Systematic Living Evidence for Clinical Trials (SyLECT): a data-driven framework for drug selection in clinical trials in motor neuron disease
Bibliographic record
Abstract
Abstract Despite many promising preclinical studies and decades of clinical trials, there remains a paucity of effective disease-modifying drugs in motor neuron disease. We aimed to develop a systematic and structured data-driven framework to identify, evaluate and prioritise candidate drugs for clinical trials, specifically for the Motor Neuron Disease-Systematic Multi-Arm Adaptive Randomised Trial (MND-SMART; NCT040302870). We developed the Systematic Living Evidence for Clinical Trials (SyLECT) platform as a modular framework integrating emerging data from different domains to inform prioritisation of candidate drugs. Current domains incorporated include published clinical, animal in vivo, and in vitro literature; in house in vitro high throughput drug screening; pathway and network analysis; and pharmacological, feasibility and clinical trial data from drug, chemical, and clinical trial databases. In this approach, we first identify a list of candidate drugs from these domains then select drugs for further consideration based on drug properties, feasibility, and expert opinion. For prioritised drugs we then generate, evaluate, and synthesise further evidence from across data domains. Using automated workflows and interactive web applications, we produce snapshot “living evidence summaries” to inform expert panel decisions on prioritisation of candidate drugs for MND-SMART. The third drug selected for MND-SMART and the first using this framework is amantadine. We demonstrated the feasibility of a systematic data-driven framework to inform prioritisation of candidate drugs for clinical trials in motor neuron disease, with potential for wider application across diseases where there is unmet clinical need. Key messages What is already known on this topic - Despite extensive preclinical research and clinical trials for disease-modifying treatments in motor neuron disease, translational success remains elusive. - Advances in research across biological domains presents a wealth of data to guide prioritisation of candidate drugs for clinical trials. What this study adds - This study demonstrates the feasibility of using a systematic, modular, data-driven framework to inform prioritisation of candidate drugs for an adaptive platform trial in motor neuron disease. How this study might affect research, practice or policy - The framework could be applied to inform prioritisation of drugs for clinical trials in other diseases, especially adaptive platform trials in neurodegenerative diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.351 | 0.429 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.020 | 0.011 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".