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Record W4407932301 · doi:10.1021/acs.oprd.4c00528

Development of a Scalable Manufacturing Process for AB-343 Drug Substance: A Potential Candidate for the Treatment of Coronavirus Infections

2025· article· en· W4407932301 on OpenAlexaff
Jeremy D. Mason, Jan Spink, Mahesh Pallerla, Zhenhua Wu, Rajeev Kumar Singh, Aravind Babu Pulipaka, Jia‐Bao Liu, Marvin M. Vega, Xu Wang, Michael J. Sofia, Ganapati REDDY PAMULAPATI

Bibliographic record

VenueOrganic Process Research & Development · 2025
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsArbutus Biopharma (Canada)
Fundersnot available
KeywordsDrugCoronavirus disease 2019 (COVID-19)CoronavirusProcess developmentDrug developmentSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Drug candidateVirologyManufacturing process2019-20 coronavirus outbreakMedicinePharmacologyNanotechnologyBiochemical engineeringMaterials scienceProcess engineeringEngineeringInfectious disease (medical specialty)Internal medicine

Abstract

fetched live from OpenAlex

A scalable process for manufacturing of the anticoronavirus clinical candidate AB-343 has been developed. The lactam-containing subunit of the molecule was prepared using a novel synthetic route involving a nitro-Michael reaction and a rhodium-catalyzed nitro group hydrogenation followed by in situ translactamization sequence as a key transformation. The drug substance was assembled via sequential amide coupling and deprotection reactions, followed by a final dehydration of a primary amide to the corresponding nitrile using T3P. AB-343 drug substance was successfully manufactured on a multikilogram scale using this route, which was suitable for supporting IND-enabling studies and Phase I clinical development.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.409
Teacher spread0.349 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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