An NMR-based pharmacometabonomic study of CYP3A4 activity

1. 2 1.5 1 Y CV Predicted 1 (Class 1) 0.5 0 -0.5 -1 50 100 150 200 250 300 350 400 450 500 550 Sample pre-dose samples post-dose samples Quinine 8.74 ppm 3-Hydroxy-Quinine 8.72 ppm ppm Figure 2. Example of 6 post-urine NMR spectra pre-urines prior to day 1 post-urines on day 15 R2 =0.27 R2 =0.21 A B p<0.05 p<0.005 1st Day 14th Day 15th Day Predicted Y Actual Y Training set Training set 14 days Independent Test set Independent test set Start taking SJW Recruitment Goal: 400 healthy individuals (100MZ:300DZ) Take Quinine Visit St. Thomas’ Hospital Predicted Y Log (actual quinine ratio) • 415 pre-urines • 412 post-urines • 315 pre-plasma • 272 post-plasma An NMR-based pharmacometabonomic study of CYP3A4 activity Gwénaëlle Le Gall1, Nilufer Rahmioglu2,James Heaton3, Norman Smith3, Ian Colquhoun1, Kourosh R Ahmadi2 and Kate Kemsley1 1Institute of Food Research, Norwich Research Park, Colney, Norwich, UK 2Department of Twin Research and Genetic Epidemiology, King's College London, London, UK 3Micro Separations Group, Pharmaceutical Science Division, King's College London, UK gwenaelle.legall@bbsrc.ac.uk Discrimination before/after St John’s Wort intervention Variability in Drug Response - “One size does not fit all” • Response to medication is highly variable, unpredictable, and at times fatal • “Personalised” treatment has the potential to increase efficacy and decrease toxicity if “response” can be predicted accurately cross validated predictions 4LV PLS-DA model cross validated predictions 6LV PLS-DA model 1.5 A B 1 0.5 0 -0.5 Y CV Predicted 1 (Class 1) -1 -1.5 • Cross Validation: • Random subset; 10 data splits 20 iterations • Sensitivity 92% • Specificity 89% • Genetic and environmental factors affect variability of the response of Drug Metabolizing Enzymes (DME) in particular the the cytochrome P450 (CYP) Cross Validation: Random subset; 10 data splits 20 iterations. • Sensitivity 94% • Specificity 95% -2 -2.5 -3 100 200 300 400 500 600 Sample Figure 1. Cross-validated PLS-DA models based on urine (A) and plasma samples (B) before and after chronic St John’s Wort exposure for two weeks and acute intake of quinine • A clear discrimination between pre and post samples is observed for both urine and plasma. Markers (not shown) include exo and endogenous compounds (quinine and derivatives, tyrosine, N-acetylated metabolites, pyruvate, acetate, glycine, etc.) The aim of the study - • Assemble a large cohort phenotyped for induced CYP3A4 activity with St. John’s Wort, a mild, herbal antidepressant - potent inducer of CYP3A4 • Obtain metabolite profiles and identify biomarkers for predicting CYP3A4 induction Quinine response in post-urines • High resolution signals of quinine at 8.74 ppm and 3-OH quinine at 8.72 ppm were used to calculate 3OHQ/Q • UPLC measurements of quinine and 3-OH quinine were performed on 367 samples Intervention study, quinine as probe drug - MLR predictive models based on NMR (A) and UPLC (B) quinine ratio Figure 3. Validated MLR models predicting the quinine ratio response of NMR (A) and UPLC data (B) based on respectively 9 buckets (buckets 101, 28, 144, 30, 124, 93, 14, 64 and 98) and 8 buckets (buckets 80, 93, 107, 153, 111,102,144 and 87); bucket 93: glycine and bucket 144: N-acetylated metabolites at 1.974 ppm; note that although r2 is lower for the UPLC model, the permutation test shows that the model is more robust giving a p value < 0.005 • Both NMR and UPLC based quinine ratios can be predicted modelling profiles from pre-urines. The Pearson correlation coefficients (r2) are high for this type of data and the p values are highly significant especially for the UPLC data 1H NMR spectra Run on 600 MHz NMR spectrometer with cryoprobe Main outcomes: • It is possible to detect urinary and plasmatic responses to St John’s Wort and quinine by 1H NMR • More importantly good prediction of the CYP3A4 induction response can be obtained using the healthy individual’s metabolite levels from pre-urine spectra MLR: Multi Liner Regression NMR: Nuclear Magnetic Resonance; PLS-DA: Partial Least Square-Discriminant Analysis; UPLC: Ultra Performance Liquid Chromatography We thank the British Biotechnological Science Research Council (BBSRC), Prof Tim Spector the director of the Department of Twin Research and Genetic Epidemiology at St Thomas Hospital in London, UK and the Twin Participants (TwinsUK)