Recent Advances in the Development and Application of Radiolabeled Kinase Inhibitors for PET Imaging
Bibliographic record
Abstract
Over the last 20 years, intensive investigation and multiple clinical successes targeting protein kinases, mostly for cancer treatment, have identified small molecule kinase inhibitors as a prominent therapeutic class. In the course of those investigations, radiolabeled kinase inhibitors for positron emission tomography (PET) imaging have been synthesized and evaluated as diagnostic imaging probes for cancer characterization. Given that inhibitor coverage of the kinome is continuously expanding, in vivo PET imaging will likely find increasing applications for therapy monitoring and receptor density studies both in- and outside of oncological conditions. Early investigated radiolabeled inhibitors, which are mostly based on clinically approved tyrosine kinase inhibitor (TKI) isotopologues, have now entered clinical trials. Novel radioligands for cancer and PET neuroimaging originating from novel but relevant target kinases are currently being explored in preclinical studies. This article reviews the literature involving radiotracer design, radiochemistry approaches, biological tracer evaluation and nuclear imaging results of radiolabeled kinase inhibitors for PET reported between 2010 and mid-2015. Aspects regarding the usefulness of pursuing selective vs. promiscuous inhibitor scaffolds and the inherent challenges associated with intracellular enzyme imaging will be discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".