An Ultrahydrating Polymer that Protects Protein Therapeutics and RNA‐Lipid Nanoparticles Against Freezing, Heat and Lyophilization Stress
Bibliographic record
Abstract
Abstract RNA and protein‐based therapeutics constitute almost half of recent drug approvals and receive considerable attention within biotechnology industries. Ensuring their stability and longevity in the context of heat, freezing, and lyophilization processes are paramount to a successful deployment. However, the advancement of formulations designed to achieve this goal is still in its nascent phase. To address these challenges, a new class of semi‐dendritic hydrophilic polymer with extended linear units is reported, which showcase very high hydration. These novel polymers demonstrated exceptional efficacy in preserving messenger RNA‐ (mRNA‐) and self‐amplifying RNA‐ (saRNA‐) lipid nanoparticles during freezing and lyophilization. Additionally, they have been found to protect therapeutic proteins against external stressors such as freezing, heat, and lyophilization. These polymers are non‐toxic, which enables their utilization at high concentrations and eliminates the requirement for removal prior to administration. It is found that their unique topology contributes to the high hydration. These excipients are anticipated to create new prospects in biotechnology, food science, and cryopreservation.
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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) | 0.000 | 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 | 0.000 |
| 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".