Lipid Nanoparticle Delivery Systems to Enable mRNA-Based Therapeutics
Bibliographic record
Abstract
The world raced to develop vaccines to protect against the rapid spread of SARS-CoV-2 infection upon the recognition of COVID-19 as a global pandemic. A broad spectrum of candidates was evaluated, with mRNA-based vaccines emerging as leaders due to how quickly they were available for emergency use while providing a high level of efficacy. As a modular technology, the mRNA-based vaccines benefitted from decades of advancements in both mRNA and delivery technology prior to the current global pandemic. The fundamental lessons of the utility of mRNA as a therapeutic were pioneered by Dr. Katalin Kariko and her colleagues, perhaps most notably in collaboration with Drew Weissman at University of Pennsylvania, and this foundational work paved the way for the development of the first ever mRNA-based therapeutic authorized for human use, COMIRNATY®. In this Special Issue of Pharmaceutics, we will be honoring Dr. Kariko for her great contributions to the mRNA technology to treat diseases with unmet needs. In this review article, we will focus on the delivery platform, the lipid nanoparticle (LNP) carrier, which allowed the potential of mRNA therapeutics to be realized. Similar to the mRNA technology, the development of LNP systems has been ongoing for decades before culminating in the success of the first clinically approved siRNA-LNP product, ONPATTRO®, a treatment for an otherwise fatal genetic disease called transthyretin amyloidosis. Lessons learned from the siRNA-LNP experience enabled the translation into the mRNA platform with the eventual authorization and approval of the mRNA-LNP vaccines against COVID-19. This marks the beginning of mRNA-LNP as a pharmaceutical option to treat genetic diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".