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Record W4402945815 · doi:10.1016/j.ijpharm.2024.124780

A novel temperature-controlled media milling device to produce drug nanocrystals at the laboratory scale

2024· article· en· W4402945815 on OpenAlexfundno aff
Elise Catlin, Octavio E. Fandiño, Lucía López-Vidal, M.E. Sangalli, Ryan F. Donnelly, Santiago Daniel Palma, Alejandro J. Paredes

Bibliographic record

VenueInternational Journal of Pharmaceutics · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug Solubulity and Delivery Systems
Canadian institutionsnot available
FundersQueen's UniversityQueen's University BelfastEngineering and Physical Sciences Research CouncilAcademy of Medical SciencesSociety for Applied Microbiology
KeywordsNanocrystalNanotechnologyMaterials scienceDrugPharmaceutical technologyScale (ratio)Chemical engineeringChemistryPharmacologyChromatographyEngineeringMedicinePhysics

Abstract

fetched live from OpenAlex

Poor aqueous solubility of preexisting and emerging drug molecules is a common issue faced in the field of pharmaceutics. To address this, particle size reduction techniques, including drug micro- and nanonisation have been widely employed. Nanocrystals (NCs), drug particles with particle sizes below 1 µm, offer high drug content, improved dissolution, and long-acting capabilities. Media milling is the most used method to prepare NCs using of-the-shelf machinery, both at the laboratory and industrial scales. However, early NCs development, especially when limited amounts of the active are available, require the use of milligram-scale media milling. This study introduces a novel mini-scale milling device (Mini-mill) that incorporates temperature control through a water-cooled jacket. The device was used to produce NCs of three model hydrophobic drugs, itraconazole, ivermectin and curcumin, with lowest particle sizes of 162.5 ± 0.4 nm, 178 ± 2 nm, and 116.7 ± 0.7 nm, respectively. Precise control of milling temperature was achieved at 15, 45, and 75°C, with drug dependent particle size reduction trends, with no adverse effects on the milling materials or polymorphic changes in the NCs, as confirmed by calorimetric analysis. Finally, a scale-up feasibility study was carried out in a lab-scale NanoDisp®, confirming that the novel Mini-mills are a material-efficient tool for early formulation development, with potential for scale-up to commercial mills.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.425
Teacher spread0.353 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations14
Published2024
Admission routes1
Has abstractyes

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