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Record W3112742288 · doi:10.1080/15412555.2020.1853091

Lung Deposition Using the Respimat<sup>®</sup> Soft Mist™ Inhaler Mono and Fixed-Dose Combination Therapies: An <i>In Vitro</i>/<i>In Silico</i> Analysis

2020· article· en· W3112742288 on OpenAlexaboutno aff
Anna-Maria Ciciliani, Mark W. Denny, Peter Langguth, Thomas Voshaar, Herbert Wachtel

Bibliographic record

VenueCOPD Journal of Chronic Obstructive Pulmonary Disease · 2020
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsnot available
FundersBoehringer Ingelheim
KeywordsInhalerIn silicoCOPDPulmonary diseaseIn vitroMistDeposition (geology)LungMedicineDry-powder inhalerMaterials scienceChemistryBiologyInternal medicineAsthmaPhysics

Abstract

fetched live from OpenAlex

Tiotropium and olodaterol are mainstay treatments for chronic obstructive pulmonary disease (COPD) and yield important clinical improvements, especially when used in fixed-dose combination. Whilst previous studies have shown consistent delivery of tiotropium to the lungs with the Respimat® inhaler, no such study has been carried out for olodaterol or the components of their fixed-dose combination (TIO/OLO). Combining in vitro and in silico models, we measured the amount of drug retained in the mouth–throat area, entering the trachea and reaching the lung periphery. We applied a hybrid deposition model that considered the experimentally determined output of an Alberta throat model (in vitro – dose to lung) combined with a computational fluid dynamic model of the lungs (in silico). Regardless of the COPD breathing pattern, ≥50% of the nominal dose of either tiotropium, olodaterol, or TIO and OLO in the fixed-dose combination reached the lung. Of the dose reaching the lungs, greater than 50% is deposited in the lung periphery (from generation 8 onwards). Our study demonstrated that aerosol delivery via the Respimat inhaler achieved high deposition deep into the lung periphery with all formulations evaluated.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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 designSimulation or modeling
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

Citations15
Published2020
Admission routes1
Has abstractyes

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