Preclinical Development and Characterization of Novel Adeno-Associated Viral Vectors for the Treatment of Lipoprotein Lipase Deficiency
Bibliographic record
Abstract
Lipoprotein lipase deficiency (LPLD) results from mutations within the lipoprotein lipase (LPL) gene that lead to a complete lack of catalytically active LPL protein. Glybera was one of the first adeno-associated virus (AAV) gene replacement therapy to receive European Medicines Agency regulatory approval for the treatment of LPLD. However, Glybera is no longer marketed potentially due to a combination of economical, manufacturing, and vector-related issues. The aim of this study was to develop a more efficacious AAV gene therapy vector for LPLD. Following preclinical biodistribution, efficacy and non-Good Laboratory Practice toxicity studies with novel AAV1 and AAV8-based vectors in mice, we identified AAV8 pVR59. AAV8 pVR59 delivered a codon-optimized, human gain-of-function hLPLS447X transgene driven by a CAG promoter in an AAV8 capsid. AAV8 pVR59 was significantly more efficacious, at 10- to 100-fold lower doses, compared with an AAV1 vector based on Glybera, when delivered intramuscularly or intravenously, respectively, in mice with LPLD. Efficient gene transfer was observed within the injected skeletal muscle and liver following delivery of AAV8 pVR59, with long-term correction of LPLD phenotypes, including normalization of plasma triglycerides and lipid tolerance, for up to 6 months post-treatment. While intramuscular delivery of AAV8 pVR59 was well tolerated, intravenous administration augmented liver pathology. These results highlight the feasibility of developing a superior AAV vector for the treatment of LPLD and provide critical insight for initiating studies in larger animal models. The identification of an AAV gene therapy vector that is more efficacious at lower doses, when paired with recent advances in production and manufacturing technologies, will ultimately translate to increased safety and accessibility for patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".