Global Cancer Drug Development—A Report From the 2022 Accelerating Anticancer Agent Development and Validation Meeting
Bibliographic record
Abstract
Rapidly expanding systemic treatment options, combined with improved screening, diagnostic, surgical, and radiotherapy techniques, have led to improved survival outcomes for many cancers over time. However, these overall survival gains have disproportionately benefited patients in high-income countries, whereas patients in low- and middle-income countries (LMICs) continue to experience challenges in accessing timely and guideline concordant care. In September 2022, the Accelerating Anticancer Agent Development and Validation workshop was held, focusing on global cancer drug development. Panelists discussed key barriers such as the lack of diagnostic services and human resources, drug accessibility and affordability, lack of research infrastructure, and regulatory and authorization challenges, with a particular focus on Africa and Latin America. Potential opportunities to improve access and affordability were reviewed, such as the importance of prioritizing investments in diagnostics, investing health infrastructure and work force planning, coordinated drug procurement efforts and streamlined regulatory processing, incentivized pricing through regulatory change, and the importance of developing and promoting clinical trials that can answer relevant clinical questions for patients in LMICs. As a cancer community, we must continue to advocate for and work toward equitable access to high-quality interventions for patients, regardless of their geographical location.
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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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".