Radiopharmaceuticals for diagnostic oncology
Bibliographic record
Abstract
Medicinal Chemistry and the Radiopharmaceutical Sciences The radiopharmaceutical sciences are founded in Medicinal Chemistry and applied in Nuclear Medicine. The radiopharmaceutical sciences include a large component of drug design and development, and encompass the physics and chemistry of radionuclide production using nuclear reactors and charged particle accelerators (c.g. cyclotrons), as well as the synthesis and radiolabelling of precursor molecules, radiation dosimetry, biological testing, pharmacokinetic and kinetic modeling, and the administration of diagnostic and therapeutic doses of radiopharmaceuticals to patients. Radiopharmacy, as it pertains more narrowly to the practice of pharmacy, is a subdiscipline of medicinal and pharmaceutical chemistry in many Schools of Pharmacy. In several countries, radiopharmacy (or nuclear pharmacy) practice is recognized as a professional sub-specialty in Pharmacy, and is governed by state regulatory authority. Radiopharmaceutical research and radiopharmacy practice are also found within academic departments in chemistry and physics, and are practiced in nuclear medicine departments of major hospitals, worldwide. My research has evolved from medicinal chemistry, with a focus on the design, synthesis, radiolabelling, and biological and clinical evaluation of novel compounds that are of medicinal interest. Personal Contributions to the Submitted Publications Radiopharmaceutical chemistry and the development of radiopharmaceuticals is the pervading theme of the research publications arising from my research activities. The publications presented in this submission are based on research conducted primarily at the Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, and the Cross Cancer Institute (Edmonton). Research collaborations have included scientists from the Deutsches Krebsforschungszentrum (Heidelberg), the Tuebingen Universitaet PET Center (Tuebingen), the Daniel den Hoed Cancer Hospital (Rotterdam), the Rega Institute (Leuven), Ansto (Sydney), the University College of London Medical School (London) and the Peter MacCallum Cancer Institute (Melbourne). Publications submitted date from 1970 through 2000. The early publications (to the late 1970's) reflect research that I have either conducted or assisted in personally at the research bench. The majority of the remaining papers reflect my research leadership in terms of concepts, experimental design and execution, and manuscript preparation. It is my practice to actively participate in all publications that I co-author, and in general, to place the names of junior scientists (e.g. graduate students) ahead of senior co-authors. In no case am I a co—author because of administrative positions I have held (e.g. Division head; Director). The senior collaborators who have influenced the scientific evolution of my work are named in the following paragraphs. Junior scientists (e.g. graduate students, post-doctoral fellows, research associates) have made important contributions, especially at the research bench. The manuscripts are grouped into three main categories, which are introduced in the following paragraphs. Published abstracts, and international patents on hypoxia imaging, gene therapy imaging, and drug delivery based on cyclodextrins, are not included in this material.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.051 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".