Global health and equity in access to genomics and clinical trials with novel therapies
Bibliographic record
Abstract
Access to effective and timely cancer therapy should be a fundamental right for patients with malignancy. The diagnosis, management, and treatment of cancer is changing rapidly, which is in large part related to a deepening and evolving understanding of biology and of genomic and immune interactions between malignant cells and the host. Therapy is increasingly stratified on the basis of predictive biomarkers that are identified by profiling blood and tumour tissue. This rapid progress in understanding cancer biology has led to enhanced clinical trial activity for many targeted and immunotherapeutic drugs for common and rare cancers. Clinical trial activity has changed profoundly in the past 5–10 years, with an emphasis on biomarker-driven, early-phase trials to show activity, sensitivity, and resistance. About 50% of new trials are related to immunotherapy. Trials now are smaller in size, accrue fewer patients per site, and have changed the operational dynamics at cancer centres overall. The pathway to effective novel therapies incorporates predictive biomarkers, stratified clinical trials, and regulatory and funding approval. The pathway from research to clinical implementation can allow for equity gaps in access based on access to genomic profiling, access to and inclusiveness of participation in clinical trials, and applicability of final regulatory and funding approvals.1Sullivan R Pramesh CS Booth CM Cancer patients need better care, not just more technology.Nature. 2017; 549: 325-328Crossref PubMed Scopus (29) Google Scholar Access and availability of genomic or molecular profiling, availability or eligibility for clinical trials and novel therapies, and timeliness of evidence-based funding approval can all cause inequality of access and treatment in patients within a country and between countries.2Cherny NI Sullivan R Torode J Saar M Eniu A ESMO International Consortium Study on the availability, out-of-pocket costs and accessibility of antineoplastic medicines in countries outside of Europe.Ann Oncol. 2017; 28: 2633-2647Crossref PubMed Scopus (56) Google Scholar To address systematic gaps in access to effective therapies in a timely fashion, it is important to plan and identify real-world and equitable clinical trials. This would include consideration of inclusiveness of populations at risk on the basis of race, ethnicity, pharmacogenomics, geography, and language. Ensuring availability of information, profiling, and policies to support real-time interpretation or translation will ensure equitable access to novel therapies for patients. WHO regularly updates its guidance on essential medicines for key diseases, including cancer, to highlight fundamental and evidence-based therapies that should be made available.3WHO20th model list of essential medicines. World Health Organization, Geneva2017Google Scholar, 421st WHO Expert CommitteeThe selection and use of essential medicines.http://www.who.int/medicines/publications/essentialmedicines/trs-1006-2017/en/Google Scholar I declare no competing interests.
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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 | 0.031 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.003 |
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".