Lipid Nanoparticle Delivery Systems to Enable mRNA-Based Therapeutics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 | 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 it