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Ideation and implementation of an open science drug discovery business model – M4K Pharma

2018· preprint· en· W2902080463 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWellcome Open Research · 2018
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsDalton Pharma Services (Canada)Structural Genomics ConsortiumUniversity of Toronto
FundersStructural Genomics ConsortiumBrain Tumour CharityOntario Institute for Cancer ResearchWellcome TrustWellcome
KeywordsIntellectual propertyBusinessVenture capitalIdeationGovernment (linguistics)Business modelInvestment (military)Key (lock)Foundation (evidence)Asset (computer security)FinanceIndustrial organizationMarketingComputer sciencePolitical scienceComputer security

Abstract

fetched live from OpenAlex

M4K Pharma was incorporated to launch an open science drug discovery program that relies on regulatory exclusivity as its primary intellectual property and commercial asset, in lieu of patents.In many cases and in key markets, using regulatory exclusivity can provide equivalent commercial protection to patents, while also being compatible with open science. The model is proving attractive to government, foundation and individual funders, who collectively have different expectations for returns on investment compared with biotech, pharmaceutical companies, or venture capital investors.In the absence of these investor-driven requirements for returns, it should be possible to commercialize therapeutics at affordable prices.M4K is piloting this open science business model in a rare paediatric brain tumour, but there is no reason it should not be more widely applicable.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0040.003
Open science0.0050.014
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.327
GPT teacher head0.499
Teacher spread0.173 · 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