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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".