Engineered nanomedicine for myeloma and bone microenvironment targeting
Bibliographic record
Abstract
Bone is a favorable microenvironment for tumor growth and a frequent destination for metastatic cancer cells. Targeting cancers within the bone marrow remains a crucial oncologic challenge due to issues of drug availability and microenvironment-induced resistance. Herein, we engineered bone-homing polymeric nanoparticles (NPs) for spatiotemporally controlled delivery of therapeutics to bone, which diminish off-target effects and increase local drug concentrations. The NPs consist of poly(D,L-lactic-co-glycolic acid) (PLGA), polyethylene glycol (PEG), and bisphosphonate (or alendronate, a targeting ligand). The engineered NPs were formulated by blending varying ratios of the synthesized polymers: PLGA-b-PEG and alendronate-conjugated polymer PLGA-b-PEG-Ald, which ensured long circulation and targeting capabilities, respectively. The bone-binding ability of Ald-PEG-PLGA NPs was investigated by hydroxyapatite binding assays and ex vivo imaging of adherence to bone fragments. In vivo biodistribution of fluorescently labeled NPs showed higher retention, accumulation, and bone homing of targeted Ald-PEG-PLGA NPs, compared with nontargeted PEG-PLGA NPs. A library of bortezomib-loaded NPs (bone-targeted Ald-Bort-NPs and nontargeted Bort-NPs) were developed and screened for optimal physiochemical properties, drug loading, and release profiles. Ald-Bort-NPs were tested for efficacy in mouse models of multiple myeloma (MM). Results demonstrated significantly enhanced survival and decreased tumor burden in mice pretreated with Ald-Bort-NPs versus Ald-Empty-NPs (no drug) or the free drug. We also observed that bortezomib, as a pretreatment regimen, modified the bone microenvironment and enhanced bone strength and volume. Our findings suggest that NP-based anticancer therapies with bone-targeting specificity comprise a clinically relevant method of drug delivery that can inhibit tumor progression in MM.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".