PP22. PROGRESSING RADIOTHERAPY-DRUG COMBINATIONS TOWARDS EARLY PHASE CLINICAL TRIALS
Bibliographic record
Abstract
The Radiotherapy-Drug Combinations consortium (RaDCom) works with UK-based investigators to design and deliver high quality preclinical projects evaluating specific radiotherapy-drug combinations. We have several collaborations with industry, from in vitro projects to understand the novel agent in the context of radiobiology, through to preclinical studies that will generate data to support the development of radiotherapy combination trials. RaDCom facilitates the coordination of industry interactions, triage new proposals, monitor active projects, and engages with the radiotherapy community to promote collaboration and networking (via a capability map). The CRUK New Agents Committee Preclinical Combination Grant scheme provides one of the funding options for these studies, with the potential to feed into early phase clinical trials via the ECMC Combinations Alliance. RaDCom also supports broader radiotherapy research initiatives, by working to improve preclinical quality assurance and identifying a route to registration for radiotherapy-drug treatments. These activities will place the UK at the forefront of radiotherapy-drug preclinical research and provide a significant incentive for pharmaceutical companies to invest in this area and utilise the RaDCom network. Further information can be found on our webpage: http://ctrad.ncri.org.uk/research-support/radiation-drug-combinations-radcom Successful projects from RaDCom can then move into early phase combinations trials within the Combinations Alliance. The Combinations Alliance supports early phase combination studies in the UK via the ECMC (Experimental Cancer Medicine Centres) network. It focuses on translational research, and enables clinical project teams to work with disease experts to set up investigator led trials. The CRUK Centre of Drug Development (CDD) supports these studies with further management and coordination ensuring more robust timelines and delivery. The Combinations Alliance framework and funding structure allows more combination studies to be established in UK broadening academic access to innovative drugs. It also increases the commercial value for each drug and decreases the risks for later development. Further information can be found on our webpage: http://www.ecmcnetwork.org.uk/ca
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.306 | 0.270 |
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".