PET Tracer Kinetic Modeling In Drug

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Dale Morris
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1 PET Tracer Kinetic Modeling In Drug Discovery Research Applications Sandra M. Sanabria-Bohórquez Imaging Merck & Co., Inc.

2 Positron Emission Tomography - PET PET is an advanced d imaging i technique permitting the assessment of biochemical and physiological processes within the living organism in a fully quantitative and non-invasive manner. Extremely powerful and versatile technique for brain studies: perfusion, metabolism, receptor systems, enzymes, etc CNS Drug development studies: BBB penetration. Distribution of target. Target occupancy - dosage regimen. Target changes in patient populations Target changes over time

Tracer plasma concentration Labeled metabolites What can influence tracer brain uptake?

3 You have the PET ligand, now what?? What scanning protocol? Static snapshot images vs. Dynamic imaging data What needs to be measured? Tracer plasma concentration Labeled metabolites What can influence tracer brain uptake? Peripheral metabolism/binding Plasma protein binding Anesthesia What simplifications can be made? Shorter scanning time Input functions Prepare for clinical studies

4 Scanning Protocol: Static images Receptor distribution information mglur5 receptors - [ 8 F]F-PEB Rhesus low high Benzodiazepine receptors - [ C]Flumazenil Baboon low high

5 Scanning Protocol: Static images Where is the glucose being utilized? Conscious Propofol anesthesia low high What is the effect of MPTP treatment? [ 8 F]FDG uptake in rhesus monkey brain Baseline Post MPTP low high Presynaptic dopaminergic integrity [ 8 F]FMT uptake in rhesus brain

Dynamic PET imaging [ 8 F] F-PEB (mglur5) T=.25 min 32 min 65 min 9 min 3 min 7 min Time Activity Curves TAC Tracer distribution as a function of time 4 3.5 3 2.

6 Dynamic PET imaging [ 8 F] F-PEB (mglur5) T=.25 min 32 min 65 min 9 min 3 min 7 min Time Activity Curves TAC Tracer distribution as a function of time Available information: Rate of influx Receptor availability Rate of trapping SUV time (min) Striatum Thalamus Frontal Cx Parietal Cx Temporal Cx Occipital Cx. Cerebellum Pons White-matter

s. Bound 8 F 8 F 8 F 8 F 8 F 8 F 8 F 8 F 8 F 8 F 8 F 8 F

7 Quantification of dynamic PET data What needs to be measured? Receptor-Ligand interactions PET Plasma Free + n.s. Bound 8 F 8 F 8 F 8 F 8 F 8 F 8 F 8 F 8 F 8 F 8 F 8 F BBB Blood sampling: Total plasma concentration Metabolite measurement

8 Neuroreceptor quantification Compartmental t model formulation Tracer T= k 3 = f ND k on B max k 4 = k off K d = k on /k off dc ( t) = KCP ( t) 2 3 dt dc S ( t ) = k3 CND ( t ) k4 CS ( t ) dt ( k + k ) C ( t) ND + ND k 4 C S ( t) TAC = CP IRF( K, k2, k3, k4) Impulse Response Function SUV 5 5 C P time (min)

9 Neuroreceptor quantification Compartmental t model formulation k 3 = f ND k on B max k 4 = k off TAC K d = k on /k off = C p IRF ( K, k 2, k 3, k 4 ) 5 4 PET fitted curve SUV 3 2 Tracer bound to receptor ND: Free + n.s. C P Time (min)

10 Neuroreceptor quantification Compartmental t model formulation k 3 = f ND k on B max k 4 = k off TAC = C p K d = k on /k off ) IRF ( K, k 2, k 3, k 4) SUV PET fitted curve Tracer bound to receptor ND: Free + n.s. Tracer specific binding indexes (macro parameters): Binding Potential BP ND = k 3 /k 4 = f ND B max /K d Distribution Volume V T = K /k 2 (+ k 3 /k 4 ) = K /k 2 (+ f ND B max /K d ) C P Time (min)

11 Neuroreceptor quantification Graphical methods using plasma input Logan analysis for reversible tracer kinetics SUV TACs t TAC( τ ) dτ TAC( t) = VT t CP ( τ ) dτ TAC( t) + it int 2 C P y x Slope = V T Time (min) V T = K/k 2 (+ k/k 3 4 ) ml/cm 3 22 ml/cm 3 5 ml/cm 3 y K k 3 4 k 2 k x

12 Neuroreceptor quantification Graphical methods using plasma input Patlak analysis for irreversible tracer kinetics SUV 2 TACs t CP ( τ ) dτ TAC ( t ) = K i CP ( t) CP ( t) + it int C y x C P time (min) K i = K k 3 /(k 2 +k 3 ) y 5 Slope = K i.3e-2 min - 9.9e-3 min - 5.8e-3 min - K k 3 5 k x

13 Neuroreceptor quantification Data driven methods Data driven methods: No assumption is made on the number of tissue compartments or how the compartments are linked. n Plasma C T C T = β t TAC αie i CP ( t ) i= C T C T Spectral analysis solution: Basis space and non-negative least square

14 Neuroreceptor quantification Data driven methods an example TAC = n i= β t ie i CP ( t) α 2 3 Parameters: n K = i= n VT = i= α α i i β i n all β > λ, i β t K lim α e i with β > λ, β i = i i n t i= n = λ α time (min) 5 x β -2 -

15 Neuroreceptor quantification Reference region is available! Reference tissue models: A region of the brain is devoid of receptors, no tracer plasma PK is necessary. Plasma K k 2 C ND k 3 K C Target C ND S regions k 2 ' k 4 Reference region (Indirect input) BBB Simplified graphical methods: Reversible kinetics: Logan analysis Irreversible kinetics: Patlak analysis

9 weeks..8 low high Binding Pot tential 6.6.4.2.

16 Kinetic modeling in rodent studies Disease progression Use of [ C]DTBZ to assess PD progression in MitoPark Transgenic Mouse MRI Wild type 8 weeks Transgenic 6 weeks Transgenic 9 weeks..8 low high Binding Pot tential Age (weeks) Wildtype Transgenic Negligible [ C]DTBZ binding in cerebellum. SRTM was used to estimate VMAT2 availability in striatum.

Why bother with kinetic modeling? Accurate target availability estimates are possible, less bias due to CBF changes. Explore simplified approaches: Image derived methods to assess tracer plasma PK.

17 Why bother with kinetic modeling? Accurate target availability estimates are possible, less bias due to CBF changes. Explore simplified approaches: Image derived methods to assess tracer plasma PK. All regions share the same input n TAC = i= α β t ie i CPlasma ( t) SUV V time (min) SUV time (min) Optimal scanning procedures: shorter scans, more scans per animal & less burden on investigators!

18 Why bother with kinetic modeling? More reliable: PK-PD relationships Disease progression markers Good understanding of tracer kinetics in preclinical studies will assist in better clinical study planning and potential simpler protocols: Potential regions of interest (+ in vitro data) Image derived input functions Scan length

19 From rhesus to humans Use of NHP for tracer discovery: Brain penetration, kinetics, occupancy studies. Preparation for FIH studies: Tracer kinetics: potential protocol simplifications CB receptor imaging Dosimetry studies in HNP Histamine H3 receptor imaging GlyT imaging H3 receptor tracer CB receptor tracer

20 Conclusions Kinetic modeling in the preclinical space: Initial rigorous tracer characterization will result in simpler experimental protocol and more reliable outcomes. Understanding of potential confounding issues: anesthesia, CBF, etc Preparing for clinical studies: Dosimetry and biodistribution Explore novel techniques to reduce burden in subjects/patients and investigators. Kinetic modeling in the clinical studies: All of the above Simpler protocols, simpler but reliable analysis techniques.

Using mathematical models & approaches to quantify BRAIN (dynamic) Positron Emission Tomography (PET) data

Using mathematical models & approaches to quantify BRAIN (dynamic) Positron Emission Tomography (PET) data Imaging Seminars Series Stony Brook University, Health Science Center Stony Brook, NY - January