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Record W2912908908 · doi:10.1002/cpt.1385

Human Cardiac Ventricular‐Like Organoid Chambers and Tissue Strips From Pluripotent Stem Cells as a Two‐Tiered Assay for Inotropic Responses

2019· article· en· W2912908908 on OpenAlexaff
Wendy Keung, Patrick K.W. Chan, Peter Backeris, Eugene K. Lee, Nicodemus Wong, Andy O.T. Wong, Gabriel Wong, Camie W. Chan, Bernard Fermini, Kevin D. Costa, Ronald A. Li

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsNovelis (Canada)
FundersInnovation and Technology CommissionPfizer
KeywordsInotropeOrganoidInduced pluripotent stem cellFalse positive paradoxDrug discoveryDrugPharmacologyStem cellAssay sensitivityBiologyMedicineInternal medicineBioinformaticsPathologyCell biologyComputer scienceEmbryonic stem cell

Abstract

fetched live from OpenAlex

Traditional drug discovery is an inefficient process. Human pluripotent stem cell-derived cardiomyocytes can potentially fill the gap between animal and clinical studies, but conventional two-dimensional cultures inadequately recapitulate the human cardiac phenotype. Here, we systematically examined the pharmacological responses of engineered human ventricular-like cardiac tissue strips (hvCTS) and organoid chambers (hvCOC) to 25 cardioactive compounds covering various drug classes. While hvCTS effectively detected negative and null inotropic effects, the sensitivity to positive inotropes was modest. We further quantified the predictive capacity of hvCTS in a blinded screening, with accuracies for negative, positive, and null inotropic effects at 100%, 86%, and 80%, respectively. Interestingly, hvCOC, with a pro-maturation milieu that yields physiologically complex parameters, displayed enhanced positive inotropy. Based on these results, we propose a two-tiered screening system for avoiding false positives and negatives. Such an approach would facilitate drug discovery by leading to better overall success.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.044
GPT teacher head0.385
Teacher spread0.341 · 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 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

Citations55
Published2019
Admission routes1
Has abstractyes

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