(FDHT) in Prostate Tumors as Measured by PET

Pharmacokinetic Assessment of the Uptakeof 16b-18F-Fluoro-5a-Dihydrotestosterone(FDHT) in Prostate Tumors as Measuredby PET

Bradley J. Beattie*1, Peter M. Smith-Jones*2, Yuliya S. Jhanwar3, Heiko Schoder2, C. Ross Schmidtlein4,Michael J. Morris5, Pat Zanzonico4, Olivia Squire2, Gustavo S.P. Meirelles6, Ron Finn2, Mohammad Namavari7,Shangde Cai2, Howard I. Scher5, Steven M. Larson2, and John L. Humm4

1Department of Neurology, Memorial Sloan-Kettering Cancer Center, New York, New York; 2Department of Radiology, MemorialSloan-Kettering Cancer Center, New York, New York; 3Department of Radiology, St. Luke’s-Roosevelt Hospital, New York, NewYork; 4Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York; 5Genitourinary OncologyService, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York; 6Department of Radiology,Federal University of Sao Paulo, Sao Paulo, Brazil; and 7Department of Radiology and Bioengineering, Bio-X Program, StanfordUniversity, Stanford, California

The aim of this study was to develop a clinically applicable non-invasive method to quantify changes in androgen receptor (AR)levels based on 18F-16b-fluoro-5a-dihydrotestosterone (18F-FDHT) PET in prostate cancer patients undergoing therapy.Methods: Thirteen patients underwent dynamic 18F-FDHT PETover a selected tumor. Concurrent venous blood samples wereacquired for blood metabolite analysis. A second cohort of 25patients injected with 18F-FDHT underwent dynamic PET of theheart. These data were used to generate a population-based in-put function, essential for pharmacokinetic modeling. Linearcompartmental pharmacokinetic models of increasing complex-ity were tested on the tumor tissue data. Four suitable modelswere applied and compared using the Bayesian information cri-terion (BIC). Model 1 consisted of an instantaneously equilibrat-ing space, followed by a unidirectional trap. Models 2a and 2bcontained a reversible space between the instantaneously equil-ibrating space and the trap, into which metabolites were ex-cluded (2a) or allowed (2b). Model 3 built on model 2b with theaddition of a second reversible space preceding the unidirec-tional trap and from which metabolites were excluded. Results:The half-life of the 18F-FDHT in blood was between 6 and 7 min.As a consequence, the uptake of 18F-FDHT in prostate cancer le-sions reached a plateau within 20 min as the blood-borne activitywas consumed. Radiolabeled metabolites were shown not tobind to ARs in in vitro studies with CWR22 cells. Model 1 pro-duced reasonable and robust fits for all datasets and was judgedbest by the BIC for 16 of 26 tumor scans. Models 2a, 2b, and 3were judged best in 7, 2, and 1 cases, respectively. Conclusion:Our study explores the clinical potential of using 18F-FDHT PETto estimate free AR concentration. This process involved the es-timation of a net uptake parameter such as the ktrap of model 1that could serve as a surrogate measure of AR expression in met-

astatic prostate cancer. Our initial studies suggest that a simplebody mass–normalized standardized uptake value correlatesreasonably well to model-based ktrap estimates, which we sur-mise may be proportional to AR expression. Validation studiesto test this hypothesis are underway.

Key Words: molecular imaging; PET/CT; radiotracer tissue ki-netics; FDHT; dynamic PET; pharmacokinetic modeling; pros-tate cancer

J Nucl Med 2010; 51:183–192DOI: 10.2967/jnumed.109.066159

The androgen receptor (AR) is known to be important inthe development and progression of prostate cancer.Castration-resistant prostate cancers in particular harbora series of oncogenic alterations in AR including over-expression, increased copy number, mutations that affectligand specificity, and an increase in the enzyme levelsresponsible for antigen synthesis (1). It is for this reasonthat there is increasing interest in the development oftherapies directed at these alterations.

Currently, a direct biopsy of a metastatic lesion isrequired to assess the AR status in tumors when treatmentis being considered. Although technically feasible, thisprocedure is invasive, costly, not a part of routine practice,and difficult to repeat. Moreover, the AR status determinedhistopathologically in one metastasis may not be represen-tative of all metastatic lesions.

A PET ligand that could provide a signal that is pre-dictive of AR expression levels in prostate cancer not onlywould have great potential in the diagnosis of this diseasebut also could have implications in determining the

Received Jul. 27, 2009; revision accepted Oct. 7, 2009.For correspondence or reprints contact: John L. Humm, Memorial

Sloan-Kettering Cancer Center, 1275 York Ave., New York, NY 10021.E-mail: [email protected]*Contributed equally to this work.COPYRIGHT ª 2010 by the Society of Nuclear Medicine, Inc.

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PHARMACOKINETIC ASSESSMENT OF 18F-FDHT • Beattie et al. 183

Journal of Nuclear Medicine, published on January 15, 2010 as doi:10.2967/jnumed.109.066159by on January 31, 2018. For personal use only. jnm.snmjournals.org Downloaded from

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appropriate therapy and in assessing its efficacy. This isespecially pertinent in an era when targeted therapies arebecoming available clinically, as molecular imaging ap-proaches can potentially be used to select patients likely torespond to such therapies and to monitor the therapeuticeffectiveness of these treatments.

18F-16b-fluoro-5a-dihydrotestosterone (18F-FDHT) isa structural analog of 5a-dihydrotestosterone (DHT), theprincipal intraprostatic form of androgen (2). Amongfluorinated androgen analogs studied in animals, 18F-FDHTuptake in the prostate was blocked (reduced ;10-fold) bythe coadministration of cold testosterone and yielded thehighest levels of unmetabolized radioligand in blood up to45 min after injection and the highest prostate-to-bone andprostate-to-muscle activity concentration ratios up to 4 hafter injection. Thus, 18F-FDHT appears to bind specificallyto ARs in vivo and to have the most favorable targetingproperties for noninvasive imaging among AR-bindingradiotracers studied to date. In addition, like androgensgenerally, most of the 18F-FDHT in circulation is bound tosex hormone–binding globulin (3). Such plasma–proteinbinding presumably serves to retard degradation of endog-enous androgens and to facilitate their transport into cells.These considerations led to the selection of 18F-FDHT asthe lead radiopharmaceutical for further evaluation inclinical studies. Two clinical studies subsequently demon-strated successful PET of prostate cancer using 18F-FDHT(4–6). These studies showed rapid tumor uptake andsystemic metabolism of 18F-FDHT and provided someevidence that the foci of activity seen on 18F-FDHT PETimages correlates with AR-expressing tissue as demon-strated by immunohistochemical staining. In this article, wedescribe our initial investigations seeking to use pharma-cokinetic modeling of 18F-FDHT time–activity data inprostate tumors, as measured by dynamic PET, to assessrelative levels of AR in such tumors.

MATERIALS AND METHODS

PatientsPatients were selected prospectively under the auspices of the

Memorial Sloan-Kettering Cancer Center Institutional ReviewBoard (protocol 00-095). All who agreed to participate in thestudy signed informed consent forms. The study cohort consistedof 13 patients who then underwent dynamic 18F-FDHT PET overa selected metastatic tumor site, located in either the pelvis or theabdomen. Because satisfactory region-of-interest (ROI)–basedblood time–activity data (i.e., input functions) could not, ingeneral, be obtained from these images, arterial input data wereextracted from a larger cohort of 25 patients, who underwentdynamic 18F-FDHT PET of the heart. Data from ROIs placed overthe aorta in these patients were averaged to generate a population-based input function used in the pharmacokinetic modeling.

For the 13 patients recruited into the first cohort, there werea total of thirty 18F-FDHT scans obtained, consisting of 3 patientsfor whom a single 18F-FDHT baseline scan only was obtained, 3patients for whom 1 pre- and 1 posttherapy 18F-FDHT scan were

obtained, and 7 patients for whom a baseline scan followed by 2posttherapy scans were obtained.

Radiochemical Synthesis of 18F-FDHT18F-FDHT was synthesized as previously described (5). The

total radiochemistry synthesis time was approximately 100 min,and the radiochemical yield to end of bombardment was nearly30%. Chemical and radiochemical quality assurance (QA) wasperformed by radio–thin-layer chromatography and reversed-phase high-performance liquid chromatography (HPLC) by coe-lution with a fully characterized nonradioactive standard. AdditionalQA included confirmation of color, appearance, radioactive half-life, pH, sterility, and apyrogenicity. All QA results were in fullaccordance with the approved specifications. The radiochemicalpurity was greater than 99%.

To maintain the chemical stability of the 18F-FDHT compound,the final product was formulated in a 5% ethanol solution,precluding the use of a bolus injection because of the burningsensation at the injection site associated with the intravenousadministration of alcohol. As a consequence, injections weremanually performed using a lead-shielded syringe for 40 s to 2min, and the rate was reduced based on patient feedback.

Binding Studies with 18F-FDHTDisplacement studies were performed with 18F-FDHT and

CWR22-rv1 cells with FDHT and DHT as competitors. Briefly,triplicate samples of cells were mixed with 20,000 counts perminute of 18F-FDHT and increasing amounts of cold competitor (1pM to 1 mM). The solutions were then shaken on an orbital shakerat ambient temperature, and after 60 min the cells were isolatedand washed with ice-cold Tris-buffered saline using a M-24T cellharvester (Brandel). All of the isolated cell samples were counted,with appropriate standards of total activity and blank controls, andthe specific uptake of 18F-FDHT determined. These data wereplotted against the concentration of the cold competitor to givesigmoidal displacement curves ( ½Fig: 1�Fig. 1). The inhibitory concen-tration of 50% was determined using a 1-site model and a least-squares curve-fitting routine (Origin; OriginLab). The r2 of thecurve fit was 0.99.

FIGURE 1. Displacement binding of 18F-FDHT and final18F-FDHT metabolite to CWR22-rv1 cells by DHT.

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A sample of 18F-FDHT drug product was evaporated to removethe ethanol and mixed with a 10% suspension of rat liverhomogenate in phenazine methosulfate for 30 min at 37�C. Atthe end of this period, the solution was diluted to 40% acetonitrileand the precipitated proteins pelleted by centrifugation. The me-tabolized 18F-FDHT supernatant was then purified by reversed-phase HPLC (C18 Ultrasphere column [Beckman]; 5 mm, 250 ·4.6 mm) using an gradient elution of 40% acetonitrile/10 mMphosphoric acid up to 90% acetonitrile/10 mM phosphoric acidover 10 min at a flow rate of 1 mL/min. Under these conditions,18F-FDHT elutes at around 8 min, and the main metabolite elutesat 4 min. The purified metabolite was then used in a displacementbinding study as described above.

PETEach patient underwent at least 1 whole-body 18F-FDG PET/

CT scan that was followed within 1–7 d by dynamic and whole-body 18F-FDHT PET scans. The initial 18F-FDG PET/CT scanwas used to identify one or more lesions to be followed in thesubsequent dynamic 18F-FDHT scans. A subset of the patientsreceived up to 2 additional sets of scans, approximately 4 and 12wk later during their course of treatment.

All studies were performed on either a Discovery LS or anAdvance PET scanner (GE Healthcare). Before the 18F-FDGscans, patients were required to fast for at least 6 h and a bloodsample was obtained to measure the serum glucose level. Nofasting was required for the 18F-FDHT scan. For all 18F-FDHTscans, each patient had 1 peripheral intravenous line placed intoeach arm, 1 for injection and 1 for blood sampling. Scanning in allcases was performed in 2-dimensional mode (septa-in).

The dynamic 18F-FDHT PET emission scan was initiated co-incident with the start of the injection. The first 2 patientsunderwent a dynamic scan of 55 min. Analysis of this dynamicimaging data, along with plasma metabolite analysis, showed littleto no change in activity levels in the tumor occurring beyond 20 minafter injection and almost complete metabolism of the compound.The protocol was, therefore, modified for patients 3–13, reducingthe duration of the dynamic PET scan to 30 min. After the dynamicscan, patients were allowed to dismount from the table to rest forapproximately 10 min. Patients were encouraged to urinate beforethe acquisition of a whole-body PET scan, which was used todetermine overall biodistribution and to explore other potentialmetastatic sites. The whole-body scan was obtained from the skullbase to the pelvic floor. All images were reconstructed using bothfiltered backprojection (FBP) and iterative reconstruction.

The 25 patients from the second cohort underwent an almostidentical scan procedure, except that the 30-min dynamic scan wasobtained over the chest to include the heart rather than overa metastatic index lesion. ROI data were derived from the aorta ofthese patients and were used to generate a population averageinput function for 18F-FDHT.

Radiometabolite Analysis of 18F-FDHTPatients who underwent 18F-FDHT scans had blood samples

drawn to determine the clearance of 18F-FDHT and the rate ofmetabolite formation. Blood samples were drawn at all or some ofthe following time points after injection with 18F-FDHT: 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, and 50 min. Patients whounderwent 30-min dynamic scans did not have the 40- and 50-minblood samples drawn, but whenever possible a blood sample wasdrawn at a late time point after the whole-body PET scan was

obtained. The activities in whole blood, plasma, 30-kDa filteredplasma, and acetonitrile-precipitated plasma were determined. Inaddition, the relative amounts of 18F-FDHT and 18F-FDHTmetabolites in the plasma were determined by HPLC.

Aliquots of whole blood were transferred to preweighed tubes.Portions of the blood samples were centrifuged and aliquots ofplasma transferred into preweighed tubes. The remaining plasmasamples underwent ultrafiltration using 30-kDa spin filters (Cen-tricon YM-30; Millipore), and aliquots of the filtrate were trans-ferred to preweighed tubes. 18F activity in all samples was assayedin a g-counter (LKB Wallac 1282; Compugamma) calibrated for18F, corrected for decay to the time of injection, and expressed asa percentage of the injected dose per gram.

The counted plasma samples were mixed with 0.45 mL ofacetonitrile containing unlabeled FDHT (0.05 mg/mL) as a refer-ence compound. After centrifugation at 2,000g for 5 min, theprecipitated proteins were also counted in a g-counter. The protein-free supernatant was analyzed by reversed-phase C18 HPLC. TheShimadzu HPLC system consisted of 2 LC10AT pumps anda SCL10A controller coupled to a SPD-10A ultraviolet detectorand a Packard Radiometric 625TR flow detector (500 mL/cell, withbismuth germanate crystals) in series. The reversed-phase C18

column (Ultrasphere [Beckman]; 5 mm, 250 · 4.6 mm) was eluted,applying a gradient from 40% acetonitrile/10 mM phosphoric acidto 90% acetonitrile/10 mM phosphoric acid over 10 min at a flowrate of 1 mL/min. Under these conditions, 18F-FDHT elutes ataround 8 min and the main metabolized product at 4 min. The datawere expressed as a percentage of the total activity in plasma.

ROI AnalysisOne nuclear medicine reader analyzed the ROIs in this in-

vestigation. The tumor was identified by displaying the 18F-FDGPET/CT scan alongside the whole-body 18F-FDHT and regionaldynamic 18F-FDHT scans. Separate ROIs circumscribing a homo-geneous region well within the borders of the index tumor weredrawn on images from a summed frame, iteratively reconstructed,covering the last 15 min of the 18F-FDHT PET emission data.ROIs were also generated for the descending aorta (3 studies in 2patients) or iliac arteries (20 studies in 9 patients) using summedframes covering the first 2 min after 18F-FDHT injection. All ROIswere applied to FBP-reconstructed images to generate curves ofmean activity concentration versus time. FBP images were usedhere to avoid problems associated with spatially variant conver-gence rates encountered with iterative reconstruction methods.Multiple ROIs were weighted (by their fractional volume) andsummed for structures extending over several adjacent images.

Because of the relatively large diameter of the descendingaorta, we chose the 3 curves, so derived, as the reference againstwhich to judge the accuracy of the iliac artery–derived curves andthe venous blood samples. A comparison of the blood time–activity curves both within and among studies suggested that datafrom the venous blood samples and from ROIs drawn over theiliac arteries likely do not accurately represent the true blood–activity time course at early times after 18F-FDHT injection. Onlytime–activity curves generated from ROIs drawn over the de-scending aorta showed the expected first-pass peak ( ½Fig: 2�Fig. 2). Atlate times (.15 min) all curves tended to plateau, with the aorta-derived and venous blood–derived data reaching approximatelythe same activity concentration level.

The descending aorta was within the field of view for only 2patients in the first cohort; therefore, it was decided to use data

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from a second separate cohort of patients for which dynamic PETscans of 18F-FDHT were obtained over the heart. These data wereused to establish a population-based blood time–activity curve.Curves derived from ROIs drawn over the aorta from this cohort of25 patients were converted to standardized uptake values (SUVs 5

tissue activity per kg divided by injected activity per kg of bodyweight) and averaged. Individual radiolabeled metabolite andparent 18F-FDHT input functions were calculated for each patientby first scaling the population-based blood time–activity curve sothat it matched the venous blood sample activity level at late timesand then multiplying the result by a population-based metabolite-fraction time–activity curve derived from the original cohort. Theparent 18F-FDHT input function could then be calculated bysubtracting this metabolite input function from the scaled pop-ulation-based blood time–activity curve (Fig. 2).

Pharmacokinetic ModelingA series of linear compartmental pharmacokinetic models of

increasing complexity were tested on the tumor tissue data andcompared using the Bayesian information criterion (BIC) asformulated by Schwartz (7). The definition of BIC is given as

BIC 5 n lnRSS

n

� �1k lnðnÞ;

where n is the number of data points, RSS the residual sum ofsquares from the estimated model, and k the number of freeparameters used in the model fit.

The BIC provide a statistical metric with which to compare theresults of different model fits to the data. The model yielding thelowest BIC value is judged to be of sufficient dimension (i.e., haveenough parameters) to describe the data. The BIC increase asa function of the residual sum of squares of the fitted data, but theyalso increase as the number of model parameters used to fit thedata is increased. In this way, the introduction of more thana requisite number of compartments is penalized. Hence, lowerBIC imply either fewer explanatory variables, better fit, or both.

The patient data were fitted to 4 different compartmentalmodels that were then compared using the BIC metric. Thesemodels are diagrammed in ½Fig: 3�Figure 3. The first and simplest ofthese models, model 1, consisted of an instantaneously equili-brating space (often also referred to as a fractional bloodvolume), followed by a unidirectional trap, thus requiring just2 parameters. The instantaneously equilibrating space wasassumed to contain both parent 18F-FDHT and its metabolites,whereas the trap was assumed to contain only parent 18F-FDHT.The exclusion of the metabolites from the final compartment wasjustified on the basis of our in vitro studies demonstrating that theradiolabeled metabolites of 18F-FDHT do not bind to CWR22-rv1 cells.

The next 2 models (2a and 2b) are similar to the first modelexcept for the addition of a reversible space between the in-stantaneously equilibrating space and the trap. The 2 variantsdiffered in their exclusion (2a) and allowance (2b) of metabolitesinto this intermediate space. When allowed, the metabolites wereforced to enter and exit this space using rate constants constrainedto equal that of the parent 18F-FDHT; thus, each of the 2 variantsrequired 4 parameters. We chose to equate these rate constantsunder the assumption that the associated space represents a regionof disrupted vasculature into which radiolabeled molecules boundto plasma proteins were leaking. For the most complex modeltried, model 3, an additional reversible space preceding theunidirectional trap was added, requiring a total of 6 parameters.In this model, metabolites were allowed to enter the first reversiblespace (again using rate constants constrained to equal that of 18F-FDHT) but not allowed to enter the second.

In each of these models, we have assumed that the entirety ofthe parent 18F-FDHT in the blood is equally available for uptakeinto the tumor, whether it is bound to plasma proteins or to steroidhormone–binding globulin or other constituents of the blood orfree in the plasma. Therefore, our input functions are based onwhole-blood activity concentrations.

The 18F time–activity data for each lesion in each patient PETstudy was independently fitted by each of the described modelvariants using the SAAM II software package (University ofWashington). Parameter values were adjusted to minimize the sumof the weighted squared differences between the model estimateand the corresponding measured values. The weight for each PETtime point was chosen to be the inverse of the duration of itsframe. The parent 18F-FDHT and metabolite input functionsapplied to the models were the individually scaled population-based curves as described above. BIC values for each of the fitswere calculated by SAAM II.

RESULTS

Clinical Image Data

Thirteen patients were studied in this protocol. Most ofthe metastatic sites were in bone. A summary of the patientscans and treatment regimens is provided in ½Table 1�Table 1. Adetailed description of these scans is beyond the scope ofthis article. However, in brief, several of these patientsexhibited concordant PET lesion detection by 18F-FDHTand 18F-FDG PET, with patient 12 showing 18F-FDGpositivity and 18F-FDHT negativity and patient 4 exhibitingonly abnormal 18F-FDHT uptake. Two of the patients (12and 13) did not demonstrate 18F-FDHT uptake in any

FIGURE 2. Time–activity curves in SUV units for bloodsamples (total, 18F-FDHT and metabolites) and PET-deriveddata from iliac artery and aorta. SUVbw 5 SUV normalized bybody weight.

RGB

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tumors and were, thus, excluded from further analysis.Some of the patients demonstrated mixed findings betweenthe SUVs measured for corresponding lesions, with highSUV on 18F-FDG and low SUV on 18F-FDHT scans, or viceversa. Most patients with positive 18F-FDHT baseline scanfindings exhibited uptake on posttherapy scans at reducedlevels. However, there were instances of increased uptakeon the follow-up 18F-FDHT PET scans, for example,patient 1. One patient (patient 10) had multiple positivelesions on the baseline 18F-FDG scan and a negativebaseline 18F-FDHT scan result but new abnormal 18F-FDHT uptake in these regions on the later PET scans.Although several of the patients had some soft-tissue areasof abnormal 18F-FDG uptake without corresponding 18F-FDHT uptake, patient 7 showed abnormal 18F-FDG and18F-FDHT uptake in lymph nodes in the left neck and

retroperitoneum, with increasing 18F-FDHT uptake onsubsequent scans.

From this patient cohort in Table 1, the following valuesare observed: the average age of the patients was 66.7 y,with a range of 49–79 y. The median prostate-specificantigen value was 94.65, with a minimum of 0.49 anda maximum of 885.05. The average Gleason score for thesepatients was 7, with a range of 5–10. The Karnofskyperformance status (KPS) values were either 80 (6 patients)or 90 (7 patients). Nine patients had bone disease, 8 hadvisceral or lymph node lesions, and 4 had both bone andvisceral or lymph node disease.

Metabolite Analysis

There was no free 18F-FDHT, as determined by size-exclusion filtration of serum samples. Recovery of 18F

FIGURE 3. Four compartmentalmodels used to fit 18F-FDHT data totumor uptake profiles. V*FDHT 5 volumetime FDHT concentration; V*metab 5

volume times metabolism concentration.

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activity from the acetonitrile-precipitated serum sampleswas around 89% and showed no trend with increasingmetabolite levels. A series of HPLC chromatograms for 1patient study is shown in½Fig: 4� Figure 4. The earliest samples,obtained at 1 min after injection, show the activity elutingat 7.6 min as 18F-FDHT. Later serum samples showincreasing amounts of a metabolite eluting at 4 min. Theserum count data and HPLC analysis data were combinedto give a time–activity curve for total blood activity and18F-FDHT and radiolabeled metabolite levels (½Fig: 5� Fig. 5)

Compartmental Analysis

Twenty-six tumor time–activity datasets were fitted byeach of the 4 compartmental models proposed. All datasetsfit well to model 1; for 16 of the datasets, the BIC foundmodel 1 to be of the most appropriate dimension. By thesecriteria, model 2a was most appropriate for 7 cases, andmodel 2b was best in 2 cases. However, in both of the casesfor model 2b, the distribution volumes for the metaboliteswere found to be quite high (28% and 67%), possiblyinconsistent with our in vitro findings that the metabolites

TABLE 1. Summary of Patient Scans and Treatment Regimens

Patient

no.

Age

(y)

Prostate-specific

antigen at baseline

Gleason

score KPS

No. of

PET scans

Site of

disease

Therapeutic

regimen

1 65 885.05 9 80 3 Bone Docetaxel (Sanofi-Aventis)

2 68 96.62 10 80 3 Bone, lymph node,

prostatic mass,soft tissue

Docetaxel (Sanofi-Aventis)

3 75 10.44 5 90 1 Bone 17AAG (AG Scientific),

docetaxel (Sanofi-Aventis)

4 73 24.90 7 90 3 Lymph node Abiraterone (Cougar Biotechnology)5 59 7.52 9 90 3 Bone, lymph node Abiraterone (Cougar Biotechnology)

6 49 27.04 7 90 3 Lymph node MDV3100 (Medivation, Inc.)

7 53 96.45 9 90 3 Bone, lymph node 17AAG (AG Scientific),

docetaxel (Sanofi-Aventis)8 74 47.22 7 80 2 Bone Docetaxel (Sanofi-Aventis)

9 70 0.49 7 90 2 Bone 17AAG (AG Scientific),

docetaxel (Sanofi-Aventis)

10 59 5.28 9 80 3 Liver Docetaxel (Sanofi-Aventis)11 76 10.42 6 90 2 Bone Palliative radiotherapy to sacrum

12 67 17.51 10 80 1 Bone, lymph node,

liver

5-Fluorouracil (Pharmacia and Upjohn),

oxaliplatin (Sanofi-Aventis)13 79 1.52 7 80 1 * Bicalutamide (AstraZeneca)

*Reading for patient 13 had confidence of metastases of equivocal; therefore, sites of disease were not identified.

FIGURE 4. Series of HPLC elution profiles showing pro-gressive decrease in 18F-FDHT (retention time, 6.7 min) andincrease in metabolites (retention time, 4.0 min).

FIGURE 5. Time course of 18F-FDHT and metabolite levelsin serial patient sera. %ID/L 5 percentage injected dose perliter.

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do not enter or bind to the cell membrane. The mostcomplex model, model 3, was best by the BIC for only 1dataset. In this case, the unidirectional rate constant ktrap

went to zero.As can be seen from the results shown in½Table 2� Table 2, the

pattern of the model selection does not appear to correlatewith individual tumors; that is, repeated studies of the sametumor yielded different models judged as the most appro-priate. In the few cases in which either model 2b or model 3was judged best, the BIC value was not substantially better(i.e., lower) than its values for models 1 and 2a, suggestingperhaps that their seeming superiority might be due torandom correlations in the noise. Thus, we present param-eter values for only models 1 and 2a in Table 2, leaving thedescription of models 2b and 3 results to the ‘‘Discussion’’section. Also in Table 2 are the estimation of the fraction ofthe measured tumor tissue activity that is bound to AR at 30min after injection and SUV at that time for models 1 and2a. The correlation between the ktrap term and the 30-minSUV of model 1 is shown in½Fig: 6� Figure 6, suggesting thepossibility of using a simplified imaging protocol that doesnot involve blood sampling or modeling.

Sample results of the compartmental modeling arepresented in½Fig: 7� Figure 7A, which shows the fits for model 1to the measured tumor time–activity data for 18F-FDHT inpatient 1. This patient underwent 1 pre- and 2 posttherapy18F-FDHT scans, and for all of these scans model 1 wasselected by the BIC. The results consistently show whatmight be interpreted as a progressive reduction in ARconcentration (or a reduction in AR-expressing viable cells)in response to therapy, but alternative interpretations cannotbe ruled out on the basis of these data alone.

DISCUSSION

The objectives of this study were to define the pharma-cokinetic properties of the tracer, determine the metabolismof 18F-FDHT in blood, and determine whether the datacould support a model having parameters that might proveto be correlated with AR concentration. The prospects formeasuring a parameter correlated to AR concentration arefounded on the assumption that the rate of 18F-FDHT to ARbinding is proportional to the product of the unboundconcentrations of 18F-FDHT and AR, for which theproportionality constant is the bimolecular association rateconstant kon. In compartmental modeling terms, this meansidentifying a rate constant that is equal to (or at leastconsistently proportional to) the product of kon and theconcentration of unbound AR (the latter of which isessentially a constant, given tracer levels of 18F-FDHT).It was our goal in this study to ascertain the limits of ourdynamic PET measurements in resolving model parametersthat might prove to be so correlated.

Of the compartmental models tested, 2 were frequentlychosen by the BIC as being most consistent with theavailable data. The first of these, model 1, fits reasonably

well to all datasets. Model 1 is a simple 2-parameter model,with 1 parameter being the rate constant associated witha unidirectional trap (at least over the time period of theseexperiments) of 18F-FDHT into the tumor. Thus, in model 1the resultant ktrap term comprises a combination of in-tracellular transport and association with the bindingdomain. As a consequence, tumor-to-tumor (or tumorresponse) differences may be the result of changes inintracellular transport in addition to any change in ARconcentration. For example, P-glycoprotein–mediated dif-ferences in the efflux of DHT between various tumor celllines has been reported (8), differences that could bereflected in this rate constant.

The results for model 2a suggest that in some cases itmay be possible to derive a parameter that is more directlyrelated to the free AR concentration, but our results alsosuggest that the determination of this parameter is lessrobust. More robust was the determination of the compositeparameter describing the steady-state net influx rate con-stant Ki, which in the case of model 2a is equal tok1ktrap=ðk21ktrapÞ. The value for Ki was in general foundto be close to the ktrap value of model 1. However, therewere some cases in which the final trapping term, and thusKi, went to zero. Although infrequent, this emphasizes thatover the time frame of the current datasets (30 min)a significant portion of the measured PET signal might bedue to the distribution of 18F-FDHT into reversible pre-cursor compartments—that is, upstream transport eventsrather than binding to AR.

Similar quality fits to the data can be obtained with models1 and 2a (Figs. 7A and 7B, corresponding to different modelfits to the same patient data), with associated differences ininterpretation. Lower residuals were obtained with model 2athan with model 1, but according to the BIC the degree ofimprovement in the fit did not justify the additional param-eters. This does not in and of itself mean that the model 2ainterpretation is wrong, but rather its compartments merelycannot be reliably discriminated by these data. With this inmind, one would have to accept the possibility of the model2a interpretation, in which there is little to no binding of 18F-FDHT to AR in the tumor (Fig. 7B) at baseline and onlymoderate binding at 3 and 6 mo after therapy, compared withthe perhaps more plausible interpretation, that of model 1, ofprogressive response to therapy.

To distinguish which of the foregoing scenarios is mostlikely, it is necessary to consider other relevant data.Evidence that 18F-FDHT uptake is indeed measuring thebinding of 18F-FDHT to AR is provided by in vitro and invivo blocking studies, the latter conducted in baboons (2).In the baboon study, 18F-FDHT was coadministered withrelatively high levels of nonradiolabeled testosterone,which was shown to reduce the amount of 18F-FDHTuptake into the prostate. If we assume that the transportof 18F-FDHT is not itself saturable, then the reduced uptakeis most likely explained by a reduction in free AR becauseof its binding to testosterone-derived DHT. This same study

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jnm066159-pm n 1/10/10




also showed a loss of 18F-FDHT from baboon prostate afterabout 3 h, perhaps from a slowly equilibrating compartmentbut also possibly due to reversible binding of 18F-FDHT toAR. Furthermore, biopsy samples from lesions positive for18F-FDHT also stained positively for AR by immunohisto-chemistry (5).

Although significant questions remain regarding thepotential of 18F-FDHT to directly measure free AR con-centration, taken together the results of this and otherstudies suggest the potential that clinically useful informa-tion may be provided from net uptake parameters such asthe ktrap of model 1 or the Ki of model 2. Indeed, thisparameter may be more useful clinically, because it is thebinding of the AR–testosterone complex to androgen

response elements on the DNA that affects gene expression(8), not AR expression levels per se.

Caveats of our analysis included our inability to obtaininput functions from arterial blood sampling (which wouldimpede patient accrual and limit protocol utility). Becausemost patients did not have lesions close to the heart,we—for the time being—overcame this limitation throughthe use of a population-derived input function (derivedfrom the aorta ROIs of 25 patients) for modeling. Theparent 18F-FDHT input function was then calculated bysubtracting the metabolite input function (determined fromvenous blood samples) from the scaled population-basedblood time–activity curve, as shown in Figure 2. In general,the high rate of 18F-FDHT metabolism is a difficulty in thatit gives rise to what is in effect a second input functionresulting from the recirculation of the radiolabeled metab-olites into the blood. However, our metabolite analysisshowed that the 18F species appears to be tightly bound toplasma proteins and thus remains in the blood compart-ment. Whereas egress of these metabolites into the inter-stitium cannot be ruled out, we have demonstrated by invitro studies with CWR22 cell lines that these metabolitesdo not bind to AR.

CONCLUSION

Independent of these difficulties, the success of model 1in fitting the tumor time–activity curves suggests thepotential for simpler acquisition protocols involvinga static PET measurement with and without a simultaneousvenous blood sample. As described here and in a previouspublication from our group (5), the metabolism of 18F-FDHT is such that the parent compound is almost entirelyeliminated from the blood by 15 min. After this time, thearea under the input function curve increases by less than

FIGURE 6. Scattergram showing relationship betweenk trap parameter values calculated in model 1 with 30-minSUV. These data are fitted with line forced through origin.SUVbw 5 SUV normalized by body weight.

FIGURE 7. (A) Tumor time vs. activity concentration data for 3 studies (1 pretherapy and 2 posttherapy) of patient 1 fitted withmodel 1. (B) Same 3 datasets as in A, this time fitted by model 2a. Time course of modeled reversible compartment for each ofthese is also shown. SUVbw5 SUV normalized by body weight; Rev cmpt 5 reversible compartment.

jnm066159-pm n 1/10/10




1% per minute. This finding, coupled with an assumptionof rapid equilibration of any reversible spaces within themodel, gives rise to a system in which the PET tumoractivity measurement rapidly reaches a plateau con-founded primarily by metabolites whose concentration isproportional to the metabolite level in the blood. If weassume a reasonable value for this proportionality con-stant, then an offset correcting the measured tumoractivity concentration for metabolites can be estimated.The activity in the blood sample would also be used toscale the area under the population–based 18F-FDHT inputcurve. The metabolite-corrected tumor tissue value di-vided by the scaled area under the curve is then equal toktrap. If the contribution of the metabolites to the tumortissue measurement is small, then a simple SUV-typemeasure may suffice.

In the future, we plan further validation of our simplifiedapproach by testing the correlation of 18F-FDHT SUV (.15min after injection) with histologic AR staining intensity andby testing the correlation of changes in SUV with measuresof clinical outcome. We also hope to modify the formulationof the injectate to avoid the problems with injection andtherefore input function variability.

ACKNOWLEDGMENTS

This work was supported by the Memorial Sloan-Kettering Center grants P50-CA92629 (‘‘SPORE in Pros-tate Cancer’’), P50 CA086438 (‘‘In Vivo Center forMolecular and Cellular Imaging’’), and K23: CA102544.

REFERENCES

1. Chen Y, Sawyers CL, Scher HI. Targeting the androgen receptor pathway in

prostate cancer. Curr Opin Pharmacol. 2008;8:440–448.

2. Choe YS, Lidstrom PJ, Chi DY, Bonasera TA, Welch MJ, Katzenellenbogen JA.

Synthesis of 11 b-[18F]fluoro-5 a-dihydrotestosterone and 11 b-[18F]fluoro-19-

nor-5 a-dihydrotestosterone: preparation via halofluorination-reduction, receptor

binding, and tissue distribution. J Med Chem. 1995;38:816–825.

3. Bonasera TA, Oneil JP, Xu M, et al. Preclinical evaluation of fluorine-18-labeled

androgen receptor ligands in baboons. J Nucl Med. 1996;37:1009–1015.

4. Dehdashti F, Picus J, Michalski JM, et al. Positron tomographic assessment of androgen

receptors in prostatic carcinoma. Eur J Nucl Med Mol Imaging. 2005;32:344–350.

5. Larson SM, Morris M, Gunther I, et al. Tumor localization of 16b-18F-fluoro-5a-

dihydrotestosterone versus 18F-FDG in patients with progressive, metastatic

prostate cancer. J Nucl Med. 2004;45:366–373.

6. Zanzonico PB, Finn R, Pentlow KS, et al. PET-based radiation dosimetry in man

of 18F-fluorodihydrotestosterone, a new radiotracer for imaging prostate cancer.

J Nucl Med. 2004;45:1966–1971.

7. Schwartz G. Estimating dimension of a model. Ann Stat. 1978;6:461–464.

8. Fedoruk MN, Gimenez-Bonafe P, Guns ES, Mayer LD, Nelson CC. P-glycoprotein

increases the efflux of the androgen dihydrotestosterone and reduces androgen

responsive gene activity in prostate tumor cells. Prostate. 2004;59:77–90.

jnm066159-pm n 1/10/10




Doi: 10.2967/jnumed.109.066159Published online: January 15, 2010.JNM Scher, Steven M. Larson and John L. HummPat Zanzonico, Olivia Squire, Gustavo S.P. Meirelles, Ron Finn, Mohammad Namavari, Shangde Cai, Howard I. Bradley J. Beattie, Peter M. Smith-Jones, Yuliya S. Jhanwar, Heiko Schöder, C. Ross Schmidtlein, Michael J. Morris, -Dihydrotestosterone (FDHT) in Prostate Tumors as Measured by PET

αF-Fluoro-518-βPharmacokinetic Assessment of the Uptake of 16

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(FDHT) in Prostate Tumors as Measured by PET

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