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Record W7009805696

The Evolving Landscape of Radioisotopes in Modern Medicine

2023· other· en· W7009805696 on OpenAlexafffund

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2023
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsTRIUMF
FundersTRIUMF
KeywordsModalitiesRadionuclide therapyCyclotronBroad spectrumModern medicinePersonalized medicineIdentification (biology)
DOInot available

Abstract

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Introduction After decades of development, an increasing repertoire of radioisotopes are experiencing rapid growth in demand, both for diagnostic molecular imaging (MI), but also targeted radionuclide therapy (TRT) – two modalities with great potential for the identification and treatment of difficult-to-treat diseases, including micro-metastatic cancers, antibiotic-resistant bacterial infections and viral infections. Clinical MI agents (specifically PET and SPECT radiotracers) were dominated for years by a small group of short-lived, main-group positron-, and metallic single-photon emitting radioisotopes. However, recent advances in technologies in and around solid targets and metal isotope production are now enabling cyclotron centres to produce and distribute many emerging and important radionuclides for clinical use. On the TRT front, recent clinical results demonstrating the efficacy of beta- and alpha-emitting radiopharmaceuticals toward advanced, metastatic disease have triggered a global pursuit for new drugs. Couple this with increasing supply of promising alpha-, beta- and Auger-emitting radionuclides, personalized diagnostic, therapeutic and theranostic medicine is closer to reality now than ever before. Researchers at facilities such as TRIUMF are playing an active and important role in developing and translating new technologies that are paving the way for the discovery and translation of radioisotopes and radiopharmaceuticals that will ultimately enable the paradigm of personalized molecular medicine. Description of the Work or Project Many of the ~1400 medical cyclotrons around the world today operate between 16 and 24 MeV [1], an ideal range for producing, among others, isotopes including 99mTc [2,3], 68Ga [4], 64Cu and 89Zr [5]. Efforts at TRIUMF have led to the development of a solid target transfer and irradiation system, and solid target processing chemistry which has demonstrated a high-yield, automated method for producing GBq-TBq quantities of these isotopes using up to 500 μA of ~13-22 MeV protons. Fully automated dissolution/separation processes along with regulatory filings now allow for cyclotron-produced materials to substitute for other sources used in the clinic today. On the therapeutic isotope front TRIUMF is scaling-up processes to produce 225Ac via the high-energy proton irradiation of 232Th, with the aim of implementing a scalable and routine production operation capable of supporting multiple clinical trials [6]. Targets containing 0.5 mm thick, 11 g thorium foils were irradiated to12,500 μAh with ~450 MeV protons using TRIUMF’s 500 MeV Isotope Production Facility (IPF), producing GBq quantities of 225Ac, 225Ra, 228Th, 212Pb, among a number of other alpha-emitting isotopes of interest [7]. A discussion will include recent experiences with target chemistry automation, product quality control, and Th-spallation waste handling and disposal. Conclusions This presentation will provide a summary update on the development and implementation of several newer technologies toward direct cyclotron-production of various emerging radionuclides across a fleet of 13 to 520 MeV cyclotrons located at TRIUMF and its partner institutions. References [1] Accelerator Knowledge Portal https://nucleus.iaea.org/sites/accelerators/Pages/Cyclotron.aspx [2] Beaver, J.E., Hupf, H.B. (1971). J Nucl Med. 12(11), 739–41. PMID 5113635 [3] Bénard, F. et al. (2014). J.Nucl.Med. 55(6), 1017-22. https://doi.org/10.2967/jnumed.113.133413 [4] Thisgaard, H. et al. (2021). EJNMMI Radiopharmacy and Chemistry. 6:1. https://doi.org/10.1186/s41181-020-00114-9 [5] Oehlke, E. et al. (2015). Nucl. Med. Biol. 42, 842-49. http://dx.doi.org/10.1016/j.nucmedbio.2015.06.005 [6] Robertson, A.K.H. et al. (2020). Inorg. Chem. 59(17), pp. 12156-165. https://doi.org/10.1021/acs.inorgchem.0c01081 [7] Robertson A.K.H., Kunz, P., Hoehr, C., Schaffer, P. (2020). Physics Review C, 102, 044613. https://journals.aps.org/prc/abstract/10.1103/PhysRevC.102.044613

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.007
Scholarly communication0.0040.007
Open science0.0020.003
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0140.006

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 head0.014
GPT teacher head0.154
Teacher spread0.139 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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