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Record W2801547046 · doi:10.18433/jpps29788

Positron Emission Tomography (PET) and Pharmacokinetics: Classical Blood Sampling Versus Image-Derived Analysis of [18F]FAZA and [18F]FDG in a Murine Tumor Bearing Model

2018· article· en· W2801547046 on OpenAlexafffundvenue
Hans-Soenke Jans, Xiaohong Yang, Dion R. Brocks, Piyush Kumar, Melinda Wuest, Leonard I. Wiebe

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2018
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsPositron emission tomographyBlood samplingPharmacokineticsCardiac PETNuclear medicineBlood volumePet imagingPositronMedicineIsofluranePharmacologyInternal medicineAnesthesia

Abstract

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PURPOSE: Pharmacokinetic (PK) data are generally derived from blood samples withdrawn serially over a defined period after dosing. In small animals, blood sampling after dosing presents technical difficulties, particularly when short time intervals and frequent sampling are required. Positron emission tomography (PET) is a non-invasive functional imaging technique that can provide semi-quantitative temporal data for defined volume regions of interest (vROI), to support kinetic analyses in blood and other tissues. The application of preclinical small-animal PET to determine and compare PK parameters for [18F]FDG and [18F]FAZA, radiopharmaceuticals used clinically for assessing glucose metabolism and hypoxic fractions, respectively, in the same mammary EMT6 tumor-bearing mouse model, is reported here. METHODS: Two study groups were used: normal BALB/c mice under isoflurane anesthesia were intravenously injected with either [18F]FDG or [18F]FAZA. For the first group, blood-sampling by tail artery puncture was used to collect blood samples which were then analyzed with Radio-microTLC. Dynamic PET experiments were performed with the second group of mice and analyzed for blood input function and tumor uptake utilizing a modified two compartment kinetic model. Heart and inferior vena cava vROIs were sampled to obtain image-derived data. PK parameters were calculated from blood samples and image-derived data. Time-activity curves (TACs) were also generated over regions of liver, kidney and urinary bladder to depict clearance profiles for each radiotracer. RESULTS: PK values generated by classical blood sampling and PET image-derived analysis were comparable to each other for both radiotracers. Heart vROI data were suitable for analysis of [18F]FAZA kinetics, but metabolic uptake of radioactivity mandated the use of inferior vena cava vROIs for [18F]FDG analysis. While clearance (CL) and blood half-life (t½) were similar for both [18F]FDG and [18F]FAZA for both sampling methods, volume of distribution yielded larger differences, indicative of limitations such as partial volume effects within quantitative image-derived data. [18F]FDG underwent faster blood clearance and had a shorter blood half-life than [18F]FAZA. Kinetic analysis of tumor uptake from PET image data showed higher uptake and longer tumor tissue retention of [18F]FDG, indicative of the tumor's glucose metabolism rate, versus lower tumor uptake and retention of [18F]FAZA. While [18F]FAZA possesses a somewhat greater hepatobiliary clearance , [18F]FDG clears faster through the renal system which results in faster radioactivity accumulation in the urinary bladder. CONCLUSIONS: The present study provides a working example of the applicability of functional PET imaging as a suitable tool to determine PK parameters in small animals. The comparative analysis in the current study demonstrates that it is feasible to use [18F]FDG PET and [18F]FAZA PET in the same model to analyze their blood PK parameters, and to estimate kinetic parameters for these tracers in tumor. This non-invasive imaging-based determination of tissue kinetic parameters facilitates translation from pre-clinical to clinical phases of drug development. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.083
GPT teacher head0.441
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2018
Admission routes3
Has abstractyes

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