Hypersensitivity and Loss of Disease Site Targeting Caused by Antibody Responses to PEGylated Liposomes
Bibliographic record
Abstract
The systemic application of nucleic acid drugs requires delivery systems that overcome the poor pharmacokinetics, limited biodistribution, and inefficient uptake of nucleic acids. PEGylated liposomes show considerable promise because of their intrinsic ability to accumulate at disease sites and facilitate transfection of target cells. Unlike many viral vectors, PEGylated liposomes are generally considered to be nonimmunogenic. We have developed a PEGylated liposome for the systemic administration of plasmid DNA that achieves high levels of selective gene expression at distal tumor sites. Here we report that the in vivo efficacy and safety of these systems can be severely compromised following repeat administration. This phenomenon is characterized by a loss of disease site targeting, accelerated clearance from the blood, and acute hypersensitivity. These effects are fully attributable to a surprisingly robust, long-lived antibody response generated against polyethylene glycol (PEG) that results from the strong adjuvant effect of the plasmid payload. Importantly, immunogenicity may be substantially reduced by modifying the alkyl chain of the PEG-lipid conjugate, thereby allowing successful repeat dosing of the modified plasmid formulations without adverse side effects. Immunogenicity is a relevant concern for a number of nonviral delivery systems given the potent immunostimulatory properties of many nucleic acid drugs.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".