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Record W2381189906 · doi:10.1039/9781849737821-00483

Designing Polymeric Binders for Pharmaceutical Applications

2013· book-chapter· en· W2381189906 on OpenAlexaff
Nicolas Bertrand, P. Colin, Maxime Ranger, Jeanne Leblond Chain

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldChemistry
TopicAntimicrobial agents and applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsNanotechnologyDrugChemistryComputational biologyPharmacologyMedicineMaterials scienceBiology

Abstract

fetched live from OpenAlex

The properties of polyvalent polymers to form supramolecular complexes with biological substrates offer many attractive therapeutic possibilities. Polymeric binders are macromolecules designed to exert a pharmacological effect by selectively interacting with exogenous or endogenous substrates. They can be employed to prevent the harmful effects of toxins, inhibit virus colonization or even trigger apoptosis of diseased cells. This chapter presents the fundamentals of developing polymeric binders as new drug entities. The basics of finding the right target, establishing structure–activity relationships and measuring efficacy are highlighted, with numerous examples of polymeric binders at different development stages, including commercialization. Orally administered scavengers represent the most advanced examples in clinical use. Their binding in the gastrointestinal tract results in either local or systemic therapeutic effects. Although they are designed to be non-absorbable, their low systemic exposure is not always devoid of side effects. The required approaches to confirm innocuousness of the macromolecules and the challenges encountered during the clinical phases are also presented.

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)0.000
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0260.001

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 head0.045
GPT teacher head0.288
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2013
Admission routes1
Has abstractyes

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