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Record W3026314653 · doi:10.3389/fcimb.2020.00228

Utilizing Organoid and Air-Liquid Interface Models as a Screening Method in the Development of New Host Defense Peptides

2020· review· en· W3026314653 on OpenAlexafffund
Ka-Yee Grace Choi, Bing Wu, Amy Huei‐Yi Lee, Beverlie Baquir, Robert E. W. Hancock

Bibliographic record

VenueFrontiers in Cellular and Infection Microbiology · 2020
Typereview
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
FundersCanadian Institutes of Health ResearchKillam TrustsSimon Fraser UniversityMichael Smith Health Research BC
KeywordsComputational biologyAntimicrobial peptidesDrug developmentBiologyInnate immune systemOrganoidDrug discoveryInduced pluripotent stem cellDrugImmune systemPeptideCell biologyBioinformaticsBiochemistryPharmacologyEmbryonic stem cellImmunology

Abstract

fetched live from OpenAlex

Host defence peptides (HDPs), also known as antimicrobial peptides, are naturally occurring polypeptides (~12-50 residues) composed of cationic and hydrophobic amino acids that adopt an amphipathic conformation upon folding usually after contact with membranes. HDPs have a variety of biological activities including immunomodulatory, anti-inflammatory, anti-bacterial and anti-biofilm functions. Although HDPs have the potential to address the global threat of antibiotic resistance and to treat immune and inflammatory disorders, they have yet to achieve this promise. Indeed, there are several challenges associated with bringing peptide-based drug candidates from the lab bench to clinical practice, including identifying appropriate indications, stability, toxicity and cost. These challenges can be addressed in part by the development of innate defence regulator (IDR) peptides and peptidomimetics, which are synthetic derivatives of HDPs with similar or better efficacy, increased stability, and reduced toxicity and cost of the original HDP. However, one of the largest gaps between basic research and clinical application is the validity and translatability of conventional model systems, such as cell lines and animal models, for screening HDPs and their derivatives as potential drug therapies. Indeed, such translation has often relied on animal models, which have only limited validity. Here we discuss the recent development of human organoids for disease modeling and drug screening, assisted by the use of omics analyses. Organoids, developed from primary cells, cell lines, or human pluripotent stem cells, are three-dimensional, self-organizing structures that closely resemble their corresponding in vivo organs with regards to immune responses, tissue organization and physiological properties; thus, organoids represent a reliable method for studying efficacy, formulation, toxicity and to some extent drug stability and pharmacodynamics. The use of patient-derived organoids enables the study of patient-specific efficacy, toxicogenomics and drug response predictions. We outline how organoids and omics data analysis can be leveraged to aid in the clinical translation of IDR peptides.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.036
GPT teacher head0.286
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations54
Published2020
Admission routes2
Has abstractyes

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