Delivering Innovation to Oncology Drug Development through Cancer Drug DISCO (Development Incentive Strategy using Comparative Oncology): Perspectives, Gaps and Solutions
Bibliographic record
Abstract
Abstract The field of Comparative Oncology has historically sought to conduct clinical trials in dogs with naturally occurring cancer to answer drug development questions relevant to human Oncology that cannot be easily answered in conventional animal cancer models or in human clinical trials. This approach to delivering Oncology drug development innovation has produced several notable successes and has gained support from many groups within the human Oncology drug development community. However, widespread adoption of this paradigm has not yet occurred. As an immediate follow up to an international meeting of experts and stakeholders in the field at the Pre-Congress One Health committee meeting of the World Small Animal Veterinary Association (WSAVA) in Toronto, Canada in July 2019, we hypothesized that new commercial incentives for Comparative Oncology would jointly accelerate and optimize cancer drug development for humans and pet dogs by more closely aligning the human and animal health pharmaceutical/biotech industries. This enhanced animal and human pharmaceutical/biotech proximity will create new commercial transactions between animal health and human health companies, and will reposition Comparative Oncology canine trials as interspersed with, and parallel to, human development rather than merely as preclinical models. We refer to this new animal-human pharma proximity and the resultant outcomes (new commercial incentives and parallel repositioning of Comparative Oncology) as Drug Development Incentive Strategy using Comparative Oncology (DISCO). The gaps and solutions to implementation of Drug DISCO are provided herein as the first output of the WSAVA One Health committee meeting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".