The Biomolecular Corona of Lipid Nanoparticles for Gene Therapy
Bibliographic record
Abstract
Gene therapy holds great potential for treating almost any disease by gene silencing, protein expression, or gene correction. To efficiently deliver the nucleic acid payload to its target tissue, the genetic material needs to be combined with a delivery platform. Lipid nanoparticles (LNPs) have proven to be excellent delivery vectors for gene therapy and are increasingly entering into routine clinical practice. Over the past two decades, the optimization of LNP formulations for nucleic acid delivery has led to a well-established body of knowledge culminating in the first-ever RNA interference therapeutic using LNP technology, i.e., Onpattro, and many more in clinical development to deliver various nucleic acid payloads. Screening a lipid library in vivo for optimal gene silencing potency in hepatocytes resulted in the identification of the Onpattro formulation. Subsequent studies discovered that the key to Onpattro’s liver tropism is its ability to form a specific “biomolecular corona”. In fact, apolipoprotein E (ApoE), among other proteins, adsorbed to the LNP surface enables specific hepatocyte targeting. This proof-of-principle example demonstrates the use of the biomolecular corona for targeting specific receptors and cells, thereby opening up the road to rationally designing LNPs. To date, however, only a few studies have explored in detail the corona of LNPs, and how to efficiently modulate the corona remains poorly understood. In this review, we summarize recent discoveries about the biomolecular corona, expanding the knowledge gained with other nanoparticles to LNPs for nucleic acid delivery. In particular, we address how particle stability, biodistribution, and targeting of LNPs can be influenced by the biological environment. Onpattro is used as a case study to describe both the successful development of an LNP formulation for gene therapy and the key influence of the biological environment. Moreover, we outline the techniques available to isolate and analyze the corona of LNPs, and we highlight their advantages and drawbacks. Finally, we discuss possible implications of the biomolecular corona for LNP delivery and we examine the potential of exploiting the corona as a targeting strategy beyond the liver to develop next-generation gene therapies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".