Companion Diagnostic <sup>64</sup>Cu-Liposome Positron Emission Tomography Enables Characterization of Drug Delivery to Tumors and Predicts Response to Cancer Nanomedicines
Bibliographic record
Abstract
Deposition of liposomal drugs into solid tumors is a potentially rate-limiting step for drug delivery and has substantial variability that may influence probability of response. Tumor deposition is a shared mechanism for liposomal therapeutics such that a single companion diagnostic agent may have utility in predicting response to multiple nanomedicines. Methods: We describe the development, characterization and preclinical proof-of-concept of the positron emission tomography (PET) agent, MM-DX-929, a drug-free untargeted 100 nm PEGylated liposome stably entrapping a chelated complex of 4-DEAP-ATSC and 64 Cu (copper-64). MM-DX-929 is designed to mimic the biodistribution of similarly sized therapeutic agents and enable quantification of deposition in solid tumors. Results: MM-DX-929 demonstrated sufficient in vitro and in vivo stability with PET images accurately reflecting the disposition of liposome nanoparticles over the time scale of imaging. MM-DX-929 is also representative of the tumor deposition and intratumoral distribution of three different liposomal drugs, including targeted liposomes and those with different degrees of PEGylation. Furthermore, stratification using a single pre-treatment MM-DX-929 PET assessment of tumor deposition demonstrated that tumors with high MM-DX-929 deposition predicted significantly greater anti-tumor activity after multi-cycle treatments with different liposomal drugs. In contrast, MM-DX-929 tumor deposition was not prognostic in untreated tumor-bearing xenografts, nor predictive in animals treated with small molecule chemotherapeutics. Conclusions: These data illustrate the potential of MM-DX-929 PET as a companion diagnostic strategy to prospectively select patients likely to respond to liposomal drugs or nanomedicines of similar molecular size.
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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 | 0.000 | 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) | 0.002 | 0.001 |
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".