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Record W4386531039 · doi:10.4155/bio-2023-0092

Volumetric Absorptive Microsampling Coupled with Hybridization LC–MS/MS for Quantitation of Antisense Oligonucleotides

2023· article· en· W4386531039 on OpenAlexaff
Ming-Luan Chen, Kevork Mekhssian, Muskaan Dutt, Jeff Plomley, Anahita Keyhani

Bibliographic record

VenueBioanalysis · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsAltasciences (Canada)
Fundersnot available
KeywordsOligonucleotideChromatographyChemistryDNABiochemistry

Abstract

fetched live from OpenAlex

Background: Volumetric absorptive microsampling has emerged as a less invasive alternative to venous sampling for small-molecule pharmacokinetic studies, but its application to novel therapeutics such as antisense oligonucleotides (ASOs) is not well-established. Results: A workflow was developed using Mitra microsampling coupled with hybridization LC–MS/MS for accurate determination of fomivirsen, a 21-mer ASO, in human blood. Quantitative recovery was achieved regardless of blood hematocrit level or microsample age by implementing impact-assisted extraction. A thorough method evaluation confirmed sensitivity, linearity, precision/accuracy, matrix effect, metabolite interference and four months of microsample stability. Conclusion: The combined impact-assisted extraction and hybridization LC–MS/MS workflow demonstrated the successful quantitation of fomivirsen, establishing the validity and applicability of the approach for ASO drug candidates.

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)0.001
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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.035
GPT teacher head0.302
Teacher spread0.267 · 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
GenreMethods

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

Citations5
Published2023
Admission routes1
Has abstractyes

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