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Record W2137053703 · doi:10.1517/17460441.3.6.677

Strategies to assess blood–brain barrier penetration

2008· article· en· W2137053703 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueExpert Opinion on Drug Discovery · 2008
Typearticle
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsBlood–brain barrierPenetration (warfare)MedicineNeuroscienceBiologyComputational biologyCentral nervous systemEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: The principles and screening strategies for brain penetration in drug discovery are important in identifying drug candidates with desirable CNS properties. OBJECTIVE: Define key variables and assays that are essential for determining brain penetration. METHODS: This review covers issues, methods, and strategies for assessing brain penetration of small molecules in drug discovery. RESULTS/CONCLUSION: Brain penetration is assessed using both initial rate and extent at steady-state. Unbound drug is the active species that exerts pharmacological effects. Low brain penetration can be due to low blood-brain barrier (BBB) permeability, P-glycoprotein (Pgp) efflux, or high plasma protein binding. Successful methods include: parallel artificial membrane permeability assay (PAMPA)-BBB permeability, MDR1-MDCKII for Pgp efflux, B-P dialysis for fraction unbound, and in vivo B/P ratio to extrapolate unbound brain drug concentration.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.614

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)0.0000.000

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 head0.069
GPT teacher head0.370
Teacher spread0.301 · 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