Clinical trials to assess adjuvant therapeutics for severe malaria
Bibliographic record
Abstract
Despite potent anti-malarial treatment, mortality rates associated with severe falciparum malaria remain high. To attempt to improve outcome, several trials have assessed a variety of potential adjunctive therapeutics, however none to date has been shown to be beneficial. This may be due, at least partly, to the therapeutics chosen and clinical trial design used. Here, we highlight three themes that could facilitate the choice and evaluation of putative adjuvant interventions for severe malaria, paving the way for their assessment in randomized controlled trials. Most clinical trials of adjunctive therapeutics to date have been underpowered due to the large number of participants required to reach mortality endpoints, rendering these study designs challenging and expensive to conduct. These limitations may be mitigated by the use of risk-stratification of participants and application of surrogate endpoints. Appropriate surrogate endpoints include direct measures of pathways causally involved in the pathobiology of severe and fatal malaria, including markers of host immune and endothelial activation and microcirculatory dysfunction. We propose using circulating markers of these pathways to identify high-risk participants that would be most likely to benefit from adjunctive therapy, and further by adopting these biomarkers as surrogate endpoints; moreover, choosing interventions that target deleterious host immune responses that directly contribute to microcirculatory dysfunction, multi-organ dysfunction and death; and, finally, prioritizing where possible, drugs that act on these pathways that are already approved by the FDA, or other regulators, for other indications, and are known to be safe in target populations, including children. An emerging understanding of the critical role of the host response in severe malaria pathogenesis may facilitate both clinical trial design and the search of effective adjunctive therapeutics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".