Download One size does not fit all - Institute of Food Research

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
of Twin Research and Genetic Epidemiology, King's College London, London, UK
3Micro Separations Group, Pharmaceutical Science Division, King's College London, UK
2Department
[email protected]
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
cross validated predictions 4LV PLS-DA model
cross validated predictions 6LV PLS-DA model
• “Personalised” treatment has the potential to
increase efficacy and decrease toxicity if “response”
can be predicted accurately
1
0.5
A
0
-0.5
-1
-1.5
-2
pre-dose samples -2.5
• Genetic and environmental factors affect variability of the response of Drug
Metabolizing Enzymes (DME) in particular the the cytochrome P450 (CYP)
Y CV Predicted 1 (Class 1)
Y CV Predicted 1 (Class 1)
1.5
Cross Validation:
Random subset; 10 data splits 20 iterations
Sensitivity 92%
Specificity 89%
post-dose samples -3
100
200
300
400
Sample
2
1.5
0.5
0
-0.5
500
-1
600
B
1
Cross Validation:
Random subset; 10 data splits 20 iterations.
• Sensitivity 94%
• Specificity 95%
50 100 150 200 250 300 350 400 450 500 550
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, Nacetylated 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
Quinine response in post-urines
Quinine
8.74 ppm
• Obtain metabolite profiles and identify biomarkers for
predicting CYP3A4 induction
3-Hydroxy-Quinine
8.72 ppm
ppm
Intervention study, quinine as probe
drug pre-urines
postprior to
day 1
Figure 2. Example of 6 post-urine
NMR spectra
• 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
MLR predictive models based on NMR
(A) and UPLC (B) quinine ratio
urines on
day 15
Predicted versus actual plot, from 8-variate model (blue triangles = independent test set)
3.5
3
1.5
R2
=0.27
p<0.05
A
1
Day
Day
Y
Predicted
Predicted y
Day
15th
Actual Y
14th
Actual Y
2.5
1st
2
1.5
1
Start
taking
SJW
1H
Recruitment Goal:
400 healthy individuals
(100MZ:300DZ)
0
0.5
Take
Quinine
NMR spectra
• 415 pre-urines
• 315 pre-plasma
• 412 post-urines
• 272 post-plasma
Run on 600 MHz NMR spectrometer with cryoprobe
Visit St.
Thomas’
Hospital
0.5
0
Training set
0.5
14 days
R2 =0.21
p<0.005
B
Training set
Independent
Test set
1
1.5
2
Predicted Y
2.5
3
Independent test set
-0.5
3.5
Predicted Y
-0.5
0
0.5
Log(actual quinine ratio)
1
1.5
Log (actual 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
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)