Pathogen-Specific Actinium-225 and Lutetium-177 Labeled Antibodies for Treatment of Biofilm-Associated Implant Infections: Initial In Vivo Proof-of-Concept
Bibliographic record
Abstract
Background: the primary challenge with implant infections is the formation of biofilm, which harbors dormant bacteria that reduce the effectiveness of antibiotics and amplify antibiotic resistance, exacerbating the global antimicrobial resistance crisis. A potential novel treatment strategy is radioimmunotherapy, which uses antibodies linked to radioisotopes to deliver targeted radiation to the bacteria and biofilm. We describe the first in vivo use of targeted radiation therapy, employing Actinium-225 (α-radiation) and Lutetium-177 (β-radiation) labeled antibodies to treat a Staphylococcus aureus biofilm-associated intramedullary implant infection. Untargeted radiation in the form of unbound radionuclide treatment was also evaluated. Methods: to assess therapeutic efficacy, bacterial counts were performed on implant and surrounding bone after seven days of follow-up. Biodistribution was evaluated using SPECT/CT and ex vivo gamma counting. Results: radioimmunotherapy using an antibody against wall teichoic acid which was labeled with Actinium-225 and Lutetium-177 achieved bacterial reductions between 45% and 93% on the implant and surrounding bone. Surprisingly, a similar antimicrobial effect was observed with unbound Actinium-225 treatment reducing the bacterial load by 80% on the implant and 98% in the surrounding bone. Indications of maximum tolerated dose (MTD) with Lutetium-177 labeled antibodies were observed through hepatic and renal function evaluations. Conclusions: These results should be interpreted in the context of the study’s constraints, particularly the limited animal sample size. Nonetheless, the results suggest that in vivo applied radiation may help reduce a biofilm-associated infection at the implant site as well as in the surrounding bone. These findings encourage further investigation into the use of targeted and non-targeted radiation, potentially combined with antibiotics, to develop effective strategies for eradicating biofilm-associated implant infections.
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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) | 0.001 | 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".