Use of biomarkers to improve immunosuppressive drug development and outcomes in renal organ transplantation: A meeting report
Bibliographic record
Abstract
On September 27-28, 2018 the Food and Drug Administration (FDA) and the Critical Path Institute's Transplant Therapeutics Consortium convened a public workshop titled "Evidence-Based Treatment Decisions in Transplantation: The Right Dose & Regimen for the Right Patient/Individualized Treatment." The workshop facilitated cooperative engagement of transplant community stakeholders, including pharmaceutical industry, academic researchers, clinicians, patients, and regulators to discuss methods to advance the development of novel immunosuppressive drugs for use in solid organ transplantation. Day 1 focused on the utility of biomarkers in drug development, with considerations for seeking regulatory endorsement for use in clinical trials. Biomarkers add value to drug development by improving patient selection criteria, safety monitoring, endpoint selection, and more. Regulatory endorsement through the FDA Biomarker Qualification Program encourages the use of biomarkers in drug development by instilling confidence and consistency in biomarker interpretation across trials. Public–private partnerships or consortia allow stakeholders to share expertise, resources, and data in pursuit of biomarker qualification. Biomarkers relevant to pretransplant risk assessment, early posttransplant care, and assessment of immune response, immunosuppressive drug efficacy, and graft function as discussed on day 1 of the workshop are described.
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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 | 0.047 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".