Pharmacokinetics and Biodistribution of Human Serum Albumin-TIMP-2 Fusion Protein Using Near-Infrared Optical Imaging
Bibliographic record
Abstract
PURPOSE: TIMP-2 has been studied as an attractive cancer therapeutic candidate, and a TIMP-2 fusion protein (HSA/TIMP-2) displayed effective anticancer activity, despite a lack of information about its pharmacokinetics (PK) and biodistribution. The purpose of this work was to assess the PK and biodistribution of HSA/TIMP-2 as well as to quantify accumulated HSA/TIMP-2 in tumors. METHODS: Cy5.5 near-infrared (NIR) fluorescence was conjugated to the HSA/TIMP-2 protein (Cy5.5-HSA/TIMP-2) for monitoring spatio-temporal changes in vivo. For PK and biodistribution analysis, 0.2 μg/g body weight of Cy5.5-HSA/TIMP-2 was injected into MAT-LyLu prostate tumor xenografts, which were then imaged using an IVIS-200 optical imaging system. To quantify the accumulated HSA/TIMP-2 in tumors, we introduced a standard curve with depth-corrected fluorescence measurement. RESULTS: In the vascular tube formation assay with human umbilical vein endothelial cells (HUVECs), Cy5.5-HSA/TIMP-2 showed an antiangiogenic effect. In prostate cancer xenografts, Cy5.5-HSA/TIMP-2 exhibited a prolongation of blood half-life to 19.6 h and relatively preferential distribution to the tumor. The amount of tumor-accumulated Cy5.5-HSA/TIMP-2 was calculated to be 4.5 ± 0.5 ng/g body weight at 2 days, representing 2.25 ± 0.25% of the initial dose. CONCLUSIONS: We evaluated the pharmacokinetic profile and biodistribution of HSA/TIMP-2 with favorable results, providing new information for more effective approaches to cancer therapeutics using HSA/TIMP-2. Additionally, real-time in vivo fluorescence imaging analysis using a depth-corrected standard curve may serve as a platform to quantify biodistributed drug in anticancer therapeutic studies.
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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 | 0.001 | 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 | 0.001 |
| 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".