Challenges of globalization of cancer drug trials- recruitment in LMICs, approval in HICs
Bibliographic record
Abstract
An increasing number of cancer clinical trials are conducted in low-and-middle-income countries (LMICs). 1 Increasing the representation of LMICs in cancer drug trials is encouraging, but we are concerned about research parasitism and parachutism in these practices.2 In this commentary, we explore how LMICs may not have been served by participating in these global cancer drug trials.Despite reduced complexity and lower cost being commonly cited reasons for the industry's motivation in running cancer drug trials in LMICs, 1 there is another incentive that is less frequently discussedthe possibility of running a trial with a substandard control arm that would not be considered appropriate in a high-income country (HIC).For example, a new immune checkpoint inhibitor (ICI) (cemiplimab-rwlc) for patients with advanced Non-Small Cell Lung Cancer Treatment (NSCLC) with tumor PD-L1 expression of 50% or more was run as an international trial (EMPOWER-Lung 1) in several HICs (such as Australia and Spain) and LMICs (including Brazil, Chile, Colombia and Mexico), not including the USA.The trial compared cemiplimab against chemotherapy control between June 2017 and February 2020.3 This was done despite pembrolizumab, another ICI, having been established as the standard of care for this patient population after demonstrating improved survival versus chemotherapy in a randomized controlled trial.4 Nevertheless, the EMPOWER-Lung 1 trial enrolled patients to a trial with chemotherapy as the control arm and proved, unsurprisingly, the superiority of immunotherapy, leading to the drug's approval by the US FDA. 5 This practice of running substandard trials in LMICs to get a drug approved in HICs is unacceptable for several reasons.Firstly, the Helsinki Declaration states that control arms in randomized controlled trials (RCTs) should receive the "best-proven intervention", 6 not the standard of care in the local research setting.Investigators from HICsthe majority of which are sponsored by the pharmaceutical industryfrequently conduct clinical cancer trials in LMICs, where an inferior control arm is used
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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 | 0.062 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".