Imaging in CNS Drug Discovery

Roger N. Gunn, Ph.D.1,2,3,* & Eugenii A Rabiner1,4, FCPsych(SA)

1. Imanova Ltd, London, UK

2. Division of Brain Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK

3. Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK

4. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK

*Corresponding author

Professor Roger N Gunn,

Imanova Ltd, Burlington Danes Building,

Hammersmith Hospital, Du Cane Road,

London, W12 0NN, UK.

Email: roger.gunn@imanova.co.uk.

Telephone: +44 (0)208 008 6000. Fax: +44 (0)208 008 6491.

Seminars  in  Nuclear  Medicine  

Updates  in  Molecular  Brain  Imaging  

Vol  47,  issue  1,  Jan  2017      

(20-­‐25  double  spaced  pages  with  additional  space  for  illustrations,  tables,  etc.)

Abstract

The discovery and development of CNS drugs is an extremely challenging process

requiring large resources, timelines and associated costs. The high risk of failure

leads to high levels of risk. Over the last couple of decades PET imaging has

become a central component of the CNS drug development process, enabling

decision making in Phase I studies, where early discharge of risk provides increased

confidence to progress a candidate to more costly later phase testing at the right

dose level or alternatively to kill a compound through failure to meet key criteria. The

so called “three pillars” of drug survival, namely; tissue exposure, target engagement

and pharmacological activity, are particularly well suited for evaluation by PET

imaging. This review introduces the process of CNS drug development before

considering how PET imaging of the “three pillars” has advanced to provide valuable

tools for decision-making on the critical path of CNS drug development. Finally, we

review the advances in PET science of biomarker development and analysis that

enable sophisticated drug development studies in man.

Introduction

CNS drug discovery and development is a long and difficult process with the delivery

of a successful new medicine taking around 10 years to complete from the initial

identification of the disease and target biology through to the launch of an approved

NME. In addition, a lot of resource is consumed through high levels of attrition, for

failed drugs that never overcome all the hurdles required to realise a viable CNS

drug. These hurdles include appropriate safety, PK, BBB penetration, target

engagement, pharmacological activity and efficacy. Thus, vast costs and long

timelines are associated with the approval of each NME – with over $2 billion

required to bring each NME to market, by current estimates [1].

The drug discovery and development process can be broken down into a number of

phases. Following the identification of the putative biological target the process

passes through a screen for compounds with the right properties (Figure 1.). This

leads to libraries of compounds being generated that are then subsequently tested

pre-clinically in vitro and in vivo, before a candidate compound is taken into human

testing. In humans Phase I (Safety & Tolerability), Phase IIA (Proof of concept &

dose ranging), Phase IIB (definitive dose finding), Phase III (Pivotal placebo

controlled trials and long term safety) precede compound registration and approval

by the relevant regulatory authorities. Post approval Phase IV studies are conducted

(Post marketing surveillance studies). The size and cost of these studies increases

throughout these phases, with costs increasing in proportion with increased sample

sizes and safety monitoring requirements.

Figure 1. Phases in the Drug Discovery and Development process.

These exponentially rising costs make good decisions early in the process

imperative to killing compounds that do not have the right characteristics for a

successful drug as early as possible. For example, performing a Phase III study with

a compound that does not sufficiently engage the biological target is an easy way to

waste the order of $100 Million.

Direct assay of human brain tissues are extremely challenging, and thus historical

approaches to CNS drug development relied heavily on peripheral pharmacokinetics

(PK) and clinical measures. These lack direct quantitative information on the levels

of brain exposure to the drug, target engagement and pharmacological activity – the

so-called three pillars of drug survival [2](Figure 2). These three pillars represent the

hurdles that a candidate molecule must overcome to become a successful centrally

acting drug. In a 2012 review of decision making for 44 of its drug programs in Phase

II, Pfizer revealed that, in 43% of decisions, it was not able to conclude whether

these three pillars had been met [2]. PET neuroimaging is unique in being able to

provide a direct quantification of parameters central to the three pillars in the human

brain in vivo, through the use of radiolabelled drugs or biomarkers [1, 3].

IND$Subm

i*ed

$

NDA

$Sub

mi*ed

$

FDA$Ap

proval$

Figure 2. The 3-Pillars of CNS Drug Survival that can be assayed with PET

neuroimaging: Tissue Exposure, Target Engagement and Pharmacological Activity.

The first CNS PET studies looking at measuring target engagement of a drug were

performed in the late 80’s by Farde et al [4] who used [11C]raclopride as a radiotracer

to explore the target-engagement of antipsychotics designed to interact with the

dopamine D2-receptor. Such studies were first directly incorporated into the drug

development process in the early 90’s [5]. This has led to PET target engagement

(or occupancy) studies of novel drug candidates becoming de-rigeur in the last two

decades, as big Pharma has adopted this technology to provide confidence in brain

penetration and rich dosing information from small human cohorts in Phase 1 studies

[1, 3, 6]. These studies deliver information on two of the three pillars in small cohorts

(n=6-12 subjects) in first time in human (FTIH) studies, providing an opportunity for a

very early go-no-go decision in the development process.

Clinical'Outcomes'

''''

Tissue&&Exposure&

Target&Engagement&

Pharmacological&Ac6vity&

Plasma'PK'''''

Biodistribution studies, are ones where the drug candidate itself is directly

radiolabelled, have also been performed [7-9]. These studies provide information on

whether the drug access across the blood brain barrier and its concentration in brain

tissue.

Measures of pharmacological activity along with stratification for trial entry with

imaging agents able to quantify levels of misfolded proteins such as Aβ and tau have

provided important readouts for Pharma in neurodegenerative diseases [10].

This review focuses on PET measures of these three CNS drug survival pillars,

Tissue Exposure, Target Engagement and Pharmacological Activity as well as the

methods of Biomarker development and Quantitative analysis that are critical for

their application. In particular we highlight the more recent evolution and

sophistication of these approaches that have led to their increased value. We signify

these recent innovations in sections identified with the “+” symbol.

Biodistribution

PET radiolabelling of a small molecule drug with either C11 and F18 allows for the

introduction of an imaging tag without altering the properties of the drug compound

itself. Subsequent intravenous injection of the labelled drug and careful quantitative

analysis of the dynamic PET data allows for the direct measurement of the drugs

concentration in brain tissue. Quantitative analysis involves the fitting of an

appropriate tracer kinetic model to the data that partitions the total signal between

radioactivity emanating from blood vessels and that from brain tissue[11]. The

resultant signal from brain tissue provides information on the delivery of the drug into

brain tissue (  𝐾!) and the equilibrium partition coefficient of the free and non-

specifically bound drug, between brain tissue and plasma (  𝑉!").

Biodistribution +

Classical biodistribution studies measure the total brain drug concentration.

However, a more important measure is that of the free concentration of drug in the

brain, as this is the portion that determines the level of binding to the biological

target, and this information coupled with an estimate of the in vitro affinity allows for

a prediction of the level of target engagement under the assumption that in vitro and

in vivo affinities are equivalent.

The desire to quantify the free drug concentration in the tissue, has led to the

combination of equilibrium equilibrium dialysis measures of the tissue non-specific

binding with PET measures in order to directly convert the PET measures into the

free drug concentration in brain [12, 13].

𝐶!" = 𝑓!"  𝑉!"  𝐶!   (1)

where 𝐶!" is the free concentration of drug in brain tissue, 𝑓!" is the fraction of the

non-displaceable signal which is represented by the free drug, and 𝐶! is the

concentration of drug in plasma. Combining equation 1 with an estimate of the drug

target affinity enables estimation of target occupancy (𝑂𝑐𝑐),

𝑂𝑐𝑐 = !!"!!"!  !!

  (2)

where 𝐾! is the equilibrium dissociation rate constant for the drug.

These studies also allow for an assessment of whether there are any active transport

mechanisms for the drug, which would lead to concentration gradients across the

BBB. Under the assumption of passive diffusion the free drug concentrations in

tissue and plasma at equilibrium will be equal. Thus, combining estimates of the PET

equilibrium partition coefficient between tissue and plasma (in the absence of

specific binding) and equilibrium dialysis assays of the plasma (𝑓!) and tissue (𝑓!")

free fractions, then allows for an assessment of transport across the BBB,

𝑉!" =  !!"!!

=   !!!!"

 !!"  !!"

  (3)

where 𝐶!" is the free concentration of drug in plasma. Rearranging, yields,

 !!"  !!"

=     !!"  !!"!!

  (4)

with  !!"  !!"

 ~  1 consistent with diffusion,  !!"  !!"

>  1 consistent with active influx and

 !!"  !!"

<  1 consistent with active efflux across the BBB.

Recent developments have extended this approach to studying macromolecules

where the much slower drug kinetics require the use of longer-lived isotopes such as

Zr89, the labelling of antibody fragments or pretargeting approaches [14-16].

Appropriate quantitative analysis of these data is still an active area of research with

requirements for cold doses of the drug to block peripheral uptake, internalisation of

the drug-target complex and loss of the label from the drug contributing additional

levels of complexity, not adequately accounted for by current models.

Target Engagement

Target engagement (or occupancy studies), which utilise a PET biomarker and

varying doses of the drug under investigation, provide some of the most valuable

decision making data that can be acquired as part of a FTIH Phase I study [17-24].

Occupancy studies are superior, and are to be preferred to biodistribution studies for

the following reasons. In addition to confirming BBB penetration these provide

information on the level of target engagement and easily facilitate the assessment

and comparison of back up molecules without further extensive radiochemistryif lead

compounds fail. Combined with information on the desired levels of target

engagement to achieve efficacy and/or drug exposure that will avoid unwanted

adverse effects, occupancy studies allow drug development teams to decide on

whether to progress a drug candidate into later phase efficacy studies of larger size,

complexity and cost. In addition, these studies allow the optimisation of the likely

therapeutic dose range for later phase efficacy studies, reducing the size and cost of

these.

Occupancy studies require an radioligand appropriate for the molecular target, and if

none exist already, may require radioligand development in parallel with the drug

discovery programme, so that the radioligand will be ready for use as part of the

FTIH study (successful development efforts can take 12-24 mths). By measuring the

target availability through the outcome measure binding potential (BPND) in both a

baseline and post-drug scan it is possible to calculate the fractional occupancy of the

target by the drug candidate,

𝑂𝑐𝑐 =  !"!"!"#$%&'$  !  !"!"

!"#$

!"!"!"#$%&'$   (5)

By performing a number of such scans at different doses it is possible to relate the

concentration of drug in plasma to the level of target engagement. These data can

be fitted to an “Emax” model in order to estimate the drugs 𝐸𝐶!",

𝑂𝑐𝑐 =   !!!  !""!"#

!!!!!"!"!   (6)

where 𝐸𝐶!"  is the concentration of drug that leads to half maximal target

engagement, 𝑁 is the Hill coefficient describing cooperativity (𝑁 =1 for classical

antagonist drug binding) and 𝑂𝑐𝑐!"# is the maximal level of target engagement.

Figure 3: PET Target Engagement studies of four different molecular targets. Top

rows illustrate baseline scans and bottom rows illustrate the same subject imaged

GlyT1 – [11C]GSK931145 Mu Opioid – [11C]Carfentanil

Histamine H3 – [11C]GSK189254 SERT – [11C]DASB

after the administration of a drug candidate. Reduction in the overall signal and

heterogeneity confirms that the drug crosses the BBB and behinds to that molecular

target.

Target Engagement +

Classical target engagement studies have looked at a single time point post drug

dosing and vary only the administered dose of the drug. If the drug kinetics are

“Direct”, meaning that the brain free drug concentration can be assumed to be in

equilibrium with the free plasma concentration at all times, and that the drug-target

residence time is suitably finite, then the 𝐸𝐶!" can be readily estimated from such

experiments. If these conditions are not met and the drug kinetics are “Indirect” then

improved experimental designs are required which involve the measurement of

occupancy at different time points post-administration and varying doses [25-27].

Such experimental designs allow for an assessment of whether an Indirect

relationship is present and if it is, the modelling of the drug PK – target occupancy

can be achieved with a more sophisticated model that accounts for these kinetics.

!  !""(!)!  !

=  𝑘!"  𝐶! 𝑡   1 − 𝑂𝑐𝑐(𝑡) −  𝑘!""  𝑂𝑐𝑐 𝑡     (7)

where 𝑘!" and 𝑘!"" are the target association and dissociation rate constants,

respectively, and 𝐸𝐶!" ( = 𝑘!""/𝑘!") which is equivalent to the drug plasma

concentration that achieves half maximal occupancy.

Figure 4. Examples of Direct and Indirect drug PK – target occupancy kinetics. (left)

Direct PK-RO kinetics are described by a classical Emax model independent of the

time at which target occupancy measures are taken, (right) Indirect PK-RO kinetics

exhibit hysteresis where later target occupancy measures have a higher occupancy

value in relation to plasma levels in contrast to measures at earlier time points such

that an Emax model no longer describes the relationship.

Whilst early studies used fixed designs with a pre-set range of doses, the latest

studies benefit from adaptive designs that use data from the study as it proceeds to

efficiently calculate the optimal doses and scan timings . This means that the

information acquired from scans is maximised and that study size and duration can

be minimized [25, 27]. Typically, initial doses and timings are obtained from

preclinical data and information on the drugs PK. Following the acquisition of a first

cohort of human subjects (usually 2 are sufficient) D-optimal design theory allows

doses and timings to be selected for the subsequent cohort. This process continues

throughout the study until the required precision has been obtained on the 𝐸𝐶!".

FTIH target engagement studies are all performed following a single dose (SD) of

drug. However, drugs are usually ultimately given to patients as a repeat dosing

Plasma Concentration10-1 100 101 102 103

Occ

upan

cy

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

3 hr4 to 27 hrs32 to 61 hrs

Plasma Concentration10-3 10-2 10-1 100 101

Occ

upan

cy

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

up to 4 hr10 to 16 hrs23 to 33 hrs

Direct PK-RO relationship Indirect PK-RO relationship

(RD) either once or twice daily and therefore a drug development team is actually

interested in knowing what level of target engagement is reached at RD. If the drug

PK – occupancy relationship is Direct then the 𝐸𝐶!" measured from a SD PET study

will translate to an RD study and only RD PK is needed. However, if the relationship

is Indirect then one either needs to measure the occupancy from a RD PET study or

predict it from the SD PET study using an appropriate mathematical model [25, 26].

Figure 5. Predicting RD drug PK – RO relationships from SD data. Biomathematical

characterisation of the relationship between drug PK and brain target occupancy

from a SD PET study through modelling of discrete measures (white dots) of drug

PK and PET occupancy (top). This model may then be applied to RD drug PK data

to predict RD brain target occupancy (bottom).

The Indirect model described in equation 7 has been successfully applied to

Duloxetine to predict its RD occupancy at the serotonin transporter from SD PET

data [25]. A direct measurement of RD occupancy by conducting a RD PET study

carries the significant risk the up- or down-regulation of the molecular target is

induced by multiple dosing of the drug. Recent developments by Rabiner et al. (in

preparation) show that the parsimonious explanation for consistent differences in

RD Plasma Concentration RD Occupancy PK

– R

O M

odel

SD Occupancy SD Plasma Concentration

SD RD PK Model

𝐸𝐶!" estimates obtained from SD and RD occupancy studies for D2 receptor

antagonists is upregulation of the D2 receptor between the two PET measures.

Under such conditions, and assuming the administration of the drug does not change

the target affinity for either the drug or the radioligand, the 𝐸𝐶!" can be obtained

along with an estimate of the level of upregulation from,

𝑂𝑐𝑐 = !!  !!  !  !"!"  

1 −     !!"#!!  !"!"  

  !!  !  !"!"   (8)

where U is the level of upregulation (U=1 corresponds to no change in target

number).

Figure 6. Upregulation of target concentration between baseline and post-drug

follow up scan leads to incorrect estimation of 𝐸𝐶!" if it is not properly accounted for.

Upregulation of the target, for example through repeat dose antagonist stimulation,

will lead to an underestimation of the true occupancy and hence underestimate the

potency of the drug candidate.

Plasma Concentration10-1 100 101 102 103

Occ

upan

cy

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Measured OccupancyTrue Occupancy

Pharmacological Activity

Estimation of pharmacological activity with PET aims to detect treatment induced

changes “downstream” of target engagement, though its utility in drug development

is not well established. [15O]H2O and 18F-FDG have been used in the past to

measure cerebral blood flow and brain glucose utilisation as indices of brain activity,

but have been supplanted by functional MRI for this purpose [28]. Irrespective of the

methodology used, the link between novel drug administration and changes in brain

activity has not been established with sufficient specificity to provide confidence for

go-no-go decision making. 18F-FDG PET imaging of the brain may find utility in the

development of treatments for conditions such as Alzheimer’s disease [29], but more

work remains to be done in demonstrating its utility for drug development.

Alternative approaches to imaging pharmacodynamics responses to drug

administration include the indexing of neuroinflammation (e.g. using a variety of

radioligands for the 18 kDa translocator protein – TSPO – to index microglial

activation) [30], evaluating the changes in misfolded protein deposition (using Ab and

tau radioligands) [10, 31] and assaying changes in synaptic neurotransmitter release

[32].

In the last decade, radioligands have been developed to quantify misfolded protein

concentrations in the brain following the seminal work of Klunk and Mathis in

Pittsburgh who introduced [11C]PiB for imaging amyloid in the brain [33]. Three

fluorinated analogues that are FDA approved for imaging Aβ and a number of tau

tracers are in development including [18F]AV1451, [18F]GE5351 and [11C]PBB3 [34-

36].

Aβ agents have already been employed in clinical trials to quantify the level of Aβ in

the brain pre- and post-treatment with novel drug candidates [10]. These imaging

tools are also being use to stratify entry of subjects into clinical trials where an Aβ+

status gives increased confidence that the subject is part of the target trial

population.

Neuroinflammation is a complex physiological process present in a variety of

neuropsychiatric diseases, including neurodegenerative disorders, viral infections

such as HIV and HTLV-1 and mood disorders. Molecular imaging of

neuroinflammation offers the prospect of assessing disease progression and the

effect of medication. The 18 kDa translocator protein (TSPO) has been studied

extensively as molecular marker of activated microglia [37, 38]. The development of

2nd generation PET ligands such as [11C]PBR28, [18F]DPA-713 and [18F]FEPPA

has provided improved tools to quantify TSPO availability, compared to the original

TSPO ligand [11C]PK11195 [39-42]. The high variability in binding of these ligands

has been addressed by the discovery of the rs6971 SNP in the TSPO gene, which

allows an efficient method to prescreen study populations to reduce experimental

variability [43]. [11C]PBR28 has been used recently to index the pharmacodynamic

effects of a novel myeloperoxidase inhibitor AZ3241 [44].

Assessment of synaptic neurotransmission processes has been enabled for the

dopamine system through the development of radioligands such as [11C]raclopride

and [11C]PHNO, that are sensitive to changes in endogenous dopamine levels with

radioligands [32, 45]. These tools have been used to show enhanced dopamine

release in schizophrenics in the striatum [46, 47], but not in extra-striatal areas [48].

Similar techniques have demonstrated altered opioid neurotransmission in

pathological gamblers [49, 50] and patients with alcohol dependence. Recent

developments offer the prospect of these techniques being extended to the

acetylcholine [51, 52], GABA [53] and serotonin [54, 55]. Assessment of changes in

evoked neurotransmitter release offers the means to assess the effect of drug

therapies on these neurotransmitter systems.

Pharmacological Activity +

Imaging tau is rapidly evolving with putative agents for this only being introduced in

the last few years and their characterization and validation is still under way.

However, there is already a strong interest in using these imaging tools in clinical

trials with an understanding that tau levels may be more closely linked with cognitive

function and the potential for more relevant readouts in trials evaluating tau targeted

therapies [34, 56].

Biomarker Development

Radiolabelling of a drug candidate for a biodistribution study requires the

radiochemistry expertise to attach a suitable positron emitting radionuclide to the

drug candidate and therefore the challenges are restricted to a problem in

radiochemistry. The development of a successful PET biomarker is a much more

challenging problem, which whilst requiring the equivalent radiochemistry expertise

also includes many other complexities surrounding the compound to radiolabel. This

is like a mini drug development programme in itself with the compound needing

appropriate characteristics such that it readily crosses the BBB, binds with high

enough affinity and selectivity to the biological target, low enough non-specific

binding so background signal does not dominate and suitable kinetics so that an

appropriate outcome measure can be estimated [57, 58]. Since the first development

of radioligands for GPCR targets in the late 80’s, initial screening of compounds has

focussed on identifying those with suitable lipophilicty (typically Log P 1-3) to ensure

BBB penetration and nM or better affinity to yield signal at the target. Radiolabelling

feasibility and pre-clinical evaluation follow prior to translation and evaluation in man.

To date, their exists around 30 protein targets for which successful PET radioligands

have been developed (see Table 1) [59].

Target System Description Radioligand

Dopamine D2/3 receptor antagonist

[11C]raclopride [18F]fallypride [11C]FLB457

D2/3 receptor agonist [11C]-(+)-PHNO [11C]NPA

D1 receptor antagonist [11C]NNC112 [11C]SCH23390

DAT antagonist [11C]PE2I [11C]CFT

Substrate for AADC [18F]DOPA [18F]FMT

VMAT2 antagonist [11C]DTBZ [18F]AV133

Serotonin SERT antagonist

[11C]DASB [11C]AFM

5-HT1A receptor antagonist [11C-carbonyl]WAY100635 [18F]FCWAY [11C]CUMI101

5-HT2A receptor antagonist [11C]MDL100907 [18F]Altanserin

5-HT2A receptor agonist [11C]CIMBI-36

5-HT1B receptor antagonist [11C]P943 [11C]AZ10419369

5-HT6 receptor antagonist [11C]GSK215083 Norepinepherine

NET antagonist [18F]MeNER [11C]MRB

Opioid m opioid receptor agonist [11C]carfentanil Non-selective opioid receptor antagonist [11C]diprenorphine

Acetylcholine α4β2 nAChR receptor antagonist 2-[18F]-FA-85380 M2 receptor agonist [18F]FP-TZTP

Glycine GlyT1 antagonist

[11C]GSK931145 [18F]MK-6577

GABA GABAA receptor antagonist [11C]flumazenil Glutamate allosteric mGluR5 receptor

antagonist [11C]ABP688 [18F]FPEB

Substance P NK1 receptor antagonist [18F]SPA-RQ Enzymes MAO B antagonist [11C]deprenyl

MAO A antagonist [11C]clorgyline PDE4 antagonist [11C]rolipram

PDE10 antagonist [11C]IMA107 [18F]MNI659

TSPO TSPO ligand

[11C]PK11195 [11C]PBR28 [18F]FEPPA

Cannabinoid CB1 receptor antagonist

[11C]OMAR [18F]MK9470

FAAH antagonist [11C]CURB β-Amyloid

β-sheet fibrils of β-Amyloid

[11C]PiB [11C]AZD4694 [18F]GE067 [18F]BAY949172 [18F]AV45

Tau PHF -tau

[18F]AV1451 [18F]THK5351 [11C]PBB3

Table 1. List of selected drug development targets and some of the PET radiotracers that allow their measurement.

Biomarker Development +

In recent years, the discovery and development of PET biomarkers has increased in

sophistication with the introduction of in silico biomathematical models that provide a

quantitative prediction of the compounds in vivo performance based on in vitro data

for initial screening of compound libraries [60, 61]. Such in silico methods can use

estimates of the molecular size and lipholicity to predict the delivery rate constant

(K1) in to brain tissue, affinity and non-specific binding assays (from equilibrium

dialysis assays) combined with target density information to enable simulation of

brain tissue kinetics in target and reference regions. From this it is possible to predict

in vivo signal to noise levels in the outcome measure of interest (usually the binding

potential – BPND) and thus rank candidates in terms of their likely performance in

vivo. Combining this with information on ease of radiolabelling based on the

compound structure allows for an early assessment about whether a compound

series contains candidates that would make PET imaging tractable and if so

prioritises the order in which compounds are selected for in vivo testing.

Analysis

Strategies for the acquisition and analysis of PET data range from the more complex

fully quantitative through to simpler semi-quantitative methods (e.g. SUVR). In all the

drug development applications discussed above (excepting SUVR for Aβ

stratification of subjects as Aβ -/ Aβ +), quantitative methods are necessary. For

example in Biodistribution studies, the use of sophisticated analysis methods allows

for the partitioning of the measured PET signal between tissue and blood, and for

therapeutic intervention studies it ensures that the changes in the outcome measure

relate to differences in target availability rather than blood flow.

Fully quantitative methods are comprised of blood data processing (if blood sampling

is performed), image processing, and kinetic modeling steps to derive regional or

parametric image estimates of the outcome measure of choice. Such analysis

workflows include image registration methods to correct for subject motion and align

anatomical information from co-acquired MR scans, along with tracer kinetic

modeling methods that model the kinetic behavior accounting for all the relevant

kinetics in the system and estimation of outcome measures that are directly related

to the target biology under investigation [59].

Analysis +

Quantitative imaging analysis has historically required many human steps with

several software packages and ‘home brew’ scripts needed to perform the full

analysis workflow from acquired data to end results. The software and workflow

employed, along with their myriad options are often detailed in individuals’ notebooks

and neither controlled nor entirely reproducible. This can lead to inefficiency,

inconsistency through human error, and end results that are difficult or impossible to

reproduce later.

Quality, consistency and reproducibility of PET analysis are entirely attainable, and

software tools now exist to facilitate this (MIAKAT; www.miakat.org). Such software

tools provide complete, audit trailed analysis of the data allowing for transparency on

what analyses have been applied and allowing replication of the analysis from the

primary data at any point. Such advances in analysis tools provide increased

confidence in the analysis of PET drug development studies.

Summary

In summary, PET molecular imaging provides a unique window into the brain for

CNS drug hunters (Figure 7). It allows direct assays of the free concentration of the

drug in brain tissue, the level of target engagement at particular administered doses

and the level of downstream pharmacological activity. These three pillars of drug

survival can all be assessed early in FTIH, in small studies (N~10) providing critical

decision making data for early discharge of risk. It should be noted that the same

techniques can be applied in early preclinical testing, where these three pillars can

also be assessed. Finally, PET molecular imaging is taking on an ever increasing

role in later phase CNS studies in neurodegenerative diseases, where Aβ scans are

now used routinely for the stratification of subjects into clinical trials and for providing

readouts on therapeutic interventions.

Figure 7. Summary of where PET imaging impacts across the Drug Development

pipeline.

Tissue&Exposure&

Target&Engagement&

Pharmacological&Ac6vity&

Pa6ent&Stra6fica6on&

Disclosure/Conflict of Interest

RG is a consultant for Abbvie, GlaxoSmithKline, and UCB.

ER is a consultant for Opiant Pharmaceuticals and GlaxoSmithKline.

 

 

References  

1.   Hargreaves  RJ,  Hoppin  J,  Sevigny  J,  et  al:  Optimizing  Central  Nervous  System  Drug  Development  Using  Molecular  Imaging:  Clin  Pharmacol  Ther  2015;  98:47-­‐60.  

2.   Morgan  P,  Van  Der  Graaf  PH,  Arrowsmith  J,  et  al:  Can  the  flow  of  medicines  be  improved?  Fundamental  pharmacokinetic  and  pharmacological  principles  toward  improving  Phase  II  survival:  Drug  Discov  Today  2012;  17:419-­‐24.  

3.   Gunn  R,  Rabiner  I:  Making  drug  development  visible  -­‐  and  viable:  Drug  Discovery  Today  2014;  19:1-­‐3.  

4.   Farde  L,  Wiesel  FA,  Halldin  C,  et  al:  Central  D2-­‐dopamine  receptor  occupancy  in  schizophrenic  patients  treated  with  antipsychotic  drugs:  Arch  Gen  Psychiatry  1988;  45:71-­‐6.  

5.   Bench  CJ,  Lammertsma  AA,  Dolan  RJ,  et  al:  Dose  dependent  occupancy  of  central  dopamine  D2  receptors  by  the  novel  neuroleptic  CP-­‐88,059-­‐01:  a  study  using  positron  emission  tomography  and  11C-­‐raclopride:  Psychopharmacology  (Berl)  1993;  112:308-­‐14.  

6.   Hargreaves  RJ,  Rabiner  EA:  Translational  PET  imaging  research:  Neurobiol  Dis  2014;  61:32-­‐8.  7.   Cunningham  VJ,  Parker  CA,  Rabiner  EA,  et  al:  PET  studies  in  drug  development:  

Methodological  considerations:  Drug  Discov  Today  Technol  2005;  2:311-­‐5.  8.   Matthews  PM,  Rabiner  I,  Gunn  R:  Non-­‐invasive  imaging  in  experimental  medicine  for  drug  

development:  Curr  Opin  Pharmacol  2011;  11:501-­‐7.  9.   Papisov  MI,  Belov  V,  Fischman  AJ,  et  al:  Delivery  of  proteins  to  CNS  as  seen  and  measured  by  

positron  emission  tomography:  Drug  Deliv  Transl  Res  2012;  2:201-­‐9.  10.   Rinne  JO,  Brooks  DJ,  Rossor  MN,  et  al:  11C-­‐PiB  PET  assessment  of  change  in  fibrillar  amyloid-­‐

beta  load  in  patients  with  Alzheimer's  disease  treated  with  bapineuzumab:  a  phase  2,  double-­‐blind,  placebo-­‐controlled,  ascending-­‐dose  study:  Lancet  Neurol  2010;  9:363-­‐72.  

11.   Gunn  RN,  Gunn  SR,  Cunningham  VJ:  Positron  emission  tomography  compartmental  models:  J  Cereb  Blood  Flow  Metab  2001;  21:635-­‐52.  

12.   Gunn  RN,  Summerfield  SG,  Salinas  CA,  et  al:  Combining  PET  biodistribution  and  equilibrium  dialysis  assays  to  assess  the  free  brain  concentration  and  BBB  transport  of  CNS  drugs:  J  Cereb  Blood  Flow  Metab  2012;  32:874-­‐83.  

13.   Summerfield  SG,  Lucas  AJ,  Porter  RA,  et  al:  Toward  an  improved  prediction  of  human  in  vivo  brain  penetration:  Xenobiotica  2008;  38:1518-­‐35.  

14.   Jansen  MH,  Lagerweij  T,  Sewing  AC,  et  al:  Bevacizumab  targeting  diffuse  intrinsic  pontine  glioma:  results  of  89Zr-­‐bevacizumab  PET  imaging  in  brain  tumor  models:  Mol  Cancer  Ther  2016.  

15.   Luo  H,  Hernandez  R,  Hong  H,  et  al:  Noninvasive  brain  cancer  imaging  with  a  bispecific  antibody  fragment,  generated  via  click  chemistry:  Proc  Natl  Acad  Sci  U  S  A  2015;  112:12806-­‐11.  

16.   Altai  M,  Perols  A,  Tsourma  M,  et  al:  Feasibility  of  Affibody-­‐Based  Bioorthogonal  Chemistry-­‐Mediated  Radionuclide  Pretargeting:  J  Nucl  Med  2016;  57:431-­‐6.  

17.   Ashworth  S,  Berges  A,  Rabiner  EA,  et  al:  Unexpectedly  high  affinity  of  a  novel  histamine  H(3)  receptor  antagonist,  GSK239512,  in  vivo  in  human  brain,  determined  using  PET:  Br  J  Pharmacol  2014;  171:1241-­‐9.  

18.   Brooks  DJ,  Doder  M,  Osman  S,  et  al:  Adenosine  A(2A)  receptor  occupancy  by  istradefylline.  An  C-­‐11  KW-­‐6002  PET  study  in  healthy  subjects:  Neurology  2005;  64:A235-­‐A235.  

19.   Castner  SA,  Murthy  NV,  Ridler  K,  et  al:  Relationship  Between  glycine  Transporter  1  Inhibition  as  Measured  with  Positron  Emission  Tomography  and  Changes  in  Cognitive  Performances  in  Nonhuman  Primates:  Neuropsychopharmacology  2014.  

20.   Comley  RA,  Salinas  CA,  Slifstein  M,  et  al:  Monoamine  transporter  occupancy  of  a  novel  triple  reuptake  inhibitor  in  baboons  and  humans  using  positron  emission  tomography:  J  Pharmacol  Exp  Ther  2013;  346:311-­‐7.  

21.   Rabiner  E,  Bhagwagar  Z,  Gunn  R,  et  al:  Attenuation  of  preferential  occupancy  of  5-­‐HT1A  autoreceptors  by  pindolol  in  depressed  patients:  effect  of  SSRIs  or  an  endophenotype  of  the  depressed  state?:  Neuroimage  2002;  16:S67-­‐S67.  

22.   Rabiner  EA,  Gunn  RN,  Wilkins  MR,  et  al:  Evaluation  of  EMD  128  130  occupancy  of  the  5-­‐HT1A  and  the  D-­‐2  receptor:  a  human  PET  study  with  C-­‐11  WAY-­‐100635  and  C-­‐11  raclopride:  Journal  of  Psychopharmacology  2002;  16:195-­‐199.  

23.   Ridler  K,  Gunn  RN,  Searle  GE,  et  al:  Characterising  the  plasma-­‐target  occupancy  relationship  of  the  neurokinin  antagonist  GSK1144814  with  PET:  J  Psychopharmacol  2014;  28:244-­‐53.  

24.   Searle  GE,  Beaver  JD,  Tziortzi  A,  et  al:  Mathematical  modelling  of  [(1)(1)C]-­‐(+)-­‐PHNO  human  competition  studies:  Neuroimage  2013;  68:119-­‐32.  

25.   Abanades  S,  van  der  Aart  J,  Barletta  JA,  et  al:  Prediction  of  repeat-­‐dose  occupancy  from  single-­‐dose  data:  characterisation  of  the  relationship  between  plasma  pharmacokinetics  and  brain  target  occupancy:  J  Cereb  Blood  Flow  Metab  2011;  31:944-­‐52.  

26.   Salinas  C,  Weinzimmer  D,  Searle  G,  et  al:  Kinetic  analysis  of  drug-­‐target  interactions  with  PET  for  characterization  of  pharmacological  hysteresis:  J  Cereb  Blood  Flow  Metab  2013;  33:700-­‐7.  

27.   Zamuner  S,  Di  Iorio  VL,  Nyberg  J,  et  al:  Adaptive-­‐optimal  design  in  PET  occupancy  studies:  Clin  Pharmacol  Ther  2010;  87:563-­‐71.  

28.   Borsook  D,  Becerra  L,  Hargreaves  R:  A  role  for  fMRI  in  optimizing  CNS  drug  development:  Nat  Rev  Drug  Discov  2006;  5:411-­‐24.  

29.   Cummings  JL,  Banks  SJ,  Gary  RK,  et  al:  Alzheimer's  disease  drug  development:  translational  neuroscience  strategies:  CNS  Spectr  2013;  18:128-­‐38.  

30.   Garvey  LJ,  Pavese  N,  Politis  M,  et  al:  Increased  microglia  activation  in  neurologically  asymptomatic  HIV-­‐infected  patients  receiving  effective  ART:  AIDS  2014;  28:67-­‐72.  

31.   Wang  L,  Benzinger  TL,  Su  Y,  et  al:  Evaluation  of  Tau  Imaging  in  Staging  Alzheimer  Disease  and  Revealing  Interactions  Between  beta-­‐Amyloid  and  Tauopathy:  JAMA  Neurol  2016.  

32.   Laruelle  M:  Imaging  synaptic  neurotransmission  with  in  vivo  binding  competition  techniques:  a  critical  review:  J  Cereb  Blood  Flow  Metab  2000;  20:423-­‐51.  

33.   Klunk  WE,  Engler  H,  Nordberg  A,  et  al:  Imaging  brain  amyloid  in  Alzheimer's  disease  with  Pittsburgh  Compound-­‐B:  Ann  Neurol  2004;  55:306-­‐19.  

34.   Ossenkoppele  R,  Schonhaut  DR,  Scholl  M,  et  al:  Tau  PET  patterns  mirror  clinical  and  neuroanatomical  variability  in  Alzheimer's  disease:  Brain  2016;  139:1551-­‐67.  

35.   Lockhart  SN,  Baker  SL,  Okamura  N,  et  al:  Dynamic  PET  Measures  of  Tau  Accumulation  in  Cognitively  Normal  Older  Adults  and  Alzheimer's  Disease  Patients  Measured  Using  [18F]  THK-­‐5351:  PLoS  One  2016;  11:e0158460.  

36.   Kimura  Y,  Ichise  M,  Ito  H,  et  al:  PET  Quantification  of  Tau  Pathology  in  Human  Brain  with  11C-­‐PBB3:  J  Nucl  Med  2015;  56:1359-­‐65.  

37.   Vivash  L,  O'Brien  TJ:  Imaging  Microglial  Activation  with  TSPO  PET:  Lighting  Up  Neurologic  Diseases?:  J  Nucl  Med  2016;  57:165-­‐8.  

38.   Airas  L,  Rissanen  E,  Rinne  JO:  Imaging  neuroinflammation  in  multiple  sclerosis  using  TSPO-­‐PET:  Clin  Transl  Imaging  2015;  3:461-­‐473.  

39.   Kreisl  WC,  Fujita  M,  Fujimura  Y,  et  al:  Comparison  of  [(11)C]-­‐(R)-­‐PK  11195  and  [(11)C]PBR28,  two  radioligands  for  translocator  protein  (18  kDa)  in  human  and  monkey:  Implications  for  positron  emission  tomographic  imaging  of  this  inflammation  biomarker:  Neuroimage  2010;  49:2924-­‐32.  

40.   Kreisl  WC,  Lyoo  CH,  Liow  JS,  et  al:  (11)C-­‐PBR28  binding  to  translocator  protein  increases  with  progression  of  Alzheimer's  disease:  Neurobiol  Aging  2016;  44:53-­‐61.  

41.   Rusjan  PM,  Wilson  AA,  Bloomfield  PM,  et  al:  Quantitation  of  translocator  protein  binding  in  human  brain  with  the  novel  radioligand  [18F]-­‐FEPPA  and  positron  emission  tomography:  J  Cereb  Blood  Flow  Metab  2011;  31:1807-­‐16.  

42.   Yokokura  M,  Terada  T,  Bunai  T,  et  al:  Depiction  of  microglial  activation  in  aging  and  dementia:  Positron  emission  tomography  with  [11C]DPA713  versus  [11C](R)PK11195:  J  Cereb  Blood  Flow  Metab  2016.  

43.   Owen  DR,  Yeo  AJ,  Gunn  RN,  et  al:  An  18-­‐kDa  translocator  protein  (TSPO)  polymorphism  explains  differences  in  binding  affinity  of  the  PET  radioligand  PBR28:  J  Cereb  Blood  Flow  Metab  2012;  32:1-­‐5.  

44.   Jucaite  A,  Svenningsson  P,  Rinne  JO,  et  al:  Effect  of  the  myeloperoxidase  inhibitor  AZD3241  on  microglia:  a  PET  study  in  Parkinson's  disease:  Brain  2015;  138:2687-­‐700.  

45.   Shotbolt  P,  Tziortzi  AC,  Searle  GE,  et  al:  Within-­‐subject  comparison  of  [(11)C]-­‐(+)-­‐PHNO  and  [(11)C]raclopride  sensitivity  to  acute  amphetamine  challenge  in  healthy  humans:  J  Cereb  Blood  Flow  Metab  2012;  32:127-­‐36.  

46.   Abi-­‐Dargham  A,  Gil  R,  Krystal  J,  et  al:  Increased  striatal  dopamine  transmission  in  schizophrenia:  confirmation  in  a  second  cohort:  Am  J  Psychiatry  1998;  155:761-­‐7.  

47.   Laruelle  M,  Abi-­‐Dargham  A,  Gil  R,  et  al:  Increased  dopamine  transmission  in  schizophrenia:  relationship  to  illness  phases:  Biol  Psychiatry  1999;  46:56-­‐72.  

48.   Slifstein  M,  van  de  Giessen  E,  Van  Snellenberg  J,  et  al:  Deficits  in  prefrontal  cortical  and  extrastriatal  dopamine  release  in  schizophrenia:  a  positron  emission  tomographic  functional  magnetic  resonance  imaging  study:  JAMA  Psychiatry  2015;  72:316-­‐24.  

49.   Colasanti  A,  Guo  Q,  Giannetti  P,  et  al:  Hippocampal  Neuroinflammation,  Functional  Connectivity,  and  Depressive  Symptoms  in  Multiple  Sclerosis:  Biol  Psychiatry  2016;  80:62-­‐72.  

50.   Mick  I,  Myers  J,  Ramos  AC,  et  al:  Blunted  Endogenous  Opioid  Release  Following  an  Oral  Amphetamine  Challenge  in  Pathological  Gamblers:  Neuropsychopharmacology  2016;  41:1742-­‐50.  

51.   Esterlis  I,  Hannestad  JO,  Bois  F,  et  al:  Imaging  changes  in  synaptic  acetylcholine  availability  in  living  human  subjects:  J  Nucl  Med  2013;  54:78-­‐82.  

52.   Hillmer  AT,  Esterlis  I,  Gallezot  JD,  et  al:  Imaging  of  cerebral  alpha4beta2*  nicotinic  acetylcholine  receptors  with  (-­‐)-­‐[18F]Flubatine  PET:  Implementation  of  bolus  plus  constant  infusion  and  sensitivity  to  acetylcholine  in  human  brain:  Neuroimage  2016;  141:71-­‐80.  

53.   Frankle  WG,  Cho  RY,  Narendran  R,  et  al:  Tiagabine  increases  [11C]flumazenil  binding  in  cortical  brain  regions  in  healthy  control  subjects:  Neuropsychopharmacology  2009;  34:624-­‐33.  

54.   Finnema  SJ,  Varrone  A,  Hwang  TJ,  et  al:  Fenfluramine-­‐induced  serotonin  release  decreases  [11C]AZ10419369  binding  to  5-­‐HT1B-­‐receptors  in  the  primate  brain:  Synapse  2010;  64:573-­‐7.  

55.   Ridler  K,  Plisson  C,  Rabiner  EA,  et  al:  Characterization  of  in  vivo  pharmacological  properties  and  sensitivity  to  endogenous  serotonin  of  [11C]  P943:  a  positron  emission  tomography  study  in  Papio  anubis:  Synapse  2011;  65:1119-­‐27.  

56.   Brier  MR,  Gordon  B,  Friedrichsen  K,  et  al:  Tau  and  Abeta  imaging,  CSF  measures,  and  cognition  in  Alzheimer's  disease:  Sci  Transl  Med  2016;  8:338ra66.  

57.   Honer  M,  Gobbi  L,  Martarello  L,  et  al:  Radioligand  development  for  molecular  imaging  of  the  central  nervous  system  with  positron  emission  tomography:  Drug  Discov  Today  2014;  19:1936-­‐44.  

58.   Pike  VW:  Considerations  in  the  Development  of  Reversibly  Binding  PET  Radioligands  for  Brain  Imaging:  Curr  Med  Chem  2016;  23:1818-­‐69.  

59.   Gunn  RN,  Slifstein  M,  Searle  GE,  et  al:  Quantitative  imaging  of  protein  targets  in  the  human  brain  with  PET:  Phys  Med  Biol  2015;  60:R363-­‐411.  

60.   Guo  Q,  Brady  M,  Gunn  RN:  A  biomathematical  modeling  approach  to  central  nervous  system  radioligand  discovery  and  development:  J  Nucl  Med  2009;  50:1715-­‐23.  

61.   Zhang  L,  Villalobos  A,  Beck  EM,  et  al:  Design  and  selection  parameters  to  accelerate  the  discovery  of  novel  central  nervous  system  positron  emission  tomography  (PET)  ligands  and  

their  application  in  the  development  of  a  novel  phosphodiesterase  2A  PET  ligand:  J  Med  Chem  2013;  56:4568-­‐79.