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Record W4289942999 · doi:10.3390/molecules27154959

Application of Cleavable Linkers to Improve Therapeutic Index of Radioligand Therapies

2022· review· en· W4289942999 on OpenAlexafffund
Joseph Lau, Hwan Lee, Julie Rousseau, François Bénard, Kuo‐Shyan Lin

Bibliographic record

VenueMolecules · 2022
Typereview
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchLeading Edge Endowment Fund
KeywordsTherapeutic indexLimitingRadioligandNephrotoxicityMedicinePeptidomimeticPharmacologyPharmacokineticsDrugComputational biologyCancer researchChemistryPeptideKidneyInternal medicineBiochemistryBiologyReceptor

Abstract

fetched live from OpenAlex

Radioligand therapy (RLT) is an emergent drug class for cancer treatment. The dose administered to cancer patients is constrained by the radiation exposure to normal tissues to maintain an appropriate therapeutic index. When a radiopharmaceutical or its radiometabolite is retained in the kidneys, radiation dose deposition in the kidneys can become a dose-limiting factor. A good exemplar is [177Lu]Lu-DOTATATE, where patients receive a co-infusion of basic amino acids for nephroprotection. Besides peptides, there are other classes of targeting vectors like antibody fragments, antibody mimetics, peptidomimetics, and small molecules that clear through the renal pathway. In this review, we will review established and emerging strategies that can be used to mitigate radiation-induced nephrotoxicity, with a focus on the development and incorporation of cleavable linkers for radiopharmaceutical designs. Finally, we offer our perspectives on cleavable linkers for RLT, highlighting future areas of research that will help advance the technology.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.046
GPT teacher head0.370
Teacher spread0.324 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2022
Admission routes2
Has abstractyes

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