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Record W1984138060 · doi:10.2217/fnl.15.3

Conquering the barriers: are antibody therapeutics feasible for CNS indications?

2015· article· en· W1984138060 on OpenAlexaff
Danica Stanimirovic, Kristin Kemmerich

Bibliographic record

VenueFuture Neurology · 2015
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMedicineAntibodyIntensive care medicineNeuroscienceImmunologyPsychology

Abstract

fetched live from OpenAlex

The major hurdles on the development path of CNS therapeutics include an inherent complexity of the CNS, highly restricted drug access to CNS targets by the blood–brain barrier (BBB), paucity of translational preclinical disease models, safety risks, and lack of clinical biomarkers to aid in patient selection and early assessment of efficacy in clinical trials. Drug development for CNS is therefore highly complex and risky – more expensive and lengthy than for any other indication. The CNS drugs have one of the highest pipeline attrition rates and longest development times (114 months); clinical trial failures tend to occur later in the clinical development, making the cost of developing a CNS drug among the highest of any therapeutic indication.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.304

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 head0.071
GPT teacher head0.373
Teacher spread0.302 · 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 teacher head, not a consensus.

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

Citations4
Published2015
Admission routes1
Has abstractyes

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