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A Systematic Investigation into the Influence of Net Charge on the Biological Distribution of Radiometalated Peptides Using [<sup>68</sup>Ga]Ga-DOTA-TATE Derivatives

2023· article· en· W4321186608 on OpenAlexafffund
Shvan J. Raheem, Akam K. Salih, Moralba Dominguez Garcia, Jessica C. Sharpe, Behzad M. Toosi, Eric W. Price

Bibliographic record

VenueBioconjugate Chemistry · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of Saskatchewan
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsSaskatchewan Health Research FoundationUniversity of Saskatchewan
KeywordsBiodistributionChemistryDOTAPeptideIn vivoNephrotoxicityRadionuclide therapyRadiochemistryBiophysicsChelationIn vitroNuclear medicineBiochemistryOrganic chemistryToxicity

Abstract

fetched live from OpenAlex

Recently, several radiometalated peptides have been approved for clinical imaging and/or therapy (theranostics) of several types of cancer; nonetheless, the primary challenge that most of these peptides confront is significant renal uptake and retention, which is often dose limiting and can cause nephrotoxicity. In response to this, numerous methods have been employed to reduce the uptake of radiometalated peptides in the kidneys, and among these is adding a linker to modulate polarity and/or charge. To better understand the influence of net charge on the biodistribution of radiometalated peptides, we selected the clinically popular construct DOTA-TATE (NETSPOT/LUTATHERA) as a model system. We synthesized derivatives using manual solid-phase peptide synthesis methods including mechanical and ultrasonic agitation to effectively yield the gold standard DOTA-TATE and a series of derivatives with different net charges (+2, +1, 0, −1, −2). Dynamic PET imaging from 0 to 90 min in healthy female mice (CD1) revealed high accumulation and retention of activity in the kidneys for the net-neutral (0) charged [ 68 Ga]Ga-DOTA-TATE and even higher for positively charged derivatives, whereas negatively charged derivatives exhibited low accumulation and fast renal excretion. Ex vivo biodistribution at 2 h post injection demonstrated a significant retention of [ 68 Ga]Ga-DOTA-TATE (∼74 %ID/g) in the kidneys, which increased as the net positive charge per molecule increased to +1 and +2 (∼272 %ID/g and ∼333 %ID/g, respectively), but the −1 and −2 net charged molecules exhibited lower renal uptake (∼15 %ID/g and 16 %ID/g, respectively). Interestingly, the net −2 charged [ 68 Ga]Ga-DOTA-(Glu) 2 -PEG 4 -TATE was stable in blood serum but had much higher healthy organ uptake (lungs, liver, spleen) than the net −1 compound, suggesting instability in vivo . Although the [ 68 Ga]Ga-DOTA-PEG 4 -TATE derivative with a net charge of 0 also showed a decrease in kidney uptake, it also showed instability in blood serum and in vivo . Despite the superior pharmacokinetics of the net −1 charged [ 68 Ga]Ga-DOTA-Glu-PEG 4 -TATE in healthy mice with respect to kidney uptake and overall profile, dynamic PET images and ex vivo biodistribution in male mice (NSG) bearing AR42J (SSTR2 overexpressing) subcutaneous tumor xenografts showed significantly diminished tumor uptake when compared to the gold standard [ 68 Ga]Ga-DOTA-TATE. Taken together, these findings indicate unambiguously that kidney uptake and retention are significantly influenced by the net charge of peptide-based radiotracers. In addition, it was illustrated that the negatively charged peptides had substantially decreased kidney uptake, but in this instantiation the tumor uptake was also impaired.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.054
GPT teacher head0.307
Teacher spread0.252 · 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

Citations15
Published2023
Admission routes2
Has abstractyes

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