Enzyme-targeted, Nucleoside-based Radiopharmaceuticals for Scintigraphic Monitoring of Gene Transfer and Expression
Bibliographic record
Abstract
Enzymes, the expression products of transferred or native genes, offer unique windows of opportunity for clinical diagnosis and therapy. Although some expression products can be monitored in plasma, nuclear medicine imaging (SPECT and PET) offers the unique ability to selectively measure the intensity and regional/spatial distribution of gene expression both in vivo, in situ. Importantly, the superior sensitivity and moderate spatial resolution of the nuclear techniques also enable in vivo kinetic characterization of enzyme-substrate interaction. Indeed, the non-invasive, whole-body assessment of gene expression can only be achieved through imaging techniques. Given today's technology, nuclear imaging techniques uniquely provide the necessary sensitivity required to evaluate the success of the gene delivery and expression (transcription and translation), and to detect unwanted expression by non-target tissues. Enzymes are a special class of proteinacious gene expression products that selectively bind specific substrates for the purpose of molecular biotransformation rather than for signal transduction. In general, enzymes have received much less attention for imaging than receptors and antibodies, despite the enzymes' high substrate specificity and the potential for kinetic evaluation. Enzymes are attractive targets for diagnostic imaging and radioisotope radiotherapy because they convert multiple molecular copies of the substrate (radiotracer) per molecule of enzyme, thereby greatly increasing the ultimate sensitivity relative to the sensitivity offered by receptors that bind with 1:1 stoichiometry. Not surprisingly, enzymes have been the preferred molecular targets to date for scintigraphic imaging of gene therapy. This overview describes opportunities and advances in the utilization of radiolabelled nucleosides and nucleoside bases for imaging in gene therapy, with emphasis on the exploitation of enzyme systems for scintigraphic imaging of gene expression in gene therapy of cancer. Herpes simplex virus type-1 thymidine kinase and bacterial/fungal cytosine deaminase are discussed within the context of gene therapy issues such as gene vectors for targeting and delivery, the bystander effect, and radionucleoside delivery. The utilization of nucleosides as markers of tissue proliferation is discussed with respect to selected enzyme targets.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".