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Medicinal (Radio) Chemistry: Building Radiopharmaceuticals for theFuture

2023· review· en· W4385969165 on OpenAlexaff
Martha Sahylí Ortega Pijeira, Paulo Sérgio Gonçalves Nunes, Samila Leon Chaviano, Aida Mary Abreu Diaz, Jean DaSilva, Eduardo Ricci‐Júnior, Luciana Magalhães Rebêlo Alencar, Xiaoyuan Chen, Ralph Santos‐Oliveira

Bibliographic record

VenueCurrent Medicinal Chemistry · 2023
Typereview
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité LavalUniversité de MontréalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsContext (archaeology)Drug developmentChemistryMedicineNeurosciencePharmacologyDrugPsychologyBiology

Abstract

fetched live from OpenAlex

Radiopharmaceuticals are increasingly playing a leading role in diagnosing, monitoring, and treating disease. In comparison with conventional pharmaceuticals, the development of radiopharmaceuticals does follow the principles of medicinal chemistry in the context of imaging-altered physiological processes. The design of a novel radiopharmaceutical has several steps similar to conventional drug discovery and some particularity. In the present work, we revisited the insights of medicinal chemistry in the current radiopharmaceutical development giving examples in oncology, neurology, and cardiology. In this regard, we overviewed the literature on radiopharmaceutical development to study overexpressed targets such as prostate-specific membrane antigen and fibroblast activation protein in cancer; β-amyloid plaques and tau protein in brain disorders; and angiotensin II type 1 receptor in cardiac disease. The work addresses concepts in the field of radiopharmacy with a special focus on the potential use of radiopharmaceuticals for nuclear imaging and theranostics.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.004

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 head0.234
GPT teacher head0.515
Teacher spread0.281 · 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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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