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Record W2970730415 · doi:10.1039/c9lc00492k

Microphysiological lung models to evaluate the safety of new pharmaceutical modalities: a biopharmaceutical perspective

2019· review· en· W2970730415 on OpenAlexaff
Garrett R. Ainslie, Myrtle Davis, Lorna Ewart, Linda A. Lieberman, David J. Rowlands, Andrew J. Thorley, Gorm Yoder, Anne Ryan

Bibliographic record

VenueLab on a Chip · 2019
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsDiscovery Centre
FundersNovartis Institutes for BioMedical Research
KeywordsBiopharmaceuticalPerspective (graphical)Drug developmentModalitiesRisk analysis (engineering)BusinessPharmaceutical industryDrugBiochemical engineeringEngineeringPharmacologyComputer scienceMedicineBiotechnologyBiologyArtificial intelligence

Abstract

fetched live from OpenAlex

The lung is a complex organ; it is both the initial barrier for inhaled agents and the site of metabolism and therapeutic effect for a subset of systemically administered drugs. Comprised of more than 40 cell types that are responsible for various important functions, the lung's complexity contributes to the subsequent challenges in developing complex in vitro co-culture models (also called microphysiological systems (MPS), complex in vitro models or organs-on-a-chip). Although there are multiple considerations and limitations in the development and qualification of such in vitro systems, MPS exhibit great promise in the fields of pharmacology and toxicology. Successful development and implementation of MPS models may enable mechanistic bridging between non-clinical species and humans, and increase clinical relevance of safety endpoints, while decreasing overall animal use. This article summarizes, from a biopharmaceutical industry perspective, essential elements for the development and qualification of lung MPS models. Its purpose is to guide MPS developers and manufacturers to expedite MPS utilization for safety assessment in the biopharmaceutical industry.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.927
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.214
GPT teacher head0.448
Teacher spread0.234 · 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

Citations65
Published2019
Admission routes1
Has abstractyes

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