Modeling a novel radiotracer: “a” checklist · fate of the tracer in the human body, in...

Modeling a novel radiotracer: “a” checklist

Ronald Boellaard

Department of Nuclear Medicine & PET Research VU University Medical Center, Amsterdam, The Netherlands

Email: r.boellaard@vumc.nl

Overview of presentation..

• Preclinical evaluations: a very short overview

• Modeling a novel tracer:– Introduction into models– A ‘checklist’– Checklist examples

Starting point

Target defined

Lead compound identified

Failed drug New molecule

Pre-PET evaluation

Workup of new molecule

• Affinity for target

Pharmacology

Ex vivo autoradiographyEx vivo autoradiography[[33H]R116301 (gerbil)H]R116301 (gerbil)

StrStr

OBOBLCLC

StrStr

CxCx

0.0025 0.01 0.04 0.16 0.63 2.5 10

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Ex vivo receptor binding Ex vivo receptor binding [[33H]subP (gerbil striatum) H]subP (gerbil striatum)

Pre-PET evaluation

Workup of new molecule

• Affinity for target

• Selectivity– Lack of affinity for other targets

• Amenable to radiolabelling with C-11 or F-18– C-11

• on-site cyclotron required• intervention studies possible

– F-18• good statistics• limited repeat studies

Pre-PET evaluation

Workup of new molecule

• Affinity for target

• Selectivity

• Amenable to radiolabelling with C-11 or F-18

• Substrate for P-gp?

• Toxicity– Tracer alone: acute toxicity– Pharmacological dose: full blown toxicity– C-11 versus F-18 labelling– Better after preclinical evaluation?

Preclinical evaluationEx vivo biodistribution in rats

• Tracer alone– Sufficient uptake in tissue (e.g. VT > 1)– Biodistribution comparable with in vitro data– Specific to non-specific signal (> 2)– Identification of labelled metabolites

• Blocking studies– Specificity and selectivity in vivo

• Animal model of disease– Proof of concept– Genetically modified animals (mice)

• Radiation dosimetry!

Preclinical evaluation

PET studies in rats

• Same issues as for biodistribution studies

Wild type mouseMDR1a(+/+)/1b(+/+)

Double gene knock-out mouseMDR1a( -/-)/1b(-/-)

Pgp function: [11C]verapamil – modulation of BBB

Preclinical evaluation

PET studies in rats

• Same issues as for biodistribution studies

• Assessment of kinetics– Reversible versus irreversible– Time required to reach transient equilibrium

• Intervention studies– Combination with in vivo microdialysis

• Kinetics of labelled metabolites– Penetration of blood-brain barrier– Synthesise labelled metabolites?

Modeling a novel radiotracer: introduction into models…..

Clinical evaluationGMP cleanroom required

Overview of presentation..

• Preclinical evaluations: a very short overview

• Modeling a novel tracer:– Introduction into models– A ‘checklist’– Checklist examples

Clinical evaluation

Tracer alone studies

• Signal?

• Distribution

• Kinetic modeling

• Specific to non-specific ratio

• Level of non-specific binding

• Labelled metabolites

• Quality input function– Stickiness tracer– Size and accuracy parent fraction

Tracer Kinetic Modeling: clinical evaluation

Tracer Model:

Purpose:

Method:

Mathematical description of thefate of the tracer in the humanbody, in particular in the organunder study

To quantify functional entitiesgiven the distribution ofradioactivity (over time)

Divide possible distribution oftracer in a limited number ofdiscrete compartments

PET pharmacokinetic modeling

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Input function

PET, TAC

Model

K1,…VT, BP

Examples of PET pharmacokinetic models

plasma input single tissue compartment model (1T-2k)

Ctissue=f+sb+nsbK1=EF

k2

Cplasma CPET

Requires-Dynamic PET scan (TAC)-Metabolite corrected plasma input function

PET pharmacokineticparameter:VT=K1/k2

C’free’

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Cbound

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Examples of PET pharmacokinetic modelsplasma input two tissue compartment model (2T-4k)

Requires-Dynamic PET scan (TAC)-Metabolite corrected plasma input function

VT=K1/k2*[1+BP]BPnd=k3/k4

Examples of PET pharmacokinetic modelsreference tissue model

C’f+ns

Cf+ns+sp

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K1

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Target

Reference

Requires-Dynamic PET scan (TAC)-Definition of a reference region or reference TAC

Simplified methods

Model simplifications/assumptions– Logan (plasma and/or reference input)– Patlak

Model linearisation– Blomquist– Ichise (MRTM0-4)

Model basis function implementation – RPM– Spectral analysis/Basis pursuit/RS-ESA

Uptake & uptake ratio’s– SUV (standardised uptake value)– SUVr, ratio of act.conc. target/reference regions

Logan plot

Simplified methods

Why using simplified methods ?– Clinical applicability, ease of (practical) use– Computational speed– Reduce sensitivity to noise (less fit parameters)– Possibility to generate parametric images

Purpose of simplified method evaluations:Look for method that is most suitable for a given (clinical) task

Potential limitations of simplified methods

Simplified methods often do not provide information on microparameters

Model simplification & linearisationNoise induced bias

Basis function methodSelection of basis range = bias versus precision trade-off

Uptake/uptake ratioDoes not provide a pharmacokinetic parameterEffects of global and regional flow differences

Overview of presentation..

• Preclinical evaluations: a very short overview

• Modeling a novel tracer:– Introduction into models– A ‘checklist’……..finally !– Checklist examples

Modeling a novel tracer : example of a recipe

1. Start with dynamic PET studies to measure kinetics• Duration 60, 90, 120, 150 min ?• Frame durations should match kinetics, start with short frames (<5s) !• Include arterial blood sampling with metabolite analysis

2. Generate regional time activity curves (TAC)• VOI versus cluster analysis• Heterogeneity within VOI

3. Determine optimal plasma input model• Processing of plasma input: metabolite analysis weakest link !• Approaches:

• Data driven approach: try models with increasing complexity• Hypothesis drives approach: define (complex) model based on

existing (preclinical) data and then simplify.

Data driven approach:

e.g. start analyzing VOI TAC using plasma input models

• One tissue compartment model– 2 rate constants + Vb (blood volume)

• Irreversible two tissue comparment model– 3 rate constants + Vb

• Reversible two tissue comparment model– 4 rate constants + Vb

Modeling a novel tracer : example of a recipe

Modeling a novel tracer : example of a recipe

1. Start with dynamic PET studies to measure kinetics• Include arterial blood sampling with metabolite analysis

2. Generate regional time activity curves (TAC)

3. Determine optimal plasma input model• Processing of plasma input: metabolite analysis weakest link !

4. Validate reference tissue models and reference regionsagainst plasma input model/results

5. Validate (use of) simplified methods

6. Validate parametric methods

Measures used for model evaluation

Fit accuracy – how well does model fit thru PET data

Accuracy / bias – how accurate are observed parameters

Precision - reproducibility / test-retest variability

Correlation with other measures (MMSE, age, gender etc)

Discriminating ability

- individual (important when tracer is intended for diagnostic purposes)

- group level (tracer ‘only’ useful in research setting)

Model development

Multiple data sets required/optimal• Baseline data

– Does it enter brain ?– Expected distribution OK ?

• Blocking or displacement data– Ideally, data over entire range of occupancies– Same ligand or other specific & selective ligand (pref)– Specificity– Reference regions (‘free of displacement’)

• Test-retest data• Patient and healthy subject data

– collect demographic, blood, tissue, sample or other data

• Simulation studies

Overview of presentation..

• Preclinical evaluations: a very short overview

• Modeling a novel tracer:– Introduction into models– A ‘checklist’– Checklist examples

Model evaluation

processing of measured arterial input function

min

Whole blood activity

Plasma PK11195 activity

Model evaluation

processing of measured arterial input functionEffects of using different fitting routines for plasma/whole blood and/or metabolite correction of input function on observed Vd and BP in case of missing data or ‘outliers’ (Lubberink et al., abstract NRM, 2004)

VT, missing data BP, outliers

Fit accuracy – ‘incorrect’ model selection by

parent fraction fitting

NB1: ‘suboptimal’ plasma input processing may affect model selection and

results !

NB2: metabolite may enter brain (may need to use parent + metabolite as input)

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AIC 1T2k SCH 1T2k AIC 2T4k SCH 2T4k AIC 2T3k SCH 2T3k

Strategies/measures used for model evaluation

Accuracy of fit

- WRSE=(weighted) residual square error=how well does fit go thru data

- AIC = Akaike Information Criterion = which model fits best using least number of fit parameters

- Number of outliers = how frequent does a model provide results outside physiological realistic range

Accuracy / bias

Precision / reproducibility / test-retest variability

Discriminating ability

- individual

- group level

Evaluation of model based on ‘fit accuracy’ using WRSE & AIC

Example for PK11195 (Kropholler et al. JCBFM 2005)

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ROI gray matter size (ml)

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1TC2TC-3k2TC-4k

‘Standard’ models show a preference for the 2TC-4k model for all ROI sizes. Reducing ROI size (=increasing noise) increases preference for models with lower number of parameters.

“Fit accuracy” - comparison of SRTM with FRTM - PIB

Chart Titley = 0.9708xR2 = 0.8544

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Linear (FRTM BP)

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-AIC indicate preference for FRTM-SRTM less outliers-SRTM correlates well with FRTM-SRTM is preferred model

Measures used for model evaluation

Fit accuracy – how well does model fit thru PET data

Accuracy / bias – how accurate are observed parameters

Precision - reproducibility / test-retest variability

Correlation with other measures (MMSE, age, gender etc)

Discriminating ability

- individual

- group level

Evaluation of model accuracy – clinical datavalidation of reference tissue model

Correlation with ‘gold standard’ (may be experimental data as well)

PK11195, BPSRTM versus DVR2T-4k -1

y = 0.9973x + 0.0125R2 = 0.8373

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Kropholler et al., JCBFM 2007, Schuitemaker et al., JCBFM, 2007

Correlation between DVR-1 and BP-SRTM improves from 0.004 to

0.62 when using metabolite input corrected kinetic models

Effects of metabolites on DVR-1 and BP-SRTM

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BP STRM

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PMfvdm2T4k

Measures used for model evaluation

Fit accuracy – how well does model fit thru PET data

Accuracy / bias – how accurate are observed parameters

Precision - reproducibility / test-retest variability

Correlation with other measures (MMSE, age, gender etc)

Discriminating ability

- individual

- group level

Example of test-retest variability - PIB

Subjects: 6 AD patients and 3 age matched healthy controls

Repeat dynamic scans (23 frames; 90 minutes total) in 3D

acquisition mode, following bolus injection of 370 MBq [11C]PIB.

Test and retest scans were performed on the same day.

Arterial whole blood sampling together with plasma parent tracer and

metabolite concentrations measurements

Example of test-retest variability - PIB

• T-RT depends on VOI size (noise)• SRTM better TRT than PI 2T-4k• SRTM better TRT for AD than for control subjects

Tolboom et al. JNM 2008

Assessment of accuracy and precision with

simulations

Why using simulations ?– Simulations are useful when ‘gold standard’ is not available or when parameters

cannot be changed in a controlled way (patients as opposed to animal studies)– Simulations are useful to study e.g. effect of noise level, flow and flow differences,

blood volume fraction etc on bias and precision (‘sensitivity analysis’)

Simulation study design / setup– Generate TAC using a typical input function and PET pharmacokinetic parameters– Make 10000 noisy realisations– Fit each ‘noisy’ TAC with model or simplified method

(noise model, weighting factors, optim.alg. Yaqub et al.PMB 2006)– Bias = ratio or difference between average (or median) observed parameter value

over simulated ‘true’ valuePrecision = SD or COV of observed parameter value

Evaluation of parametric methods – PK11195

simulations

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Logan (DVR-1) ICH1 ICH2

R.Logan RPM1 RPM2 (*)

Schuitemaker et al. JCBFM 2007

RefLogan

ICH1

ICH2

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RPM2

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Parametric analysis & sims using PPET. Boellaard et al. NRM2006

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Evaluation of parametric methods – PK11195

simulations – effect of regional flow difference on SRTM and ref.par.methods

Schuitemaker et al.JCBFM 2007

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“Normal flow” ‘Reduced’ flow

Measures used for model evaluation

Fit accuracy – how well does model fit thru PET data

Accuracy / bias – how accurate are observed parameters

Precision - reproducibility / test-retest variability

Correlation with other measures (MMSE, age, gender etc)

Discriminating ability

- individual (important when tracer is intended for diagnostic purposes)

- group level (tracer ‘only’ useful in research setting)

[11C]PIB versus [18F]FDDNP

AD 1

PIBFDDNP

AD 2

PIBFDDNP

CONTROL

PIBFDDNP

Receptor Parametric Mapping; cerebellum = reference

Courtesy of B.N.M. van Berckel and N. Tolboom

Measures used for model evaluation

Fit accuracy – how well does model fit thru PET data

Accuracy / bias – how accurate are observed parameters

Precision - reproducibility / test-retest variability

Correlation with other measures (MMSE, age, gender etc)

Discriminating ability

- individual

- group level

Discrimination between subject groups

FP-B-CIT

Parametric methods evaluation

discrimination ability

< 0.001NSLeft frontal

0.043NSRight frontal

0.002NSLeft lateraltemporal lobe

< 0.001NSRight lateral temporal lobe

< 0.0010.1Left thalamus

< 0.0010.1Right thalamus

BPVdRegion

< 0.001NSLeft frontal

0.043NSRight frontal

0.002NSLeft lateraltemporal lobe

< 0.001NSRight lateral temporal lobe

< 0.0010.1Left thalamus

< 0.0010.1Right thalamus

BPVdRegion

Effects of plasma versus reference input parametric kinetic methods on SPM analysis, young versus old & AD (Schuitemaker et al., NeuroImage 2007.)

Region/p-values

Modeling a novel tracer - summary

1. Start with dynamic PET studies to measure kinetics• Include arterial blood sampling with metabolite analysis

2. Generate regional time activity curves (TAC)

3. Determine optimal plasma input model• Processing of plasma input: metabolite analysis weakest link !

4. Validate reference tissue models and reference regionsagainst plasma input model/results

5. Validate (use of) simplified methods

6. Validate parametric methods

7. Use simulations to perform ‘sensitivity’ analysis for allmodels and methods considered

Model development –requires a lot of data collection/studiesMultiple data sets required/optimal• Baseline data• Blocking or displacement data

– Ideally, data over entire range of occupancies– Same ligand or other specific & selective ligand (pref)– Specificity– Reference regions (‘free of displacement’)

• Test-retest data• Patient and healthy subject data

– collect demographic, blood, tissue, sample or other(imaging) data

• Simulation studies

Acknowledgements

VUMC, Amsterdam, NLAdriaan LammertsmaMaqsood YaqubMark LubberinkBart van BerckelOtto HoekstraNelleke TolboomMarc KrophollerUrsula KlumpersBert WindhorstGert LuurtsemaAlie Schuitemaker