The case for conducting first-in-human (phase 0 and phase 1) clinical trials in low and middle income countries
Bibliographic record
Abstract
BACKGROUND: Despite the increase in the number of clinical trials in low and middle income countries (LMICs), there has been little serious discussion of whether First in Human (FIH; phase 0 and phase 1) clinical trials should be conducted in LMICs, and if so, under what conditions. Based on our own experience, studies and consultations, this paper aims to stimulate debate on our contention that for products meant primarily for conditions most prevalent in LMICs, FIH trials should preferably be done first in those countries. DISCUSSION: There are scientific and pragmatic arguments that support conducting FIH trials in LMIC. Furthermore, the changing product-development and regulatory landscape, and the likelihood of secondary benefits such as capacity building for innovation and for research ethics support our argument. These arguments take into account the critical importance of protecting human subjects of research while developing capacity to undertake FIH trials. SUMMARY: While FIH trials have historically not been conducted in LMICs, the situation in some of these countries has changed. Hence, we have argued that FIH should be conducted in LMICs for products meant primarily for conditions that are most prevalent in those contexts; provided the necessary protections for human subjects are sufficient.
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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 | 0.758 | 0.680 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.045 | 0.039 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".