Bibliographic record
Abstract
Nanoparticle drug delivery vehicles can be used for in vivo delivery of a variety of bioactive molecules including nucleic acid polymers and small molecule drugs for applications ranging from gene therapy to improving the efficacy of anticancer drugs. This thesis explores different variations of lipid nanoparticles (LNP) formulated with microfluidic mixing method, for encapsulation of a wide range of bioactive agents. The first part of the thesis focuses on the effect of cationic lipid content of the LNP and its effect on encapsulation of negatively charged small cargo. The ability of the rapid mixing/cationic lipid protocol to encapsulate small negatively charged molecules and short oligonucleotides in LNP systems containing ionizable cationic lipids and/or permanently positively charged cationic lipids is explored. It was found that encapsulation of small cargo is dependent on molecule size and charge and that permanently charged LNP are more efficacious at encapsulating small, charged cargo than ionizable LNP. It is also shown that charged prodrug forms of molecules that are neutral in their native form can be encapsulated and delivered using this method. The results of these studies can be used as general guidelines for entrapment and delivery of novel bioactive molecules that are typically difficult to formulate. While the cationic lipid plays an obvious role in entrapment and delivery of negatively charged molecules, the role of helper lipids is not very clear. The second chapter of the thesis looks at the effect of helper lipid titration and variation in in vitro cellular uptake and knockdown and in vivo hepatocyte gene silencing, using ionizable-LNP containing siRNA. It was found that while in vitro knockdown might not be greatly affected when the conventional 10 mol% DSPC (1,2-distearoyl-sn-glycero-3-phosphocholine) is varied, in vivo hepatocyte gene silencing was almost fully disrupted by helper lipid variations. The results of these studies could lead to a more targeted LNP simply by varying the helper lipid that enables avoiding certain cell types or targets specific tissues.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".