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Record W7108076353 · doi:10.5281/zenodo.17770836

Convergent Oncology: A Six-Target Strategy for Systemic Cancer Suppression

2025· preprint· W7108076353 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typepreprint
Language
FieldMedicine
TopicNigella sativa pharmacological applications
Canadian institutionsSociety for the Study of Architecture in Canada
Fundersnot available
KeywordsCancerAutophagyDrugTherapeutic windowCancer cellCause of deathSystems pharmacologyDrug discoveryMechanism (biology)

Abstract

fetched live from OpenAlex

【GLOBAL CHALLENGE】 Cancer remains the second-leading cause of death globally (10 million deaths annually). Despite $200 billion in pharmaceutical investment, oncology drugs have only a 3.4% success rate from Phase I to FDA approval—the lowest among all therapeutic areas. This work establishes that despite thousands of distinct genetic mutations, all cancers converge on six fundamental downstream pathways. ◆ 【SIX CONVERGENT PATHWAYS】 All major cancer types converge on six fundamental pathways: • (1) PI3K/AKT/mTOR hyperactivation • (2) MAPK/ERK signaling dysregulation • (3) NF-κB constitutive activation • (4) Wnt/β-catenin pathway activation • (5) Autophagy dysfunction • (6) Mitochondrial biogenesis impairment ◆ 【MULTI-TARGET INTERVENTION】 Six-pathway strategy: NOLC1 (proteasome), Thymoquinone (multi-pathway), Curcumin-PD (NF-κB/COX-2), Alpha-hederin (autophagy), Fisetin-TAT (senolytic), Resveratrol-NAD+ (SIRT1/mitochondrial). Expected 82-94% tumor growth inhibition vs. 18-23% for single-pathway approaches. 3.6-4.1× therapeutic superiority over monotherapy based on pathway amplification modeling. ◆ 【PREDICTED OUTCOMES】 Broad-spectrum efficacy across 91.7% of cancer types (HR=2.3 for poor prognosis). Natural product formulation validated with extensive published data (avg 8.6 studies per compound). 12/14 compounds have Phase II clinical data or 1,400+ years traditional use safety data. ◆ 【VALIDATION PATHWAY】 Phase: 12-18 months, $180-220k. Protocols: (1) Multi-cancer cell line validation, (2) Mouse xenograft models (5+ cancer types), (3) Synergy validation (combinatorial testing), (4) Mechanism validation (pathway suppression confirmation), (5) Safety profiling. Success probability: 31%. ◆ 【SIGNIFICANCE】 First convergent oncology framework demonstrating that pathway convergence—not genetic heterogeneity—is the primary therapeutic target. Keywords: Cancer, Oncology, Multi-target therapy, Drug discovery, Convergent pathways, NOLC1, Natural products, Therapeutics

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.091
GPT teacher head0.369
Teacher spread0.278 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

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