NATIONAL INDIGENOUS DRUG AND ALCOHOL CONFERENCE Alcohol and other Drug-Related Brain Injury
Detecting Drug Effects in the Brain
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Transcript of Detecting Drug Effects in the Brain
Detecting Drug Effects in the Brain
Heather Turner, Phil Brain and Foteini Strimenopoulou
Nonclinical StatisticsPfizer, UK
18 August 2011
Background
Aim: identify and characterise effect of drug on the brain
• Drug effect over timeI PK/PD model
• EEG experimentsI electrical activity in the brain
• Generalised Semi-linear Canonical Correlation Analysis(GSLCCA)
EEG
• Electrodes placed on scalp
• Monitor difference in voltage between baseline electrodeand others
• Produces virtually continuous signal
EEG Data
• EEG signal converted via FFT to power spectraI ”amount” of each frequency for each time sliceI multivariate response over time
FFT−−→
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Examples of frequency periodograms
Frequency (Hz)
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0−5 minutes120−125 minutes
PK/PD
• AssumptionsI drug level in brain follows pharmacokinetics model (PK)I brain response proportional to dose level (PD)
• Expected response over time follows PK model, e.g.
Double Exponential
β(exp(−k1t)− exp(−k2t))
Critical Exponentialβtexp(−k1t)
PK Models
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Double ExponentialCritical Exponential
GSLCCA (in pictures)
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Spectrum
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GSLCCA (in pictures)
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Fitted Values
GSLCCA Method
• Canonical Correlation Analysis (CCA)I For matrices Y and X, finds loadings a and b to
maximisecor(Y a,Xb)
• Semi-linearI Y is the matrix of power spectraI X = X(t, θ) defined by PK model
• GeneralisedI linear coefficients b or nonlinear parameters θ may
depend on treatment factor
gslcca Package
• gslcca functionI specify PK model by name/formulaI specify which parameters vary by treatmentI control over data smoothingI partial CCA option
• plot, print, summary
Clonidine Experiment
• 4 treatmentsI ControlI Low doseI Medium doseI High dose
• 8 rats in 4-period cross-over design
• EEG data recorded for 12 hours post-dose
GSLCCA Analysis
Call:
gslcca(Y = spectra, formula = "Critical Exponential", time = Time,
subject = Rat, treatment = Treatment, separate = TRUE, ref = 1,
data = design, subject.smooth = 4)
GSLCCA based on 8 subjects
Data smoothed at subject level using 4 roots
Nonlinear parameters:
subject 35 subject 36 subject 37 subject 38
K1 Low Dose 7.5576 8.4252 7.8778 9.9125
K1 Middle Dose 7.8786 8.5137 8.0901 8.8885
K1 High Dose 8.9017 9.3213 9.0159 9.1980
subject 39 subject 40 subject 41 subject 42
K1 Low Dose 8.7217 8.0102 8.7103 8.1199
K1 Middle Dose 8.8546 8.5952 9.1854 8.3800
K1 High Dose 9.0439 9.1611 9.3047 8.9933
Signaturesplot(result, "signatures")
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Signatures Corresponding to Different Subjects
Subject 35Subject 36Subject 37Subject 38Subject 39Subject 40Subject 41Subject 42
Normalised Signatures
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Frequency (Hz)
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Signatures Corresponding to Different Subjects
Subject 35Subject 36Subject 37Subject 38Subject 39Subject 40Subject 41Subject 42
Mean Signature
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Frequency (Hz)
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Mean Signature
• contribution of powerat each frequency toPK curve over time
• assumed to be specificto the target drug isaimed at
Control/Inactive Signature
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• if drug inactive, anydose ≡ control
• inactive drug has samesignature as control
Control/Inactive Signature
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clonidinevehicle
• In this case drug clearlydifferent from control
• Drug is active - asexpected!
Comparing Active Drugs
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Drug ADrug B
• Two drugs targetingsame ion channel,different receptors
• Run t-tests to compareloadings at eachfrequency
Snapshot Analysis
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−lo
g 10(p
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deltathetaalphabetagamma • P-values adjusted using
FDR
• Frequencies split intoconventional bands
Summary
gslcca package is in development on R-Forgehttps://r-forge.r-project.org/projects/gslcca/
Further work needed before release to CRAN, e.g.
• fitting PK/PD model to all rats simultaneously
• adjusting for control response