Selecting amantadine as the 3rd experimental arm for MND-SMART using a data-driven framework
Bibliographic record
Abstract
There is a pressing need to identify effective disease modifying treatments for motor neuron disease (MND). Motor Neuron Disease-Systematic Multi-Arm Adaptive Randomised Trial (MND-SMART; NCT04302870) is a phase III multi-arm multi-stage trial testing a pipeline of candidate drugs. Building upon systematic reviews to inform initial drug selection, we aimed to develop and implement a multimodal data-driven framework to inform ongoing selection. We gathered, synthesised, integrated and reported evidence from published literature through Repurposing Living Systematic Review (ReLiSyR-MND), in vitro high throughput drug screening, pathway and network analysis, and mining drug, chemical and clinical trial registry databases. We identified candidate drugs (ReLiSyR: 303, drug screening: 287, network analysis: 1144). We longlisted 49 drugs and synthesised further evidence across domains. We shortlisted 9 drugs and selected amantadine for evaluation in MND-SMART. Amantadine showed good efficacy and safety across 59 clinical publications, reduced TDP-43 aggregates on in vitro screening and was predicted to be active on multiple targets associated with MND. Amantadine was added as the third experimental arm of MND-SMART in April 2023. We demonstrated the feasibility and synergistic benefits of a systematic, multimodal, data-driven framework to inform drug selection for MND clinical trials, with potential for application across other disease areas.
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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 | 0.141 | 0.258 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".