Lipid-based nanoparticles deliver mRNA to reverse the pathogenesis of lysosomal acid lipase deficiency in a preclinical model
Bibliographic record
Abstract
Abstract Lysosomal acid lipase (LAL) is the only known enzyme that degrades cholesteryl esters (CEs) and triglycerides (TGs) in the lysosomes. LAL deficiency (LAL-D) results in hepatosplenomegaly with extensive accumulation of CEs and TGs, and can in the most severe cases be a life-threatening condition in early infancy. Using messenger ribonucleic acid (mRNA) for protein replacement is an innovative approach for the treatment of genetic disorders, but is challenged by a safe and efficient mRNA delivery. Here, we generated a combinatorial library of lipid-based nanoparticles (LNPs) for mRNA delivery and screened it in vitro and in vivo, which yielded a new formulation with a superior potency than an FDA-approved nanoformulation. This formulation efficiently delivered LAL mRNA and restored LAL activity in liver and spleen, mediating significant reversal of the pathological progression in an aggressive preclinical model of LAL-D. In vivo, the new formulation also promoted a more sustained and quantitatively higher LAL expression. In addition, repeated administration regimen mitigated hepatosplenomegaly, and targeted lipidomic analysis revealed strong diminution of CEs and TGs and of toxic lipid species in the liver and spleen. Transcriptomic analysis showed significant attenuation of inflammatory processes, fibrosis and several pathological pathways associated to LAL-D. These findings provide strong evidence that the intracellular production of LAL via mRNA-LNP is a very promising approach for the chronic treatment of LAL-D and support the clinical translation of mRNA therapy to overcome the challenges associated with traditional enzyme replacement therapies. One Sentence Summary Screening of a mRNA-LNPs library yielded a formulation with outmost potency and mitigated the progression of LAL deficiency in a preclinical model.
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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) | 0.001 | 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 | 0.000 |
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".