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Record W3206474164 · doi:10.18433/jpps31788

Solid Lipid Nanoparticles: An Approach to Improve Oral Drug Delivery

2021· review· en· W3206474164 on OpenAlexvenueno aff
Néstor Mendoza‐Muñoz, Zaida Urbán‐Morlán, Gerardo Leyva‐Gómez, María L. Zambrano‐Zaragoza, Elizabeth Piñón‐Segundo, David Quintanar‐Guerrero

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsnot available
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoConsejo Nacional de Ciencia y Tecnología
KeywordsSolid lipid nanoparticleBioavailabilityDrugPharmacologyMedicineDrug deliveryNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

Nanoparticles have shown overall beneficial effects in drug administration. Specifically, solid lipid nanoparticles (SLN) have emerged as an alternative to polymer-based systems. However, the oral administration of SLN, the first choice for conventional medications, has not been addressed due to the taboo surrounding the complicated transit that this delivery route entails. This review focuses on the encapsulation of drugs into SLN as a strategy for improving oral administration. Examples of applications of SLN to enhance the absorption and bioavailability of poorly-soluble drugs and protect acid-labile active molecules are discussed. This work also emphasizes the importance of developing SLN-based systems to treat health issues such as neurological diseases and cancer, and combat antibiotic resistance, three significant and increasingly common current public health problems. The review sections clarify how SLN can improve bioavailability, target therapeutic agents, and reduce side effects.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.092
GPT teacher head0.446
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations77
Published2021
Admission routes1
Has abstractyes

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