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Record W6940366733 · doi:10.7939/r3-ej44-ga91

Evolving In Vitro and In Silico Methods for Predicting Performance in Respiratory Drug Delivery Applications

2022· dissertation· en· W6940366733 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2022
Typedissertation
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsnot available
Fundersnot available
KeywordsIn silicoIn vivoIn vitroInhalerBudesonideIn vitro toxicologyDry-powder inhaler

Abstract

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The overarching theme of this work is the investigation and development of in vitro and in silico methods used to characterize inhaled pharmaceutical aerosols. The ultimate aim is to expand and strengthen the links between applied science and clinical practice for inhaled medications. Chapter 1 introduces the structure of the thesis. Chapter 2 consists of a review of literature. Relevant metrics used in the characterization of inhaled therapies are discussed, together with advanced in vitro and in silico methods for characterizing respiratory tract deposition and drug disposition. Chapter 3 describes an in vitro study on deposition from commercially available pharmaceutical inhalers in the Alberta Idealized Throat. This mouth-throat geometry has been used to accurately characterize aerosol deposition in terms of extrathoracic and total lung doses, though its ability to replicate in vivo deposition from some inhalers requires careful consideration of the underlying aerosol mechanics. We hypothesized that differences between in vitro and in vivo data may be partly caused by variations in factors not typically considered during in vitro testing, primarily the insertion angle of the inhaler into the mouth-throat geometry itself. Three of six examined inhalers showed sensitivity to the angle of insertion. For DPIs, this sensitivity may be reduced using larger diameter mouthpieces and smaller particle sizes in powder formulations. For pMDIs, lower momentum sprays demonstrated more consistent performance. Consideration of these factors in future devices and formulations may improve the consistency of dosing during real-world use. Chapter 4 describes a combined in vitro – in silico methodology to predict systemic exposure of budesonide from dry powder inhalers, incorporating in vitro measurements of intrathoracic particle size distributions, regional lung deposition modeling, and pharmacokinetics. Good agreement between predictions and in vivo data were obtained without the requirement of extraneous fit factors, suggesting the model is robust for well-characterized therapeutic agents like budesonide. Comparatively modest deposition in the small conducting airways was predicted to occur with each dry powder inhaler despite large in vitro differences in performance. Tracheobronchial deposition was predicted to correlate poorly with systemic drug concentrations, suggesting that overt reliance on systemic exposure data in establishing bioequivalence of locally acting inhaled therapies may not properly elucidate differences between formulations. Rather, a combination of methods like that proposed in the present work may aid in better predicting bioequivalence. In Chapter 5, a novel in vitro – in silico methodology was developed to characterize nebulizer performance in the context of methacholine challenge testing. The incorporation of experimental methods with hygroscopic theory and lung deposition modeling allowed for the quantification of regional deposition of methacholine and better estimation of the provocative dose than is provided by existing methods, which likely overestimate the relevant dose by considerable and device-dependent margins. Measurements of airstream conditions suggested that droplets exiting nebulizer mouthpieces exist in highly concentrated states compared to stock solutions, and upon inhalation these droplets can be expected to undergo significant hygroscopic growth. The procedure outlined in Chapter 5 may serve as step towards standardizing the determination of provocative doses obtained with methacholine challenge testing, which could improve the translatability of results currently obtained with disparate methods and protocols. Finally, Chapter 6 summarizes major conclusions, identifies contributions to knowledge, and proposes potential avenues for future work. Methods described in this thesis provide a framework for improving upon the standard pharmacopeial methods used to characterize pharmaceutical aerosols. With increased focus on the use of inhaled aerosols as a vehicle for both local and systemic delivery of medication, such methods are of interest in streamlining the drug development process and in optimizing future devices and formulations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.010
GPT teacher head0.258
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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