

19. Summary of chemical peaks in volunteers and Crohn’s , UC patients What are we detecting? Possible Key chemicals • In excess of 20 chemicals modulated; • Likely key chemicals: • ethyl esters propanoic or butanoic acids, butanoic acid • methyl ester, 3-methyl butanoic acid, 1-butanol, 1-propanol and indole HA=hydrogen azide (HN3); APossible Key chemicalsA=acetic acid (CH3COOH); PG=propylene glycol (C3H6(OH)2); A=aldehydes; K=ketones; OA=organic acids.

20. Conclusions Clear disease separation between controls, ulcerative colitis and Crohn’s disease Able to detect between disease flares and remission VOC correlation with GC Potential first line diagnostic modality in patients with suspected IBD Inexpensive novel tool, non invasive and potentially provides point of care diagnosis

21. Acknowledgements: Nathalie Ouaret (PhD) Nabil Quraishi (MSc) Eic Westinbrink (PhD) Nicki O’Connel, RGN Catherine Bailey, RGN BROAD Foundation BRET BDRF Thank you - Questions